From 021990a6ee0e6e1fd89fb027ac030aa80cd53c65 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Mon, 5 Jan 2026 19:47:15 -0700 Subject: [PATCH 01/50] DOC-1867 AI Gateway # Conflicts: # modules/ROOT/nav.adoc --- modules/ROOT/nav.adoc | 2 + modules/ai-agents/pages/ai-gateway.adoc | 164 ++++++++++++++++++++++++ modules/shared/images/ai-gateway.png | Bin 0 -> 211123 bytes 3 files changed, 166 insertions(+) create mode 100644 modules/ai-agents/pages/ai-gateway.adoc create mode 100644 modules/shared/images/ai-gateway.png diff --git a/modules/ROOT/nav.adoc b/modules/ROOT/nav.adoc index ce168d2ab..a985d235a 100644 --- a/modules/ROOT/nav.adoc +++ b/modules/ROOT/nav.adoc @@ -87,6 +87,8 @@ **** xref:ai-agents:mcp/remote/manage-servers.adoc[Manage Servers] **** xref:ai-agents:mcp/remote/scale-resources.adoc[Scale Resources] **** xref:ai-agents:mcp/remote/monitor-activity.adoc[Monitor Activity] +*** xref:ai-agents:mcp/remote/pipeline-patterns.adoc[MCP Server Patterns] +** xref:ai-agents:ai-gateway.adoc[] * xref:develop:connect/about.adoc[Redpanda Connect] ** xref:develop:connect/connect-quickstart.adoc[Quickstart] diff --git a/modules/ai-agents/pages/ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway.adoc new file mode 100644 index 000000000..351727938 --- /dev/null +++ b/modules/ai-agents/pages/ai-gateway.adoc @@ -0,0 +1,164 @@ += AI Gateway Quickstart +:description: Learn how to configure the AI Gateway for Redpanda Cloud, including the LLM proxy and the MCP proxy. +:page-beta: true + +[NOTE] +==== +This private beta documentation introduces AI Gateway in a *Self-Managed* deployment for internal testing. However, it will be released for *Redpanda Cloud BYOC* deployments only. This is a living document, and the UX/API will evolve quickly. +==== + +The Redpanda AI Gateway is a production-grade proxy that provides unified access to multiple Large Language Model (LLM) providers and Model Context Protocol (MCP) servers through a single endpoint. It maintains centralized control over routing, rate limiting, cost optimization, security, and observability. + +== Prerequisites + +* Access to the AI Gateway UI (provided by your administrator) +* API key for at least one LLM provider (OpenAI, Anthropic, or AWS Bedrock) +* (Optional) MCP server endpoints if you plan to use tool aggregation + +== Get started + +Before users can create gateways, an administrator must enable LLM providers and models. + +=== Step 1: Enable a provider + +Providers represent upstream services (Anthropic, OpenAI, AWS Bedrock, custom) and associated credentials. Providers are disabled by default. An administrator must enable them explicitly by adding credentials. + +. Navigate to *Providers*. +. Select a provider (for example, *Anthropic*). +. On the *Configuration* tab, enter your API Key. + +=== Step 2: Enable models + +The model catalog is the set of models made available through the gateway. Models are disabled by default. An administrator must enable them explicitly. + +The infrastructure that is serving the model is different based on the provider you select. For example, AWS Bedrock has different reliability and availability metrics than Anthropic. When you consider all the metrics, you can design your gateway to use different providers for different use cases. + +. Navigate to *Models*. +. Enable the models you want exposed through gateways. + +NOTE: Requests use the `vendor/model_id` format (for example, `openai/gpt-4o`, `anthropic/claude-3-5-sonnet-20241022`). + +=== Step 3: Create a gateway + +A gateway is a logical configuration boundary (policies + routing + observability) on top of a single deployment. It's a "virtual gateway" that you can create per team, environment (staging/production), product, or customer. + +. Navigate to *Gateways*. +. Click *Create Gateway*. +. Choose a name, workspace, and optional metadata. +. After creation, copy the *Gateway Endpoint* from the gateway detail page. + +TIP: A _workspace_ is conceptually similar to a _resource group_ in Redpanda streaming. + +=== Step 4: Configure LLM routing + +On the Gateways page, select the *LLM* tab to configure rate limits, spend limits, and routing policies. + +The LLM routing pipeline visually represents the request lifecycle: + +. Rate Limit (first): For example, global rate limit of 100 requests/second +. Spend Limit / Monthly Budget (second): For example, $15K/month with blocking enforcement +. Routing to a primary provider pool with optional fallback provider pool(s): For example, primary route to Anthropic pool, fallback to Bedrock pool + +*Load balancing / multi-provider distribution:* +If a provider pool contains multiple providers, you can distribute traffic (for example, balancing across Anthropic and Bedrock). + +TIP: Provider pool (UI) = Backend pool (API) + +=== Step 5: Configure MCP tools + +NOTE: Model Context Protocol (MCP) is a standard for connecting AI agents to external tools and data sources. MCP servers expose tools that agents can discover and call. + +On the Gateways page, select the *MCP* tab to configure tool discovery and tool execution. + +You can aggregate multiple MCP servers behind a single endpoint. For example: + +* Data catalog API +* MCP orchestrator +* Research memory store +* Vector search service + +*How MCP works:* + +* You configure MCP server endpoints in the MCP gateway. +* The gateway presents a single aggregated MCP surface to the agent. +* Agents can list/search tools and call them through the gateway. + +*MCP orchestrator* + +The orchestrator is a built-in MCP server that enables programmatic tool calling. The agent can generate JavaScript to call multiple tools in a single orchestrated step, which reduces the number of round trips. For example, a workflow requiring 47 file reads can be reduced from 49 round trips to just 1. + +=== Step 6: Understand tiered tool loading (token savings) + +When many tools are aggregated, listing all tools can consume significant tokens. Tiered tool loading effectively behaves as tiered/lazy tool discovery: + +* Instead of returning all tools, the MCP gateway initially returns: +** a *tool search* capability, and +** the *MCP orchestrator* +* The agent then searches for the specific tool it needs and retrieves only that subset. + +This can reduce token usage significantly (for example, 80-90% depending on how many servers/tools are configured). + +== Observability + +After traffic flows through a gateway, you can inspect: + +* Request volume +* Token usage +* Estimated spend +* Latency +* Per-model breakdown + +This is central to governance: You can see and control usage by gateway boundary (for example, by team, environment, customer, or product). + +== CEL routing + +The AI Gateway uses Common Expression Language (CEL) for flexible routing and policy application. CEL expressions let you create sophisticated routing rules based on request properties without code changes. Use CEL to: + +* Route requests to specific providers based on model family +* Apply different rate limits based on user tiers +* Enforce policies based on request content + +An inline editor in the UI helps you discover available request fields (headers, path, body, and so on). + +=== Practical CEL examples + +Route based on model family: + +[,cel] +---- +request.body.model.startsWith("anthropic/") +---- + +Apply a rule to all requests: + +[,cel] +---- +true +---- + +Route based on a header (for example, product tier): + +[,cel] +---- +request.headers['tier'][0] == "premium" +---- + +Guard for field existence: + +[,cel] +---- +has(request.body.max_tokens) && request.body.max_tokens > 1000 +---- + +== Architecture + +The AI Gateway is the policy-controlled choke point for both LLM inference and tool access in agentic systems. It is a core building block in the Agentic Data Plane: Agents reason, plan and execute, invoking LLMs and tools, while logs, metrics, and traces flow to the customer's observability stack. + +image::shared:ai-gateway.png[AI Gateway architecture] + +== Common gateway patterns + +* *Team isolation*: Create separate gateways for each team to track usage and enforce budgets independently. +* *Environment separation*: Use different gateways for staging and production with appropriate rate limits. +* *Failover*: Configure a primary provider pool with a fallback pool for high availability. +* *A/B testing*: Distribute traffic across providers to compare performance and cost. \ No newline at end of file diff --git a/modules/shared/images/ai-gateway.png b/modules/shared/images/ai-gateway.png new file mode 100644 index 0000000000000000000000000000000000000000..0754146d5d0268750155ab550e490c1119e4ae35 GIT binary patch literal 211123 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ecF%f1Yj+-L=ACGAt=<$8Z0B)S*gPZGyZ;A+oa)d3 literal 0 HcmV?d00001 From 5a9cc4bced52e910a89adb9b6aa200ffc8dc5e20 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Mon, 5 Jan 2026 21:09:14 -0700 Subject: [PATCH 02/50] add questions for reviewers --- modules/ai-agents/pages/ai-gateway.adoc | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway.adoc index 351727938..2988d0199 100644 --- a/modules/ai-agents/pages/ai-gateway.adoc +++ b/modules/ai-agents/pages/ai-gateway.adoc @@ -1,11 +1,6 @@ = AI Gateway Quickstart -:description: Learn how to configure the AI Gateway for Redpanda Cloud, including the LLM proxy and the MCP proxy. -:page-beta: true +:description: Learn how to configure the AI Gateway for unified access to multiple LLM providers and MCP servers through a single endpoint. -[NOTE] -==== -This private beta documentation introduces AI Gateway in a *Self-Managed* deployment for internal testing. However, it will be released for *Redpanda Cloud BYOC* deployments only. This is a living document, and the UX/API will evolve quickly. -==== The Redpanda AI Gateway is a production-grade proxy that provides unified access to multiple Large Language Model (LLM) providers and Model Context Protocol (MCP) servers through a single endpoint. It maintains centralized control over routing, rate limiting, cost optimization, security, and observability. @@ -17,7 +12,7 @@ The Redpanda AI Gateway is a production-grade proxy that provides unified access == Get started -Before users can create gateways, an administrator must enable LLM providers and models. +Before a gateway owner can create a gateway, an administrator must enable LLM providers and models. === Step 1: Enable a provider @@ -36,7 +31,7 @@ The infrastructure that is serving the model is different based on the provider . Navigate to *Models*. . Enable the models you want exposed through gateways. -NOTE: Requests use the `vendor/model_id` format (for example, `openai/gpt-4o`, `anthropic/claude-3-5-sonnet-20241022`). +NOTE: Requests must use the `vendor/model_id` format (for example, `openai/gpt-4o`, `anthropic/claude-3-5-sonnet-20241022`). Requests that omit the vendor prefix may be rejected. === Step 3: Create a gateway @@ -57,7 +52,7 @@ The LLM routing pipeline visually represents the request lifecycle: . Rate Limit (first): For example, global rate limit of 100 requests/second . Spend Limit / Monthly Budget (second): For example, $15K/month with blocking enforcement -. Routing to a primary provider pool with optional fallback provider pool(s): For example, primary route to Anthropic pool, fallback to Bedrock pool +. Routing to a primary provider pool with optional fallback provider pools: For example, primary route to Anthropic pool, fallback to Bedrock pool *Load balancing / multi-provider distribution:* If a provider pool contains multiple providers, you can distribute traffic (for example, balancing across Anthropic and Bedrock). @@ -87,6 +82,8 @@ You can aggregate multiple MCP servers behind a single endpoint. For example: The orchestrator is a built-in MCP server that enables programmatic tool calling. The agent can generate JavaScript to call multiple tools in a single orchestrated step, which reduces the number of round trips. For example, a workflow requiring 47 file reads can be reduced from 49 round trips to just 1. +*REVIEWERS: When/how exactly do you use the orchestrator? Also what happens after they create a gateway? Please provide an example of how to validate end-to-end routing against the gateway endpoint!* + === Step 6: Understand tiered tool loading (token savings) When many tools are aggregated, listing all tools can consume significant tokens. Tiered tool loading effectively behaves as tiered/lazy tool discovery: @@ -110,6 +107,8 @@ After traffic flows through a gateway, you can inspect: This is central to governance: You can see and control usage by gateway boundary (for example, by team, environment, customer, or product). +*REVIEWERS: Where do those metrics appear in the UI, or how does a user validate observability after setup?* + == CEL routing The AI Gateway uses Common Expression Language (CEL) for flexible routing and policy application. CEL expressions let you create sophisticated routing rules based on request properties without code changes. Use CEL to: From 8188598ec13d989db2fc40a01f494a62f8d72930 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Tue, 6 Jan 2026 13:02:38 -0700 Subject: [PATCH 03/50] incorporate review comments --- modules/ROOT/nav.adoc | 4 +- modules/ai-agents/pages/ai-gateway.adoc | 66 ++++++++++++------ modules/ai-agents/pages/index.adoc | 8 +-- .../{images => partials}/ai-gateway.png | Bin 4 files changed, 48 insertions(+), 30 deletions(-) rename modules/shared/{images => partials}/ai-gateway.png (100%) diff --git a/modules/ROOT/nav.adoc b/modules/ROOT/nav.adoc index a985d235a..500dccbda 100644 --- a/modules/ROOT/nav.adoc +++ b/modules/ROOT/nav.adoc @@ -69,7 +69,8 @@ ** xref:security:secrets.adoc[Secrets] ** xref:security:cloud-safety-reliability.adoc[Safety and Reliability] -* xref:ai-agents:index.adoc[AI Agents] +* xref:ai-agents:index.adoc[Agentic Data Plane] +** xref:ai-agents:ai-gateway.adoc[] ** xref:ai-agents:mcp/overview.adoc[MCP Overview] ** xref:ai-agents:mcp/local/index.adoc[Redpanda Cloud Management MCP Server] *** xref:ai-agents:mcp/local/overview.adoc[Overview] @@ -88,7 +89,6 @@ **** xref:ai-agents:mcp/remote/scale-resources.adoc[Scale Resources] **** xref:ai-agents:mcp/remote/monitor-activity.adoc[Monitor Activity] *** xref:ai-agents:mcp/remote/pipeline-patterns.adoc[MCP Server Patterns] -** xref:ai-agents:ai-gateway.adoc[] * xref:develop:connect/about.adoc[Redpanda Connect] ** xref:develop:connect/connect-quickstart.adoc[Quickstart] diff --git a/modules/ai-agents/pages/ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway.adoc index 2988d0199..1e77e9709 100644 --- a/modules/ai-agents/pages/ai-gateway.adoc +++ b/modules/ai-agents/pages/ai-gateway.adoc @@ -2,13 +2,15 @@ :description: Learn how to configure the AI Gateway for unified access to multiple LLM providers and MCP servers through a single endpoint. +NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and later. + The Redpanda AI Gateway is a production-grade proxy that provides unified access to multiple Large Language Model (LLM) providers and Model Context Protocol (MCP) servers through a single endpoint. It maintains centralized control over routing, rate limiting, cost optimization, security, and observability. == Prerequisites * Access to the AI Gateway UI (provided by your administrator) -* API key for at least one LLM provider (OpenAI, Anthropic, or AWS Bedrock) -* (Optional) MCP server endpoints if you plan to use tool aggregation +* API key for at least one LLM provider: OpenAI or Anthropic +* Optional: MCP server endpoints if you plan to use tool aggregation == Get started @@ -16,22 +18,47 @@ Before a gateway owner can create a gateway, an administrator must enable LLM pr === Step 1: Enable a provider -Providers represent upstream services (Anthropic, OpenAI, AWS Bedrock, custom) and associated credentials. Providers are disabled by default. An administrator must enable them explicitly by adding credentials. +Providers represent upstream services (Anthropic, OpenAI) and associated credentials. Providers are disabled by default. An administrator must enable them explicitly by adding credentials. . Navigate to *Providers*. -. Select a provider (for example, *Anthropic*). +. Select a provider (for example, Anthropic). . On the *Configuration* tab, enter your API Key. === Step 2: Enable models The model catalog is the set of models made available through the gateway. Models are disabled by default. An administrator must enable them explicitly. -The infrastructure that is serving the model is different based on the provider you select. For example, AWS Bedrock has different reliability and availability metrics than Anthropic. When you consider all the metrics, you can design your gateway to use different providers for different use cases. +The infrastructure that is serving the model is different based on the provider you select. For example, OpenAI has different reliability and availability metrics than Anthropic. When you consider all the metrics, you can design your gateway to use different providers for different use cases. . Navigate to *Models*. . Enable the models you want exposed through gateways. -NOTE: Requests must use the `vendor/model_id` format (for example, `openai/gpt-4o`, `anthropic/claude-3-5-sonnet-20241022`). Requests that omit the vendor prefix may be rejected. +==== Model naming convention + +Model provider requests must use the `vendor/model_id` format in the model property of the request body, and include the `rp-aigw-id` header with the gateway ID the request is being sent to. The following example routes OpenAI API calls through Redpanda's AI Gateway for centralized control. + +[source,python] +---- +# Example: Using the OpenAI Python SDK with AI Gateway +from openai import OpenAI + +client = OpenAI( + base_url="https://gw.ai.panda.com", <1> + api_key="your-api-key", +) + +# Add header per request +response = client.chat.completions.create( + model="openai/gpt-5", <2> + messages=[{"role": "user", "content": "Hello!"}], + extra_headers={ + "rp-aigw-id": "gateway-abc" # Override for this request + } <3> +) +---- +<1> This redirects the OpenAI client to the AI Gateway endpoint. +<2> The `model` property uses the `vendor/model_id` format as required by the AI Gateway. +<3> Includes the `rp-aigw-id` header to specify which gateway configuration to use. === Step 3: Create a gateway @@ -40,22 +67,23 @@ A gateway is a logical configuration boundary (policies + routing + observabilit . Navigate to *Gateways*. . Click *Create Gateway*. . Choose a name, workspace, and optional metadata. -. After creation, copy the *Gateway Endpoint* from the gateway detail page. - ++ TIP: A _workspace_ is conceptually similar to a _resource group_ in Redpanda streaming. +. After creation, copy the *Gateway Endpoint* from the gateway detail page. + === Step 4: Configure LLM routing On the Gateways page, select the *LLM* tab to configure rate limits, spend limits, and routing policies. The LLM routing pipeline visually represents the request lifecycle: -. Rate Limit (first): For example, global rate limit of 100 requests/second -. Spend Limit / Monthly Budget (second): For example, $15K/month with blocking enforcement -. Routing to a primary provider pool with optional fallback provider pools: For example, primary route to Anthropic pool, fallback to Bedrock pool +. Rate Limit: For example, global rate limit of 100 requests/second +. Spend Limit / Monthly Budget: For example, $15K/month with blocking enforcement +. Routing to a primary provider pool with optional fallback provider pools: For example, primary route to Anthropic pool, fallback to OpenAI pool *Load balancing / multi-provider distribution:* -If a provider pool contains multiple providers, you can distribute traffic (for example, balancing across Anthropic and Bedrock). +If a provider pool contains multiple providers, you can distribute traffic (for example, balancing across Anthropic and OpenAI). TIP: Provider pool (UI) = Backend pool (API) @@ -63,7 +91,7 @@ TIP: Provider pool (UI) = Backend pool (API) NOTE: Model Context Protocol (MCP) is a standard for connecting AI agents to external tools and data sources. MCP servers expose tools that agents can discover and call. -On the Gateways page, select the *MCP* tab to configure tool discovery and tool execution. +On the Gateways page, select the *MCP* tab to configure tool discovery and tool execution. You can aggregate multiple MCP servers behind a single endpoint. For example: @@ -84,9 +112,9 @@ The orchestrator is a built-in MCP server that enables programmatic tool calling *REVIEWERS: When/how exactly do you use the orchestrator? Also what happens after they create a gateway? Please provide an example of how to validate end-to-end routing against the gateway endpoint!* -=== Step 6: Understand tiered tool loading (token savings) +=== Step 6: Understand deferred tool loading (token savings) -When many tools are aggregated, listing all tools can consume significant tokens. Tiered tool loading effectively behaves as tiered/lazy tool discovery: +When many tools are aggregated, listing all tools can consume significant tokens. Deferred tool loading effectively behaves as lazy tool discovery: * Instead of returning all tools, the MCP gateway initially returns: ** a *tool search* capability, and @@ -119,7 +147,7 @@ The AI Gateway uses Common Expression Language (CEL) for flexible routing and po An inline editor in the UI helps you discover available request fields (headers, path, body, and so on). -=== Practical CEL examples +=== CEL examples Route based on model family: @@ -149,12 +177,6 @@ Guard for field existence: has(request.body.max_tokens) && request.body.max_tokens > 1000 ---- -== Architecture - -The AI Gateway is the policy-controlled choke point for both LLM inference and tool access in agentic systems. It is a core building block in the Agentic Data Plane: Agents reason, plan and execute, invoking LLMs and tools, while logs, metrics, and traces flow to the customer's observability stack. - -image::shared:ai-gateway.png[AI Gateway architecture] - == Common gateway patterns * *Team isolation*: Create separate gateways for each team to track usage and enforce budgets independently. diff --git a/modules/ai-agents/pages/index.adoc b/modules/ai-agents/pages/index.adoc index 9ac867a96..b5a5737db 100644 --- a/modules/ai-agents/pages/index.adoc +++ b/modules/ai-agents/pages/index.adoc @@ -1,8 +1,4 @@ -= AI Agents in Redpanda Cloud -:description: Learn about AI agents and the tools Redpanda Cloud provides for building them. += Agentic Data Plane +:description: Learn about the Redpanda Agentic Data Plane, including the AI Gateway, AI agents, and MCP servers. :page-layout: index :page-aliases: develop:agents/about.adoc, develop:ai-agents/about.adoc - -AI agents are configurable assistants that autonomously perform specialist tasks by leveraging large language models (LLMs) and connecting to external data sources and tools. - -Redpanda Cloud provides two complementary Model Context Protocol (MCP) options to help you build AI agents. diff --git a/modules/shared/images/ai-gateway.png b/modules/shared/partials/ai-gateway.png similarity index 100% rename from modules/shared/images/ai-gateway.png rename to modules/shared/partials/ai-gateway.png From c072cc04292d2006de4835710b763a3279d1f84e Mon Sep 17 00:00:00 2001 From: micheleRP Date: Tue, 6 Jan 2026 13:26:21 -0700 Subject: [PATCH 04/50] configure AI Gateway LLM & MCP endpoints in Claude Code & similar tools --- modules/ai-agents/pages/ai-gateway.adoc | 204 +++++++++++++++++++++++- 1 file changed, 197 insertions(+), 7 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway.adoc index 1e77e9709..5df70c215 100644 --- a/modules/ai-agents/pages/ai-gateway.adoc +++ b/modules/ai-agents/pages/ai-gateway.adoc @@ -6,6 +6,13 @@ NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and The Redpanda AI Gateway is a production-grade proxy that provides unified access to multiple Large Language Model (LLM) providers and Model Context Protocol (MCP) servers through a single endpoint. It maintains centralized control over routing, rate limiting, cost optimization, security, and observability. +Common gateway patterns: + +* *Team isolation*: Create separate gateways for each team to track usage and enforce budgets independently. +* *Environment separation*: Use different gateways for staging and production with appropriate rate limits. +* *Failover*: Configure a primary provider pool with a fallback pool for high availability. +* *A/B testing*: Distribute traffic across providers to compare performance and cost. + == Prerequisites * Access to the AI Gateway UI (provided by your administrator) @@ -20,7 +27,7 @@ Before a gateway owner can create a gateway, an administrator must enable LLM pr Providers represent upstream services (Anthropic, OpenAI) and associated credentials. Providers are disabled by default. An administrator must enable them explicitly by adding credentials. -. Navigate to *Providers*. +. In AI Gateways, navigate to *Providers*. . Select a provider (for example, Anthropic). . On the *Configuration* tab, enter your API Key. @@ -106,7 +113,7 @@ You can aggregate multiple MCP servers behind a single endpoint. For example: * The gateway presents a single aggregated MCP surface to the agent. * Agents can list/search tools and call them through the gateway. -*MCP orchestrator* +*MCP orchestrator:* The orchestrator is a built-in MCP server that enables programmatic tool calling. The agent can generate JavaScript to call multiple tools in a single orchestrated step, which reduces the number of round trips. For example, a workflow requiring 47 file reads can be reduced from 49 round trips to just 1. @@ -177,9 +184,192 @@ Guard for field existence: has(request.body.max_tokens) && request.body.max_tokens > 1000 ---- -== Common gateway patterns +== Integrate with AI agents and tools -* *Team isolation*: Create separate gateways for each team to track usage and enforce budgets independently. -* *Environment separation*: Use different gateways for staging and production with appropriate rate limits. -* *Failover*: Configure a primary provider pool with a fallback pool for high availability. -* *A/B testing*: Distribute traffic across providers to compare performance and cost. \ No newline at end of file +The AI Gateway provides standardized endpoints that work with various AI development tools and agents. This section shows how to configure popular tools to use your AI Gateway endpoints. + +=== MCP server endpoint + +If you've configured MCP tools in your gateway, AI agents can connect to the aggregated MCP endpoint: + +* MCP endpoint URL: `https://gw.ai.panda.com/mcp` + +* Headers required: +** `Authorization: Bearer your-api-key` +** `rp-aigw-id: your-gateway-id` + +This endpoint aggregates all MCP servers configured in your gateway, providing a unified interface for tool discovery and execution. + +=== Environment variables + +For consistent configuration across tools, set these environment variables: + +[source,bash] +---- +export REDPANDA_GATEWAY_URL="https://gw.ai.panda.com" +export REDPANDA_GATEWAY_ID="your-gateway-id" +export REDPANDA_API_KEY="your-api-key" +---- + +Many tools and SDKs can automatically use these environment variables when configured appropriately. + +=== Claude Code + +Configure Claude Code to use AI Gateway endpoints by creating or editing your MCP configuration file. + +*For Claude Desktop (with VS Code extension):* + +Create or edit `.vscode/settings.json`: + +[source,json] +---- +{ + "claude.mcpServers": { + "redpanda-ai-gateway": { + "command": "node", + "args": ["/path/to/mcp-redpanda-gateway/index.js"], + "env": { + "GATEWAY_ENDPOINT": "https://gw.ai.panda.com", + "GATEWAY_ID": "your-gateway-id", + "API_KEY": "your-api-key" + } + } + } +} +---- + +*For Claude Code CLI:* + +Create or edit `~/.claude/config.json`: + +[source,json] +---- +{ + "mcpServers": { + "redpanda-ai-gateway": { + "command": "npx", + "args": ["@redpanda/mcp-ai-gateway"], + "env": { + "REDPANDA_GATEWAY_URL": "https://gw.ai.panda.com", + "REDPANDA_GATEWAY_ID": "your-gateway-id", + "REDPANDA_API_KEY": "your-api-key" + } + } + }, + "apiProviders": { + "redpanda": { + "baseURL": "https://gw.ai.panda.com", + "headers": { + "rp-aigw-id": "your-gateway-id" + } + } + } +} +---- + +=== VS Code extensions + +Configure VS Code extensions that support OpenAI-compatible APIs: + +*Continue extension:* + +Edit your Continue config file (`~/.continue/config.json`): + +[source,json] +---- +{ + "models": [ + { + "title": "Redpanda AI Gateway - GPT-4", + "provider": "openai", + "model": "openai/gpt-4", + "apiBase": "https://gw.ai.panda.com", + "apiKey": "your-api-key", + "requestOptions": { + "headers": { + "rp-aigw-id": "your-gateway-id" + } + } + }, + { + "title": "Redpanda AI Gateway - Claude", + "provider": "anthropic", + "model": "anthropic/claude-3-5-sonnet-20241022", + "apiBase": "https://gw.ai.panda.com", + "apiKey": "your-api-key", + "requestOptions": { + "headers": { + "rp-aigw-id": "your-gateway-id" + } + } + } + ] +} +---- + +=== Cursor IDE + +Configure Cursor to route requests through the AI Gateway: + +. Open Cursor Settings (*Cursor* → *Settings* or `Cmd+,`) +. Navigate to *AI* settings +. Add a custom OpenAI-compatible provider: + +[source,json] +---- +{ + "cursor.ai.providers.openai.apiBase": "https://gw.ai.panda.com", + "cursor.ai.providers.openai.defaultHeaders": { + "rp-aigw-id": "your-gateway-id" + } +} +---- + +=== Custom applications + +For custom applications using OpenAI or Anthropic SDKs: + +*OpenAI SDK (Python):* + +[source,python] +---- +from openai import OpenAI + +client = OpenAI( + base_url="https://gw.ai.panda.com", + api_key="your-api-key", + default_headers={ + "rp-aigw-id": "your-gateway-id" + } +) +---- + +*Anthropic SDK (Python):* + +[source,python] +---- +from anthropic import Anthropic + +client = Anthropic( + base_url="https://gw.ai.panda.com", + api_key="your-api-key", + default_headers={ + "rp-aigw-id": "your-gateway-id" + } +) +---- + +*Node.js with OpenAI SDK:* + +[source,javascript] +---- +import OpenAI from 'openai'; + +const openai = new OpenAI({ + baseURL: 'https://gw.ai.panda.com', + apiKey: process.env.OPENAI_API_KEY, + defaultHeaders: { + 'rp-aigw-id': 'your-gateway-id' + } +}); +---- From 1588b36a216726945617c7f88978d162ede8d55e Mon Sep 17 00:00:00 2001 From: micheleRP Date: Tue, 6 Jan 2026 15:27:54 -0700 Subject: [PATCH 05/50] update Claude Code example + index page --- modules/ai-agents/pages/ai-gateway.adoc | 37 +++++++++---------------- modules/ai-agents/pages/index.adoc | 3 ++ 2 files changed, 16 insertions(+), 24 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway.adoc index 5df70c215..d82b4861c 100644 --- a/modules/ai-agents/pages/ai-gateway.adoc +++ b/modules/ai-agents/pages/ai-gateway.adoc @@ -215,30 +215,20 @@ Many tools and SDKs can automatically use these environment variables when confi === Claude Code -Configure Claude Code to use AI Gateway endpoints by creating or editing your MCP configuration file. +Configure Claude Code to use AI Gateway endpoints using HTTP transport for the MCP connection. -*For Claude Desktop (with VS Code extension):* +*For Claude Code CLI:* -Create or edit `.vscode/settings.json`: +Use the `claude mcp add` command to configure the HTTP transport: -[source,json] +[source,bash] ---- -{ - "claude.mcpServers": { - "redpanda-ai-gateway": { - "command": "node", - "args": ["/path/to/mcp-redpanda-gateway/index.js"], - "env": { - "GATEWAY_ENDPOINT": "https://gw.ai.panda.com", - "GATEWAY_ID": "your-gateway-id", - "API_KEY": "your-api-key" - } - } - } -} +claude mcp add --transport http redpanda-aigateway https://gw.ai.panda.com/mcp \ + --header "Authorization: Bearer YOUR_API_KEY" \ + --header "rp-aigw-id: GATEWAY_ID" ---- -*For Claude Code CLI:* +*Alternative configuration via config file:* Create or edit `~/.claude/config.json`: @@ -247,12 +237,11 @@ Create or edit `~/.claude/config.json`: { "mcpServers": { "redpanda-ai-gateway": { - "command": "npx", - "args": ["@redpanda/mcp-ai-gateway"], - "env": { - "REDPANDA_GATEWAY_URL": "https://gw.ai.panda.com", - "REDPANDA_GATEWAY_ID": "your-gateway-id", - "REDPANDA_API_KEY": "your-api-key" + "transport": "http", + "url": "https://gw.ai.panda.com/mcp", + "headers": { + "Authorization": "Bearer YOUR_API_KEY", + "rp-aigw-id": "GATEWAY_ID" } } }, diff --git a/modules/ai-agents/pages/index.adoc b/modules/ai-agents/pages/index.adoc index b5a5737db..0e31f53f1 100644 --- a/modules/ai-agents/pages/index.adoc +++ b/modules/ai-agents/pages/index.adoc @@ -2,3 +2,6 @@ :description: Learn about the Redpanda Agentic Data Plane, including the AI Gateway, AI agents, and MCP servers. :page-layout: index :page-aliases: develop:agents/about.adoc, develop:ai-agents/about.adoc + + +The Redpanda Agentic Data Plane platform provides AI agents with secure and governed access to enterprise data by acting as an intermediary layer between the data ecosystem and the agents themselves. It enables agents to safely discover, query, and act on data drawn from a wide range of sources while seamlessly combining real-time and historical context. Governance controls, access policies, and audit trails ensure that all interactions with data are traceable and compliant with organizational standards. From 9c757421202cbf5a342805f74dbd54aef17d3dc3 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 9 Jan 2026 13:31:40 -0700 Subject: [PATCH 06/50] revert nav title --- modules/ROOT/nav.adoc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ROOT/nav.adoc b/modules/ROOT/nav.adoc index 500dccbda..2e268d196 100644 --- a/modules/ROOT/nav.adoc +++ b/modules/ROOT/nav.adoc @@ -69,7 +69,7 @@ ** xref:security:secrets.adoc[Secrets] ** xref:security:cloud-safety-reliability.adoc[Safety and Reliability] -* xref:ai-agents:index.adoc[Agentic Data Plane] +* xref:ai-agents:index.adoc[AI Agents] ** xref:ai-agents:ai-gateway.adoc[] ** xref:ai-agents:mcp/overview.adoc[MCP Overview] ** xref:ai-agents:mcp/local/index.adoc[Redpanda Cloud Management MCP Server] From fce266b3dc9e17765b56946b51279e3cf80a2183 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Sun, 11 Jan 2026 15:14:52 -0700 Subject: [PATCH 07/50] edits --- modules/ai-agents/pages/ai-gateway.adoc | 52 ++++++++----------------- 1 file changed, 16 insertions(+), 36 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway.adoc index d82b4861c..2e327da98 100644 --- a/modules/ai-agents/pages/ai-gateway.adoc +++ b/modules/ai-agents/pages/ai-gateway.adoc @@ -4,7 +4,7 @@ NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and later. -The Redpanda AI Gateway is a production-grade proxy that provides unified access to multiple Large Language Model (LLM) providers and Model Context Protocol (MCP) servers through a single endpoint. It maintains centralized control over routing, rate limiting, cost optimization, security, and observability. +The Redpanda AI Gateway is a production-grade proxy that provides unified access to multiple Large Language Model (LLM) providers and Model Context Protocol (MCP) servers through a single endpoint. MCP servers expose tools that agents can discover and call. An AI Gateway maintains centralized control over routing, rate limiting, cost optimization, security, and observability. Common gateway patterns: @@ -21,7 +21,7 @@ Common gateway patterns: == Get started -Before a gateway owner can create a gateway, an administrator must enable LLM providers and models. +Before you can create a gateway, an administrator must enable LLM providers and models. === Step 1: Enable a provider @@ -29,11 +29,11 @@ Providers represent upstream services (Anthropic, OpenAI) and associated credent . In AI Gateways, navigate to *Providers*. . Select a provider (for example, Anthropic). -. On the *Configuration* tab, enter your API Key. +. On the *Configuration* tab for the provider, click *Add configuration* and enter your API Key. === Step 2: Enable models -The model catalog is the set of models made available through the gateway. Models are disabled by default. An administrator must enable them explicitly. +The model catalog is the set of models made available through the gateway. Models are disabled by default. After enabling a provider, an administrator can enable its models. The infrastructure that is serving the model is different based on the provider you select. For example, OpenAI has different reliability and availability metrics than Anthropic. When you consider all the metrics, you can design your gateway to use different providers for different use cases. @@ -81,13 +81,13 @@ TIP: A _workspace_ is conceptually similar to a _resource group_ in Redpanda str === Step 4: Configure LLM routing -On the Gateways page, select the *LLM* tab to configure rate limits, spend limits, and routing policies. +On the Gateways page, select the *LLM* tab to configure rate limits, spend limits, routing, and provider pools with fallback options. The LLM routing pipeline visually represents the request lifecycle: -. Rate Limit: For example, global rate limit of 100 requests/second -. Spend Limit / Monthly Budget: For example, $15K/month with blocking enforcement -. Routing to a primary provider pool with optional fallback provider pools: For example, primary route to Anthropic pool, fallback to OpenAI pool +. Rate Limit: For example, global rate limit of 100 requests/second. +. Spend Limit / Monthly Budget: For example, $15K/month with blocking enforcement, so it blocks requests after that budget is exceeded. +. Routing to a primary provider pool with optional fallback provider pools: For example, primary route to Anthropic backend pool, and if that fails, it will fallback to OpenAI pool. *Load balancing / multi-provider distribution:* If a provider pool contains multiple providers, you can distribute traffic (for example, balancing across Anthropic and OpenAI). @@ -96,39 +96,19 @@ TIP: Provider pool (UI) = Backend pool (API) === Step 5: Configure MCP tools -NOTE: Model Context Protocol (MCP) is a standard for connecting AI agents to external tools and data sources. MCP servers expose tools that agents can discover and call. +On the Gateways page, select the *MCP* tab to configure your MCP tool discovery and tool execution. This MCP proxy is an aggregator of MCP servers, allowing multiple MCP servers behind a single endpoint. Agents can then find tools and call them through the gateway. To configure the MCP proxy, add the following: -On the Gateways page, select the *MCP* tab to configure tool discovery and tool execution. +* Display name: When you drag a provider pool, you give it a name. +* Model dropdown: Choose a model from the available models in the catalog. +* Load Balancing options: If you have multiple providers, you can load balance requests between them; for example, round robin. -You can aggregate multiple MCP servers behind a single endpoint. For example: +MCP tools include a data catalog API, the memory store, a vector search service, and an MCP orchestrator. The *MCP orchestrator* is a built-in MCP server that enables programmatic tool calling. Agents can generate code to call multiple tools in a single orchestrated step, which reduces the number of round trips. For example, a workflow requiring 47 file reads can be reduced from 49 round trips to just 1. To add other tools, (for example, Slack), add the Slack MCP server endpoint. -* Data catalog API -* MCP orchestrator -* Research memory store -* Vector search service - -*How MCP works:* - -* You configure MCP server endpoints in the MCP gateway. -* The gateway presents a single aggregated MCP surface to the agent. -* Agents can list/search tools and call them through the gateway. - -*MCP orchestrator:* - -The orchestrator is a built-in MCP server that enables programmatic tool calling. The agent can generate JavaScript to call multiple tools in a single orchestrated step, which reduces the number of round trips. For example, a workflow requiring 47 file reads can be reduced from 49 round trips to just 1. +When many tools are aggregated, listing all tools can consume significant tokens. With *deferred tool loading*, instead of returning all tools, the MCP gateway initially returns a tool search capability and the MCP orchestrator. The agent then searches for the specific tool it needs and retrieves only that subset. That way, the exchange of messages between the MCP gateway and the agent is small. This can reduce token usage significantly when you have many tools configured. *REVIEWERS: When/how exactly do you use the orchestrator? Also what happens after they create a gateway? Please provide an example of how to validate end-to-end routing against the gateway endpoint!* -=== Step 6: Understand deferred tool loading (token savings) - -When many tools are aggregated, listing all tools can consume significant tokens. Deferred tool loading effectively behaves as lazy tool discovery: - -* Instead of returning all tools, the MCP gateway initially returns: -** a *tool search* capability, and -** the *MCP orchestrator* -* The agent then searches for the specific tool it needs and retrieves only that subset. - -This can reduce token usage significantly (for example, 80-90% depending on how many servers/tools are configured). +*REVIEWERS: How do users connect to the ADP catalog + MCP servers exposed through RPCN?* == Observability @@ -152,7 +132,7 @@ The AI Gateway uses Common Expression Language (CEL) for flexible routing and po * Apply different rate limits based on user tiers * Enforce policies based on request content -An inline editor in the UI helps you discover available request fields (headers, path, body, and so on). +The editor in the UI helps you discover available request fields (headers, path, body, and so on). === CEL examples From 85257c9ac1525e0be51d2dde03246d4c082523ac Mon Sep 17 00:00:00 2001 From: micheleRP Date: Sun, 11 Jan 2026 16:05:52 -0700 Subject: [PATCH 08/50] Add comprehensive AI Gateway documentation partials Added 7 new documentation files for AI Gateway: - what-is-ai-gateway.adoc: Overview, problem/solution framing, common patterns - quickstart-enhanced.adoc: Step-by-step quickstart with time markers - observability-logs.adoc: Request logs, filtering, and debugging - observability-metrics.adoc: Dashboards, analytics, and cost tracking - migration-guide.adoc: Safe migration from direct provider integration - cel-routing-cookbook.adoc: CEL routing patterns with examples - mcp-aggregation-guide.adoc: MCP aggregation and orchestration All files follow Redpanda documentation standards: - Sentence case headings - Imperative verbs for action headings - AsciiDoc format - Comprehensive placeholders for product-specific details Co-Authored-By: Claude Sonnet 4.5 --- .../partials/cel-routing-cookbook.adoc | 880 +++++++++++++++++ .../partials/mcp-aggregation-guide.adoc | 923 ++++++++++++++++++ .../ai-agents/partials/migration-guide.adoc | 866 ++++++++++++++++ .../partials/observability-logs.adoc | 631 ++++++++++++ .../partials/observability-metrics.adoc | 751 ++++++++++++++ .../partials/quickstart-enhanced.adoc | 503 ++++++++++ .../partials/what-is-ai-gateway.adoc | 419 ++++++++ 7 files changed, 4973 insertions(+) create mode 100644 modules/ai-agents/partials/cel-routing-cookbook.adoc create mode 100644 modules/ai-agents/partials/mcp-aggregation-guide.adoc create mode 100644 modules/ai-agents/partials/migration-guide.adoc create mode 100644 modules/ai-agents/partials/observability-logs.adoc create mode 100644 modules/ai-agents/partials/observability-metrics.adoc create mode 100644 modules/ai-agents/partials/quickstart-enhanced.adoc create mode 100644 modules/ai-agents/partials/what-is-ai-gateway.adoc diff --git a/modules/ai-agents/partials/cel-routing-cookbook.adoc b/modules/ai-agents/partials/cel-routing-cookbook.adoc new file mode 100644 index 000000000..44a505cfb --- /dev/null +++ b/modules/ai-agents/partials/cel-routing-cookbook.adoc @@ -0,0 +1,880 @@ += CEL routing: deep dive & cookbook + +== Overview + +Redpanda AI Gateway uses CEL (Common Expression Language) for dynamic request routing. CEL expressions evaluate request properties (headers, body, context) and determine which model or provider should handle each request. + +*CEL enables*: +* User-based routing (free vs premium tiers) +* Content-based routing (by prompt topic, length, complexity) +* Environment-based routing (staging vs production models) +* Cost controls (reject expensive requests in test environments) +* A/B testing (route percentage of traffic to new models) +* Geographic routing (by region header) +* Custom business logic (any condition you can express) + +== CEL basics + +=== What is cel? + +CEL (Common Expression Language) is a non-Turing-complete expression language designed for fast, safe evaluation. It's used by Google (Firebase, Cloud IAM), Kubernetes, Envoy, and other systems. + +*Key Properties*: +* *Safe*: Cannot loop infinitely or access system resources +* *Fast*: Evaluates in microseconds +* *Readable*: Similar to Python/JavaScript expressions +* *Type-safe*: Errors caught at configuration time, not runtime + +=== CEL syntax primer + +*Comparison Operators*: +[source,cel] +---- +== // equal +!= // Not equal +< // Less than +> // Greater than +<= // Less than or equal +>= // Greater than or equal +---- + + +*Logical Operators*: +[source,cel] +---- +&& // AND +|| // OR +! // NOT +---- + + +*Ternary Operator* (most common pattern): +[source,cel] +---- +condition ? value_if_true : value_if_false +---- + + +*Functions*: +[source,cel] +---- +.size() // Length of string or array +.contains("text") // String contains substring +.startsWith("x") // String starts with +.endsWith("x") // String ends with +.matches("regex") // Regex match +has(field) // Check if field exists +---- + + +*Examples*: +[source,cel] +---- +// Simple comparison +request.headers["tier"] == "premium" + +// Ternary (if-then-else) +request.headers["tier"] == "premium" ? "openai/gpt-4o" : "openai/gpt-4o-mini" + +// Logical AND +request.headers["tier"] == "premium" && request.headers["region"] == "us" + +// String contains +request.body.messages[0].content.contains("urgent") + +// Size check +request.body.messages.size() > 10 +---- + + +== Request object schema + +CEL expressions evaluate against the `request` object, which contains: + +// PLACEHOLDER: Confirm exact schema + +=== `request.headers` (map) + +All HTTP headers (lowercase keys). + +[source,cel] +---- +request.headers["x-user-tier"] // Custom header +request.headers["x-customer-id"] // Custom header +request.headers["user-agent"] // Standard header +request.headers["x-request-id"] // Standard header +---- + + +*Note*: Header names are case-insensitive in HTTP, but CEL requires lowercase keys. + +=== `request.body` (object) + +The JSON request body (for `/chat/completions`). + +[source,cel] +---- +request.body.model // String: Requested model +request.body.messages // Array: Conversation messages +request.body.messages[0].role // String: "system", "user", "assistant" +request.body.messages[0].content // String: Message content +request.body.messages.size() // Int: Number of messages +request.body.max_tokens // Int: Max completion tokens (if set) +request.body.temperature // Float: Temperature (if set) +request.body.stream // Bool: Streaming enabled (if set) +---- + + +*Note*: Fields are optional. Use `has()` to check existence: +[source,cel] +---- +has(request.body.max_tokens) ? request.body.max_tokens : 1000 +---- + + +=== `request.path` (string) + +The request path. + +[source,cel] +---- +request.path == "/v1/chat/completions" +request.path.startsWith("/v1/") +---- + + +=== `request.method` (string) + +The HTTP method. + +[source,cel] +---- +request.method == "POST" +---- + + +// PLACEHOLDER: Are there other fields? User context? Gateway context? Timestamp? + +== CEL routing patterns + +Each pattern follows this structure: +* *When to use*: Scenario description +* *Expression*: CEL code +* *What happens*: Routing behavior +* *Verify*: How to test +* *Cost/performance impact*: Implications + +''' + +=== Pattern 1: tier-based routing + +*When to use*: Different user tiers (free, pro, enterprise) should get different model quality + +*Expression*: +[source,cel] +---- +request.headers["x-user-tier"] == "enterprise" ? "openai/gpt-4o" : +request.headers["x-user-tier"] == "pro" ? "anthropic/claude-sonnet-3.5" : +"openai/gpt-4o-mini" +---- + + +*What happens*: +* Enterprise users → GPT-4o (best quality) +* Pro users → Claude Sonnet 3.5 (balanced) +* Free users → GPT-4o-mini (cost-effective) + +*Verify*: +[source,python] +---- +# Test enterprise +response = client.chat.completions.create( + model="auto", # PLACEHOLDER: How to trigger CEL routing? + messages=[{"role": "user", "content": "Test"}], + extra_headers={"x-user-tier": "enterprise"} +) +# Check logs: Should route to openai/gpt-4o + +# Test free +response = client.chat.completions.create( + model="auto", + messages=[{"role": "user", "content": "Test"}], + extra_headers={"x-user-tier": "free"} +) +# Check logs: Should route to openai/gpt-4o-mini +---- + + +*Cost Impact*: +* Enterprise: ~$5.00 per 1K requests +* Pro: ~$3.50 per 1K requests +* Free: ~$0.50 per 1K requests + +*Use Case*: SaaS product with tiered pricing where model quality is a differentiator + +''' + +=== Pattern 2: environment-based routing + +*When to use*: Prevent staging from using expensive models + +*Expression*: +[source,cel] +---- +request.headers["x-environment"] == "production" + ? "openai/gpt-4o" + : "openai/gpt-4o-mini" +---- + + +*What happens*: +* Production → GPT-4o (best quality) +* Staging/dev → GPT-4o-mini (10x cheaper) + +*Verify*: +[source,python] +---- +# Set environment header +response = client.chat.completions.create( + model="auto", + messages=[{"role": "user", "content": "Test"}], + extra_headers={"x-environment": "staging"} +) +# Check logs: Should route to gpt-4o-mini +---- + + +*Cost Impact*: +* Prevents staging from inflating costs +* Example: Staging with 100K test requests/day + * GPT-4o: $500/day ($15K/month) + * GPT-4o-mini: $50/day ($1.5K/month) + * *Savings: $13.5K/month* + +*Use Case*: Protect against runaway staging costs + +''' + +=== Pattern 3: content-length guard rails + +*When to use*: Block or downgrade long prompts to prevent cost spikes + +*Expression (Block)*: +[source,cel] +---- +request.body.messages.size() > 10 || request.body.max_tokens > 4000 + ? "reject" + : "openai/gpt-4o" +---- + + +*What happens*: +* Requests with >10 messages or >4000 max_tokens → Rejected with 400 error +* Normal requests → GPT-4o + +*Expression (Downgrade)*: +[source,cel] +---- +request.body.messages.size() > 10 || request.body.max_tokens > 4000 + ? "openai/gpt-4o-mini" // Cheaper model + : "openai/gpt-4o" // Normal model +---- + + +*What happens*: +* Long conversations → Downgraded to cheaper model +* Short conversations → Premium model + +*Verify*: +[source,python] +---- +# Test rejection +response = client.chat.completions.create( + model="auto", + messages=[{"role": "user", "content": f"Message {i}"} for i in range(15)], + max_tokens=5000 +) +# Should return 400 error (rejected) + +# Test normal +response = client.chat.completions.create( + model="auto", + messages=[{"role": "user", "content": "Short message"}], + max_tokens=100 +) +# Should route to gpt-4o +---- + + +*Cost Impact*: +* Prevents unexpected bills from verbose prompts +* Example: Block requests >10K tokens (would cost $0.15 each) + +*Use Case*: Staging cost controls, prevent prompt injection attacks that inflate token usage + +''' + +=== Pattern 4: topic-based routing + +*When to use*: Route different question types to specialized models + +*Expression*: +[source,cel] +---- +request.body.messages[0].content.contains("code") || +request.body.messages[0].content.contains("debug") || +request.body.messages[0].content.contains("programming") + ? "openai/gpt-4o" // Better at code + : "anthropic/claude-sonnet-3.5" // Better at general writing +---- + + +*What happens*: +* Coding questions → GPT-4o (optimized for code) +* General questions → Claude Sonnet (better prose) + +*Verify*: +[source,python] +---- +# Test code question +response = client.chat.completions.create( + model="auto", + messages=[{"role": "user", "content": "Debug this Python code: ..."}] +) +# Check logs: Should route to gpt-4o + +# Test general question +response = client.chat.completions.create( + model="auto", + messages=[{"role": "user", "content": "Write a blog post about AI"}] +) +# Check logs: Should route to claude-sonnet-3.5 +---- + + +*Cost Impact*: +* Optimize model selection for task type +* Could improve quality without increasing costs + +*Use Case*: Multi-purpose chatbot with both coding and general queries + +''' + +=== Pattern 5: geographic/regional routing + +*When to use*: Route by user region for compliance or latency optimization + +*Expression*: +[source,cel] +---- +request.headers["x-user-region"] == "eu" + ? "openai/gpt-4o-eu" // PLACEHOLDER: If regional models exist + : "openai/gpt-4o" +---- + + +*What happens*: +* EU users → EU-region model (GDPR compliance) +* Other users → Default region + +*Verify*: +[source,python] +---- +response = client.chat.completions.create( + model="auto", + messages=[{"role": "user", "content": "Test"}], + extra_headers={"x-user-region": "eu"} +) +# Check logs: Should route to EU model +---- + + +*Cost Impact*: Neutral (same model, different region) + +*Use Case*: GDPR compliance, data residency requirements + +''' + +=== Pattern 6: customer-specific routing + +*When to use*: Different customers have different model access (enterprise features) + +*Expression*: +[source,cel] +---- +request.headers["x-customer-id"] == "customer_vip_123" + ? "anthropic/claude-opus-4" // Most expensive, best quality + : "anthropic/claude-sonnet-3.5" // Standard +---- + + +*What happens*: +* VIP customer → Best model +* Standard customers → Normal model + +*Verify*: +[source,python] +---- +response = client.chat.completions.create( + model="auto", + messages=[{"role": "user", "content": "Test"}], + extra_headers={"x-customer-id": "customer_vip_123"} +) +# Check logs: Should route to claude-opus-4 +---- + + +*Cost Impact*: +* VIP: ~$7.50 per 1K requests +* Standard: ~$3.50 per 1K requests + +*Use Case*: Enterprise contracts with premium model access + +''' + +=== Pattern 7: a/b testing (percentage-based routing) + +*When to use*: Test new models with a percentage of traffic + +// PLACEHOLDER: Confirm if CEL can access random functions or if A/B testing requires different mechanism + +*Expression (if random is available)*: +[source,cel] +---- +// PLACEHOLDER: Verify CEL random function availability +random() < 0.10 + ? "anthropic/claude-opus-4" // 10% traffic to new model + : "openai/gpt-4o" // 90% traffic to existing model +---- + + +*Alternative (Hash-Based)*: +[source,cel] +---- +// Use customer ID hash for stable routing +hash(request.headers["x-customer-id"]) % 100 < 10 + ? "anthropic/claude-opus-4" + : "openai/gpt-4o" +---- + + +*What happens*: +* 10% of requests → New model (Opus 4) +* 90% of requests → Existing model (GPT-4o) + +*Verify*: +[source,python] +---- +# Send 100 requests, count which model was used +for i in range(100): + response = client.chat.completions.create( + model="auto", + messages=[{"role": "user", "content": f"Test {i}"}], + extra_headers={"x-customer-id": f"customer_{i}"} + ) +# Check logs: ~10 should use opus-4, ~90 should use gpt-4o +---- + + +*Cost Impact*: +* Allows safe, incremental rollout of new models +* Monitor quality/cost for new model before full adoption + +*Use Case*: Evaluate new models in production with real traffic + +''' + +=== Pattern 8: complexity-based routing + +*When to use*: Route simple queries to cheap models, complex queries to expensive models + +*Expression*: +[source,cel] +---- +request.body.messages.size() == 1 && +request.body.messages[0].content.size() < 100 + ? "openai/gpt-4o-mini" // Simple, short question + : "openai/gpt-4o" // Complex or long conversation +---- + + +*What happens*: +* Single short message (<100 chars) → Cheap model +* Multi-turn or long messages → Premium model + +*Verify*: +[source,python] +---- +# Test simple +response = client.chat.completions.create( + model="auto", + messages=[{"role": "user", "content": "Hi"}] # 2 chars +) +# Check logs: Should route to gpt-4o-mini + +# Test complex +response = client.chat.completions.create( + model="auto", + messages=[ + {"role": "user", "content": "Long question here..." * 10}, + {"role": "assistant", "content": "Response"}, + {"role": "user", "content": "Follow-up"} + ] +) +# Check logs: Should route to gpt-4o +---- + + +*Cost Impact*: +* Can reduce costs significantly if simple queries are common +* Example: 50% of queries are simple, save 90% on those = 45% total savings + +*Use Case*: FAQ chatbot with mix of simple lookups and complex questions + +''' + +=== Pattern 9: time-based routing + +*When to use*: Use cheaper models during off-peak hours + +// PLACEHOLDER: Confirm if CEL has access to current timestamp + +*Expression (if time functions available)*: +[source,cel] +---- +// PLACEHOLDER: Verify CEL time function availability +now().hour >= 22 || now().hour < 6 // 10pm - 6am + ? "openai/gpt-4o-mini" // Off-peak: cheaper model + : "openai/gpt-4o" // Peak hours: best model +---- + + +*What happens*: +* Off-peak hours (10pm-6am) → Cheap model +* Peak hours (6am-10pm) → Premium model + +*Cost Impact*: +* Optimize for user experience during peak usage +* Save costs during low-traffic hours + +*Use Case*: Consumer apps with time-zone-specific usage patterns + +''' + +=== Pattern 10: fallback chain (multi-level) + +*When to use*: Complex fallback logic beyond simple primary/secondary + +*Expression*: +[source,cel] +---- +request.headers["x-priority"] == "critical" + ? "openai/gpt-4o" // First choice for critical + : request.headers["x-user-tier"] == "premium" + ? "anthropic/claude-sonnet-3.5" // Second choice for premium + : "openai/gpt-4o-mini" // Default for everyone else +---- + + +*What happens*: +* Critical requests → Always GPT-4o +* Premium non-critical → Claude Sonnet +* Everyone else → GPT-4o-mini + +*Verify*: Test with different header combinations + +*Cost Impact*: Ensures SLA for critical requests while optimizing costs elsewhere + +*Use Case*: Production systems with SLA requirements + +''' + +== Advanced CEL patterns + +=== Pattern: default values with `has()` + +*Problem*: Field might not exist in request + +*Expression*: +[source,cel] +---- +has(request.body.max_tokens) && request.body.max_tokens > 2000 + ? "openai/gpt-4o" // Long response expected + : "openai/gpt-4o-mini" // Short response +---- + + +*What happens*: Safely checks if `max_tokens` exists before comparing + +=== Pattern: multiple conditions with parentheses + +*Expression*: +[source,cel] +---- +(request.headers["x-user-tier"] == "premium" || + request.headers["x-customer-id"] == "vip_123") && +request.headers["x-environment"] == "production" + ? "openai/gpt-4o" + : "openai/gpt-4o-mini" +---- + + +*What happens*: Premium users OR VIP customer, AND production → GPT-4o + +=== Pattern: regex matching + +*Expression*: +[source,cel] +---- +request.body.messages[0].content.matches("(?i)(urgent|asap|emergency)") + ? "openai/gpt-4o" // Route urgent requests to best model + : "openai/gpt-4o-mini" +---- + + +*What happens*: Messages containing "urgent", "ASAP", or "emergency" (case-insensitive) → GPT-4o + +=== Pattern: string array contains + +*Expression*: +[source,cel] +---- +["customer_1", "customer_2", "customer_3"].exists(c, c == request.headers["x-customer-id"]) + ? "openai/gpt-4o" // Whitelist of customers + : "openai/gpt-4o-mini" +---- + + +*What happens*: Only specific customers get premium model + +=== Pattern: reject invalid requests + +*Expression*: +[source,cel] +---- +!has(request.body.messages) || request.body.messages.size() == 0 + ? "reject" // PLACEHOLDER: Confirm "reject" is supported + : "openai/gpt-4o" +---- + + +*What happens*: Requests without messages are rejected (400 error) + +== Test CEL expressions + +=== Option 1: CEL editor in UI (if available) + +// PLACEHOLDER: Add screenshot if UI has CEL editor with test mode + +1. Navigate to Gateway → Routing Rules +2. Enter CEL expression +3. Click "Test" +4. Input test headers/body +5. View evaluated result + +=== Option 2: send test requests + +[source,python] +---- +def test_cel_routing(headers, messages): + """Test CEL routing with specific headers and messages""" + response = client.chat.completions.create( + model="auto", # PLACEHOLDER: Confirm trigger for CEL routing + messages=messages, + extra_headers=headers, + max_tokens=10 # Keep it cheap + ) + + # Check logs to see which model was used + print(f"Headers: {headers}") + print(f"Routed to: {response.model}") # PLACEHOLDER: Does response include actual model? + +# Test tier-based routing +test_cel_routing( + {"x-user-tier": "premium"}, + [{"role": "user", "content": "Test"}] +) +test_cel_routing( + {"x-user-tier": "free"}, + [{"role": "user", "content": "Test"}] +) +---- + + +=== Option 3: cli test (if available) + +[source,bash] +---- +# PLACEHOLDER: If CLI tool exists for testing CEL +rpk cloud ai-gateway test-cel \ + --gateway-id gw_abc123 \ + --expression 'request.headers["tier"] == "premium" ? "openai/gpt-4o" : "openai/gpt-4o-mini"' \ + --header 'tier: premium' \ + --body '{"messages": [{"role": "user", "content": "Test"}]}' + +# Expected output: openai/gpt-4o +---- + + +== Common CEL errors + +=== Error: "unknown field" + +*Symptom*: +[source,text] +---- +Error: Unknown field 'request.headers.x-user-tier' +---- + + +*Cause*: Wrong syntax (dot notation instead of bracket notation for headers) + +*Fix*: +[source,cel] +---- +// Wrong +request.headers.x-user-tier + +// Correct +request.headers["x-user-tier"] +---- + + +=== Error: "type mismatch" + +*Symptom*: +[source,text] +---- +Error: Type mismatch: expected bool, got string +---- + + +*Cause*: Forgot comparison operator + +*Fix*: +[source,cel] +---- +// Wrong (returns string) +request.headers["tier"] + +// Correct (returns bool) +request.headers["tier"] == "premium" +---- + + +=== Error: "field does not exist" + +*Symptom*: +[source,text] +---- +Error: No such key: max_tokens +---- + + +*Cause*: Accessing field that doesn't exist in request + +*Fix*: +[source,cel] +---- +// Wrong (crashes if max_tokens not in request) +request.body.max_tokens > 1000 + +// Correct (checks existence first) +has(request.body.max_tokens) && request.body.max_tokens > 1000 +---- + + +=== Error: "index out of bounds" + +*Symptom*: +[source,text] +---- +Error: Index 0 out of bounds for array of size 0 +---- + + +*Cause*: Accessing array element that doesn't exist + +*Fix*: +[source,cel] +---- +// Wrong (crashes if messages empty) +request.body.messages[0].content.contains("test") + +// Correct (checks size first) +request.body.messages.size() > 0 && request.body.messages[0].content.contains("test") +---- + + +== CEL performance considerations + +=== Expression complexity + +*Fast* (<1ms evaluation): +[source,cel] +---- +request.headers["tier"] == "premium" ? "openai/gpt-4o" : "openai/gpt-4o-mini" +---- + + +*Slower* (~5-10ms evaluation): +[source,cel] +---- +request.body.messages[0].content.matches("complex.*regex.*pattern") +---- + + +*Recommendation*: Keep expressions simple. Complex regex can add latency. + +=== Number of evaluations + +Each request evaluates CEL expression once. Total latency impact: +* Simple expression: <1ms +* Complex expression: ~5-10ms + +*Acceptable for most use cases.* + +== CEL function reference + +// PLACEHOLDER: Comprehensive list of available CEL functions in AI Gateway + +=== String functions + +| Function | Description | Example | +|----------|-------------|---------| +| `size()` | String length | `"hello".size() == 5` | +| `contains(s)` | String contains | `"hello".contains("ell")` | +| `startsWith(s)` | String starts with | `"hello".startsWith("he")` | +| `endsWith(s)` | String ends with | `"hello".endsWith("lo")` | +| `matches(regex)` | Regex match | `"hello".matches("h.*o")` | + +=== Array functions + +| Function | Description | Example | +|----------|-------------|---------| +| `size()` | Array length | `[1,2,3].size() == 3` | +| `exists(x, cond)` | Any element matches | `[1,2,3].exists(x, x > 2)` | +| `all(x, cond)` | All elements match | `[1,2,3].all(x, x > 0)` | + +=== Utility functions + +| Function | Description | Example | +|----------|-------------|---------| +| `has(field)` | Field exists | `has(request.body.max_tokens)` | + +// PLACEHOLDER: Other functions like hash(), random(), now()? + +== Next steps + +* *Apply CEL Routing* → [Gateway Configuration Guide](// PLACEHOLDER: link) +* *Test Routing* → [End-to-End Validation](// PLACEHOLDER: link) +* *Monitor Routing Decisions* → [Observability: Logs](// PLACEHOLDER: link) +* *Optimize Costs* → [Cost Optimization Guide](// PLACEHOLDER: link) +* *Multi-Tenancy Patterns* → [Multi-Tenancy Guide](// PLACEHOLDER: link) + +== Related pages + +* [Quickstart](// PLACEHOLDER: link) +* [Provider Pools & Fallback](// PLACEHOLDER: link) +* [Rate Limiting](// PLACEHOLDER: link) +* [Observability](// PLACEHOLDER: link) diff --git a/modules/ai-agents/partials/mcp-aggregation-guide.adoc b/modules/ai-agents/partials/mcp-aggregation-guide.adoc new file mode 100644 index 000000000..c09684357 --- /dev/null +++ b/modules/ai-agents/partials/mcp-aggregation-guide.adoc @@ -0,0 +1,923 @@ += MCP aggregation & orchestration guide + +== Overview + +AI Gateway provides MCP (Model Context Protocol) aggregation, allowing AI agents to access tools from multiple MCP servers through a single unified endpoint. This eliminates the need for agents to manage multiple MCP connections and significantly reduces token costs through deferred tool loading. + +*MCP Aggregation Benefits*: +* *Single Endpoint*: One MCP endpoint aggregates all approved MCP servers +* *Token Reduction*: 80-90% fewer tokens through deferred tool loading +* *Centralized Governance*: Admin-approved MCP servers only +* *Orchestration*: JavaScript-based orchestrator reduces multi-step round trips +* *Security*: Controlled tool execution environment + +== What is mcp? + +*Model Context Protocol (MCP)* is a standard for exposing tools (functions) that AI agents can discover and invoke. MCP servers provide tools like: +* Database queries +* File system operations +* API integrations (CRM, payment, analytics) +* Search (web, vector, enterprise) +* Code execution +* Workflow automation + +*Without AI Gateway*: +* Agent connects to each MCP server individually +* Agent loads ALL tools from ALL servers upfront (high token cost) +* No centralized governance or security +* Complex configuration + +*With AI Gateway*: +* Agent connects to gateway's unified `/mcp` endpoint +* Gateway aggregates tools from approved MCP servers +* Deferred loading: Only search + orchestrator tools sent initially +* Agent queries for specific tools when needed (token savings) +* Centralized governance and observability + +== Architecture + +[source,text] +---- +┌─────────────────┐ +│ AI Agent │ +│ (Claude, GPT) │ +└────────┬────────┘ + │ + │ 1. Discover tools via /mcp endpoint + │ 2. Invoke specific tool + │ +┌────────▼────────────────────────────────┐ +│ AI Gateway (MCP Aggregator) │ +│ │ +│ ┌─────────────────────────────────┐ │ +│ │ Deferred Tool Loading │ │ +│ │ (Send search + orchestrator │ │ +│ │ initially, defer others) │ │ +│ └─────────────────────────────────┘ │ +│ │ +│ ┌─────────────────────────────────┐ │ +│ │ Orchestrator (JavaScript) │ │ +│ │ (Reduce round trips for │ │ +│ │ multi-step workflows) │ │ +│ └─────────────────────────────────┘ │ +│ │ +│ ┌─────────────────────────────────┐ │ +│ │ Approved MCP Server Registry │ │ +│ │ (Admin-controlled) │ │ +│ └─────────────────────────────────┘ │ +└────────┬────────────────────────────────┘ + │ + │ Routes to appropriate MCP server + │ + ┌────▼─────┬──────────┬─────────┐ + │ │ │ │ +┌───▼────┐ ┌──▼─────┐ ┌──▼──────┐ ┌▼──────┐ +│ MCP │ │ MCP │ │ MCP │ │ MCP │ +│Database│ │Filesystem│ │ Slack │ │Search │ +│Server │ │ Server │ │ Server │ │Server │ +└────────┘ └────────┘ └─────────┘ └───────┘ +---- + + +== MCP request lifecycle + +=== 1. tool discovery (initial connection) + +*Agent Request*: +[source,http] +---- +GET /mcp/tools +Headers: + Authorization: Bearer {TOKEN} + rp-aigw-id: {GATEWAY_ID} + rp-aigw-mcp-deferred: true # Enable deferred loading +---- + + +*Gateway Response* (with deferred loading): +[source,json] +---- +{ + "tools": [ + { + "name": "search_tools", + "description": "Query available tools by keyword or category", + "input_schema": { + "type": "object", + "properties": { + "query": {"type": "string"}, + "category": {"type": "string"} + } + } + }, + { + "name": "orchestrator", + "description": "Execute multi-step workflows with JavaScript logic", + "input_schema": { + "type": "object", + "properties": { + "workflow": {"type": "string"}, + "context": {"type": "object"} + } + } + } + ] +} +---- + + +*Note*: Only 2 tools returned initially (search + orchestrator), not all 50+ tools from all MCP servers. + +*Token Savings*: +* Without deferred loading: ~5,000-10,000 tokens (all tool definitions) +* With deferred loading: ~500-1,000 tokens (2 tool definitions) +* *80-90% reduction* + +=== 2. tool query (when agent needs specific tool) + +*Agent Request*: +[source,http] +---- +POST /mcp/tools/search_tools +Headers: + Authorization: Bearer {TOKEN} + rp-aigw-id: {GATEWAY_ID} +Body: +{ + "query": "database query" +} +---- + + +*Gateway Response*: +[source,json] +---- +{ + "tools": [ + { + "name": "execute_sql", + "description": "Execute SQL query against the database", + "mcp_server": "database-server", + "input_schema": { + "type": "object", + "properties": { + "query": {"type": "string"}, + "database": {"type": "string"} + }, + "required": ["query"] + } + }, + { + "name": "list_tables", + "description": "List all tables in the database", + "mcp_server": "database-server", + "input_schema": { + "type": "object", + "properties": { + "database": {"type": "string"} + } + } + } + ] +} +---- + + +*Agent receives only relevant tools* based on query. + +=== 3. tool execution + +*Agent Request*: +[source,http] +---- +POST /mcp/tools/execute_sql +Headers: + Authorization: Bearer {TOKEN} + rp-aigw-id: {GATEWAY_ID} +Body: +{ + "query": "SELECT * FROM users WHERE tier = 'premium' LIMIT 10", + "database": "prod" +} +---- + + +*Gateway*: +1. Routes to appropriate MCP server (database-server) +2. Executes tool +3. Returns result + +*Gateway Response*: +[source,json] +---- +{ + "result": [ + {"id": 1, "name": "Alice", "tier": "premium"}, + {"id": 2, "name": "Bob", "tier": "premium"}, + ... + ] +} +---- + + +*Agent receives result* and can continue reasoning. + +== Deferred tool loading: deep dive + +=== How it works + +*Traditional MCP (No Deferred Loading)*: +1. Agent connects to MCP endpoint +2. Gateway sends ALL tools from ALL MCP servers (50+ tools) +3. Agent includes ALL tool definitions in EVERY LLM request +4. High token cost: ~5,000-10,000 tokens per request + +*Deferred Loading (AI Gateway)*: +1. Agent connects to MCP endpoint with `rp-aigw-mcp-deferred: true` header +2. Gateway sends only 2 tools: `search_tools` + `orchestrator` +3. Agent includes only 2 tool definitions in LLM request (~500-1,000 tokens) +4. When agent needs specific tool: + * Agent calls `search_tools` with query (e.g., "database") + * Gateway returns matching tools + * Agent calls specific tool (e.g., `execute_sql`) +5. Total token cost: Initial 500-1,000 + per-query ~200-500 + * *Still 80-90% lower than loading all tools* + +=== When to use deferred loading + +*Use Deferred Loading When*: +* You have 10+ tools across multiple MCP servers +* Agents don't need all tools for every request +* Token costs are a concern +* Agents can handle multi-step workflows (search → execute) + +*Don't Use Deferred Loading When*: +* You have <5 tools total (overhead not worth it) +* Agents need all tools for every request (rare) +* Latency is more important than token costs (deferred adds 1 round trip) + +=== Configure deferred loading + +// PLACEHOLDER: Add UI path or configuration method + +*Option 1: Enable at Gateway Level* (recommended) +[source,yaml] +---- +# PLACEHOLDER: Actual configuration format +mcp: + deferred_loading: true # Default for all agents using this gateway +---- + + +*Option 2: Enable Per-Request* (agent-controlled) +[source,python] +---- +# Agent includes header +headers = { + "rp-aigw-id": "gw_abc123", + "rp-aigw-mcp-deferred": "true" # Enable for this request +} +---- + + +=== Measure token savings + +*Compare token usage before/after deferred loading*: + +1. *Check Logs Without Deferred Loading*: + * Filter: Gateway = your-gateway, Model = your-model, Date = before enabling + * Average tokens per request: // PLACEHOLDER: measure + +2. *Enable Deferred Loading* + +3. *Check Logs After Deferred Loading*: + * Filter: Same gateway/model, Date = after enabling + * Average tokens per request: // PLACEHOLDER: measure + +4. *Calculate Savings*: + ``` + Savings % = ((Before - After) / Before) × 100 + ``` + +*Expected Results*: 80-90% reduction in average tokens per request + +== Orchestrator: multi-step workflows + +=== What is the orchestrator? + +The *orchestrator* is a special tool that executes JavaScript workflows, reducing multi-step interactions from multiple round trips to a single request. + +*Without Orchestrator*: +1. Agent: "Search vector database for relevant docs" → Round trip 1 +2. Agent receives results, evaluates: "Results insufficient" +3. Agent: "Fallback to web search" → Round trip 2 +4. Agent receives results, processes → Round trip 3 +5. *Total: 3 round trips* (high latency, 3× token cost) + +*With Orchestrator*: +1. Agent: "Execute workflow: Search vector DB → if insufficient, fallback to web search" +2. Gateway executes entire workflow in JavaScript +3. Agent receives final result → *1 round trip* + +*Benefits*: +* *Latency Reduction*: 1 round trip vs 3+ +* *Token Reduction*: No intermediate LLM calls needed +* *Reliability*: Workflow logic executes deterministically +* *Cost*: Single LLM call instead of multiple + +=== When to use orchestrator + +*Use Orchestrator When*: +* Multi-step workflows with conditional logic (if/else) +* Fallback patterns (try A, if fails, try B) +* Sequential tool calls with dependencies +* Loop-based operations (iterate, aggregate) + +*Don't Use Orchestrator When*: +* Single tool call (no benefit) +* Agent needs to reason between steps (orchestrator is deterministic) +* Workflow requires LLM judgment at each step + +=== Orchestrator example: search with fallback + +*Scenario*: Search vector database; if results insufficient, fallback to web search. + +*Without Orchestrator* (3 round trips): +[source,python] +---- +# Agent's internal reasoning (3 separate LLM calls) + +# Round trip 1: Search vector DB +vector_results = call_tool("vector_search", {"query": "Redpanda pricing"}) + +# Round trip 2: Agent evaluates results +if len(vector_results) < 3: + # Round trip 3: Fallback to web search + web_results = call_tool("web_search", {"query": "Redpanda pricing"}) + results = web_results +else: + results = vector_results + +# Agent processes final results +---- + + +*With Orchestrator* (1 round trip): +[source,python] +---- +# Agent invokes orchestrator once +results = call_tool("orchestrator", { + "workflow": """ + // JavaScript workflow + const vectorResults = await tools.vector_search({ + query: context.query + }); + + if (vectorResults.length < 3) { + // Fallback to web search + const webResults = await tools.web_search({ + query: context.query + }); + return webResults; + } + + return vectorResults; + """, + "context": { + "query": "Redpanda pricing" + } +}) + +# Agent receives final results directly +---- + + +*Savings*: +* *Latency*: ~3-5 seconds (3 round trips) → ~1-2 seconds (1 round trip) +* *Tokens*: ~1,500 tokens (3 LLM calls) → ~500 tokens (1 LLM call) +* *Cost*: ~$0.0075 → ~$0.0025 (67% reduction) + +=== Orchestrator API + +// PLACEHOLDER: Confirm orchestrator API details + +*Tool Name*: `orchestrator` + +*Input Schema*: +[source,json] +---- +{ + "workflow": "string (JavaScript code)", + "context": "object (variables available to workflow)" +} +---- + + +*Available in Workflow*: +* `tools.{tool_name}(params)`: Call any tool from approved MCP servers +* `context.{variable}`: Access context variables +* Standard JavaScript: `if`, `for`, `while`, `try/catch`, `async/await` + +*Security*: +* Sandboxed execution (no file system, network, or system access) +* Timeout: // PLACEHOLDER: e.g., 30 seconds +* Memory limit: // PLACEHOLDER: e.g., 128MB + +*Limitations*: +* Cannot call external APIs directly (must use MCP tools) +* Cannot import npm packages (built-in JS only) +* // PLACEHOLDER: Other limitations? + +=== Orchestrator example: data aggregation + +*Scenario*: Fetch user data from database, calculate summary statistics. + +[source,python] +---- +results = call_tool("orchestrator", { + "workflow": """ + // Fetch all premium users + const users = await tools.execute_sql({ + query: "SELECT * FROM users WHERE tier = 'premium'", + database: "prod" + }); + + // Calculate statistics + const stats = { + total: users.length, + by_region: {}, + avg_spend: 0 + }; + + let totalSpend = 0; + for (const user of users) { + // Count by region + if (!stats.by_region[user.region]) { + stats.by_region[user.region] = 0; + } + stats.by_region[user.region]++; + + // Sum spend + totalSpend += user.monthly_spend; + } + + stats.avg_spend = totalSpend / users.length; + + return stats; + """, + "context": {} +}) +---- + + +*Output*: +[source,json] +---- +{ + "total": 1250, + "by_region": { + "us-east": 600, + "us-west": 400, + "eu": 250 + }, + "avg_spend": 149.50 +} +---- + + +*vs Without Orchestrator*: +* Would require fetching all users to agent → agent processes → 2 round trips +* Orchestrator: All processing in gateway → 1 round trip + +=== Orchestrator best practices + +*DO*: +* Use for deterministic workflows (same input → same output) +* Use for sequential operations with dependencies +* Use for fallback patterns +* Handle errors with `try/catch` +* Keep workflows readable (add comments) + +*DON'T*: +* Use for workflows requiring LLM reasoning at each step (let agent handle that) +* Execute long-running operations (timeout will hit) +* Access external resources (use MCP tools instead) +* Execute untrusted user input (security risk) + +== MCP server administration + +=== Add MCP servers + +// PLACEHOLDER: Add UI path for MCP server management + +*Prerequisites*: +* MCP server URL +* Authentication method (if required) +* List of tools to enable + +*Steps*: +1. *Navigate to MCP Servers*: + * Console → AI Gateway → MCP Servers → Add Server + +2. *Configure Server*: + ```yaml + # PLACEHOLDER: Actual configuration format + name: database-server + url: https://mcp-database.example.com + authentication: + type: bearer_token + token: ${SECRET_REF} # Reference to secret + enabled_tools: + * execute_sql + * list_tables + * describe_table + ``` + +3. *Test Connection*: + * Gateway attempts connection to MCP server + * Verifies authentication + * Retrieves tool list + +4. *Enable Server*: + * Server status: Active + * Tools available to agents + +*Common MCP Servers*: +* *Database*: PostgreSQL, MySQL, MongoDB query tools +* *Filesystem*: Read/write/search files +* *API Integrations*: Slack, GitHub, Salesforce, Stripe +* *Search*: Web search, vector search, enterprise search +* *Code Execution*: Python, JavaScript sandboxes +* *Workflow*: Zapier, n8n integrations + +=== MCP server approval workflow + +*Why Approval is Required*: +* Security: Prevent agents from accessing unauthorized systems +* Governance: Control which tools are available +* Cost: Some tools are expensive (API calls, compute) +* Compliance: Audit trail of approved tools + +*Approval Process*: +// PLACEHOLDER: Confirm if there's an approval workflow or if admins directly enable servers + +1. *Request*: User/team requests MCP server +2. *Review*: Admin reviews security, cost, necessity +3. *Approval/Rejection*: Admin decision +4. *Configuration*: If approved, admin adds server to gateway + +*Rejected Server Behavior*: +* Server not listed in tool discovery +* Agent cannot query or invoke tools from this server +* Requests return `403 Forbidden` + +=== Restrict MCP server access + +*Per-Gateway Restrictions*: +[source,yaml] +---- +# PLACEHOLDER: Actual configuration format +gateways: + - name: production-gateway + mcp_servers: + allowed: + - database-server # Only this server allowed + denied: + - filesystem-server # Explicitly denied + + - name: staging-gateway + mcp_servers: + allowed: + - "*" # All approved servers allowed +---- + + +*Use Cases*: +* Production gateway: Only production-safe tools +* Staging gateway: All tools for testing +* Customer-specific gateway: Only tools relevant to customer + +=== MCP server versioning + +// PLACEHOLDER: How is MCP server versioning handled? + +*Challenge*: MCP server updates may change tool schemas + +*Recommendations*: +1. *Pin Versions* (if supported): + ```yaml + mcp_servers: + * name: database-server + version: "1.2.3" # Pin to specific version + ``` + +2. *Test in Staging First*: + * Update MCP server in staging gateway + * Test agent workflows + * Promote to production when validated + +3. *Monitor Breaking Changes*: + * Subscribe to MCP server changelogs + * Set up alerts for schema changes + +== MCP observability + +=== Logs + +MCP tool invocations appear in request logs with: +* Tool name +* MCP server +* Input parameters +* Output result +* Execution time +* Errors (if any) + +*Filter Logs by MCP*: +[source,text] +---- +Filter: request.path.startsWith("/mcp") +---- + + +*Common Log Fields*: +| Field | Description | Example | +|-------|-------------|---------| +| Tool | Tool invoked | `execute_sql` | +| MCP Server | Which server handled it | `database-server` | +| Input | Parameters sent | `{"query": "SELECT ..."}` | +| Output | Result returned | `[{"id": 1, ...}]` | +| Latency | Tool execution time | `250ms` | +| Status | Success/failure | `200`, `500` | + +=== Metrics + +// PLACEHOLDER: Confirm if MCP-specific metrics exist + +*MCP-Specific Metrics* (if available): +* MCP requests per second +* Tool invocation count (by tool, by MCP server) +* MCP latency (p50, p95, p99) +* MCP error rate (by server, by tool) +* Orchestrator execution count +* Orchestrator execution time + +*Dashboard*: MCP Analytics +* Top tools by usage +* Top MCP servers by latency +* Error rate by MCP server +* Token savings from deferred loading + +=== Debug MCP issues + +*Issue: "Tool not found"* + +*Possible Causes*: +1. MCP server not added to gateway +2. Tool not enabled in MCP server configuration +3. Deferred loading enabled but agent didn't query for tool first + +*Solution*: +1. Verify MCP server is active: // PLACEHOLDER: UI path +2. Verify tool is in enabled_tools list +3. If deferred loading: Agent must call `search_tools` first + +*Issue: "MCP server timeout"* + +*Possible Causes*: +1. MCP server is down/unreachable +2. Tool execution is slow (e.g., expensive database query) +3. Gateway timeout too short + +*Solution*: +1. Check MCP server health +2. Optimize tool (e.g., add database index) +3. Increase timeout: // PLACEHOLDER: How to configure? + +*Issue: "Orchestrator workflow failed"* + +*Possible Causes*: +1. JavaScript syntax error +2. Tool invocation failed inside workflow +3. Timeout exceeded +4. Memory limit exceeded + +*Solution*: +1. Test workflow syntax in JavaScript playground +2. Check logs for tool error inside orchestrator +3. Simplify workflow or increase timeout +4. Reduce data processing in workflow + +== Security considerations + +=== Tool execution sandboxing + +// PLACEHOLDER: Confirm sandboxing implementation + +*Orchestrator Sandbox*: +* No file system access +* No network access (except via MCP tools) +* No system calls +* Memory limit: // PLACEHOLDER: e.g., 128MB +* Execution timeout: // PLACEHOLDER: e.g., 30s + +*MCP Tool Execution*: +* Tools execute in MCP server's environment (not gateway) +* Gateway does not execute tool code (only proxies requests) +* Security is MCP server's responsibility + +=== Authentication + +*Gateway → MCP Server*: +* Bearer token (most common) +* API key +* mTLS (for high-security environments) + +*Agent → Gateway*: +* Standard gateway authentication (Redpanda Cloud token) +* `rp-aigw-id` header identifies gateway (and its approved MCP servers) + +=== Audit trail + +All MCP operations logged: +* Who (agent/user) invoked tool +* When (timestamp) +* What tool was invoked +* What parameters were sent +* What result was returned +* Whether it succeeded or failed + +*Use Case*: Compliance, security investigation, debugging + +=== Restrict dangerous tools + +*Recommendation*: Don't enable destructive tools in production gateways + +*Examples of Dangerous Tools*: +* File deletion (`delete_file`) +* Database writes without safeguards (`execute_sql` with UPDATE/DELETE) +* Payment operations (`charge_customer`) +* System commands (`execute_bash`) + +*Best Practice*: +* Read-only tools in production gateway +* Write tools only in staging gateway (with approval workflows) +* Wrap dangerous operations in MCP server with safeguards (e.g., "require confirmation token") + +== MCP + LLM routing + +=== Combine MCP with CEL routing + +*Use Case*: Route agents to different MCP servers based on customer tier + +*CEL Expression*: +[source,cel] +---- +request.headers["x-customer-tier"] == "enterprise" + ? "gateway-with-premium-mcp-servers" + : "gateway-with-basic-mcp-servers" +---- + + +*Result*: +* Enterprise customers: Access to proprietary data, expensive APIs +* Basic customers: Access to public data, free APIs + +=== MCP with provider pools + +*Scenario*: Different agents use different models + different tools + +*Configuration*: +* Gateway A: GPT-4o + database + CRM MCP servers +* Gateway B: Claude Sonnet + web search + analytics MCP servers + +*Use Case*: Optimize model-tool pairing (some models better at certain tools) + +== Integration examples + +=== Python (openai sdk) + +[source,python] +---- +from openai import OpenAI + +# Initialize client with MCP endpoint +client = OpenAI( + base_url="https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1", + api_key=os.getenv("REDPANDA_CLOUD_TOKEN"), + default_headers={ + "rp-aigw-id": os.getenv("GATEWAY_ID"), + "rp-aigw-mcp-deferred": "true" # Enable deferred loading + } +) + +# Discover tools +tools_response = requests.get( + "https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/mcp/tools", + headers={ + "Authorization": f"Bearer {os.getenv('REDPANDA_CLOUD_TOKEN')}", + "rp-aigw-id": os.getenv("GATEWAY_ID"), + "rp-aigw-mcp-deferred": "true" + } +) +tools = tools_response.json()["tools"] + +# Agent uses tools +response = client.chat.completions.create( + model="anthropic/claude-sonnet-3.5", + messages=[ + {"role": "user", "content": "Query the database for premium users"} + ], + tools=tools, # Pass MCP tools to agent + tool_choice="auto" +) + +# Handle tool calls +if response.choices[0].message.tool_calls: + for tool_call in response.choices[0].message.tool_calls: + # Execute tool via gateway + tool_result = requests.post( + f"https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/mcp/tools/{tool_call.function.name}", + headers={ + "Authorization": f"Bearer {os.getenv('REDPANDA_CLOUD_TOKEN')}", + "rp-aigw-id": os.getenv("GATEWAY_ID") + }, + json=json.loads(tool_call.function.arguments) + ) + + # Continue conversation with tool result + response = client.chat.completions.create( + model="anthropic/claude-sonnet-3.5", + messages=[ + {"role": "user", "content": "Query the database for premium users"}, + response.choices[0].message, + { + "role": "tool", + "tool_call_id": tool_call.id, + "content": json.dumps(tool_result.json()) + } + ] + ) +---- + + +=== Claude code cli + +[source,bash] +---- +# Configure gateway with MCP +export CLAUDE_API_BASE="https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1" +export ANTHROPIC_API_KEY="your-redpanda-token" + +# Claude Code automatically discovers MCP tools from gateway +claude code + +# Agent can now use aggregated MCP tools +---- + + +=== LangChain + +[source,python] +---- +from langchain_openai import ChatOpenAI +from langchain.agents import initialize_agent, Tool + +# Initialize LLM with gateway +llm = ChatOpenAI( + base_url="https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1", + api_key=os.getenv("REDPANDA_CLOUD_TOKEN"), + default_headers={ + "rp-aigw-id": os.getenv("GATEWAY_ID") + } +) + +# Fetch MCP tools from gateway +# PLACEHOLDER: LangChain-specific integration code + +# Create agent with MCP tools +agent = initialize_agent( + tools=mcp_tools, + llm=llm, + agent="openai-tools", + verbose=True +) + +# Agent can now use MCP tools +response = agent.run("Find all premium users in the database") +---- + + +== Next steps + +* *Configure MCP Servers* → [MCP Server Administration Guide](// PLACEHOLDER: link) +* *Write Orchestrator Workflows* → [Orchestrator Examples](// PLACEHOLDER: link) +* *Monitor MCP Usage* → [Observability: MCP Metrics](// PLACEHOLDER: link) +* *Optimize Token Costs* → [Cost Optimization Guide](// PLACEHOLDER: link) +* *Build Agentic Workflows* → [Agent Patterns Guide](// PLACEHOLDER: link) + +== Related pages + +* [Quickstart](// PLACEHOLDER: link) +* [CEL Routing](// PLACEHOLDER: link) +* [Observability: Logs](// PLACEHOLDER: link) +* [Security & Data Handling](// PLACEHOLDER: link) diff --git a/modules/ai-agents/partials/migration-guide.adoc b/modules/ai-agents/partials/migration-guide.adoc new file mode 100644 index 000000000..ffdd54a17 --- /dev/null +++ b/modules/ai-agents/partials/migration-guide.adoc @@ -0,0 +1,866 @@ += Migration guide: from direct provider integration to AI gateway + +== Overview + +This guide helps you migrate existing applications from direct LLM provider integrations (OpenAI, Anthropic, etc.) to Redpanda AI Gateway. The migration is designed to be *incremental and reversible*, allowing you to test thoroughly before fully committing. + +*Migration Time*: 10-30 minutes for most applications +*Downtime Required*: None (supports parallel operation) +*Rollback Difficulty*: Easy (feature flag or environment variable) + +== Prerequisites + +Before migrating, ensure you have: +* ✅ AI Gateway configured in your Redpanda Cloud account +* ✅ Providers and models enabled (see [Admin Guide: Providers](// PLACEHOLDER: link)) +* ✅ Gateway created with appropriate policies (see [Gateway Creation Guide](// PLACEHOLDER: link)) +* ✅ Your gateway ID (`rp-aigw-id` header value) +* ✅ Your gateway endpoint URL + + + +== Migration strategy + +=== Recommended approach: parallel operation + +Run both direct and gateway-routed requests simultaneously to validate behavior before full cutover. + +[source,text] +---- +┌─────────────────┐ +│ Application │ +└────────┬────────┘ + │ + ┌────▼─────┐ + │ Feature │ + │ Flag │ + └────┬─────┘ + │ + ┌────▼──────────────┐ + │ │ +┌───▼─────┐ ┌─────▼─────┐ +│ Direct │ │ Gateway │ +│Provider │ │ Route │ +└─────────┘ └───────────┘ +---- + + +*Benefits*: +* No downtime +* Easy rollback +* Compare results side-by-side +* Gradual traffic shift + +== Step-by-step migration + +=== Step 1: add environment variables + +Add gateway configuration to your environment without removing existing provider keys (yet). + +*.env (or equivalent)* +[source,bash] +---- +# Existing (keep these for now) +OPENAI_API_KEY=sk-... +ANTHROPIC_API_KEY=sk-ant-... + +# New gateway configuration +REDPANDA_AI_GATEWAY_URL=https://{GATEWAY_ENDPOINT} +REDPANDA_AI_GATEWAY_ID={GATEWAY_ID} +REDPANDA_AI_GATEWAY_TOKEN={YOUR_TOKEN} + +# Feature flag (start with gateway disabled) +USE_AI_GATEWAY=false +---- + + +=== Step 2: update your code + +==== Option a: OpenAI SDK (recommended for most use cases) + +*Before (Direct OpenAI)* +[source,python] +---- +from openai import OpenAI + +client = OpenAI( + api_key=os.getenv("OPENAI_API_KEY") +) + +response = client.chat.completions.create( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}] +) +---- + + +*After (Gateway-Routed with Feature Flag)* +[source,python] +---- +from openai import OpenAI +import os + +# Feature flag determines which client to use +use_gateway = os.getenv("USE_AI_GATEWAY", "false").lower() == "true" + +if use_gateway: + client = OpenAI( + base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} + ) + model = "openai/gpt-4o" # Add vendor prefix +else: + client = OpenAI( + api_key=os.getenv("OPENAI_API_KEY") + ) + model = "gpt-4o" # Original model name + +response = client.chat.completions.create( + model=model, + messages=[{"role": "user", "content": "Hello"}] +) +---- + + +*Better: Abstraction Function* +[source,python] +---- +from openai import OpenAI +import os + +def get_llm_client(): + """Returns configured OpenAI client (direct or gateway-routed)""" + use_gateway = os.getenv("USE_AI_GATEWAY", "false").lower() == "true" + + if use_gateway: + return OpenAI( + base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} + ) + else: + return OpenAI(api_key=os.getenv("OPENAI_API_KEY")) + +def get_model_name(base_model: str) -> str: + """Returns model name with vendor prefix if using gateway""" + use_gateway = os.getenv("USE_AI_GATEWAY", "false").lower() == "true" + return f"openai/{base_model}" if use_gateway else base_model + +# Usage +client = get_llm_client() +response = client.chat.completions.create( + model=get_model_name("gpt-4o"), + messages=[{"role": "user", "content": "Hello"}] +) +---- + + +==== Option b: Anthropic SDK + +*Before (Direct Anthropic)* +[source,python] +---- +from anthropic import Anthropic + +client = Anthropic( + api_key=os.getenv("ANTHROPIC_API_KEY") +) + +response = client.messages.create( + model="claude-sonnet-3.5", + max_tokens=1024, + messages=[{"role": "user", "content": "Hello"}] +) +---- + + +*After (Gateway via OpenAI-Compatible Wrapper)* + +Because AI Gateway provides an OpenAI-compatible endpoint, we recommend migrating Anthropic SDK usage to OpenAI SDK for consistency: + +[source,python] +---- +from openai import OpenAI +import os + +use_gateway = os.getenv("USE_AI_GATEWAY", "false").lower() == "true" + +if use_gateway: + # Use OpenAI SDK with gateway + client = OpenAI( + base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} + ) + + response = client.chat.completions.create( + model="anthropic/claude-sonnet-3.5", + max_tokens=1024, + messages=[{"role": "user", "content": "Hello"}] + ) +else: + # Keep existing Anthropic SDK + from anthropic import Anthropic + client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY")) + + response = client.messages.create( + model="claude-sonnet-3.5", + max_tokens=1024, + messages=[{"role": "user", "content": "Hello"}] + ) +---- + + +*Alternative: Keep Anthropic SDK with base_url Override* + +// PLACEHOLDER: Verify if Anthropic SDK supports base_url override for OpenAI-compatible endpoints + +[source,python] +---- +from anthropic import Anthropic + +use_gateway = os.getenv("USE_AI_GATEWAY", "false").lower() == "true" + +if use_gateway: + client = Anthropic( + base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), # If supported + api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} + ) +else: + client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY")) +---- + + +==== Option c: multiple providers + +*Before (Separate SDKs)* +[source,python] +---- +from openai import OpenAI +from anthropic import Anthropic + +openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) +anthropic_client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY")) + +# Different code paths +if use_openai: + response = openai_client.chat.completions.create(...) +else: + response = anthropic_client.messages.create(...) +---- + + +*After (Unified via Gateway)* +[source,python] +---- +from openai import OpenAI + +# Single client for all providers +client = OpenAI( + base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} +) + +# Same code, different models +if use_openai: + response = client.chat.completions.create( + model="openai/gpt-4o", + messages=[...] + ) +else: + response = client.chat.completions.create( + model="anthropic/claude-sonnet-3.5", + messages=[...] + ) +---- + + +=== Step 3: test gateway connection + +Before changing the feature flag, verify gateway connectivity: + +*Python Test Script* +[source,python] +---- +from openai import OpenAI +import os + +def test_gateway_connection(): + client = OpenAI( + base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} + ) + + try: + response = client.chat.completions.create( + model="openai/gpt-4o-mini", # Use cheap model for testing + messages=[{"role": "user", "content": "Test"}], + max_tokens=10 + ) + print("✅ Gateway connection successful") + print(f"Response: {response.choices[0].message.content}") + return True + except Exception as e: + print(f"❌ Gateway connection failed: {e}") + return False + +if __name__ == "__main__": + test_gateway_connection() +---- + + +*Expected Output*: +[source,text] +---- +✅ Gateway connection successful +Response: Hello +---- + + +*Common Issues*: +* `401 Unauthorized` → Check `REDPANDA_AI_GATEWAY_TOKEN` +* `404 Not Found` → Check `REDPANDA_AI_GATEWAY_URL` (should end with `/v1/chat/completions` or base path) +* `Model not found` → Ensure model is enabled in gateway configuration +* No `rp-aigw-id` header → Verify header is set in `default_headers` + +See [Troubleshooting Guide](// PLACEHOLDER: link) for more details. + +=== Step 4: verify in observability dashboard + +After successful test: +1. Open AI Gateway observability dashboard +2. Navigate to // PLACEHOLDER: specific UI path, e.g., "Gateways → {GATEWAY_NAME} → Logs" +3. Verify your test request appears +4. Check fields: + * ✅ Model: `openai/gpt-4o-mini` + * ✅ Provider: OpenAI + * ✅ Status: 200 + * ✅ Token count: ~10 prompt + ~10 completion + * ✅ Cost: // PLACEHOLDER: expected cost + +*If request doesn't appear*: Check [End-to-End Validation Guide](// PLACEHOLDER: link) + +=== Step 5: enable gateway for subset of traffic + +Gradually roll out gateway usage: + +*Staged Rollout Strategy*: +1. *Week 1*: Internal testing only (dev team accounts) +2. *Week 2*: 10% of production traffic +3. *Week 3*: 50% of production traffic +4. *Week 4*: 100% of production traffic + +*Implementation Options*: + +*Option A: Environment-Based* +[source,python] +---- +# Enable gateway in staging first +use_gateway = os.getenv("ENVIRONMENT") in ["staging", "production"] +---- + + +*Option B: Percentage-Based* +[source,python] +---- +import random + +# Route 10% of traffic through gateway +use_gateway = random.random() < 0.10 +---- + + +*Option C: User-Based* +[source,python] +---- +# Enable for internal users first +use_gateway = user.email.endswith("@yourcompany.com") +---- + + +*Option D: Feature Flag Service* (recommended) +[source,python] +---- +# LaunchDarkly, Split.io, etc. +use_gateway = feature_flags.is_enabled("ai-gateway", user_context) +---- + + +=== Step 6: monitor and compare + +During parallel operation, compare metrics: + +*Metrics to Monitor*: +| Metric | Direct | Gateway | Notes | +|--------|--------|---------|-------| +| Success rate | // track | // track | Should be identical | +| Latency p50 | // track | // track | Gateway adds ~// PLACEHOLDER: Xms | +| Latency p99 | // track | // track | Watch for outliers | +| Error rate | // track | // track | Should be identical | +| Cost per 1K requests | // track | // track | Compare estimated costs | + +*Monitoring Code Example*: +[source,python] +---- +import time + +def call_llm_with_metrics(use_gateway: bool, model: str, messages: list): + start_time = time.time() + + try: + client = get_llm_client(use_gateway) + response = client.chat.completions.create( + model=model, + messages=messages + ) + + latency = time.time() - start_time + + # Log metrics + metrics.record("llm.request.success", 1, tags={ + "routing": "gateway" if use_gateway else "direct", + "model": model + }) + metrics.record("llm.request.latency", latency, tags={ + "routing": "gateway" if use_gateway else "direct" + }) + + return response + + except Exception as e: + metrics.record("llm.request.error", 1, tags={ + "routing": "gateway" if use_gateway else "direct", + "error": str(e) + }) + raise +---- + + +=== Step 7: full cutover + +Once metrics confirm gateway reliability: + +1. *Set feature flag to 100%*: + ```bash + USE_AI_GATEWAY=true + ``` + +2. *Deploy updated configuration* + +3. *Monitor for 24-48 hours* + +4. *Remove direct provider credentials* (optional, for security): + ```bash + # .env + # OPENAI_API_KEY=sk-... # Remove after confirming gateway stability + # ANTHROPIC_API_KEY=sk-ant-... # Remove after confirming gateway stability + + REDPANDA_AI_GATEWAY_URL=https://{GATEWAY_ENDPOINT} + REDPANDA_AI_GATEWAY_ID={GATEWAY_ID} + REDPANDA_AI_GATEWAY_TOKEN={YOUR_TOKEN} + ``` + +5. *Remove direct integration code* (optional, for cleanup): + ```python + # Remove feature flag logic, keep only gateway path + client = OpenAI( + base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} + ) + ``` + +== Rollback procedure + +If issues arise, rollback is simple: + +*Emergency Rollback (< 1 minute)*: +[source,bash] +---- +# Set feature flag back to false +USE_AI_GATEWAY=false + +# Restart application (if needed) +---- + + +*Gradual Rollback*: +[source,python] +---- +# Reduce gateway traffic percentage +use_gateway = random.random() < 0.50 # Back to 50% +use_gateway = random.random() < 0.10 # Back to 10% +use_gateway = False # Back to 0% +---- + + +*Keep direct provider credentials until you're confident in gateway stability.* + +== Framework-specific migration + +=== LangChain + +*Before* +[source,python] +---- +from langchain_openai import ChatOpenAI + +llm = ChatOpenAI( + model="gpt-4o", + api_key=os.getenv("OPENAI_API_KEY") +) +---- + + +*After* +[source,python] +---- +from langchain_openai import ChatOpenAI + +use_gateway = os.getenv("USE_AI_GATEWAY", "false").lower() == "true" + +if use_gateway: + llm = ChatOpenAI( + model="openai/gpt-4o", + base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} + ) +else: + llm = ChatOpenAI( + model="gpt-4o", + api_key=os.getenv("OPENAI_API_KEY") + ) +---- + + +=== LlamaIndex + +*Before* +[source,python] +---- +from llama_index.llms.openai import OpenAI + +llm = OpenAI(model="gpt-4o") +---- + + +*After* +[source,python] +---- +from llama_index.llms.openai import OpenAI + +use_gateway = os.getenv("USE_AI_GATEWAY", "false").lower() == "true" + +if use_gateway: + llm = OpenAI( + model="openai/gpt-4o", + api_base=os.getenv("REDPANDA_AI_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), + additional_kwargs={"headers": {"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")}} + ) +else: + llm = OpenAI(model="gpt-4o") +---- + + +// PLACEHOLDER: Verify LlamaIndex syntax for custom headers + +=== Vercel AI SDK (javascript/typescript) + +*Before* +[source,typescript] +---- +import { openai } from '@ai-sdk/openai'; + +const model = openai('gpt-4o'); +---- + + +*After* +[source,typescript] +---- +import { openai } from '@ai-sdk/openai'; + +const useGateway = process.env.USE_AI_GATEWAY === 'true'; + +const model = useGateway + ? openai('openai/gpt-4o', { + baseURL: process.env.REDPANDA_AI_GATEWAY_URL, + apiKey: process.env.REDPANDA_AI_GATEWAY_TOKEN, + headers: { + 'rp-aigw-id': process.env.REDPANDA_AI_GATEWAY_ID, + }, + }) + : openai('gpt-4o'); +---- + + +// PLACEHOLDER: Verify Vercel AI SDK syntax + +== Migration checklist + +Use this checklist to track your migration: + +* [ ] *Prerequisites* + * [ ] Gateway configured and tested + * [ ] Providers enabled + * [ ] Models enabled + * [ ] Gateway ID and endpoint URL obtained + +* [ ] *Code Changes* + * [ ] Environment variables added + * [ ] Feature flag implemented + * [ ] Client initialization updated + * [ ] Model name prefix added (vendor/model_id) + * [ ] Headers added (rp-aigw-id) + +* [ ] *Testing* + * [ ] Gateway connection test passes + * [ ] Test request visible in observability dashboard + * [ ] Integration tests pass with gateway + * [ ] End-to-end tests pass with gateway + +* [ ] *Staged Rollout* + * [ ] Week 1: Internal testing (dev team only) + * [ ] Week 2: 10% production traffic + * [ ] Week 3: 50% production traffic + * [ ] Week 4: 100% production traffic + +* [ ] *Monitoring* + * [ ] Success rate comparison (direct vs gateway) + * [ ] Latency comparison (direct vs gateway) + * [ ] Error rate comparison (direct vs gateway) + * [ ] Cost comparison (direct vs gateway) + +* [ ] *Cleanup* (optional, after 30 days stable) + * [ ] Remove direct provider credentials + * [ ] Remove feature flag logic + * [ ] Update documentation + * [ ] Archive direct integration code + +== Common migration issues + +=== Issue: "model not found" error + +*Symptom*: +[source,text] +---- +Error: Model 'openai/gpt-4o' not found +---- + + +*Causes*: +1. Model not enabled in gateway configuration +2. Wrong model name format (missing vendor prefix) +3. Typo in model name + +*Solution*: +1. Verify model is enabled: // PLACEHOLDER: UI path or CLI command +2. Confirm format: `vendor/model_id` (e.g., `openai/gpt-4o`, not `gpt-4o`) +3. Check supported models: // PLACEHOLDER: link to model catalog + +=== Issue: missing `rp-aigw-id` header + +*Symptom*: +[source,text] +---- +Error: Missing required header 'rp-aigw-id' +---- + + +*Solution*: +[source,python] +---- +# Ensure header is set in default_headers +client = OpenAI( + base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} # ← Required +) +---- + + +=== Issue: higher latency than expected + +*Expected Gateway Overhead*: // PLACEHOLDER: Xms p50, Yms p99 + +*If latency is significantly higher*: +1. Check geographic routing (gateway → provider region) +2. Verify provider pool configuration (no unnecessary fallbacks) +3. Review CEL routing complexity +4. Check for rate limiting (adds retry latency) + +*Solution*: See [Performance Optimization Guide](// PLACEHOLDER: link) + +=== Issue: requests not appearing in dashboard + +*Causes*: +1. Wrong gateway ID +2. Request failed before reaching gateway +3. UI delay (logs may take // PLACEHOLDER: Xs to appear) + +*Solution*: See [End-to-End Validation Guide](// PLACEHOLDER: link) + +=== Issue: different response format + +*Symptom*: Response structure differs between direct and gateway + +// PLACEHOLDER: Confirm if response format is identical to OpenAI API or if there are differences + +*Solution*: +* AI Gateway should return OpenAI-compatible responses +* If differences exist, file a support ticket with request ID from logs + +== Advanced migration scenarios + +=== Scenario: custom request timeouts + +*Before* +[source,python] +---- +client = OpenAI(api_key=..., timeout=30.0) +---- + + +*After* +[source,python] +---- +client = OpenAI( + base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")}, + timeout=30.0 # Still supported +) +---- + + +=== Scenario: streaming responses + +// PLACEHOLDER: Verify streaming support + +*Before* +[source,python] +---- +stream = client.chat.completions.create( + model="gpt-4o", + messages=[...], + stream=True +) + +for chunk in stream: + print(chunk.choices[0].delta.content, end="") +---- + + +*After* +[source,python] +---- +stream = client.chat.completions.create( + model="openai/gpt-4o", # Add vendor prefix + messages=[...], + stream=True +) + +for chunk in stream: + print(chunk.choices[0].delta.content, end="") +---- + + +=== Scenario: custom headers (e.g., user tracking) + +*Before* +[source,python] +---- +response = client.chat.completions.create( + model="gpt-4o", + messages=[...], + extra_headers={"X-User-ID": user.id} +) +---- + + +*After* +[source,python] +---- +response = client.chat.completions.create( + model="openai/gpt-4o", + messages=[...], + extra_headers={ + "X-User-ID": user.id, # Custom headers still supported + "rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID") # Required gateway header + } +) +---- + + +*Note*: Gateway may use custom headers for routing (e.g., CEL expressions can reference `request.headers["X-User-ID"]`) + +== Post-migration benefits + +After successful migration, you gain: + +=== 1. simplified provider management +[source,python] +---- +# Switch providers with one config change (no code changes) +model = "anthropic/claude-sonnet-3.5" # Was openai/gpt-4o +---- + + +=== 2. unified observability +* All requests in one dashboard +* Cross-provider cost comparison +* Session reconstruction across models + +=== 3. automatic failover +* Configure once, benefit everywhere +* No application-level retry logic needed + +=== 4. cost controls +* Enforce budgets centrally +* Rate limit per team/customer +* No surprises in cloud bills + +=== 5. a/b testing +* Test new models without code changes +* Compare quality/cost/latency +* Gradual rollout via routing policies + +== Next steps + +* *Configure routing policies* → [CEL Routing Guide](// PLACEHOLDER: link) +* *Set up failover* → [Provider Pools Guide](// PLACEHOLDER: link) +* *Add rate limits* → [Rate Limiting Guide](// PLACEHOLDER: link) +* *Explore MCP* → [MCP Aggregation Guide](// PLACEHOLDER: link) +* *Optimize costs* → [Cost Optimization Guide](// PLACEHOLDER: link) + +== Get help + +If you encounter issues during migration: +* Check [Troubleshooting Guide](// PLACEHOLDER: link) +* Review [End-to-End Validation](// PLACEHOLDER: link) +* Contact support: // PLACEHOLDER: support email or portal +* Community: // PLACEHOLDER: Slack, Discord, or forum link + +''' + +*Related Pages*: +* [Quickstart](// PLACEHOLDER: link) +* [Troubleshooting](// PLACEHOLDER: link) +* [OpenAI Integration](// PLACEHOLDER: link) +* [Anthropic Integration](// PLACEHOLDER: link) +* [LangChain Integration](// PLACEHOLDER: link) diff --git a/modules/ai-agents/partials/observability-logs.adoc b/modules/ai-agents/partials/observability-logs.adoc new file mode 100644 index 000000000..fed03f2d5 --- /dev/null +++ b/modules/ai-agents/partials/observability-logs.adoc @@ -0,0 +1,631 @@ += Observability: request logs + +== Overview + +AI Gateway logs every LLM request that passes through it, capturing the full request/response history, token usage, cost, latency, and routing decisions. This page explains how to find, filter, and interpret request logs. + +*Use Logs For*: +* Debugging specific failed requests +* Reconstructing user conversation sessions +* Auditing what prompts were sent and responses received +* Understanding which provider handled a request +* Investigating latency spikes or errors for specific users + +*Use Metrics For*: Aggregate analytics, trends, cost tracking across time → See [Observability: Metrics](// PLACEHOLDER: link) + +== Where to find logs + +// PLACEHOLDER: Add exact UI navigation path + +1. *Navigate to Logs View*: + * Console → AI Gateway → // PLACEHOLDER: exact path + * Or: Gateway detail page → Logs tab + +2. *Select Gateway*: + * Filter by specific gateway, or view all gateways + * // PLACEHOLDER: screenshot of gateway selector + +3. *Set Time Range*: + * Default: Last 1 hour + * Options: Last 5 minutes, 1 hour, 24 hours, 7 days, 30 days, Custom + * // PLACEHOLDER: screenshot of time range picker + +== Request log fields + +Each log entry contains: + +=== Core request info + +| Field | Description | Example | +|-------|-------------|---------| +| *Request ID* | Unique identifier for this request | `req_abc123...` | +| *Timestamp* | When request was received (UTC) | `2025-01-11T14:32:10.123Z` | +| *Gateway ID* | Which gateway handled this request | `gw_abc123...` | +| *Gateway Name* | Human-readable gateway name | `production-gateway` | +| *Status* | HTTP status code | `200`, `400`, `429`, `500` | +| *Latency* | Total request duration (ms) | `1250ms` | + +=== Model & provider info + +| Field | Description | Example | +|-------|-------------|---------| +| *Requested Model* | Model specified in request | `openai/gpt-4o` | +| *Actual Model* | Model that handled request (may differ due to routing) | `anthropic/claude-sonnet-3.5` | +| *Provider* | Which provider handled the request | `OpenAI`, `Anthropic` | +| *Provider Pool* | Pool used (primary/fallback) | `primary`, `fallback` | +| *Fallback Triggered* | Whether fallback was used | `true`/`false` | +| *Fallback Reason* | Why fallback occurred | `rate_limit`, `timeout`, `5xx_error` | + +=== Token & cost info + +| Field | Description | Example | +|-------|-------------|---------| +| *Prompt Tokens* | Input tokens consumed | `523` | +| *Completion Tokens* | Output tokens generated | `187` | +| *Total Tokens* | Prompt + completion | `710` | +| *Estimated Cost* | Calculated cost for this request | `$0.0142` | +| *Cost Breakdown* | Per-token costs | `Prompt: $0.005, Completion: $0.0092` | + +=== Request content (expandable) + +| Field | Description | Notes | +|-------|-------------|-------| +| *Request Headers* | All headers sent | Includes `rp-aigw-id`, custom headers | +| *Request Body* | Full request payload | Includes messages, parameters | +| *Response Headers* | Headers returned | // PLACEHOLDER: Any gateway-specific headers? | +| *Response Body* | Full response payload | Includes message content, metadata | + +=== Routing & policy info + +| Field | Description | Example | +|-------|-------------|---------| +| *CEL Expression* | Routing rule applied (if any) | `request.headers["tier"] == "premium" ? ...` | +| *CEL Result* | Model selected by CEL | `openai/gpt-4o` | +| *Rate Limit Status* | Whether rate limited | `allowed`, `throttled`, `blocked` | +| *Spend Limit Status* | Whether budget exceeded | `allowed`, `blocked` | +| *Policy Stage* | Where request was processed/blocked | `rate_limit`, `routing`, `execution` | + +=== Error info (if applicable) + +| Field | Description | Example | +|-------|-------------|---------| +| *Error Code* | Gateway or provider error code | `RATE_LIMIT_EXCEEDED`, `MODEL_NOT_FOUND` | +| *Error Message* | Human-readable error | `Request rate limit exceeded for gateway` | +| *Provider Error* | Upstream provider error | `OpenAI API returned 429: Rate limit exceeded` | + +== Filter logs + +=== By gateway + +// PLACEHOLDER: Screenshot of gateway filter dropdown + +[source,text] +---- +Filter: Gateway = "production-gateway" +---- + + +Shows only requests for the selected gateway. + +*Use Case*: Isolate production traffic from staging + +=== By model + +// PLACEHOLDER: Screenshot of model filter + +[source,text] +---- +Filter: Model = "openai/gpt-4o" +---- + + +Shows only requests for specific model. + +*Use Case*: Compare quality/cost between models + +=== By provider + +[source,text] +---- +Filter: Provider = "OpenAI" +---- + + +Shows only requests handled by specific provider. + +*Use Case*: Investigate provider-specific issues + +=== By status + +[source,text] +---- +Filter: Status = "429" +---- + + +Shows only requests with specific HTTP status. + +*Common Filters*: +* `200`: Successful requests +* `400`: Bad requests (client errors) +* `401`: Authentication errors +* `429`: Rate limited requests +* `500`: Server errors +* `5xx`: All server errors + +*Use Case*: Find all failed requests + +=== By time range + +[source,text] +---- +Filter: Timestamp >= "2025-01-11T14:00:00Z" AND Timestamp <= "2025-01-11T15:00:00Z" +---- + + +*Use Case*: Investigate incident during specific time window + +=== By custom header + +[source,text] +---- +Filter: request.headers["x-user-id"] = "user_123" +---- + + +Shows only requests for specific user. + +*Use Case*: Debug user-reported issue + +=== By token range + +[source,text] +---- +Filter: Total Tokens > 10000 +---- + + +Shows only high-token requests. + +*Use Case*: Find expensive requests + +=== By latency + +[source,text] +---- +Filter: Latency > 5000ms +---- + + +Shows only slow requests. + +*Use Case*: Investigate performance issues + +=== Combined filters + +[source,text] +---- +Gateway = "production-gateway" +AND Status >= 500 +AND Timestamp >= "last 24 hours" +---- + + +Shows production server errors in last 24 hours. + +// PLACEHOLDER: Screenshot of multiple filters applied + +== Search logs + +=== Full-text search (if supported) + +// PLACEHOLDER: Confirm if full-text search is available + +[source,text] +---- +Search: "specific error message" +---- + + +Searches across all text fields (error messages, request/response content). + +=== Search by request content + +[source,text] +---- +Search in Request Body: "user's actual question" +---- + + +Find requests containing specific prompt text. + +*Use Case*: "A user said the AI gave a wrong answer about X" → Search for "X" in prompts + +=== Search by response content + +[source,text] +---- +Search in Response Body: "specific AI response phrase" +---- + + +Find responses containing specific text. + +*Use Case*: Find all requests where AI mentioned a competitor name + +== Inspect individual requests + +Click any log entry to expand full details. + +// PLACEHOLDER: Screenshot of expanded log entry + +=== Request details tab + +Shows: +* Full request headers +* Full request body (formatted JSON) +* All parameters (temperature, max_tokens, etc.) +* Custom headers used for routing + +*Example*: +[source,json] +---- +{ + "model": "openai/gpt-4o", + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant." + }, + { + "role": "user", + "content": "What is Redpanda?" + } + ], + "temperature": 0.7, + "max_tokens": 500 +} +---- + + +=== Response details tab + +Shows: +* Full response headers +* Full response body (formatted JSON) +* Finish reason (`stop`, `length`, `content_filter`) +* Response metadata + +*Example*: +[source,json] +---- +{ + "id": "chatcmpl-...", + "choices": [ + { + "message": { + "role": "assistant", + "content": "Redpanda is a streaming data platform..." + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 24, + "completion_tokens": 87, + "total_tokens": 111 + } +} +---- + + +=== Routing details tab + +Shows: +* CEL expression evaluated (if any) +* CEL result (which model was selected) +* Provider pool used (primary/fallback) +* Fallback trigger reason (if applicable) +* Rate limit evaluation (allowed/blocked) +* Spend limit evaluation (allowed/blocked) + +*Example*: +[source,yaml] +---- +CEL Expression: | + request.headers["x-user-tier"] == "premium" + ? "openai/gpt-4o" + : "openai/gpt-4o-mini" + +CEL Result: "openai/gpt-4o" + +Provider Pool: primary +Fallback Triggered: false + +Rate Limit: allowed (45/100 requests used) +Spend Limit: allowed ($1,234 / $50,000 budget used) +---- + + +=== Performance details tab + +Shows: +* Total latency breakdown + * Gateway processing time: // PLACEHOLDER: Xms + * Provider API call time: // PLACEHOLDER: Xms + * Network time: // PLACEHOLDER: Xms +* Token generation rate (tokens/second) +* Time to first token (for streaming, if supported) + +*Example*: +[source,text] +---- +Total Latency: 1,250ms +├─ Gateway Processing: 12ms +├─ Provider API Call: 1,215ms +└─ Network Overhead: 23ms + +Token Generation Rate: 71 tokens/second +---- + + +== Common log analysis tasks + +=== Task 1: "why did this request fail?" + +1. *Find the request*: + * Filter by timestamp (when user reported issue) + * Or search by request content + * Or filter by custom header (user ID) + +2. *Check Status*: + * `400` → Client error (bad request format, invalid parameters) + * `401` → Authentication issue + * `404` → Model not found + * `429` → Rate limited + * `500`/`5xx` → Provider or gateway error + +3. *Check Error Message*: + * Gateway error: Issue with configuration, rate limits, etc. + * Provider error: Issue with upstream API (OpenAI, Anthropic, etc.) + +4. *Check Routing*: + * Was fallback triggered? (May indicate primary provider issue) + * Was CEL rule applied correctly? + +*Common Causes*: +* Model not enabled in gateway +* Rate limit exceeded +* Monthly budget exceeded +* Invalid API key for provider +* Provider outage/rate limit +* Malformed request + +=== Task 2: "reconstruct a user's conversation" + +1. *Filter by user*: + ``` + Filter: request.headers["x-user-id"] = "user_123" + ``` + +2. *Sort by timestamp* (ascending) + +3. *Review conversation flow*: + * Each request shows prompt + * Each response shows AI reply + * Reconstruct full conversation thread + +*Use Case*: User says "the AI contradicted itself" → View full conversation history + +=== Task 3: "why is latency high for this user?" + +1. *Find user's requests*: + ``` + Filter: request.headers["x-user-id"] = "user_123" + AND Latency > 3000ms + ``` + +2. *Check Performance Details*: + * Is gateway processing slow? (Likely CEL complexity) + * Is provider API slow? (Upstream latency) + * Is token generation rate normal? (Tokens/second) + +3. *Compare to other requests*: + * Filter for same model + * Compare latency percentiles + * Identify if issue is user-specific or model-wide + +*Common Causes*: +* Complex CEL routing rules +* Provider performance degradation +* Large context windows (high token count) +* Network issues + +=== Task 4: "which requests used the fallback provider?" + +1. *Filter by fallback*: + ``` + Filter: Fallback Triggered = true + ``` + +2. *Group by Fallback Reason*: + * Rate limit exceeded (primary provider throttled) + * Timeout (primary provider slow) + * 5xx error (primary provider error) + +3. *Analyze pattern*: + * Is fallback happening frequently? (May indicate primary provider issue) + * Is fallback successful? (Check status of fallback requests) + +*Use Case*: Verify failover is working as expected + +=== Task 5: "what did we spend on this customer today?" + +1. *Filter by customer*: + ``` + Filter: request.headers["x-customer-id"] = "customer_abc" + AND Timestamp >= "today" + ``` + +2. *Sum estimated costs* (if UI supports): + // PLACEHOLDER: Does UI have cost aggregation for filtered results? + * Total: $X.XX + * Breakdown by model + +3. *Export to CSV* (if supported): + // PLACEHOLDER: Is CSV export available? + * For detailed billing analysis + +*Use Case*: Chargeback/showback to customers + +== Log retention + +// PLACEHOLDER: Confirm log retention policy + +*Retention Period*: // PLACEHOLDER: e.g., 30 days, 90 days, configurable + +*After Retention Period*: +* Logs are deleted automatically +* Aggregate metrics retained longer (see [Metrics](// PLACEHOLDER: link)) + +*Export Logs* (if needed for longer retention): +// PLACEHOLDER: Is log export available? Via API? CSV? + +== Log export + +// PLACEHOLDER: Confirm export capabilities + +=== Export to CSV + +// PLACEHOLDER: Add UI path for export, or indicate not available + +1. Apply filters for desired logs +2. Click "Export to CSV" +3. Download includes all filtered logs with full fields + +=== Export via API + +// PLACEHOLDER: If API is available for log export + +[source,bash] +---- +curl https://{CLUSTER_ID}.cloud.redpanda.com/api/ai-gateway/logs \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -G \ + --data-urlencode "gateway_id=gw_abc123" \ + --data-urlencode "start_time=2025-01-11T00:00:00Z" \ + --data-urlencode "end_time=2025-01-11T23:59:59Z" +---- + + +=== Integration with observability platforms + +// PLACEHOLDER: Are there integrations with external platforms? + +*Supported Integrations* (if any): +* OpenTelemetry export → Send logs to Jaeger, Datadog, New Relic +* CloudWatch Logs → For AWS deployments +* // PLACEHOLDER: Others? + +See [Observability Integrations](// PLACEHOLDER: link) for setup guides. + +== Privacy & security + +=== What is logged + +// PLACEHOLDER: Confirm what is logged by default + +*Logged by Default*: +* Request headers (including custom headers) +* Request body (full prompt content) +* Response body (full AI response) +* Token usage, cost, latency +* Routing decisions, policy evaluations + +*Not Logged* (if applicable): +* // PLACEHOLDER: Anything redacted? API keys? Specific headers? + +=== Redaction options + +// PLACEHOLDER: Are there options to redact PII or sensitive data? + +*If Redaction is Supported*: +* Configure redaction rules for specific fields +* Mask PII (email addresses, phone numbers, etc.) +* Redact custom header values + +Example: +[source,yaml] +---- +# PLACEHOLDER: Actual configuration format +redaction: + - field: request.headers.x-api-key + action: mask + - field: request.body.messages[].content + pattern: "\\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,}\\b" # Email regex + action: replace + replacement: "[REDACTED_EMAIL]" +---- + + +=== Access control + +// PLACEHOLDER: Who can view logs? RBAC? + +*Permissions Required*: +* View logs: // PLACEHOLDER: role/permission name +* Export logs: // PLACEHOLDER: role/permission name + +*Audit Trail*: +* Log access is audited (who viewed which logs, when) +* // PLACEHOLDER: Where to find audit trail? + +== Troubleshoot log issues + +=== Issue: "logs not appearing for my request" + +*Possible Causes*: +1. Log ingestion delay (wait // PLACEHOLDER: Xs) +2. Wrong gateway ID filter +3. Request failed before reaching gateway (authentication error) +4. Time range filter too narrow + +*Solution*: +1. Wait a moment and refresh +2. Remove all filters, search by timestamp +3. Check client-side error logs +4. Expand time range to "Last 1 hour" + +=== Issue: "missing request/response content" + +*Possible Causes*: +1. Payload too large (// PLACEHOLDER: size limit?) +2. Redaction rules applied +3. // PLACEHOLDER: Other reasons? + +*Solution*: +// PLACEHOLDER: How to retrieve full content if truncated? + +=== Issue: "cost estimate incorrect" + +*Possible Causes*: +1. Cost estimate based on public pricing (may differ from your contract) +2. Provider changed pricing +3. // PLACEHOLDER: Other reasons? + +*Note*: Cost estimates are approximate. Use provider invoices for billing. + +== Next steps + +* *Aggregate Analytics* → [Observability: Metrics](// PLACEHOLDER: link) +* *Set Up Alerts* → [Alerting Guide](// PLACEHOLDER: link) +* *Export to SIEM* → [Security Integration Guide](// PLACEHOLDER: link) +* *Cost Attribution* → [Cost Tracking Guide](// PLACEHOLDER: link) +* *Troubleshoot Errors* → [Troubleshooting Guide](// PLACEHOLDER: link) + +== Related pages + +* [Observability: Metrics](// PLACEHOLDER: link) +* [End-to-End Validation](// PLACEHOLDER: link) +* [Troubleshooting Common Issues](// PLACEHOLDER: link) +* [CEL Routing Deep Dive](// PLACEHOLDER: link) diff --git a/modules/ai-agents/partials/observability-metrics.adoc b/modules/ai-agents/partials/observability-metrics.adoc new file mode 100644 index 000000000..834f8c506 --- /dev/null +++ b/modules/ai-agents/partials/observability-metrics.adoc @@ -0,0 +1,751 @@ += Observability: metrics & analytics + +== Overview + +AI Gateway provides aggregate metrics and analytics dashboards to help you understand usage patterns, costs, performance, and errors across all your LLM traffic. + +*Use Metrics For*: +* Cost tracking and budget management +* Usage trends over time +* Performance monitoring (latency, error rates) +* Capacity planning +* Model/provider comparison + +*Use Logs For*: Debugging specific requests, viewing full prompts/responses → See [Observability: Logs](// PLACEHOLDER: link) + +== Where to find metrics + +// PLACEHOLDER: Add exact UI navigation path + +1. *Navigate to Analytics Dashboard*: + * Console → AI Gateway → // PLACEHOLDER: exact path + * Or: Gateway detail page → Analytics tab + +2. *Select Gateway* (optional): + * View all gateways (org-wide metrics) + * Or filter to specific gateway + +3. *Set Time Range*: + * Default: Last 7 days + * Options: Last 24 hours, 7 days, 30 days, 90 days, Custom + * // PLACEHOLDER: screenshot of time range picker + +== Key metrics + +=== Request volume + +*What it shows*: Total number of requests over time + +// PLACEHOLDER: Screenshot of request volume graph + +*Graph Type*: Time series line chart + +*Filters*: +* By gateway +* By model +* By provider +* By status (success/error) + +*Use Cases*: +* Identify usage patterns (peak hours, days of week) +* Detect traffic spikes or drops +* Capacity planning + +*Example Insights*: +* "Traffic doubles every Monday morning at 9am" → Scale infrastructure +* "Staging gateway has more traffic than prod" → Investigate runaway testing + +=== Token usage + +*What it shows*: Prompt, completion, and total tokens consumed + +// PLACEHOLDER: Screenshot of token usage graph + +*Graph Type*: Stacked area chart (prompt vs completion tokens) + +*Metrics*: +* Total tokens +* Prompt tokens (input) +* Completion tokens (output) +* Tokens per request (average) + +*Breakdowns*: +* By gateway +* By model +* By provider + +*Use Cases*: +* Understand cost drivers (prompt vs completion tokens) +* Identify verbose prompts or responses +* Optimize token usage + +*Example Insights*: +* "90% of tokens are completion tokens" → Responses are verbose, optimize max_tokens +* "Staging uses 10x more tokens than prod" → Investigate test suite + +=== Estimated spend + +*What it shows*: Calculated cost based on token usage and public pricing + +// PLACEHOLDER: Screenshot of cost tracking dashboard + +*Graph Type*: Time series line chart with cost breakdown + +*Metrics*: +* Total estimated spend +* Spend by model +* Spend by provider +* Spend by gateway +* Cost per 1K requests +* Cost per 1M tokens + +*Breakdowns*: +* By gateway (for chargeback/showback) +* By model (for cost optimization) +* By provider (for negotiation leverage) +* By custom header (if configured, e.g., `x-customer-id`) + +*Use Cases*: +* Budget tracking ("Are we staying under $50K/month?") +* Cost attribution ("Which team spent the most?") +* Model comparison ("Is Claude cheaper than GPT-4 for our use case?") +* Forecasting ("At this rate, we'll spend $X next month") + +*Important Notes*: +* *Estimates based on public pricing* (may differ from your contract) +* *Not a substitute for provider invoices* (use for approximation only) +* *Update frequency*: // PLACEHOLDER: Real-time? Hourly? Daily? + +*Example Insights*: +* "Customer A accounts for 60% of spend" → Consider rate limits or tiered pricing +* "GPT-4o is 3x more expensive than Claude Sonnet for similar quality" → Optimize routing + +=== Latency + +*What it shows*: Request duration from gateway to provider and back + +// PLACEHOLDER: Screenshot of latency histogram + +*Metrics*: +* p50 (median) latency +* p95 latency +* p99 latency +* Min/max latency +* Average latency + +*Breakdowns*: +* By gateway +* By model +* By provider +* By token range (longer responses = higher latency) + +*Use Cases*: +* Identify slow models or providers +* Set SLO targets (e.g., "p95 < 2 seconds") +* Detect performance regressions + +*Example Insights*: +* "GPT-4o p99 latency spiked to 10 seconds yesterday" → Investigate provider issue +* "Claude Sonnet is 30% faster than GPT-4o for same prompts" → Optimize for latency + +*Latency Components* (if available): +// PLACEHOLDER: Does gateway show latency breakdown? +* Gateway processing time +* Provider API time +* Network time + +=== Error rate + +*What it shows*: Percentage of failed requests over time + +// PLACEHOLDER: Screenshot of error rate graph + +*Metrics*: +* Total error rate (%) +* Errors by status code (400, 401, 429, 500, etc.) +* Errors by model +* Errors by provider + +*Graph Type*: Time series line chart with error percentage + +*Breakdowns*: +* By error type: + * Client errors (4xx) + * Rate limits (429) + * Server errors (5xx) + * Provider errors + * Gateway errors + +*Use Cases*: +* Detect provider outages +* Identify configuration issues (e.g., model not enabled) +* Monitor rate limit breaches + +*Example Insights*: +* "Error rate spiked to 15% at 2pm" → OpenAI outage, fallback to Anthropic worked +* "10% of requests fail with 'model not found'" → Model not enabled in gateway + +=== Success rate + +*What it shows*: Percentage of successful (200) requests over time + +*Metric*: `Success Rate = (Successful Requests / Total Requests) × 100` + +*Target*: Typically 99%+ for production workloads + +*Use Cases*: +* Monitor overall health +* Set up alerts (e.g., "Alert if success rate < 95%") + +=== Fallback rate + +*What it shows*: Percentage of requests that used fallback provider + +// PLACEHOLDER: Screenshot of fallback rate graph + +*Metric*: `Fallback Rate = (Fallback Requests / Total Requests) × 100` + +*Breakdowns*: +* By fallback reason: + * Rate limit exceeded + * Timeout + * 5xx error + +*Use Cases*: +* Monitor primary provider reliability +* Verify fallback is working +* Identify when to renegotiate rate limits + +*Example Insights*: +* "Fallback rate increased to 20% yesterday" → OpenAI hit rate limits, time to increase quota +* "Zero fallbacks in 30 days" → Fallback config may not be working, or primary provider is very reliable + +== Dashboard views + +=== Overview dashboard + +*Shows*: High-level metrics across all gateways + +// PLACEHOLDER: Screenshot of overview dashboard + +*Widgets*: +* Total requests (last 24h, 7d, 30d) +* Total spend (last 24h, 7d, 30d) +* Success rate (current) +* Average latency (current) +* Top 5 models by request volume +* Top 5 gateways by spend + +*Use Case*: Executive view, health at a glance + +=== Gateway dashboard + +*Shows*: Metrics for a specific gateway + +// PLACEHOLDER: Screenshot of gateway dashboard + +*Widgets*: +* Request volume (time series) +* Token usage (time series) +* Estimated spend (time series) +* Latency percentiles (histogram) +* Error rate (time series) +* Model breakdown (pie chart) +* Provider breakdown (pie chart) + +*Use Case*: Team-specific monitoring, gateway optimization + +=== Model comparison dashboard + +*Shows*: Side-by-side comparison of models + +// PLACEHOLDER: Screenshot of model comparison + +*Metrics per Model*: +* Request count +* Total tokens +* Estimated cost +* Cost per 1K requests +* Average latency +* Error rate + +*Use Case*: Evaluate whether to switch models (cost vs performance) + +*Example*: +| Model | Requests | Avg Latency | Cost per 1K | Error Rate | +|-------|----------|-------------|-------------|------------| +| openai/gpt-4o | 10,000 | 1.2s | $5.00 | 0.5% | +| anthropic/claude-sonnet-3.5 | 5,000 | 0.9s | $3.50 | 0.3% | +| openai/gpt-4o-mini | 20,000 | 0.7s | $0.50 | 1.0% | + +*Insight*: Claude Sonnet is 25% faster and 30% cheaper than GPT-4o with better reliability + +=== Provider comparison dashboard + +*Shows*: Side-by-side comparison of providers + +*Metrics per Provider*: +* Request count +* Total spend +* Average latency +* Error rate +* Fallback trigger rate + +*Use Case*: Evaluate provider reliability, negotiate contracts + +=== Cost breakdown dashboard + +*Shows*: Detailed cost analysis + +// PLACEHOLDER: Screenshot of cost breakdown + +*Widgets*: +* Spend by gateway (stacked bar chart) +* Spend by model (pie chart) +* Spend by provider (pie chart) +* Spend by custom dimension (if configured, e.g., customer ID) +* Spend trend (time series with forecast) +* Budget utilization (progress bar: $X / $Y monthly limit) + +*Use Case*: FinOps, budget management, chargeback/showback + +== Filter & group + +=== Filter by gateway + +[source,text] +---- +Filter: Gateway = "production-gateway" +---- + + +Shows metrics for specific gateway only. + +*Use Case*: Isolate prod from staging metrics + +=== Filter by model + +[source,text] +---- +Filter: Model = "openai/gpt-4o" +---- + + +Shows metrics for specific model only. + +*Use Case*: Evaluate model performance in isolation + +=== Filter by provider + +[source,text] +---- +Filter: Provider = "OpenAI" +---- + + +Shows metrics for specific provider only. + +*Use Case*: Evaluate provider reliability + +=== Filter by status + +[source,text] +---- +Filter: Status = "200" // Only successful requests +Filter: Status >= "500" // Only server errors +---- + + +*Use Case*: Focus on errors, or calculate success rate + +=== Filter by custom dimension + +// PLACEHOLDER: Confirm if custom dimensions are supported for filtering + +[source,text] +---- +Filter: request.headers["x-customer-id"] = "customer_abc" +---- + + +Shows metrics for specific customer. + +*Use Case*: Customer-specific cost tracking for chargeback + +=== Group by dimension + +*Common Groupings*: +* Group by Gateway +* Group by Model +* Group by Provider +* Group by Status +* Group by Hour/Day/Week/Month (time aggregation) + +*Example*: "Show me spend grouped by model, for production gateway, over last 30 days" + +== Alerting + +// PLACEHOLDER: Confirm if alerting is supported + +*If Alerting is Supported*: + +=== Alert types + +*Budget Alerts*: +* Alert when spend exceeds X% of monthly budget +* Alert when spend grows Y% week-over-week + +*Performance Alerts*: +* Alert when error rate > X% +* Alert when p99 latency > Xms +* Alert when success rate < X% + +*Usage Alerts*: +* Alert when request volume drops (potential outage) +* Alert when fallback rate > X% (primary provider issue) + +=== Alert channels + +// PLACEHOLDER: Supported notification channels +* Email +* Slack +* PagerDuty +* Webhook +* // PLACEHOLDER: Others? + +=== Example alert configuration + +[source,yaml] +---- +# PLACEHOLDER: Actual alert configuration format +alerts: + - name: "High Error Rate" + condition: error_rate > 5% + duration: 5 minutes + channels: [slack, email] + + - name: "Budget Threshold" + condition: monthly_spend > 80% of budget + channels: [email] + + - name: "Latency Spike" + condition: p99_latency > 5000ms + duration: 10 minutes + channels: [pagerduty] +---- + + +See [Alerting Guide](// PLACEHOLDER: link) for detailed setup. + +== Export metrics + +// PLACEHOLDER: Confirm export capabilities + +=== Export to CSV + +1. Apply filters for desired metrics +2. Click "Export to CSV" +3. Download includes time series data + +*Use Case*: Import into spreadsheet for analysis, reporting + +=== Export via API + +// PLACEHOLDER: If API is available for metrics + +[source,bash] +---- +curl https://{CLUSTER_ID}.cloud.redpanda.com/api/ai-gateway/metrics \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -G \ + --data-urlencode "gateway_id=gw_abc123" \ + --data-urlencode "start_time=2025-01-01T00:00:00Z" \ + --data-urlencode "end_time=2025-01-31T23:59:59Z" \ + --data-urlencode "metric=requests,tokens,cost" +---- + + +*Response*: +[source,json] +---- +{ + "gateway_id": "gw_abc123", + "start_time": "2025-01-01T00:00:00Z", + "end_time": "2025-01-31T23:59:59Z", + "metrics": { + "requests": 1000000, + "tokens": 500000000, + "estimated_cost": 2500.00 + } +} +---- + + +=== Integration with observability platforms + +// PLACEHOLDER: OpenTelemetry support? Other integrations? + +*Supported Integrations* (if any): +* *Prometheus*: Metrics endpoint for scraping +* *OpenTelemetry*: Export metrics to OTel collector +* *Datadog*: Direct integration +* *Grafana*: Pre-built dashboards +* // PLACEHOLDER: Others? + +See [Observability Integrations](// PLACEHOLDER: link) for setup guides. + +== Common analysis tasks + +=== Task 1: "are we staying within budget?" + +1. *View Cost Breakdown Dashboard* +2. *Check Budget Utilization Widget*: + * Current spend: $X + * Monthly budget: $Y + * Utilization: X% + * Days remaining in month: Z +3. *Forecast*: + * At current rate: $X × (30 / days_elapsed) + * On track to exceed budget? Yes/No + +*Action*: +* If approaching limit: Adjust rate limits, optimize models, pause non-prod usage +* If well under budget: Opportunity to test more expensive models + +=== Task 2: "which team is using the most resources?" + +1. *Filter by Gateway* (assuming one gateway per team) +2. *Sort by Spend* (descending) +3. *View Table*: + +| Gateway | Requests | Tokens | Spend | % of Total | +|---------|----------|--------|-------|------------| +| team-ml | 500K | 250M | $1,250 | 50% | +| team-product | 300K | 150M | $750 | 30% | +| team-eng | 200K | 100M | $500 | 20% | + +*Action*: Chargeback costs to teams, or investigate high-usage teams + +=== Task 3: "is this model worth the extra cost?" + +1. *Open Model Comparison Dashboard* +2. *Select Models to Compare*: + * Expensive model: `openai/gpt-4o` + * Cheap model: `openai/gpt-4o-mini` +3. *Compare Metrics*: + +| Metric | GPT-4o | GPT-4o-mini | Difference | +|--------|--------|-------------|------------| +| Cost per 1K requests | $5.00 | $0.50 | *10x* | +| Avg Latency | 1.2s | 0.7s | 58% *faster* (mini) | +| Error Rate | 0.5% | 1.0% | 2x errors (mini) | + +*Decision*: If mini's error rate is acceptable, save 10x on costs + +=== Task 4: "why did costs spike yesterday?" + +1. *View Cost Trend Graph* +2. *Identify Spike* (e.g., Jan 10th: $500 vs usual $100) +3. *Drill Down*: + * *By Gateway*: Which gateway caused the spike? + * *By Model*: Did someone switch to expensive model? + * *By Hour*: What time did spike occur? +4. *Cross-Reference with Logs*: + * Filter logs to spike timeframe + * Check for unusual request patterns + * Identify custom header (user ID, customer ID) if present + +*Common Causes*: +* Test suite running against prod gateway +* A/B test routing all traffic to expensive model +* User error (wrong model in config) +* Runaway loop in application code + +=== Task 5: "is provider x more reliable than provider y?" + +1. *Open Provider Comparison Dashboard* +2. *Compare Error Rates*: + +| Provider | Requests | Error Rate | Fallback Triggers | +|----------|----------|------------|-------------------| +| OpenAI | 500K | 0.8% | 50 (rate limits) | +| Anthropic | 300K | 0.3% | 5 (timeouts) | + +*Insight*: Anthropic has 62% lower error rate + +3. *Compare Latencies*: + +| Provider | p50 Latency | p99 Latency | +|----------|-------------|-------------| +| OpenAI | 1.0s | 3.5s | +| Anthropic | 0.8s | 2.5s | + +*Insight*: Anthropic is 20% faster at p50, 28% faster at p99 + +*Decision*: Prioritize Anthropic in routing pools + +== Metrics retention + +// PLACEHOLDER: Confirm metrics retention policy + +*Retention Period*: +* *High-resolution* (1-minute granularity): // PLACEHOLDER: e.g., 7 days +* *Medium-resolution* (1-hour granularity): // PLACEHOLDER: e.g., 30 days +* *Low-resolution* (1-day granularity): // PLACEHOLDER: e.g., 1 year + +*Note*: Aggregate metrics retained longer than individual request logs + +== API access to metrics + +// PLACEHOLDER: Document metrics API if available + +=== List available metrics + +[source,bash] +---- +curl https://{CLUSTER_ID}.cloud.redpanda.com/api/ai-gateway/metrics/list \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" +---- + + +*Response*: +[source,json] +---- +{ + "metrics": [ + "requests", + "tokens.prompt", + "tokens.completion", + "tokens.total", + "cost.estimated", + "latency.p50", + "latency.p95", + "latency.p99", + "errors.rate", + "success.rate", + "fallback.rate" + ] +} +---- + + +=== Query specific metric + +[source,bash] +---- +curl https://{CLUSTER_ID}.cloud.redpanda.com/api/ai-gateway/metrics/query \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "Content-Type: application/json" \ + -d '{ + "metric": "requests", + "gateway_id": "gw_abc123", + "start_time": "2025-01-01T00:00:00Z", + "end_time": "2025-01-31T23:59:59Z", + "granularity": "1d", + "group_by": ["model"] + }' +---- + + +*Response*: +[source,json] +---- +{ + "metric": "requests", + "granularity": "1d", + "data": [ + { + "timestamp": "2025-01-01T00:00:00Z", + "model": "openai/gpt-4o", + "value": 10000 + }, + { + "timestamp": "2025-01-01T00:00:00Z", + "model": "anthropic/claude-sonnet-3.5", + "value": 5000 + }, + ... + ] +} +---- + + +== Best practices + +=== 1. set up budget alerts early +* Don't wait for surprise bills +* Alert at 50%, 80%, 90% of budget +* Include multiple stakeholders (eng, finance) + +=== 2. create team dashboards +* One dashboard per team showing their gateway(s) +* Empowers teams to self-optimize +* Reduces central ops burden + +=== 3. monitor fallback rate +* Low fallback rate (0-5%): Normal, failover working +* High fallback rate (>20%): Investigate primary provider issues +* Zero fallback rate: Verify fallback config is correct + +=== 4. compare models regularly +* Run A/B tests with metrics +* Reassess as pricing and models change +* Don't assume expensive = better quality for your use case + +=== 5. track trends, not point-in-time +* Day-to-day variance is normal +* Look for week-over-week and month-over-month trends +* Seasonal patterns (e.g., more usage on weekdays) + +== Troubleshoot metrics issues + +=== Issue: "metrics don't match my provider invoice" + +*Possible Causes*: +1. Metrics are estimates based on public pricing +2. Your contract has custom pricing +3. Provider changed pricing mid-month + +*Solution*: +* Use metrics for trends and optimization decisions +* Use provider invoices for actual billing +* // PLACEHOLDER: Can users configure custom pricing in gateway? + +=== Issue: "metrics are delayed or missing" + +*Possible Causes*: +1. Metrics aggregation has delay (// PLACEHOLDER: typical delay?) +2. Time range outside retention period +3. No requests in selected time range (empty data) + +*Solution*: +1. Wait and refresh (// PLACEHOLDER: Xminutes typical delay) +2. Check retention policy +3. Verify requests were sent (check logs) + +=== Issue: "dashboard shows 'no data'" + +*Possible Causes*: +1. Filters too restrictive (no matching requests) +2. Gateway has no traffic yet +3. Permissions issue (can't access this gateway's metrics) + +*Solution*: +1. Remove filters, widen time range +2. Send test request (see [Quickstart](// PLACEHOLDER: link)) +3. Check permissions with admin + +== Next steps + +* *View Individual Requests* → [Observability: Logs](// PLACEHOLDER: link) +* *Set Up Alerts* → [Alerting Guide](// PLACEHOLDER: link) +* *Optimize Costs* → [Cost Optimization Guide](// PLACEHOLDER: link) +* *Export to BI Tools* → [Data Export Guide](// PLACEHOLDER: link) +* *Compare Models* → [Model Selection Guide](// PLACEHOLDER: link) + +== Related pages + +* [Observability: Logs](// PLACEHOLDER: link) +* [Budget & Rate Limits](// PLACEHOLDER: link) +* [Cost Optimization](// PLACEHOLDER: link) +* [Performance Benchmarks](// PLACEHOLDER: link) diff --git a/modules/ai-agents/partials/quickstart-enhanced.adoc b/modules/ai-agents/partials/quickstart-enhanced.adoc new file mode 100644 index 000000000..f0ae04cca --- /dev/null +++ b/modules/ai-agents/partials/quickstart-enhanced.adoc @@ -0,0 +1,503 @@ += AI gateway quickstart + +Get your first request routed through Redpanda AI Gateway in under 10 minutes. + +== What you'll accomplish + +✓ *2 minutes*: Route your first LLM request through the gateway +✓ *5 minutes*: See observability data in the dashboard +✓ *7 minutes*: Add a fallback provider for reliability +✓ *10 minutes*: Write your first CEL routing rule + +*Total time*: 10-15 minutes + +== Prerequisites + +Before starting, ensure you have: +* ✅ Redpanda Cloud account with BYOC // PLACEHOLDER: specific version? +* ✅ Admin access to configure providers and gateways +* ✅ API keys for at least one LLM provider (OpenAI, Anthropic, etc.) +* ✅ Python 3.8+ or Node.js 18+ (for examples) + +== Step 1: configure a provider (admin task) + +*Time: ~2 minutes* + +Providers must be configured before they can be used in gateways. + +// PLACEHOLDER: Add UI navigation path, e.g., "Console → AI Gateway → Providers → Add Provider" + +1. *Navigate to Providers*: + * Open Redpanda Cloud Console + * Go to // PLACEHOLDER: exact menu path + +2. *Add Provider*: + ``` + Provider: OpenAI + API Key: sk-... + Enabled Models: gpt-4o, gpt-4o-mini + ``` + + // PLACEHOLDER: Add screenshot of provider configuration form + +3. *Verify*: + * Provider status shows "Active" + * Models appear in model catalog + +*Alternative: CLI* (if available) +[source,bash] +---- +# PLACEHOLDER: CLI command for adding provider +rpk cloud ai-gateway provider create \ + --provider openai \ + --api-key sk-... \ + --models gpt-4o,gpt-4o-mini +---- + + +*Supported Providers*: +// PLACEHOLDER: List currently supported providers +* OpenAI +* Anthropic +* // PLACEHOLDER: Others? + +See link:// PLACEHOLDER: link[Admin Guide: Providers] for detailed configuration options. + +== Step 2: create a gateway + +*Time: ~1 minute* + +Gateways define routing policies, rate limits, and observability scope. + +// PLACEHOLDER: Add UI navigation path + +1. *Navigate to Gateways*: + * Go to // PLACEHOLDER: exact menu path + +2. *Create Gateway*: + ``` + Name: my-first-gateway + Workspace: default + Description: Quickstart gateway for testing + ``` + + // PLACEHOLDER: Add screenshot of gateway creation form + +3. *Save Gateway ID*: + After creation, copy your gateway ID (required for requests): + ``` + Gateway ID: gw_abc123... + Gateway Endpoint: https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1 + ``` + + // PLACEHOLDER: Confirm exact endpoint format + +*Recommended Gateway Patterns*: +* One gateway per environment (staging, production) +* One gateway per team (for budget isolation) +* One gateway per customer (for multi-tenant SaaS) + +See link:// PLACEHOLDER: link[Gateway Creation Guide] for best practices. + +== Step 3: send your first request + +*Time: ~2 minutes* + +Now route a request through your gateway. + +=== Python + +[source,python] +---- +from openai import OpenAI +import os + +# Configure client to use AI Gateway +client = OpenAI( + base_url="https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1", # Gateway endpoint + api_key=os.getenv("REDPANDA_CLOUD_TOKEN"), # Your Redpanda Cloud token + default_headers={ + "rp-aigw-id": "gw_abc123..." # Your gateway ID from Step 2 + } +) + +# Make a request (note the vendor/model_id format) +response = client.chat.completions.create( + model="openai/gpt-4o-mini", # Format: {provider}/{model} + messages=[ + {"role": "user", "content": "Say 'Hello from AI Gateway!'"} + ], + max_tokens=20 +) + +print(response.choices[0].message.content) +# Output: Hello from AI Gateway! +---- + + +=== Typescript/javascript + +[source,typescript] +---- +import OpenAI from 'openai'; + +const client = new OpenAI({ + baseURL: 'https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1', + apiKey: process.env.REDPANDA_CLOUD_TOKEN, + defaultHeaders: { + 'rp-aigw-id': 'gw_abc123...' + } +}); + +const response = await client.chat.completions.create({ + model: 'openai/gpt-4o-mini', + messages: [ + { role: 'user', content: 'Say "Hello from AI Gateway!"' } + ], + max_tokens: 20 +}); + +console.log(response.choices[0].message.content); +// Output: Hello from AI Gateway! +---- + + +=== Curl + +[source,bash] +---- +curl https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "rp-aigw-id: gw_abc123..." \ + -d '{ + "model": "openai/gpt-4o-mini", + "messages": [ + {"role": "user", "content": "Say \"Hello from AI Gateway!\""} + ], + "max_tokens": 20 + }' +---- + + +*Expected Response*: +[source,json] +---- +{ + "id": "chatcmpl-...", + "object": "chat.completion", + "created": 1704844800, + "model": "openai/gpt-4o-mini", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello from AI Gateway!" + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 8, + "completion_tokens": 5, + "total_tokens": 13 + } +} +---- + + +*Troubleshooting*: +* `401 Unauthorized` → Check `REDPANDA_CLOUD_TOKEN` +* `404 Not Found` → Verify `base_url` is correct +* `Model not found` → Ensure model is enabled in Step 1 +* `Missing rp-aigw-id` → Verify header is set + +See link:// PLACEHOLDER: link[Troubleshooting Guide] for more help. + +== Step 4: verify in observability dashboard + +*Time: ~1 minute* + +Confirm your request appears in the AI Gateway dashboard. + +// PLACEHOLDER: Add UI navigation path and screenshots + +1. *Navigate to Logs*: + * Go to // PLACEHOLDER: Console → AI Gateway → {Gateway Name} → Logs + +2. *Find Your Request*: + * Filter by Gateway: `my-first-gateway` + * Filter by Model: `openai/gpt-4o-mini` + * Time range: Last 5 minutes + +3. *Verify Fields*: + * ✅ Model: `openai/gpt-4o-mini` + * ✅ Provider: OpenAI + * ✅ Status: 200 + * ✅ Prompt tokens: ~8 + * ✅ Completion tokens: ~5 + * ✅ Estimated cost: // PLACEHOLDER: $X.XXXX + * ✅ Latency: // PLACEHOLDER: ~XXXms + +4. *Click Request to Expand*: + * View full prompt and response + * See request headers + * Check routing decision (which provider pool was used) + +*If request doesn't appear*: +* Wait // PLACEHOLDER: Xs (logs may have delay) +* Check gateway ID matches +* Verify request succeeded (no error in client) +* See link:// PLACEHOLDER: link[End-to-End Validation Guide] + +*🎉 Congratulations!* You've successfully routed your first request through AI Gateway and verified observability. + +''' + +== Next steps: add failover (optional, +3 minutes) + +Add automatic failover to a backup provider for reliability. + +=== Step 5: add second provider + +*Time: ~1 minute* + +Add Anthropic as a fallback option: + +// PLACEHOLDER: Add UI path + +1. *Navigate to Providers* → *Add Provider*: + ``` + Provider: Anthropic + API Key: sk-ant-... + Enabled Models: claude-sonnet-3.5 + ``` + +2. *Verify*: + * Anthropic provider status: Active + * Models appear in catalog + +=== Step 6: configure provider pool with fallback + +*Time: ~2 minutes* + +Update your gateway to use OpenAI as primary, Anthropic as fallback. + +// PLACEHOLDER: Add UI path and configuration format + +1. *Navigate to Gateway Settings*: + * Go to // PLACEHOLDER: AI Gateway → {Gateway Name} → Routing + +2. *Configure Provider Pool*: + ```yaml + # PLACEHOLDER: Confirm actual configuration format + routing: + primary_pool: + * provider: openai + models: [gpt-4o, gpt-4o-mini] + fallback_pool: + * provider: anthropic + models: [claude-sonnet-3.5] + + fallback_triggers: + * rate_limit_exceeded + * timeout + * 5xx_errors + ``` + + // PLACEHOLDER: Add screenshot of routing configuration + +3. *Save Configuration* + +=== Step 7: test failover + +*Time: ~1 minute* + +Simulate a provider failure to see fallback in action. + +// PLACEHOLDER: Add method to test failback, or skip if not easily testable + +*Option A: Disable Primary Provider Temporarily* +1. Disable OpenAI provider in settings +2. Send request with `openai/gpt-4o` model +3. Gateway should automatically route to Anthropic fallback +4. Check logs to confirm fallback was used + +*Option B: Trigger Rate Limit* +1. Send many requests rapidly to hit rate limit +2. Gateway should fallback to Anthropic +3. Check logs for "fallback_triggered" indicator + +*Verify Fallback*: +[source,python] +---- +response = client.chat.completions.create( + model="openai/gpt-4o", # Request OpenAI model + messages=[{"role": "user", "content": "Test fallback"}] +) + +# Check which provider actually handled it +# PLACEHOLDER: How to verify this - response header? Log metadata? +---- + + +*Check Dashboard*: +* Request should show: + * Requested model: `openai/gpt-4o` + * Actual provider: Anthropic (fallback) + * Fallback reason: // PLACEHOLDER: rate_limit / timeout / error + +''' + +== Next steps: add routing rule (optional, +3 minutes) + +Use CEL expressions to route requests based on headers or content. + +=== Step 8: create CEL routing rule + +*Time: ~2 minutes* + +Route premium users to better models automatically. + +// PLACEHOLDER: Add UI path for CEL configuration + +1. *Navigate to Gateway Settings*: + * Go to // PLACEHOLDER: AI Gateway → {Gateway Name} → Routing Rules + +2. *Add CEL Rule*: + ```cel + # Route based on user tier header + request.headers["x-user-tier"] == "premium" + ? "openai/gpt-4o" + : "openai/gpt-4o-mini" + ``` + + // PLACEHOLDER: Add screenshot of CEL editor with syntax highlighting + +3. *Test Rule* (if UI supports testing): + * Input test headers: `x-user-tier: premium` + * Verify output: `openai/gpt-4o` + * Input test headers: `x-user-tier: free` + * Verify output: `openai/gpt-4o-mini` + +4. *Save Rule* + +=== Step 9: test routing rule + +*Time: ~1 minute* + +Send requests with different headers and verify routing. + +*Premium User Request*: +[source,python] +---- +response = client.chat.completions.create( + model="auto", # PLACEHOLDER: or how to trigger CEL routing + messages=[{"role": "user", "content": "Hello"}], + extra_headers={"x-user-tier": "premium"} +) + +# Should route to gpt-4o (premium model) +---- + + +*Free User Request*: +[source,python] +---- +response = client.chat.completions.create( + model="auto", + messages=[{"role": "user", "content": "Hello"}], + extra_headers={"x-user-tier": "free"} +) + +# Should route to gpt-4o-mini (cost-effective model) +---- + + +*Verify in Dashboard*: +* Check request logs +* Confirm correct model was selected based on header +* View routing decision explanation + +*🎉 Congratulations!* You've configured intelligent routing based on request context. + +''' + +== What you've learned + +✅ *Provider Configuration*: Added LLM providers (OpenAI, Anthropic) +✅ *Gateway Creation*: Created your first gateway with policies +✅ *Request Routing*: Sent requests through the gateway +✅ *Observability*: Verified requests in dashboard logs +✅ *Failover*: Configured automatic fallback to backup provider (optional) +✅ *Smart Routing*: Created CEL rule for dynamic model selection (optional) + +== What's next? + +=== Immediate next steps +1. *Set Rate Limits* → link:// PLACEHOLDER: link[Rate Limiting Guide] + * Protect against runaway costs + * Prevent abuse + +2. *Add Spend Limits* → link:// PLACEHOLDER: link[Budget Controls Guide] + * Set monthly budgets per gateway + * Get alerts before limits are hit + +3. *Configure MCP Aggregation* → link:// PLACEHOLDER: link[MCP Guide] + * Give agents access to tools + * Reduce token costs with deferred loading + +=== Explore advanced features +* *A/B Testing Models* → link:// PLACEHOLDER: link[A/B Testing Guide] +* *Multi-Tenancy Patterns* → link:// PLACEHOLDER: link[Multi-Tenancy Guide] +* *Cost Optimization* → link:// PLACEHOLDER: link[Cost Optimization Guide] +* *Performance Tuning* → link:// PLACEHOLDER: link[Performance Guide] + +=== Integration guides +* link:// PLACEHOLDER: link[OpenAI SDK Integration] +* link:// PLACEHOLDER: link[Anthropic SDK Integration] +* link:// PLACEHOLDER: link[LangChain Integration] +* link:// PLACEHOLDER: link[LlamaIndex Integration] +* link:// PLACEHOLDER: link[Claude Code CLI] +* link:// PLACEHOLDER: link[VS Code Extension] +* link:// PLACEHOLDER: link[Cursor IDE] + +=== Migrate existing applications +* link:// PLACEHOLDER: link[Migration Guide: From Direct Integration to Gateway] + +== Common next questions + +*Q: How do I switch between providers without code changes?* +A: Change the model string in your gateway routing rules. No code deployment needed. + +*Q: How much latency does the gateway add?* +A: Typically // PLACEHOLDER: Xms overhead. See link:// PLACEHOLDER: link[Performance Benchmarks]. + +*Q: Can I use the same gateway for multiple applications?* +A: Yes, but we recommend separate gateways per environment or team for better cost tracking. + +*Q: How do I attribute costs to specific customers?* +A: Use CEL routing with custom headers, then filter logs by header value. See link:// PLACEHOLDER: link[Cost Attribution Guide]. + +*Q: Does the gateway work with streaming responses?* +A: // PLACEHOLDER: Yes/No, with any limitations + +*Q: What happens if the gateway goes down?* +A: // PLACEHOLDER: Describe high availability setup, or recommend keeping fallback to direct integration + +== Get help + +* *Documentation*: link:// PLACEHOLDER: actual URL[https://docs.redpanda.com/redpanda-cloud/ai-gateway/] +* *Troubleshooting*: link:// PLACEHOLDER: link[Common Issues Guide] +* *Community*: // PLACEHOLDER: Slack, Discord, forum link +* *Support*: // PLACEHOLDER: support email or portal + +''' + +*Related Pages*: +* link:// PLACEHOLDER: link[What is AI Gateway?] +* link:// PLACEHOLDER: link[Core Concepts] +* link:// PLACEHOLDER: link[End-to-End Validation] +* link:// PLACEHOLDER: link[CEL Routing Deep Dive] +* link:// PLACEHOLDER: link[Observability: Logs & Metrics] diff --git a/modules/ai-agents/partials/what-is-ai-gateway.adoc b/modules/ai-agents/partials/what-is-ai-gateway.adoc new file mode 100644 index 000000000..a98e42945 --- /dev/null +++ b/modules/ai-agents/partials/what-is-ai-gateway.adoc @@ -0,0 +1,419 @@ += What is Redpanda AI gateway? + +== Overview + +Redpanda AI Gateway is a unified access layer for LLM providers and AI tools that sits between your applications and the AI services they use. It provides centralized routing, policy enforcement, cost management, and observability for all your AI traffic. + +== The problem + +Modern AI applications face several critical challenges: + +=== 1. provider fragmentation + +* Applications hardcode provider-specific SDKs (OpenAI, Anthropic, Google, etc.) +* Switching providers requires code changes and redeployment +* Testing across providers is time-consuming and error-prone +* Provider outages directly impact your application + +=== 2. cost spirals without visibility + +* No centralized view of token usage across teams and applications +* Difficult to attribute costs to specific customers, features, or environments +* Testing and debugging can rack up unexpected bills +* No way to enforce budgets or rate limits per team/customer + +=== 3. tool coordination complexity + +* Agents need access to multiple MCP (Model Context Protocol) servers +* Managing tool discovery and execution is repetitive across projects +* High token costs from loading all available tools upfront +* No centralized governance over which tools agents can access + +=== 4. observability gaps + +* Requests scattered across multiple provider dashboards +* Can't reconstruct user sessions that span multiple models +* No unified view of latency, errors, and costs +* Debugging "the AI gave the wrong answer" requires manual log diving + +== What AI gateway solves + +Redpanda AI Gateway addresses these challenges through four core capabilities: + +=== 1. unified LLM access (single endpoint for all providers) + +// PLACEHOLDER: Add architecture diagram showing: +// - Application → AI Gateway → Multiple LLM Providers (OpenAI, Anthropic, etc.) +// - Single baseURL configuration +// - Model routing via vendor/model_id format + +*Before (Direct Integration)* + +[source,python] +---- +# OpenAI +from openai import OpenAI +client = OpenAI(api_key="sk-...") +response = client.chat.completions.create( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}] +) + +# Anthropic (different SDK, different patterns) +from anthropic import Anthropic +client = Anthropic(api_key="sk-ant-...") +response = client.messages.create( + model="claude-sonnet-3.5", + max_tokens=1024, + messages=[{"role": "user", "content": "Hello"}] +) +---- + +*After (AI Gateway - OpenAI-Compatible)* + +[source,python] +---- +from openai import OpenAI + +# Single configuration, multiple providers +client = OpenAI( + base_url="https://{GATEWAY_ENDPOINT}", + api_key="your-redpanda-token", + default_headers={"rp-aigw-id": "{GATEWAY_ID}"} +) + +# Route to OpenAI +response = client.chat.completions.create( + model="openai/gpt-4o", + messages=[{"role": "user", "content": "Hello"}] +) + +# Route to Anthropic (same code, different model string) +response = client.chat.completions.create( + model="anthropic/claude-sonnet-3.5", + messages=[{"role": "user", "content": "Hello"}] +) +---- + +*Result*: Change `model` parameter to switch providers. No code redeployment needed. + +=== 2. policy-based routing & cost control + +Define routing rules, rate limits, and budgets once; enforce them automatically: + +*Example: Tier-Based Routing* + +[source,cel] +---- +// Route premium users to best model, free users to cost-effective model +request.headers["x-user-tier"] == "premium" + ? "anthropic/claude-opus-4" + : "anthropic/claude-sonnet-3.5" +---- + +*Example: Environment-Based Budget* + +// PLACEHOLDER: Confirm exact policy configuration format + +[source,yaml] +---- +rate_limits: + staging: 100 requests/minute + production: 10000 requests/minute + +spend_limits: + staging: $500/month + production: $50000/month +---- + +*Example: Automatic Failover* + +// PLACEHOLDER: Add details on pool configuration and failback behavior + +* Primary: OpenAI GPT-4 +* Fallback: Anthropic Claude Opus on rate limits or timeouts +* Result: 99.9% uptime even during provider outages + +=== 3. MCP aggregation & orchestration + +*Agent Tool Access Without the Overhead* + +*Before*: Agent loads all tools from multiple MCP servers upfront + +* Sends 50+ tool definitions with every request +* High token costs (thousands of tokens per request) +* Slow agent startup +* No centralized governance + +*After*: AI Gateway aggregates MCP servers + +* Deferred tool loading: Only search + orchestrator tools loaded initially +* *80-90% token reduction* depending on configuration +* Agent queries for specific tools only when needed +* Centralized approval of MCP servers + +*Orchestrator for Complex Workflows* + +* Single JavaScript-based orchestrator tool +* Reduces multi-step workflows from multiple round trips to one call +* Example: "Search vector DB → if results insufficient → fallback to web search" + +// PLACEHOLDER: Add link to MCP aggregation guide when ready + +=== 4. unified observability & cost tracking + +*Single Dashboard for All LLM Traffic* + +// PLACEHOLDER: Add screenshots of: +// - Request logs view +// - Cost breakdown by model/provider +// - Latency histogram +// - Error rate tracking + +Track across all requests: + +* Volume (requests per gateway, model, provider) +* Token usage (prompt + completion tokens) +* Estimated spend (per model, with cross-provider comparison) +* Latency (p50, p95, p99) +* Errors (by type, provider, model) + +*Use Cases*: + +* "Which model is the most cost-effective for our use case?" +* "Why did this specific user request fail?" +* "How much does our staging environment cost us per week?" +* "What's the latency difference between OpenAI and Anthropic for our workload?" + +== Cost comparison example + +// PLACEHOLDER: Insert real customer data or anonymized case study + +*Scenario*: SaaS chatbot with 1M requests/month, averaging 500 prompt + 300 completion tokens + +[cols="1,1,2"] +|=== +|Configuration |Monthly Cost |Notes + +|*Direct Integration* (no gateway) +|// PLACEHOLDER: $X,XXX +|No caching, no routing optimization + +|*+ AI Gateway* (basic routing) +|// PLACEHOLDER: $X,XXX +|Provider failover, unified observability + +|*+ Caching* +|// PLACEHOLDER: $X,XXX +|// PLACEHOLDER: X% reduction from cache hits + +|*+ Deferred Tool Loading* +|// PLACEHOLDER: $X,XXX +|80-90% token reduction for agent workloads + +|*+ Tier-Based Routing* +|// PLACEHOLDER: $X,XXX +|Premium users → better model, free → cost-effective +|=== + +*Total Savings*: // PLACEHOLDER: $X,XXX/month (XX% reduction) + +*Hidden Savings*: + +* Developer time: No more managing multiple provider SDKs +* Incident response: Automatic failover reduces downtime costs +* Experimentation: Safe A/B testing without risking production + +== Common gateway patterns + +=== Pattern 1: team isolation + +*Use Case*: Multiple teams sharing infrastructure, need separate budgets and policies + +*Setup*: Create one gateway per team + +* Team A Gateway: $5K/month budget, staging + production environments +* Team B Gateway: $10K/month budget, different rate limits +* Each team sees only their traffic in observability dashboards + +// PLACEHOLDER: Link to multi-tenancy guide + +=== Pattern 2: environment separation + +*Use Case*: Prevent staging traffic from affecting production metrics + +*Setup*: Separate gateways for staging vs production + +* Staging Gateway: Lower rate limits, restricted model access, aggressive cost controls +* Production Gateway: High rate limits, all models enabled, alerting on anomalies + +=== Pattern 3: primary + fallback for reliability + +*Use Case*: Ensure uptime during provider outages + +*Setup*: Configure provider pools with automatic failover + +* Primary: OpenAI (preferred for quality) +* Fallback: Anthropic (activates on OpenAI rate limits or timeouts) +* Monitor fallback rate to detect primary provider issues early + +=== Pattern 4: a/b testing models + +*Use Case*: Compare model quality/cost without dual integration + +*Setup*: Route percentage of traffic to different models + +// PLACEHOLDER: Confirm if percentage-based routing is supported, or if it's header-based only + +* 80% traffic → claude-sonnet-3.5 +* 20% traffic → claude-opus-4 +* Compare quality metrics and costs, then adjust + +=== Pattern 5: customer-based routing + +*Use Case*: SaaS product with tiered pricing (free, pro, enterprise) + +*Setup*: CEL routing based on request headers + +[source,cel] +---- +request.headers["x-customer-tier"] == "enterprise" ? "anthropic/claude-opus-4" : +request.headers["x-customer-tier"] == "pro" ? "anthropic/claude-sonnet-3.5" : +"anthropic/claude-haiku" +---- + +== Deployment model + +// PLACEHOLDER: Verify BYOC availability and any managed offering plans + +*BYOC (Bring Your Own Cloud)* + +* Currently available: BYOC version for // PLACEHOLDER: specific Redpanda version +* Deployment: Within your Redpanda Cloud cluster +* Data residency: All traffic stays in your cloud account +* Supported clouds: // PLACEHOLDER: AWS, GCP, Azure? + +// PLACEHOLDER: If managed offering is planned, add: +// *Managed (Redpanda Cloud)* +// - Coming soon: Fully managed AI Gateway +// - No infrastructure management +// - Global deployment regions +// - Uptime SLA + +== What's supported today + +=== LLM providers + +// PLACEHOLDER: Confirm currently supported providers + +* OpenAI +* Anthropic +* // PLACEHOLDER: Google, AWS Bedrock, Azure OpenAI, others? + +=== API compatibility + +* OpenAI-compatible `/v1/chat/completions` endpoint +* // PLACEHOLDER: Streaming support? +* // PLACEHOLDER: Embeddings support? +* // PLACEHOLDER: Other endpoints? + +=== Policy features + +* CEL-based routing expressions +* Rate limiting (// PLACEHOLDER: per-gateway, per-header, per-tenant?) +* Monthly spend limits (// PLACEHOLDER: per-gateway, per-workspace?) +* Provider pools with automatic failover +* // PLACEHOLDER: Caching support? + +=== MCP support + +* MCP server aggregation +* Deferred tool loading (80-90% token reduction) +* JavaScript orchestrator for multi-step workflows +* // PLACEHOLDER: Tool execution sandboxing? + +=== Observability + +* Request logs with full prompt/response history +* Token usage tracking +* Estimated cost per request +* Latency metrics +* // PLACEHOLDER: Metrics export? OpenTelemetry support? + +== What's not supported yet + +// PLACEHOLDER: List current limitations, for example: +// - Custom model deployments (Azure OpenAI BYOK, AWS Bedrock custom models) +// - Response caching +// - Prompt templates/versioning +// - Guardrails (PII detection, content moderation) +// - Multi-region active-active deployment +// - Metrics export to external systems +// - Budget alerts/notifications + +== Architecture + +// PLACEHOLDER: Add architecture diagram showing: +// 1. Control Plane: +// - Workspace management +// - Provider/model configuration +// - Gateway creation and policy definition +// - Admin console +// +// 2. Data Plane: +// - Request ingestion +// - Policy evaluation (rate limits → spend limits → routing → execution) +// - Provider pool selection and failover +// - MCP aggregation layer +// - Response logging and metrics +// +// 3. Observability Plane: +// - Request logs storage +// - Metrics aggregation +// - Dashboard UI + +*Request Lifecycle*: + +. Application sends request to gateway endpoint with `rp-aigw-id` header +. Gateway authenticates request +. Rate limit policy evaluates (allow/deny) +. Spend limit policy evaluates (allow/deny) +. Routing policy evaluates (which model/provider to use) +. Provider pool selects backend (primary/fallback) +. Request forwarded to LLM provider +. Response returned to application +. Request logged with tokens, cost, latency, status + +*MCP Request Lifecycle*: + +. Application discovers tools via `/mcp` endpoint +. Gateway aggregates tools from approved MCP servers +. Application receives search + orchestrator tools (deferred loading) +. Application invokes specific tool +. Gateway routes to appropriate MCP server +. Tool execution result returned +. Request logged with execution time, status + +== Next steps + +* *New to AI Gateway?* → link:quickstart.adoc[Quickstart: Route your first request] +* *Understand the concepts* → link:core-concepts.adoc[Core Concepts: Gateways, Providers, Models] +* *Set up providers* → link:admin-guide-providers.adoc[Admin Guide: Configure LLM Providers] +* *Define policies* → link:routing-rate-limits.adoc[Routing & Rate Limits Guide] +* *Integrate agents* → link:mcp-aggregation-guide.adoc[MCP Aggregation Guide] +* *Monitor usage* → link:observability-logs.adoc[Observability: Logs and Metrics] + +== Get help + +* Documentation: https://docs.redpanda.com/redpanda-cloud/ai-gateway/ +* Community: // PLACEHOLDER: Slack, Discord, or forum link +* Support: // PLACEHOLDER: support email or portal + +''' + +*Related Pages*: + +* link:quickstart.adoc[Quickstart] +* link:core-concepts.adoc[Core Concepts] +* link:architecture-deep-dive.adoc[Architecture Deep Dive] +* link:use-cases-patterns.adoc[Use Cases & Patterns] From f2c87a32799c8c5c08504bfdc02c82dc6c3d6915 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Sun, 11 Jan 2026 20:03:17 -0700 Subject: [PATCH 09/50] clean up cc drafts --- modules/ROOT/nav.adoc | 10 +- .../pages/{ => ai-gateway}/ai-gateway.adoc | 4 +- .../ai-gateway}/cel-routing-cookbook.adoc | 332 ++++++++++-------- modules/ai-agents/pages/ai-gateway/index.adoc | 3 + .../ai-gateway}/mcp-aggregation-guide.adoc | 313 ++++++++++------- .../ai-gateway}/migration-guide.adoc | 250 +++++++------ .../ai-gateway}/observability-logs.adoc | 156 ++++---- .../ai-gateway}/observability-metrics.adoc | 320 ++++++++++------- .../ai-gateway}/quickstart-enhanced.adoc | 189 +++++----- .../ai-gateway}/what-is-ai-gateway.adoc | 105 +++--- modules/ai-agents/pages/index.adoc | 3 - 11 files changed, 968 insertions(+), 717 deletions(-) rename modules/ai-agents/pages/{ => ai-gateway}/ai-gateway.adoc (98%) rename modules/ai-agents/{partials => pages/ai-gateway}/cel-routing-cookbook.adoc (76%) create mode 100644 modules/ai-agents/pages/ai-gateway/index.adoc rename modules/ai-agents/{partials => pages/ai-gateway}/mcp-aggregation-guide.adoc (83%) rename modules/ai-agents/{partials => pages/ai-gateway}/migration-guide.adoc (83%) rename modules/ai-agents/{partials => pages/ai-gateway}/observability-logs.adoc (85%) rename modules/ai-agents/{partials => pages/ai-gateway}/observability-metrics.adoc (74%) rename modules/ai-agents/{partials => pages/ai-gateway}/quickstart-enhanced.adoc (75%) rename modules/ai-agents/{partials => pages/ai-gateway}/what-is-ai-gateway.adoc (82%) diff --git a/modules/ROOT/nav.adoc b/modules/ROOT/nav.adoc index 2e268d196..f64c40349 100644 --- a/modules/ROOT/nav.adoc +++ b/modules/ROOT/nav.adoc @@ -70,7 +70,15 @@ ** xref:security:cloud-safety-reliability.adoc[Safety and Reliability] * xref:ai-agents:index.adoc[AI Agents] -** xref:ai-agents:ai-gateway.adoc[] +** xref:ai-agents:ai-gateway/index.adoc[AI Gateway] +*** xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[] +*** xref:ai-agents:ai-gateway/ai-gateway.adoc[] +*** xref:ai-agents:ai-gateway/quickstart-enhanced.adoc[] +*** xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[] +*** xref:ai-agents:ai-gateway/observability-logs.adoc[] +*** xref:ai-agents:ai-gateway/observability-metrics.adoc[] +*** xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[] +*** xref:ai-agents:ai-gateway/migration-guide.adoc[] ** xref:ai-agents:mcp/overview.adoc[MCP Overview] ** xref:ai-agents:mcp/local/index.adoc[Redpanda Cloud Management MCP Server] *** xref:ai-agents:mcp/local/overview.adoc[Overview] diff --git a/modules/ai-agents/pages/ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway/ai-gateway.adoc similarity index 98% rename from modules/ai-agents/pages/ai-gateway.adoc rename to modules/ai-agents/pages/ai-gateway/ai-gateway.adoc index 2e327da98..a91980418 100644 --- a/modules/ai-agents/pages/ai-gateway.adoc +++ b/modules/ai-agents/pages/ai-gateway/ai-gateway.adoc @@ -1,5 +1,5 @@ = AI Gateway Quickstart -:description: Learn how to configure the AI Gateway for unified access to multiple LLM providers and MCP servers through a single endpoint. +:description: Quickstart to configure the AI Gateway for unified access to multiple LLM providers and MCP servers through a single endpoint. NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and later. @@ -342,3 +342,5 @@ const openai = new OpenAI({ } }); ---- + +== Next steps \ No newline at end of file diff --git a/modules/ai-agents/partials/cel-routing-cookbook.adoc b/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc similarity index 76% rename from modules/ai-agents/partials/cel-routing-cookbook.adoc rename to modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc index 44a505cfb..50bd6405c 100644 --- a/modules/ai-agents/partials/cel-routing-cookbook.adoc +++ b/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc @@ -1,10 +1,12 @@ -= CEL routing: deep dive & cookbook += DRAFT: CEL Routing Cookbook +:description: Quickstart to configure the AI Gateway for unified access to multiple LLM providers and MCP servers through a single endpoint. == Overview Redpanda AI Gateway uses CEL (Common Expression Language) for dynamic request routing. CEL expressions evaluate request properties (headers, body, context) and determine which model or provider should handle each request. -*CEL enables*: +CEL enables: + * User-based routing (free vs premium tiers) * Content-based routing (by prompt topic, length, complexity) * Environment-based routing (staging vs production models) @@ -15,19 +17,21 @@ Redpanda AI Gateway uses CEL (Common Expression Language) for dynamic request ro == CEL basics -=== What is cel? +=== What is CEL? CEL (Common Expression Language) is a non-Turing-complete expression language designed for fast, safe evaluation. It's used by Google (Firebase, Cloud IAM), Kubernetes, Envoy, and other systems. -*Key Properties*: -* *Safe*: Cannot loop infinitely or access system resources -* *Fast*: Evaluates in microseconds -* *Readable*: Similar to Python/JavaScript expressions -* *Type-safe*: Errors caught at configuration time, not runtime +Key properties: + +* Safe: Cannot loop infinitely or access system resources +* Fast: Evaluates in microseconds +* Readable: Similar to Python/JavaScript expressions +* Type-safe: Errors caught at configuration time, not runtime === CEL syntax primer -*Comparison Operators*: +Comparison operators: + [source,cel] ---- == // equal @@ -39,7 +43,8 @@ CEL (Common Expression Language) is a non-Turing-complete expression language de ---- -*Logical Operators*: +Logical operators: + [source,cel] ---- && // AND @@ -48,14 +53,16 @@ CEL (Common Expression Language) is a non-Turing-complete expression language de ---- -*Ternary Operator* (most common pattern): +Ternary operator (most common pattern): + [source,cel] ---- condition ? value_if_true : value_if_false ---- -*Functions*: +Functions: + [source,cel] ---- .size() // Length of string or array @@ -67,7 +74,8 @@ has(field) // Check if field exists ---- -*Examples*: +Examples: + [source,cel] ---- // Simple comparison @@ -106,7 +114,7 @@ request.headers["x-request-id"] // Standard header ---- -*Note*: Header names are case-insensitive in HTTP, but CEL requires lowercase keys. +NOTE: Header names are case-insensitive in HTTP, but CEL requires lowercase keys. === `request.body` (object) @@ -125,7 +133,8 @@ request.body.stream // Bool: Streaming enabled (if set) ---- -*Note*: Fields are optional. Use `has()` to check existence: +NOTE: Fields are optional. Use `has()` to check existence: + [source,cel] ---- has(request.body.max_tokens) ? request.body.max_tokens : 1000 @@ -158,19 +167,19 @@ request.method == "POST" == CEL routing patterns Each pattern follows this structure: -* *When to use*: Scenario description -* *Expression*: CEL code -* *What happens*: Routing behavior -* *Verify*: How to test -* *Cost/performance impact*: Implications -''' +* When to use: Scenario description +* Expression: CEL code +* What happens: Routing behavior +* Verify: How to test +* Cost/performance impact: Implications -=== Pattern 1: tier-based routing +=== Pattern 1: Tier-based routing -*When to use*: Different user tiers (free, pro, enterprise) should get different model quality +When to use: Different user tiers (free, pro, enterprise) should get different model quality + +Expression: -*Expression*: [source,cel] ---- request.headers["x-user-tier"] == "enterprise" ? "openai/gpt-4o" : @@ -179,12 +188,14 @@ request.headers["x-user-tier"] == "pro" ? "anthropic/claude-sonnet-3.5" : ---- -*What happens*: +What happens: + * Enterprise users → GPT-4o (best quality) * Pro users → Claude Sonnet 3.5 (balanced) * Free users → GPT-4o-mini (cost-effective) -*Verify*: +Verify: + [source,python] ---- # Test enterprise @@ -205,20 +216,20 @@ response = client.chat.completions.create( ---- -*Cost Impact*: +Cost impact: + * Enterprise: ~$5.00 per 1K requests * Pro: ~$3.50 per 1K requests * Free: ~$0.50 per 1K requests -*Use Case*: SaaS product with tiered pricing where model quality is a differentiator +Use case: SaaS product with tiered pricing where model quality is a differentiator -''' +=== Pattern 2: Environment-based routing -=== Pattern 2: environment-based routing +When to use: Prevent staging from using expensive models -*When to use*: Prevent staging from using expensive models +Expression: -*Expression*: [source,cel] ---- request.headers["x-environment"] == "production" @@ -227,11 +238,13 @@ request.headers["x-environment"] == "production" ---- -*What happens*: +What happens: + * Production → GPT-4o (best quality) * Staging/dev → GPT-4o-mini (10x cheaper) -*Verify*: +Verify: + [source,python] ---- # Set environment header @@ -244,22 +257,24 @@ response = client.chat.completions.create( ---- -*Cost Impact*: +Cost impact: + * Prevents staging from inflating costs * Example: Staging with 100K test requests/day * GPT-4o: $500/day ($15K/month) * GPT-4o-mini: $50/day ($1.5K/month) * *Savings: $13.5K/month* -*Use Case*: Protect against runaway staging costs +Use case: Protect against runaway staging costs ''' -=== Pattern 3: content-length guard rails +=== Pattern 3: Content-length guard rails -*When to use*: Block or downgrade long prompts to prevent cost spikes +When to use: Block or downgrade long prompts to prevent cost spikes + +Expression (Block): -*Expression (Block)*: [source,cel] ---- request.body.messages.size() > 10 || request.body.max_tokens > 4000 @@ -268,11 +283,12 @@ request.body.messages.size() > 10 || request.body.max_tokens > 4000 ---- -*What happens*: +What happens: * Requests with >10 messages or >4000 max_tokens → Rejected with 400 error * Normal requests → GPT-4o -*Expression (Downgrade)*: +Expression (Downgrade): + [source,cel] ---- request.body.messages.size() > 10 || request.body.max_tokens > 4000 @@ -281,11 +297,13 @@ request.body.messages.size() > 10 || request.body.max_tokens > 4000 ---- -*What happens*: +What happens: + * Long conversations → Downgraded to cheaper model * Short conversations → Premium model -*Verify*: +Verify: + [source,python] ---- # Test rejection @@ -306,19 +324,19 @@ response = client.chat.completions.create( ---- -*Cost Impact*: +Cost impact: + * Prevents unexpected bills from verbose prompts * Example: Block requests >10K tokens (would cost $0.15 each) -*Use Case*: Staging cost controls, prevent prompt injection attacks that inflate token usage +Use case: Staging cost controls, prevent prompt injection attacks that inflate token usage -''' +=== Pattern 4: Topic-based routing -=== Pattern 4: topic-based routing +When to use: Route different question types to specialized models -*When to use*: Route different question types to specialized models +Expression: -*Expression*: [source,cel] ---- request.body.messages[0].content.contains("code") || @@ -329,11 +347,13 @@ request.body.messages[0].content.contains("programming") ---- -*What happens*: +What happens: + * Coding questions → GPT-4o (optimized for code) * General questions → Claude Sonnet (better prose) -*Verify*: +Verify: + [source,python] ---- # Test code question @@ -352,19 +372,20 @@ response = client.chat.completions.create( ---- -*Cost Impact*: +Cost impact: + * Optimize model selection for task type * Could improve quality without increasing costs -*Use Case*: Multi-purpose chatbot with both coding and general queries +Use case: Multi-purpose chatbot with both coding and general queries -''' -=== Pattern 5: geographic/regional routing +=== Pattern 5: Geographic/regional routing + +When to use: Route by user region for compliance or latency optimization -*When to use*: Route by user region for compliance or latency optimization +Expression: -*Expression*: [source,cel] ---- request.headers["x-user-region"] == "eu" @@ -373,11 +394,13 @@ request.headers["x-user-region"] == "eu" ---- -*What happens*: +What happens: + * EU users → EU-region model (GDPR compliance) * Other users → Default region -*Verify*: +Verify: + [source,python] ---- response = client.chat.completions.create( @@ -389,17 +412,17 @@ response = client.chat.completions.create( ---- -*Cost Impact*: Neutral (same model, different region) +Cost impact: Neutral (same model, different region) -*Use Case*: GDPR compliance, data residency requirements +Use case: GDPR compliance, data residency requirements -''' -=== Pattern 6: customer-specific routing +=== Pattern 6: Customer-specific routing + +When to use: Different customers have different model access (enterprise features) -*When to use*: Different customers have different model access (enterprise features) +Expression: -*Expression*: [source,cel] ---- request.headers["x-customer-id"] == "customer_vip_123" @@ -408,11 +431,13 @@ request.headers["x-customer-id"] == "customer_vip_123" ---- -*What happens*: +What happens: + * VIP customer → Best model * Standard customers → Normal model -*Verify*: +Verify: + [source,python] ---- response = client.chat.completions.create( @@ -424,21 +449,22 @@ response = client.chat.completions.create( ---- -*Cost Impact*: +Cost impact: + * VIP: ~$7.50 per 1K requests * Standard: ~$3.50 per 1K requests -*Use Case*: Enterprise contracts with premium model access +Use case: Enterprise contracts with premium model access -''' === Pattern 7: a/b testing (percentage-based routing) -*When to use*: Test new models with a percentage of traffic +When to use: Test new models with a percentage of traffic // PLACEHOLDER: Confirm if CEL can access random functions or if A/B testing requires different mechanism -*Expression (if random is available)*: +Expression (if random is available): + [source,cel] ---- // PLACEHOLDER: Verify CEL random function availability @@ -448,7 +474,8 @@ random() < 0.10 ---- -*Alternative (Hash-Based)*: +Alternative (hash-based): + [source,cel] ---- // Use customer ID hash for stable routing @@ -458,11 +485,13 @@ hash(request.headers["x-customer-id"]) % 100 < 10 ---- -*What happens*: +What happens: + * 10% of requests → New model (Opus 4) * 90% of requests → Existing model (GPT-4o) -*Verify*: +Verify: + [source,python] ---- # Send 100 requests, count which model was used @@ -476,19 +505,19 @@ for i in range(100): ---- -*Cost Impact*: +Cost impact: + * Allows safe, incremental rollout of new models * Monitor quality/cost for new model before full adoption -*Use Case*: Evaluate new models in production with real traffic +Use case: Evaluate new models in production with real traffic -''' +=== Pattern 8: Complexity-based routing -=== Pattern 8: complexity-based routing +When to use: Route simple queries to cheap models, complex queries to expensive models -*When to use*: Route simple queries to cheap models, complex queries to expensive models +Expression: -*Expression*: [source,cel] ---- request.body.messages.size() == 1 && @@ -498,11 +527,13 @@ request.body.messages[0].content.size() < 100 ---- -*What happens*: +What happens: + * Single short message (<100 chars) → Cheap model * Multi-turn or long messages → Premium model -*Verify*: +Verify: + [source,python] ---- # Test simple @@ -525,21 +556,21 @@ response = client.chat.completions.create( ---- -*Cost Impact*: +Cost impact: + * Can reduce costs significantly if simple queries are common * Example: 50% of queries are simple, save 90% on those = 45% total savings -*Use Case*: FAQ chatbot with mix of simple lookups and complex questions - -''' +Use case: FAQ chatbot with mix of simple lookups and complex questions -=== Pattern 9: time-based routing +=== Pattern 9: Time-based routing -*When to use*: Use cheaper models during off-peak hours +When to use: Use cheaper models during off-peak hours // PLACEHOLDER: Confirm if CEL has access to current timestamp -*Expression (if time functions available)*: +Expression (if time functions available): + [source,cel] ---- // PLACEHOLDER: Verify CEL time function availability @@ -549,23 +580,25 @@ now().hour >= 22 || now().hour < 6 // 10pm - 6am ---- -*What happens*: +What happens: + * Off-peak hours (10pm-6am) → Cheap model * Peak hours (6am-10pm) → Premium model -*Cost Impact*: +Cost impact: + * Optimize for user experience during peak usage * Save costs during low-traffic hours -*Use Case*: Consumer apps with time-zone-specific usage patterns +Use case: Consumer apps with time-zone-specific usage patterns -''' -=== Pattern 10: fallback chain (multi-level) +=== Pattern 10: Fallback chain (multi-level) -*When to use*: Complex fallback logic beyond simple primary/secondary +When to use: Complex fallback logic beyond simple primary/secondary + +Expression: -*Expression*: [source,cel] ---- request.headers["x-priority"] == "critical" @@ -576,26 +609,27 @@ request.headers["x-priority"] == "critical" ---- -*What happens*: +What happens: + * Critical requests → Always GPT-4o * Premium non-critical → Claude Sonnet * Everyone else → GPT-4o-mini -*Verify*: Test with different header combinations +Verify: Test with different header combinations -*Cost Impact*: Ensures SLA for critical requests while optimizing costs elsewhere +Cost impact: Ensures SLA for critical requests while optimizing costs elsewhere -*Use Case*: Production systems with SLA requirements +Use case: Production systems with SLA requirements -''' == Advanced CEL patterns -=== Pattern: default values with `has()` +=== Pattern: Default values with `has()` -*Problem*: Field might not exist in request +Problem: Field might not exist in request + +Expression: -*Expression*: [source,cel] ---- has(request.body.max_tokens) && request.body.max_tokens > 2000 @@ -604,11 +638,12 @@ has(request.body.max_tokens) && request.body.max_tokens > 2000 ---- -*What happens*: Safely checks if `max_tokens` exists before comparing +What happens: Safely checks if `max_tokens` exists before comparing + +=== Pattern: Multiple conditions with parentheses -=== Pattern: multiple conditions with parentheses +Expression: -*Expression*: [source,cel] ---- (request.headers["x-user-tier"] == "premium" || @@ -619,11 +654,12 @@ request.headers["x-environment"] == "production" ---- -*What happens*: Premium users OR VIP customer, AND production → GPT-4o +What happens: Premium users OR VIP customer, AND production → GPT-4o -=== Pattern: regex matching +=== Pattern: Regex matching + +Expression: -*Expression*: [source,cel] ---- request.body.messages[0].content.matches("(?i)(urgent|asap|emergency)") @@ -632,11 +668,12 @@ request.body.messages[0].content.matches("(?i)(urgent|asap|emergency)") ---- -*What happens*: Messages containing "urgent", "ASAP", or "emergency" (case-insensitive) → GPT-4o +What happens: Messages containing "urgent", "ASAP", or "emergency" (case-insensitive) → GPT-4o + +=== Pattern: String array contains -=== Pattern: string array contains +Expression: -*Expression*: [source,cel] ---- ["customer_1", "customer_2", "customer_3"].exists(c, c == request.headers["x-customer-id"]) @@ -645,11 +682,12 @@ request.body.messages[0].content.matches("(?i)(urgent|asap|emergency)") ---- -*What happens*: Only specific customers get premium model +What happens: Only specific customers get premium model -=== Pattern: reject invalid requests +=== Pattern: Reject invalid requests + +Expression: -*Expression*: [source,cel] ---- !has(request.body.messages) || request.body.messages.size() == 0 @@ -658,7 +696,7 @@ request.body.messages[0].content.matches("(?i)(urgent|asap|emergency)") ---- -*What happens*: Requests without messages are rejected (400 error) +What happens: Requests without messages are rejected (400 error) == Test CEL expressions @@ -666,13 +704,13 @@ request.body.messages[0].content.matches("(?i)(urgent|asap|emergency)") // PLACEHOLDER: Add screenshot if UI has CEL editor with test mode -1. Navigate to Gateway → Routing Rules +1. Navigate to Gateways → Routing Rules 2. Enter CEL expression 3. Click "Test" 4. Input test headers/body 5. View evaluated result -=== Option 2: send test requests +=== Option 2: Send test requests [source,python] ---- @@ -701,7 +739,7 @@ test_cel_routing( ---- -=== Option 3: cli test (if available) +=== Option 3: CLI test (if available) [source,bash] ---- @@ -720,16 +758,18 @@ rpk cloud ai-gateway test-cel \ === Error: "unknown field" -*Symptom*: +Symptom: + [source,text] ---- Error: Unknown field 'request.headers.x-user-tier' ---- -*Cause*: Wrong syntax (dot notation instead of bracket notation for headers) +Cause: Wrong syntax (dot notation instead of bracket notation for headers) + +Fix: -*Fix*: [source,cel] ---- // Wrong @@ -742,16 +782,18 @@ request.headers["x-user-tier"] === Error: "type mismatch" -*Symptom*: +Symptom: + [source,text] ---- Error: Type mismatch: expected bool, got string ---- -*Cause*: Forgot comparison operator +Cause: Forgot comparison operator + +Fix: -*Fix*: [source,cel] ---- // Wrong (returns string) @@ -764,16 +806,17 @@ request.headers["tier"] == "premium" === Error: "field does not exist" -*Symptom*: +Symptom: + [source,text] ---- Error: No such key: max_tokens ---- -*Cause*: Accessing field that doesn't exist in request +Cause: Accessing field that doesn't exist in request -*Fix*: +Fix: [source,cel] ---- // Wrong (crashes if max_tokens not in request) @@ -786,16 +829,18 @@ has(request.body.max_tokens) && request.body.max_tokens > 1000 === Error: "index out of bounds" -*Symptom*: +Symptom: + [source,text] ---- Error: Index 0 out of bounds for array of size 0 ---- -*Cause*: Accessing array element that doesn't exist +Cause: Accessing array element that doesn't exist + +Fix: -*Fix*: [source,cel] ---- // Wrong (crashes if messages empty) @@ -810,21 +855,23 @@ request.body.messages.size() > 0 && request.body.messages[0].content.contains("t === Expression complexity -*Fast* (<1ms evaluation): +Fast (<1ms evaluation): + [source,cel] ---- request.headers["tier"] == "premium" ? "openai/gpt-4o" : "openai/gpt-4o-mini" ---- -*Slower* (~5-10ms evaluation): +Slower (~5-10ms evaluation): + [source,cel] ---- request.body.messages[0].content.matches("complex.*regex.*pattern") ---- -*Recommendation*: Keep expressions simple. Complex regex can add latency. +Recommendation: Keep expressions simple. Complex regex can add latency. === Number of evaluations @@ -866,15 +913,12 @@ Each request evaluates CEL expression once. Total latency impact: == Next steps -* *Apply CEL Routing* → [Gateway Configuration Guide](// PLACEHOLDER: link) -* *Test Routing* → [End-to-End Validation](// PLACEHOLDER: link) -* *Monitor Routing Decisions* → [Observability: Logs](// PLACEHOLDER: link) -* *Optimize Costs* → [Cost Optimization Guide](// PLACEHOLDER: link) -* *Multi-Tenancy Patterns* → [Multi-Tenancy Guide](// PLACEHOLDER: link) +* *Apply CEL routing* → [Gateway Configuration Guide](// PLACEHOLDER: link) +* *Test routing* → [End-to-End Validation](// PLACEHOLDER: link) +* *Monitor routing decisions* → [Observability: Logs](// PLACEHOLDER: link) +* *Optimize costs* → [Cost Optimization Guide](// PLACEHOLDER: link) +* *Multi-tenancy patterns* → [Multi-Tenancy Guide](// PLACEHOLDER: link) == Related pages * [Quickstart](// PLACEHOLDER: link) -* [Provider Pools & Fallback](// PLACEHOLDER: link) -* [Rate Limiting](// PLACEHOLDER: link) -* [Observability](// PLACEHOLDER: link) diff --git a/modules/ai-agents/pages/ai-gateway/index.adoc b/modules/ai-agents/pages/ai-gateway/index.adoc new file mode 100644 index 000000000..a84ffbf2a --- /dev/null +++ b/modules/ai-agents/pages/ai-gateway/index.adoc @@ -0,0 +1,3 @@ += AI Gateway +:description: Learn how to configure the AI Gateway for unified access to multiple LLM providers and MCP servers through a single endpoint. +:page-layout: index diff --git a/modules/ai-agents/partials/mcp-aggregation-guide.adoc b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc similarity index 83% rename from modules/ai-agents/partials/mcp-aggregation-guide.adoc rename to modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc index c09684357..37c03603a 100644 --- a/modules/ai-agents/partials/mcp-aggregation-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc @@ -1,19 +1,22 @@ -= MCP aggregation & orchestration guide += DRAFT: MCP Aggregation and Orchestration Guide +:description: Quickstart to configure the AI Gateway for unified access to multiple LLM providers and MCP servers through a single endpoint. == Overview AI Gateway provides MCP (Model Context Protocol) aggregation, allowing AI agents to access tools from multiple MCP servers through a single unified endpoint. This eliminates the need for agents to manage multiple MCP connections and significantly reduces token costs through deferred tool loading. -*MCP Aggregation Benefits*: -* *Single Endpoint*: One MCP endpoint aggregates all approved MCP servers -* *Token Reduction*: 80-90% fewer tokens through deferred tool loading -* *Centralized Governance*: Admin-approved MCP servers only -* *Orchestration*: JavaScript-based orchestrator reduces multi-step round trips -* *Security*: Controlled tool execution environment +MCP aggregation benefits: -== What is mcp? +* Single endpoint: One MCP endpoint aggregates all approved MCP servers +* Token reduction: 80-90% fewer tokens through deferred tool loading +* Centralized governance: Admin-approved MCP servers only +* Orchestration: JavaScript-based orchestrator reduces multi-step round trips +* Security: Controlled tool execution environment + +== What is MCO? *Model Context Protocol (MCP)* is a standard for exposing tools (functions) that AI agents can discover and invoke. MCP servers provide tools like: + * Database queries * File system operations * API integrations (CRM, payment, analytics) @@ -22,12 +25,14 @@ AI Gateway provides MCP (Model Context Protocol) aggregation, allowing AI agents * Workflow automation *Without AI Gateway*: + * Agent connects to each MCP server individually * Agent loads ALL tools from ALL servers upfront (high token cost) * No centralized governance or security * Complex configuration *With AI Gateway*: + * Agent connects to gateway's unified `/mcp` endpoint * Gateway aggregates tools from approved MCP servers * Deferred loading: Only search + orchestrator tools sent initially @@ -81,9 +86,10 @@ AI Gateway provides MCP (Model Context Protocol) aggregation, allowing AI agents == MCP request lifecycle -=== 1. tool discovery (initial connection) +=== 1. Tool discovery (initial connection) + +Agent request: -*Agent Request*: [source,http] ---- GET /mcp/tools @@ -94,7 +100,8 @@ Headers: ---- -*Gateway Response* (with deferred loading): +Gateway response (with deferred loading): + [source,json] ---- { @@ -126,16 +133,18 @@ Headers: ---- -*Note*: Only 2 tools returned initially (search + orchestrator), not all 50+ tools from all MCP servers. +Note: Only 2 tools returned initially (search + orchestrator), not all 50+ tools from all MCP servers. + +Token savings: -*Token Savings*: * Without deferred loading: ~5,000-10,000 tokens (all tool definitions) * With deferred loading: ~500-1,000 tokens (2 tool definitions) -* *80-90% reduction* +* 80-90% reduction + +=== 2. Tool query (when agent needs specific tool) -=== 2. tool query (when agent needs specific tool) +Agent request: -*Agent Request*: [source,http] ---- POST /mcp/tools/search_tools @@ -149,7 +158,8 @@ Body: ---- -*Gateway Response*: +Gateway response: + [source,json] ---- { @@ -183,11 +193,12 @@ Body: ---- -*Agent receives only relevant tools* based on query. +Agent receives only relevant tools based on query. + +=== 3. Tool execution -=== 3. tool execution +Agent request: -*Agent Request*: [source,http] ---- POST /mcp/tools/execute_sql @@ -202,12 +213,14 @@ Body: ---- -*Gateway*: +Gateway: + 1. Routes to appropriate MCP server (database-server) 2. Executes tool 3. Returns result -*Gateway Response*: +Gateway response: + [source,json] ---- { @@ -220,19 +233,21 @@ Body: ---- -*Agent receives result* and can continue reasoning. +Agent receives result and can continue reasoning. -== Deferred tool loading: deep dive +== Deferred tool loading === How it works -*Traditional MCP (No Deferred Loading)*: +Traditional MCP (No deferred loading): + 1. Agent connects to MCP endpoint 2. Gateway sends ALL tools from ALL MCP servers (50+ tools) 3. Agent includes ALL tool definitions in EVERY LLM request 4. High token cost: ~5,000-10,000 tokens per request -*Deferred Loading (AI Gateway)*: +Deferred loading (AI Gateway): + 1. Agent connects to MCP endpoint with `rp-aigw-mcp-deferred: true` header 2. Gateway sends only 2 tools: `search_tools` + `orchestrator` 3. Agent includes only 2 tool definitions in LLM request (~500-1,000 tokens) @@ -241,17 +256,19 @@ Body: * Gateway returns matching tools * Agent calls specific tool (e.g., `execute_sql`) 5. Total token cost: Initial 500-1,000 + per-query ~200-500 - * *Still 80-90% lower than loading all tools* + * Still 80-90% lower than loading all tools === When to use deferred loading -*Use Deferred Loading When*: +Use deferred loading when: + * You have 10+ tools across multiple MCP servers * Agents don't need all tools for every request * Token costs are a concern * Agents can handle multi-step workflows (search → execute) -*Don't Use Deferred Loading When*: +Don't use deferred loading when: + * You have <5 tools total (overhead not worth it) * Agents need all tools for every request (rare) * Latency is more important than token costs (deferred adds 1 round trip) @@ -260,7 +277,8 @@ Body: // PLACEHOLDER: Add UI path or configuration method -*Option 1: Enable at Gateway Level* (recommended) +Option 1: Enable at gateway level (recommended) + [source,yaml] ---- # PLACEHOLDER: Actual configuration format @@ -269,7 +287,8 @@ mcp: ---- -*Option 2: Enable Per-Request* (agent-controlled) +Option 2: Enable per-request (agent-controlled) + [source,python] ---- # Agent includes header @@ -282,24 +301,27 @@ headers = { === Measure token savings -*Compare token usage before/after deferred loading*: +Compare token usage before/after deferred loading: + +1. Check logs without deferred loading: -1. *Check Logs Without Deferred Loading*: * Filter: Gateway = your-gateway, Model = your-model, Date = before enabling * Average tokens per request: // PLACEHOLDER: measure -2. *Enable Deferred Loading* +2. Enable deferred loading + +3. Check logs after deferred loading: -3. *Check Logs After Deferred Loading*: * Filter: Same gateway/model, Date = after enabling * Average tokens per request: // PLACEHOLDER: measure -4. *Calculate Savings*: +4. Calculate savings: + ``` Savings % = ((Before - After) / Before) × 100 ``` -*Expected Results*: 80-90% reduction in average tokens per request +Expected Results: 80-90% reduction in average tokens per request == Orchestrator: multi-step workflows @@ -307,19 +329,22 @@ headers = { The *orchestrator* is a special tool that executes JavaScript workflows, reducing multi-step interactions from multiple round trips to a single request. -*Without Orchestrator*: +Without Orchestrator: + 1. Agent: "Search vector database for relevant docs" → Round trip 1 2. Agent receives results, evaluates: "Results insufficient" 3. Agent: "Fallback to web search" → Round trip 2 4. Agent receives results, processes → Round trip 3 5. *Total: 3 round trips* (high latency, 3× token cost) -*With Orchestrator*: +With Orchestrator: + 1. Agent: "Execute workflow: Search vector DB → if insufficient, fallback to web search" 2. Gateway executes entire workflow in JavaScript 3. Agent receives final result → *1 round trip* -*Benefits*: +Benefits: + * *Latency Reduction*: 1 round trip vs 3+ * *Token Reduction*: No intermediate LLM calls needed * *Reliability*: Workflow logic executes deterministically @@ -327,22 +352,25 @@ The *orchestrator* is a special tool that executes JavaScript workflows, reducin === When to use orchestrator -*Use Orchestrator When*: +Use orchestrator when: + * Multi-step workflows with conditional logic (if/else) * Fallback patterns (try A, if fails, try B) * Sequential tool calls with dependencies * Loop-based operations (iterate, aggregate) -*Don't Use Orchestrator When*: +Don't use orchestrator when: + * Single tool call (no benefit) * Agent needs to reason between steps (orchestrator is deterministic) * Workflow requires LLM judgment at each step === Orchestrator example: search with fallback -*Scenario*: Search vector database; if results insufficient, fallback to web search. +Scenario: Search vector database; if results insufficient, fallback to web search. + +Without Orchestrator (3 round trips): -*Without Orchestrator* (3 round trips): [source,python] ---- # Agent's internal reasoning (3 separate LLM calls) @@ -362,7 +390,8 @@ else: ---- -*With Orchestrator* (1 round trip): +With Orchestrator (1 round trip): + [source,python] ---- # Agent invokes orchestrator once @@ -392,18 +421,20 @@ results = call_tool("orchestrator", { ---- -*Savings*: -* *Latency*: ~3-5 seconds (3 round trips) → ~1-2 seconds (1 round trip) -* *Tokens*: ~1,500 tokens (3 LLM calls) → ~500 tokens (1 LLM call) -* *Cost*: ~$0.0075 → ~$0.0025 (67% reduction) +Savings: + +* Latency: ~3-5 seconds (3 round trips) → ~1-2 seconds (1 round trip) +* Tokens: ~1,500 tokens (3 LLM calls) → ~500 tokens (1 LLM call) +* Cost: ~$0.0075 → ~$0.0025 (67% reduction) === Orchestrator API // PLACEHOLDER: Confirm orchestrator API details -*Tool Name*: `orchestrator` +Tool name: `orchestrator` + +Input schema: -*Input Schema*: [source,json] ---- { @@ -413,24 +444,27 @@ results = call_tool("orchestrator", { ---- -*Available in Workflow*: +Available in workflow: + * `tools.{tool_name}(params)`: Call any tool from approved MCP servers * `context.{variable}`: Access context variables * Standard JavaScript: `if`, `for`, `while`, `try/catch`, `async/await` -*Security*: +Security: + * Sandboxed execution (no file system, network, or system access) * Timeout: // PLACEHOLDER: e.g., 30 seconds * Memory limit: // PLACEHOLDER: e.g., 128MB -*Limitations*: +Limitations: + * Cannot call external APIs directly (must use MCP tools) * Cannot import npm packages (built-in JS only) * // PLACEHOLDER: Other limitations? === Orchestrator example: data aggregation -*Scenario*: Fetch user data from database, calculate summary statistics. +Scenario: Fetch user data from database, calculate summary statistics. [source,python] ---- @@ -470,7 +504,8 @@ results = call_tool("orchestrator", { ---- -*Output*: +Output: + [source,json] ---- { @@ -485,20 +520,23 @@ results = call_tool("orchestrator", { ---- -*vs Without Orchestrator*: +vs Without Orchestrator: + * Would require fetching all users to agent → agent processes → 2 round trips * Orchestrator: All processing in gateway → 1 round trip === Orchestrator best practices -*DO*: +DO: + * Use for deterministic workflows (same input → same output) * Use for sequential operations with dependencies * Use for fallback patterns * Handle errors with `try/catch` * Keep workflows readable (add comments) -*DON'T*: +DON'T: + * Use for workflows requiring LLM reasoning at each step (let agent handle that) * Execute long-running operations (timeout will hit) * Access external resources (use MCP tools instead) @@ -510,16 +548,20 @@ results = call_tool("orchestrator", { // PLACEHOLDER: Add UI path for MCP server management -*Prerequisites*: +Prerequisites: + * MCP server URL * Authentication method (if required) * List of tools to enable -*Steps*: -1. *Navigate to MCP Servers*: +Steps: + +1. Navigate to MCP servers: + * Console → AI Gateway → MCP Servers → Add Server -2. *Configure Server*: +2. Configure server: + ```yaml # PLACEHOLDER: Actual configuration format name: database-server @@ -533,47 +575,54 @@ results = call_tool("orchestrator", { * describe_table ``` -3. *Test Connection*: +3. Test connection: + * Gateway attempts connection to MCP server * Verifies authentication * Retrieves tool list -4. *Enable Server*: +4. Enable server: + * Server status: Active * Tools available to agents -*Common MCP Servers*: -* *Database*: PostgreSQL, MySQL, MongoDB query tools -* *Filesystem*: Read/write/search files -* *API Integrations*: Slack, GitHub, Salesforce, Stripe -* *Search*: Web search, vector search, enterprise search -* *Code Execution*: Python, JavaScript sandboxes -* *Workflow*: Zapier, n8n integrations +Common MCP servers: + +* Database: PostgreSQL, MySQL, MongoDB query tools +* Filesystem: Read/write/search files +* API Integrations: Slack, GitHub, Salesforce, Stripe +* Search: Web search, vector search, enterprise search +* Code Execution: Python, JavaScript sandboxes +* Workflow: Zapier, n8n integrations === MCP server approval workflow -*Why Approval is Required*: +Why approval is required: + * Security: Prevent agents from accessing unauthorized systems * Governance: Control which tools are available * Cost: Some tools are expensive (API calls, compute) * Compliance: Audit trail of approved tools -*Approval Process*: +Approval process: + // PLACEHOLDER: Confirm if there's an approval workflow or if admins directly enable servers -1. *Request*: User/team requests MCP server -2. *Review*: Admin reviews security, cost, necessity -3. *Approval/Rejection*: Admin decision -4. *Configuration*: If approved, admin adds server to gateway +1. Request: User/team requests MCP server +2. Review: Admin reviews security, cost, necessity +3. Approval/Rejection: Admin decision +4. Configuration: If approved, admin adds server to gateway + +Rejected server behavior: -*Rejected Server Behavior*: * Server not listed in tool discovery * Agent cannot query or invoke tools from this server * Requests return `403 Forbidden` === Restrict MCP server access -*Per-Gateway Restrictions*: +Per-gateway restrictions: + [source,yaml] ---- # PLACEHOLDER: Actual configuration format @@ -592,7 +641,8 @@ gateways: ---- -*Use Cases*: +Use cases: + * Production gateway: Only production-safe tools * Staging gateway: All tools for testing * Customer-specific gateway: Only tools relevant to customer @@ -601,22 +651,25 @@ gateways: // PLACEHOLDER: How is MCP server versioning handled? -*Challenge*: MCP server updates may change tool schemas +Challenge: MCP server updates may change tool schemas -*Recommendations*: -1. *Pin Versions* (if supported): +Recommendations: + +1. Pin versions (if supported): ```yaml mcp_servers: * name: database-server version: "1.2.3" # Pin to specific version ``` -2. *Test in Staging First*: +2. Test in staging first: + * Update MCP server in staging gateway * Test agent workflows * Promote to production when validated -3. *Monitor Breaking Changes*: +3. Monitor breaking changes: + * Subscribe to MCP server changelogs * Set up alerts for schema changes @@ -625,6 +678,7 @@ gateways: === Logs MCP tool invocations appear in request logs with: + * Tool name * MCP server * Input parameters @@ -632,14 +686,16 @@ MCP tool invocations appear in request logs with: * Execution time * Errors (if any) -*Filter Logs by MCP*: +Filter logs by MCP: + [source,text] ---- Filter: request.path.startsWith("/mcp") ---- -*Common Log Fields*: +Common log fields: + | Field | Description | Example | |-------|-------------|---------| | Tool | Tool invoked | `execute_sql` | @@ -653,7 +709,8 @@ Filter: request.path.startsWith("/mcp") // PLACEHOLDER: Confirm if MCP-specific metrics exist -*MCP-Specific Metrics* (if available): +MCP-specific metrics (if available): + * MCP requests per second * Tool invocation count (by tool, by MCP server) * MCP latency (p50, p95, p99) @@ -661,7 +718,8 @@ Filter: request.path.startsWith("/mcp") * Orchestrator execution count * Orchestrator execution time -*Dashboard*: MCP Analytics +Dashboard: MCP Analytics + * Top tools by usage * Top MCP servers by latency * Error rate by MCP server @@ -669,39 +727,45 @@ Filter: request.path.startsWith("/mcp") === Debug MCP issues -*Issue: "Tool not found"* +Issue: "Tool not found" + +Possible causes: -*Possible Causes*: 1. MCP server not added to gateway 2. Tool not enabled in MCP server configuration 3. Deferred loading enabled but agent didn't query for tool first -*Solution*: +Solution: + 1. Verify MCP server is active: // PLACEHOLDER: UI path 2. Verify tool is in enabled_tools list 3. If deferred loading: Agent must call `search_tools` first -*Issue: "MCP server timeout"* +Issue: "MCP server timeout" + +Possible causes: -*Possible Causes*: 1. MCP server is down/unreachable 2. Tool execution is slow (e.g., expensive database query) 3. Gateway timeout too short -*Solution*: +Solution: + 1. Check MCP server health 2. Optimize tool (e.g., add database index) 3. Increase timeout: // PLACEHOLDER: How to configure? -*Issue: "Orchestrator workflow failed"* +Issue: "Orchestrator workflow failed" + +Possible causes: -*Possible Causes*: 1. JavaScript syntax error 2. Tool invocation failed inside workflow 3. Timeout exceeded 4. Memory limit exceeded -*Solution*: +Solution: + 1. Test workflow syntax in JavaScript playground 2. Check logs for tool error inside orchestrator 3. Simplify workflow or increase timeout @@ -713,32 +777,37 @@ Filter: request.path.startsWith("/mcp") // PLACEHOLDER: Confirm sandboxing implementation -*Orchestrator Sandbox*: +Orchestrator Sandbox: + * No file system access * No network access (except via MCP tools) * No system calls * Memory limit: // PLACEHOLDER: e.g., 128MB * Execution timeout: // PLACEHOLDER: e.g., 30s -*MCP Tool Execution*: +MCP tool execution: + * Tools execute in MCP server's environment (not gateway) * Gateway does not execute tool code (only proxies requests) * Security is MCP server's responsibility === Authentication -*Gateway → MCP Server*: +Gateway → MCP server: + * Bearer token (most common) * API key * mTLS (for high-security environments) -*Agent → Gateway*: +Agent → Gateway: + * Standard gateway authentication (Redpanda Cloud token) * `rp-aigw-id` header identifies gateway (and its approved MCP servers) === Audit trail All MCP operations logged: + * Who (agent/user) invoked tool * When (timestamp) * What tool was invoked @@ -746,19 +815,21 @@ All MCP operations logged: * What result was returned * Whether it succeeded or failed -*Use Case*: Compliance, security investigation, debugging +Use case: Compliance, security investigation, debugging === Restrict dangerous tools -*Recommendation*: Don't enable destructive tools in production gateways +Recommendation: Don't enable destructive tools in production gateways + +Examples of dangerous tools*: -*Examples of Dangerous Tools*: * File deletion (`delete_file`) * Database writes without safeguards (`execute_sql` with UPDATE/DELETE) * Payment operations (`charge_customer`) * System commands (`execute_bash`) -*Best Practice*: +Best practice: + * Read-only tools in production gateway * Write tools only in staging gateway (with approval workflows) * Wrap dangerous operations in MCP server with safeguards (e.g., "require confirmation token") @@ -767,9 +838,10 @@ All MCP operations logged: === Combine MCP with CEL routing -*Use Case*: Route agents to different MCP servers based on customer tier +Use case: Route agents to different MCP servers based on customer tier + +CEL expression: -*CEL Expression*: [source,cel] ---- request.headers["x-customer-tier"] == "enterprise" @@ -778,19 +850,21 @@ request.headers["x-customer-tier"] == "enterprise" ---- -*Result*: +Result: + * Enterprise customers: Access to proprietary data, expensive APIs * Basic customers: Access to public data, free APIs === MCP with provider pools -*Scenario*: Different agents use different models + different tools +Scenario: Different agents use different models + different tools + +Configuration: -*Configuration*: * Gateway A: GPT-4o + database + CRM MCP servers * Gateway B: Claude Sonnet + web search + analytics MCP servers -*Use Case*: Optimize model-tool pairing (some models better at certain tools) +Use case: Optimize model-tool pairing (some models better at certain tools) == Integration examples @@ -860,7 +934,7 @@ if response.choices[0].message.tool_calls: ---- -=== Claude code cli +=== Claude Code CLI [source,bash] ---- @@ -909,15 +983,14 @@ response = agent.run("Find all premium users in the database") == Next steps -* *Configure MCP Servers* → [MCP Server Administration Guide](// PLACEHOLDER: link) -* *Write Orchestrator Workflows* → [Orchestrator Examples](// PLACEHOLDER: link) -* *Monitor MCP Usage* → [Observability: MCP Metrics](// PLACEHOLDER: link) -* *Optimize Token Costs* → [Cost Optimization Guide](// PLACEHOLDER: link) -* *Build Agentic Workflows* → [Agent Patterns Guide](// PLACEHOLDER: link) +* *Configure MCP servers* → [MCP Server Administration Guide](// PLACEHOLDER: link) +* *Write Orchestrator workflows* → [Orchestrator Examples](// PLACEHOLDER: link) +* *Monitor MCP usage* → [Observability: MCP Metrics](// PLACEHOLDER: link) +* *Optimize token costs* → [Cost Optimization Guide](// PLACEHOLDER: link) +* *Build agentic workflows* → [Agent Patterns Guide](// PLACEHOLDER: link) == Related pages * [Quickstart](// PLACEHOLDER: link) * [CEL Routing](// PLACEHOLDER: link) * [Observability: Logs](// PLACEHOLDER: link) -* [Security & Data Handling](// PLACEHOLDER: link) diff --git a/modules/ai-agents/partials/migration-guide.adoc b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc similarity index 83% rename from modules/ai-agents/partials/migration-guide.adoc rename to modules/ai-agents/pages/ai-gateway/migration-guide.adoc index ffdd54a17..0b8260b4c 100644 --- a/modules/ai-agents/partials/migration-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc @@ -1,23 +1,28 @@ -= Migration guide: from direct provider integration to AI gateway += DRAFT: Migrate from Direct Provider Integration to AI Gateway +:description: Quickstart to configure the AI Gateway for unified access to multiple LLM providers and MCP servers through a single endpoint. == Overview This guide helps you migrate existing applications from direct LLM provider integrations (OpenAI, Anthropic, etc.) to Redpanda AI Gateway. The migration is designed to be *incremental and reversible*, allowing you to test thoroughly before fully committing. -*Migration Time*: 10-30 minutes for most applications -*Downtime Required*: None (supports parallel operation) -*Rollback Difficulty*: Easy (feature flag or environment variable) +Migration time: 10-30 minutes for most applications + +Downtime required: None (supports parallel operation) + +Rollback difficulty: Easy (feature flag or environment variable) == Prerequisites Before migrating, ensure you have: -* ✅ AI Gateway configured in your Redpanda Cloud account -* ✅ Providers and models enabled (see [Admin Guide: Providers](// PLACEHOLDER: link)) -* ✅ Gateway created with appropriate policies (see [Gateway Creation Guide](// PLACEHOLDER: link)) -* ✅ Your gateway ID (`rp-aigw-id` header value) -* ✅ Your gateway endpoint URL - +//// == Migration strategy @@ -354,8 +355,6 @@ Common issues: * `Model not found` → Ensure model is enabled in gateway configuration * No `rp-aigw-id` header → Verify header is set in `default_headers` -See [Troubleshooting Guide](// PLACEHOLDER: link) for more details. - === Step 4: Verify in observability dashboard After successful test: @@ -370,7 +369,7 @@ After successful test: * Token count: ~10 prompt + ~10 completion * Cost: // PLACEHOLDER: expected cost -*If request doesn't appear*: Check [End-to-End Validation Guide](// PLACEHOLDER: link) +*If request doesn't appear*: Verify gateway ID and authentication token are correct. === Step 5: Enable gateway for subset of traffic @@ -780,7 +779,7 @@ If latency is significantly higher: 3. Review CEL routing complexity 4. Check for rate limiting (adds retry latency) -Solution: See [Performance Optimization Guide](// PLACEHOLDER: link) +Solution: Review geographic routing and provider pool configuration. === Issue: Requests not appearing in dashboard @@ -790,7 +789,7 @@ Causes: 2. Request failed before reaching gateway 3. UI delay (logs may take // PLACEHOLDER: Xs to appear) -Solution: See [End-to-End Validation Guide](// PLACEHOLDER: link) +Solution: Verify gateway ID and check for UI delay (logs may take a few seconds to appear). === Issue: Different response format @@ -931,12 +930,5 @@ model = "anthropic/claude-sonnet-3.5" # Was openai/gpt-4o == Next steps -* Configure routing policies → [CEL Routing Guide](// PLACEHOLDER: link) -* Explore MCP → [MCP Aggregation Guide](// PLACEHOLDER: link) - -== Related pages - -* [Quickstart](// PLACEHOLDER: link) -* [OpenAI Integration](// PLACEHOLDER: link) -* [Anthropic Integration](// PLACEHOLDER: link) -* [LangChain Integration](// PLACEHOLDER: link) +* xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Configure advanced routing policies. +* xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Explore MCP aggregation. diff --git a/modules/ai-agents/pages/ai-gateway/observability-logs.adoc b/modules/ai-agents/pages/ai-gateway/observability-logs.adoc index 0203cb863..ff82edbb8 100644 --- a/modules/ai-agents/pages/ai-gateway/observability-logs.adoc +++ b/modules/ai-agents/pages/ai-gateway/observability-logs.adoc @@ -24,7 +24,7 @@ Use logs for: * Understanding which provider handled a request * Investigating latency spikes or errors for specific users -Use metrics for: Aggregate analytics, trends, cost tracking across time → See [Observability: Metrics](// PLACEHOLDER: link) +Use metrics for: Aggregate analytics, trends, cost tracking across time. See xref:ai-agents:ai-gateway/observability-metrics.adoc[]. == Where to find logs @@ -628,7 +628,7 @@ Retention period: // PLACEHOLDER: e.g., 30 days, 90 days, configurable After retention period: * Logs are deleted automatically -* Aggregate metrics retained longer (see [Metrics](// PLACEHOLDER: link)) +* Aggregate metrics retained longer (see xref:ai-agents:ai-gateway/observability-metrics.adoc[]) Export logs (if needed for longer retention): @@ -671,7 +671,6 @@ Supported integrations (if any): * CloudWatch Logs → For AWS deployments * // PLACEHOLDER: Others? -See [Observability Integrations](// PLACEHOLDER: link) for setup guides. == Privacy and security @@ -772,4 +771,4 @@ Note: Cost estimates are approximate. Use provider invoices for billing. == Next steps -* Aggregate analytics → [Observability: Metrics](// PLACEHOLDER: link) \ No newline at end of file +* xref:ai-agents:ai-gateway/observability-metrics.adoc[]: Aggregate analytics and cost tracking. \ No newline at end of file diff --git a/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc b/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc index bd8ea95e3..0dc06bc3e 100644 --- a/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc +++ b/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc @@ -24,7 +24,7 @@ Use metrics for: * Capacity planning * Model/provider comparison -Use logs for: Debugging specific requests, viewing full prompts/responses. See [Observability: Logs](// PLACEHOLDER: link) +Use logs for: Debugging specific requests, viewing full prompts/responses. See xref:ai-agents:ai-gateway/observability-logs.adoc[]. == Where to find metrics @@ -499,9 +499,6 @@ alerts: channels: [pagerduty] ---- - -See [Alerting Guide](// PLACEHOLDER: link) for detailed setup. - == Export metrics // PLACEHOLDER: Confirm export capabilities @@ -559,8 +556,6 @@ Supported integrations (if any): * Grafana: Pre-built dashboards * // PLACEHOLDER: Others? -See [Observability Integrations](// PLACEHOLDER: link) for setup guides. - == Common analysis tasks === Task 1: "Are we staying within budget?" @@ -864,9 +859,9 @@ Possible causes: Solution: 1. Remove filters, widen time range -2. Send test request (see [Quickstart](// PLACEHOLDER: link)) +2. Send test request (see xref:ai-agents:ai-gateway/quickstart-enhanced.adoc[]) 3. Check permissions with admin == Next steps -* View individual requests → [Observability: Logs](// PLACEHOLDER: link) +* xref:ai-agents:ai-gateway/observability-logs.adoc[]: View individual requests and debug issues. From 77429a31b38a19a968f73dc863110c4813c5cae1 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Wed, 21 Jan 2026 21:30:27 -0700 Subject: [PATCH 17/50] edit for user journey --- modules/ROOT/nav.adoc | 21 +- .../pages/ai-gateway/admin/setup-guide.adoc | 323 ++++++++++ .../builders/connect-your-agent.adoc | 555 +++++++++++++++++ .../builders/discover-gateways.adoc | 299 +++++++++ modules/ai-agents/pages/ai-gateway/index.adoc | 5 +- .../pages/ai-gateway/what-is-ai-gateway.adoc | 182 ++++++ .../AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md | 573 ++++++++++++++++++ 7 files changed, 1949 insertions(+), 9 deletions(-) create mode 100644 modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc create mode 100644 modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc create mode 100644 modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc create mode 100644 modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc create mode 100644 modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md diff --git a/modules/ROOT/nav.adoc b/modules/ROOT/nav.adoc index 65c8b999d..6b62b9119 100644 --- a/modules/ROOT/nav.adoc +++ b/modules/ROOT/nav.adoc @@ -71,14 +71,19 @@ * xref:ai-agents:index.adoc[Agentic AI] ** xref:ai-agents:ai-gateway/index.adoc[AI Gateway] -*** xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[Overview] -*** xref:ai-agents:ai-gateway/ai-gateway.adoc[Quickstart] -**** xref:ai-agents:ai-gateway/quickstart-enhanced.adoc[enhanced quickstart] -*** xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[MCP Aggregation Guide] -*** xref:ai-agents:ai-gateway/observability-logs.adoc[] -*** xref:ai-agents:ai-gateway/observability-metrics.adoc[] -*** xref:ai-agents:ai-gateway/migration-guide.adoc[Migrate] -*** xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[] +*** xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[What is AI Gateway?] +*** For Administrators +**** xref:ai-agents:ai-gateway/admin/setup-guide.adoc[Setup Guide] +*** For Builders +**** xref:ai-agents:ai-gateway/builders/discover-gateways.adoc[Discover Gateways] +**** xref:ai-agents:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] +*** Reference +**** xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[Architecture Deep Dive] +**** xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[MCP Aggregation Guide] +**** xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[CEL Routing Cookbook] +**** xref:ai-agents:ai-gateway/observability-logs.adoc[Request Logs] +**** xref:ai-agents:ai-gateway/observability-metrics.adoc[Metrics and Usage] +**** xref:ai-agents:ai-gateway/migration-guide.adoc[Migration Guide] *** xref:ai-agents:ai-gateway/integrations/index.adoc[Integrations] **** Claude Code ***** xref:ai-agents:ai-gateway/integrations/claude-code-admin.adoc[Admin Guide] diff --git a/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc b/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc new file mode 100644 index 000000000..150aab357 --- /dev/null +++ b/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc @@ -0,0 +1,323 @@ += AI Gateway Setup Guide +:description: Complete setup guide for administrators to enable providers, configure models, create gateways, and set up routing policies. +:page-topic-type: how-to +:personas: platform_admin + +NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and later. + +This guide walks administrators through the complete setup process for AI Gateway, from enabling LLM providers to configuring routing policies and MCP tool aggregation. + +After completing this guide, you will be able to: + +* Enable LLM providers and models in the catalog +* Create and configure gateways with routing policies, rate limits, and spend limits +* Set up MCP tool aggregation for AI agents + +== Prerequisites + +* Access to the Redpanda Cloud Console with administrator privileges +* API keys for at least one LLM provider (OpenAI or Anthropic) +* (Optional) MCP server endpoints if you plan to use tool aggregation + +== Step 1: Enable a provider + +Providers represent upstream services (Anthropic, OpenAI) and associated credentials. Providers are disabled by default and must be enabled explicitly by an administrator. + +. In the Redpanda Cloud Console, navigate to *AI Gateway* → *Providers*. +. Select a provider (for example, Anthropic or OpenAI). +. On the *Configuration* tab for the provider, click *Add configuration*. +. Enter your API Key for the provider. ++ +TIP: Store provider API keys securely. Each provider configuration can have multiple API keys for rotation and redundancy. + +. Click *Save* to enable the provider. + +Repeat this process for each LLM provider you want to make available through AI Gateway. + +== Step 2: Enable models + +The model catalog is the set of models made available through the gateway. Models are disabled by default. After enabling a provider, you can enable its models. + +The infrastructure that serves the model differs based on the provider you select. For example, OpenAI has different reliability and availability metrics than Anthropic. When you consider all metrics, you can design your gateway to use different providers for different use cases. + +. Navigate to *AI Gateway* → *Models*. +. Review the list of available models from enabled providers. +. For each model you want to expose through gateways, toggle it to *Enabled*. ++ +Common models to enable: ++ +-- +* `openai/gpt-4o` - OpenAI's most capable model +* `openai/gpt-4o-mini` - Cost-effective OpenAI model +* `anthropic/claude-sonnet-3.5` - Balanced Anthropic model +* `anthropic/claude-opus-4` - Anthropic's most capable model +-- + +. Click *Save changes*. + +Only enabled models will be accessible through gateways. You can enable or disable models at any time without affecting existing gateways. + +=== Model naming convention + +Model requests must use the `vendor/model_id` format in the model property of the request body. This format allows AI Gateway to route requests to the appropriate provider. + +Examples: + +* `openai/gpt-4o` +* `anthropic/claude-sonnet-3.5` +* `openai/gpt-4o-mini` + +== Step 3: Create a gateway + +A gateway is a logical configuration boundary (policies + routing + observability) on top of a single deployment. It's a "virtual gateway" that you can create per team, environment (staging/production), product, or customer. + +. Navigate to *AI Gateway* → *Gateways*. +. Click *Create Gateway*. +. Configure the gateway: ++ +-- +* *Name*: Choose a descriptive name (for example, `production-gateway`, `team-ml-gateway`, `staging-gateway`) +* *Workspace*: Select the workspace this gateway belongs to ++ +TIP: A workspace is conceptually similar to a resource group in Redpanda streaming. ++ +* *Description* (optional): Add context about this gateway's purpose +* *Tags* (optional): Add metadata for organization and filtering +-- + +. Click *Create*. + +. After creation, note the following information: ++ +-- +* *Gateway ID*: Unique identifier (for example, `gw_abc123`) - users include this in the `rp-aigw-id` header +* *Gateway Endpoint*: Base URL for API requests (for example, `https://gw.ai.panda.com`) +-- + +You'll share the Gateway ID and Endpoint with users who need to access this gateway. + +== Step 4: Configure LLM routing + +On the gateway details page, select the *LLM* tab to configure rate limits, spend limits, routing, and provider pools with fallback options. + +The LLM routing pipeline visually represents the request lifecycle: + +. *Rate Limit*: Global rate limit (for example, 100 requests/second) +. *Spend Limit / Monthly Budget*: Monthly budget with blocking enforcement (for example, $15K/month) +. *Routing*: Primary provider pool with optional fallback provider pools + +=== Configure rate limits + +Rate limits control how many requests can be processed within a time window. + +. In the *LLM* tab, locate the *Rate Limit* section. +. Click *Add rate limit*. +. Configure the limit: ++ +-- +* *Requests per second*: Maximum requests per second (for example, `100`) +* *Burst allowance* (optional): Allow temporary bursts above the limit +-- + +. Click *Save*. + +Rate limits apply to all requests through this gateway, regardless of model or provider. + +=== Configure spend limits and budgets + +Spend limits prevent runaway costs by blocking requests after a monthly budget is exceeded. + +. In the *LLM* tab, locate the *Spend Limit* section. +. Click *Configure budget*. +. Set the budget: ++ +-- +* *Monthly budget*: Maximum spend per month (for example, `$15000`) +* *Enforcement*: Choose *Block* to reject requests after the budget is exceeded, or *Alert* to notify but allow requests +* *Notification threshold* (optional): Alert when X% of budget is consumed (for example, `80%`) +-- + +. Click *Save*. + +Budget tracking uses estimated costs based on token usage and public provider pricing. + +=== Configure routing and provider pools + +Provider pools define which LLM providers handle requests, with support for primary and fallback configurations. + +. In the *LLM* tab, locate the *Routing* section. +. Click *Add provider pool*. +. Configure the primary pool: ++ +-- +* *Name*: For example, `primary-anthropic` +* *Providers*: Select one or more providers (for example, Anthropic) +* *Models*: Choose which models to include (for example, `anthropic/claude-sonnet-3.5`) +* *Load balancing*: If multiple providers are selected, choose distribution strategy (round-robin, weighted, etc.) +-- + +. (Optional) Click *Add fallback pool* to configure automatic failover: ++ +-- +* *Name*: For example, `fallback-openai` +* *Providers*: Select fallback provider (for example, OpenAI) +* *Models*: Choose fallback models (for example, `openai/gpt-4o`) +* *Trigger conditions*: When to activate fallback: + ** Rate limit exceeded (429 from primary) + ** Timeout (primary provider slow) + ** Server errors (5xx from primary) +-- + +. Configure routing rules using CEL expressions (optional): ++ +For simple routing, select *Route all requests to primary pool*. ++ +For advanced routing based on request properties, use CEL expressions. See xref:ai-gateway/cel-routing-cookbook.adoc[] for examples. ++ +Example CEL expression for tier-based routing: ++ +[source,cel] +---- +request.headers["x-user-tier"] == "premium" + ? "anthropic/claude-opus-4" + : "anthropic/claude-sonnet-3.5" +---- + +. Click *Save routing configuration*. + +TIP: Provider pool (UI) = Backend pool (API) + +=== Load balancing and multi-provider distribution + +If a provider pool contains multiple providers, you can distribute traffic to balance load or optimize for cost/performance: + +* *Round-robin*: Distribute evenly across all providers +* *Weighted*: Assign weights (for example, 80% to Anthropic, 20% to OpenAI) +* *Least latency*: Route to fastest provider based on recent performance +* *Cost-optimized*: Route to cheapest provider for each model + +== Step 5: Configure MCP tools (optional) + +If your users will build AI agents that need access to tools via MCP (Model Context Protocol), configure MCP tool aggregation. + +On the gateway details page, select the *MCP* tab to configure tool discovery and execution. The MCP proxy aggregates multiple MCP servers, allowing agents to find and call tools through a single endpoint. + +=== Add MCP servers + +. In the *MCP* tab, click *Add MCP server*. +. Configure the server: ++ +-- +* *Server name*: Human-readable identifier (for example, `database-server`, `slack-server`) +* *Server URL*: Endpoint for the MCP server (for example, `https://mcp-database.example.com`) +* *Authentication*: Configure authentication if required (bearer token, API key, mTLS) +* *Enabled tools*: Select which tools from this server to expose (or *All tools*) +-- + +. Click *Test connection* to verify connectivity. +. Click *Save* to add the server to this gateway. + +Repeat for each MCP server you want to aggregate. + +=== Configure deferred tool loading + +Deferred tool loading dramatically reduces token costs by initially exposing only a search tool and orchestrator, rather than listing all available tools. + +. In the *MCP* tab, locate *Deferred Loading*. +. Toggle *Enable deferred tool loading* to *On*. +. Configure behavior: ++ +-- +* *Initially expose*: Search tool + orchestrator only +* *Load on demand*: Tools are retrieved when agents query for them +* *Token savings*: Expect 80-90% reduction in token usage for tool definitions +-- + +. Click *Save*. + +See xref:ai-gateway/mcp-aggregation-guide.adoc[] for detailed information about MCP aggregation. + +=== Configure the MCP orchestrator + +The MCP orchestrator is a built-in MCP server that enables programmatic tool calling. Agents can generate JavaScript code to call multiple tools in a single orchestrated step, reducing the number of round trips. + +Example: A workflow requiring 47 file reads can be reduced from 49 round trips to just 1 round trip using the orchestrator. + +The orchestrator is enabled by default when you enable MCP tools. You can configure: + +* *Execution timeout*: Maximum time for orchestrator workflows (for example, 30 seconds) +* *Memory limit*: Maximum memory for JavaScript execution (for example, 128MB) +* *Allowed operations*: Restrict which MCP tools can be called from orchestrator workflows + +== Verify your setup + +After completing the setup, verify that the gateway is working correctly: + +=== Test the gateway endpoint + +[source,bash] +---- +curl https://{GATEWAY_ENDPOINT}/v1/models \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "rp-aigw-id: ${GATEWAY_ID}" +---- + +Expected result: List of enabled models. + +=== Send a test request + +[source,bash] +---- +curl https://{GATEWAY_ENDPOINT}/v1/chat/completions \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "rp-aigw-id: ${GATEWAY_ID}" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "openai/gpt-4o-mini", + "messages": [{"role": "user", "content": "Hello, AI Gateway!"}], + "max_tokens": 50 + }' +---- + +Expected result: Successful completion response. + +=== Check observability + +. Navigate to *AI Gateway* → *Gateways* → Select your gateway → *Analytics*. +. Verify that your test request appears in the request logs. +. Check metrics: ++ +-- +* Request count: Should show your test request +* Token usage: Should show tokens consumed +* Estimated cost: Should show calculated cost +-- + +== Share access with users + +Now that your gateway is configured, share access with users (builders): + +. Provide the *Gateway ID* (for example, `gw_abc123`) +. Provide the *Gateway Endpoint* (for example, `https://gw.ai.panda.com`) +. Share API credentials (Redpanda Cloud tokens with appropriate permissions) +. (Optional) Document available models and any routing policies +. (Optional) Share rate limits and budget information + +Users can then discover and connect to the gateway using the information provided. See xref:ai-gateway/builders/discover-gateways.adoc[] for user documentation. + +== Next steps + +*Configure and optimize:* + +// * xref:ai-gateway/admin/manage-gateways.adoc[Manage Gateways] - List, edit, and delete gateways +* xref:ai-gateway/cel-routing-cookbook.adoc[CEL Routing Cookbook] - Advanced routing patterns +// * xref:ai-gateway/admin/networking-configuration.adoc[Networking Configuration] - Configure private endpoints and connectivity + +*Monitor and observe:* + +* xref:ai-gateway/observability-metrics.adoc[Monitor Usage] - Track costs and usage across all gateways +* xref:ai-gateway/observability-logs.adoc[Request Logs] - View and filter request logs + +*Integrate tools:* + +* xref:ai-gateway/integrations/index.adoc[Integrations] - Admin guides for Claude Code, Cursor, and other tools diff --git a/modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc b/modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc new file mode 100644 index 000000000..44a439d11 --- /dev/null +++ b/modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc @@ -0,0 +1,555 @@ += Connect Your Agent +:description: Integrate your AI agent or application with Redpanda AI Gateway for unified LLM access. +:page-topic-type: how-to +:personas: app_developer + +This guide shows you how to connect your AI agent or application to a Redpanda AI Gateway. You'll configure your client SDK, make your first request, and validate the integration. + +After completing this guide, you will be able to: + +* Configure your application to use AI Gateway with OpenAI-compatible SDKs +* Make LLM requests through the gateway and handle responses appropriately +* Validate your integration end-to-end + +== Prerequisites + +* You have discovered an available gateway and noted its Gateway ID and Endpoint ++ +If not, see xref:ai-gateway/builders/discover-gateways.adoc[]. + +* You have a Redpanda Cloud API token with access to the gateway +* You have a development environment with your chosen programming language + +== Integration overview + +Connecting to AI Gateway requires three configuration changes: + +. *Change the base URL*: Point to the gateway endpoint instead of the provider's API +. *Add authentication*: Use your Redpanda Cloud token instead of provider API keys +. *Add the gateway ID header*: Include `rp-aigw-id` to identify which gateway to use + +That's it. Your existing application code doesn't need to change. + +== Quick start + +=== Environment variables + +Set these environment variables for consistent configuration: + +[source,bash] +---- +export REDPANDA_GATEWAY_URL="https://gw.ai.panda.com" +export REDPANDA_GATEWAY_ID="gw_abc123" +export REDPANDA_API_KEY="your-redpanda-cloud-token" +---- + +Replace with your actual gateway endpoint, ID, and API token. + +=== Python (OpenAI SDK) + +[source,python] +---- +import os +from openai import OpenAI + +# Configure client to use AI Gateway +client = OpenAI( + base_url=os.getenv("REDPANDA_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_API_KEY"), + default_headers={ + "rp-aigw-id": os.getenv("REDPANDA_GATEWAY_ID") + } +) + +# Make a request (same as before) +response = client.chat.completions.create( + model="openai/gpt-4o-mini", # Note: vendor/model_id format + messages=[{"role": "user", "content": "Hello, AI Gateway!"}], + max_tokens=100 +) + +print(response.choices[0].message.content) +---- + +=== Python (Anthropic SDK) + +The Anthropic SDK can also route through AI Gateway using the OpenAI-compatible endpoint: + +[source,python] +---- +import os +from anthropic import Anthropic + +client = Anthropic( + base_url=os.getenv("REDPANDA_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_API_KEY"), + default_headers={ + "rp-aigw-id": os.getenv("REDPANDA_GATEWAY_ID") + } +) + +# Make a request +message = client.messages.create( + model="anthropic/claude-sonnet-3.5", + max_tokens=100, + messages=[{"role": "user", "content": "Hello, AI Gateway!"}] +) + +print(message.content[0].text) +---- + +=== Node.js (OpenAI SDK) + +[source,javascript] +---- +import OpenAI from 'openai'; + +const openai = new OpenAI({ + baseURL: process.env.REDPANDA_GATEWAY_URL, + apiKey: process.env.REDPANDA_API_KEY, + defaultHeaders: { + 'rp-aigw-id': process.env.REDPANDA_GATEWAY_ID + } +}); + +// Make a request +const response = await openai.chat.completions.create({ + model: 'openai/gpt-4o-mini', + messages: [{ role: 'user', content: 'Hello, AI Gateway!' }], + max_tokens: 100 +}); + +console.log(response.choices[0].message.content); +---- + +=== cURL + +For testing or shell scripts: + +[source,bash] +---- +curl ${REDPANDA_GATEWAY_URL}/v1/chat/completions \ + -H "Authorization: Bearer ${REDPANDA_API_KEY}" \ + -H "rp-aigw-id: ${REDPANDA_GATEWAY_ID}" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "openai/gpt-4o-mini", + "messages": [{"role": "user", "content": "Hello, AI Gateway!"}], + "max_tokens": 100 + }' +---- + +== Model naming convention + +When making requests through AI Gateway, use the `vendor/model_id` format for the model parameter: + +* `openai/gpt-4o` +* `openai/gpt-4o-mini` +* `anthropic/claude-sonnet-3.5` +* `anthropic/claude-opus-4` + +This format tells AI Gateway which provider to route the request to. + +Example: + +[source,python] +---- +# Route to OpenAI +response = client.chat.completions.create( + model="openai/gpt-4o", + messages=[...] +) + +# Route to Anthropic (same client, different model) +response = client.chat.completions.create( + model="anthropic/claude-sonnet-3.5", + messages=[...] +) +---- + +// To see which models are available in your gateway, see xref:ai-gateway/builders/available-models.adoc[]. + +== Handle responses + +Responses from AI Gateway follow the OpenAI API format: + +[source,python] +---- +response = client.chat.completions.create( + model="openai/gpt-4o-mini", + messages=[{"role": "user", "content": "Explain AI Gateway"}], + max_tokens=200 +) + +# Access the response +message_content = response.choices[0].message.content +finish_reason = response.choices[0].finish_reason # 'stop', 'length', etc. + +# Token usage +prompt_tokens = response.usage.prompt_tokens +completion_tokens = response.usage.completion_tokens +total_tokens = response.usage.total_tokens + +print(f"Response: {message_content}") +print(f"Tokens: {prompt_tokens} prompt + {completion_tokens} completion = {total_tokens} total") +---- + +== Handle errors + +AI Gateway returns standard HTTP status codes: + +[source,python] +---- +from openai import OpenAI, OpenAIError + +client = OpenAI( + base_url=os.getenv("REDPANDA_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_API_KEY"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_GATEWAY_ID")} +) + +try: + response = client.chat.completions.create( + model="openai/gpt-4o-mini", + messages=[{"role": "user", "content": "Hello"}] + ) + print(response.choices[0].message.content) + +except OpenAIError as e: + if e.status_code == 400: + print("Bad request - check model name and parameters") + elif e.status_code == 401: + print("Authentication failed - check API token") + elif e.status_code == 404: + print("Model not found - check available models") + elif e.status_code == 429: + print("Rate limit exceeded - slow down requests") + elif e.status_code >= 500: + print("Gateway or provider error - retry with exponential backoff") + else: + print(f"Error: {e}") +---- + +Common error codes: + +* *400*: Bad request (invalid parameters, malformed JSON) +* *401*: Authentication failed (invalid or missing API token) +* *403*: Forbidden (no access to this gateway) +* *404*: Model not found (model not enabled in gateway) +* *429*: Rate limit exceeded (too many requests) +* *500/502/503*: Server error (gateway or provider issue) + +== Streaming responses + +AI Gateway supports streaming for real-time token generation: + +[source,python] +---- +response = client.chat.completions.create( + model="openai/gpt-4o-mini", + messages=[{"role": "user", "content": "Write a short poem"}], + stream=True # Enable streaming +) + +# Process chunks as they arrive +for chunk in response: + if chunk.choices[0].delta.content: + print(chunk.choices[0].delta.content, end='', flush=True) + +print() # New line after streaming completes +---- + +== Switch between providers + +One of AI Gateway's key benefits is easy provider switching without code changes: + +[source,python] +---- +# Try OpenAI +response = client.chat.completions.create( + model="openai/gpt-4o", + messages=[{"role": "user", "content": "Explain quantum computing"}] +) + +# Try Anthropic (same code, different model) +response = client.chat.completions.create( + model="anthropic/claude-sonnet-3.5", + messages=[{"role": "user", "content": "Explain quantum computing"}] +) +---- + +Compare responses, latency, and cost to determine the best model for your use case. + +== Validate your integration + +=== Test connectivity + +[source,python] +---- +import os +from openai import OpenAI + +def test_gateway_connection(): + """Test basic connectivity to AI Gateway""" + client = OpenAI( + base_url=os.getenv("REDPANDA_GATEWAY_URL"), + api_key=os.getenv("REDPANDA_API_KEY"), + default_headers={"rp-aigw-id": os.getenv("REDPANDA_GATEWAY_ID")} + ) + + try: + # Simple test request + response = client.chat.completions.create( + model="openai/gpt-4o-mini", + messages=[{"role": "user", "content": "test"}], + max_tokens=10 + ) + print("✓ Gateway connection successful") + return True + except Exception as e: + print(f"✗ Gateway connection failed: {e}") + return False + +if __name__ == "__main__": + test_gateway_connection() +---- + +=== Test multiple models + +[source,python] +---- +def test_models(): + """Test multiple models through the gateway""" + models = [ + "openai/gpt-4o-mini", + "anthropic/claude-sonnet-3.5" + ] + + for model in models: + try: + response = client.chat.completions.create( + model=model, + messages=[{"role": "user", "content": "Say hello"}], + max_tokens=10 + ) + print(f"✓ {model}: {response.choices[0].message.content}") + except Exception as e: + print(f"✗ {model}: {e}") +---- + +=== Check request logs + +After making requests, verify they appear in observability: + +. Navigate to *AI Gateway* → *Gateways* → Select your gateway → *Logs* +. Filter by your request timestamp +. Verify your requests are logged with correct model, tokens, and cost + +// See xref:ai-gateway/builders/monitor-your-usage.adoc[] for details. + +== Integrate with AI development tools + +=== Claude Code + +Configure Claude Code to use AI Gateway: + +[source,bash] +---- +claude mcp add --transport http redpanda-aigateway https://gw.ai.panda.com/mcp \ + --header "Authorization: Bearer ${REDPANDA_API_KEY}" \ + --header "rp-aigw-id: ${REDPANDA_GATEWAY_ID}" +---- + +Or edit `~/.claude/config.json`: + +[source,json] +---- +{ + "mcpServers": { + "redpanda-ai-gateway": { + "transport": "http", + "url": "https://gw.ai.panda.com/mcp", + "headers": { + "Authorization": "Bearer your-api-key", + "rp-aigw-id": "gw_abc123" + } + } + } +} +---- + +See xref:ai-gateway/integrations/claude-code-user.adoc[] for complete setup. + +=== VS Code Continue Extension + +Edit `~/.continue/config.json`: + +[source,json] +---- +{ + "models": [ + { + "title": "AI Gateway - GPT-4", + "provider": "openai", + "model": "openai/gpt-4o", + "apiBase": "https://gw.ai.panda.com", + "apiKey": "your-redpanda-api-key", + "requestOptions": { + "headers": { + "rp-aigw-id": "gw_abc123" + } + } + } + ] +} +---- + +See xref:ai-gateway/integrations/continue-user.adoc[] for complete setup. + +=== Cursor IDE + +. Open Cursor Settings (*Cursor* → *Settings* or `Cmd+,`) +. Navigate to *AI* settings +. Add custom OpenAI-compatible provider: + +[source,json] +---- +{ + "cursor.ai.providers.openai.apiBase": "https://gw.ai.panda.com", + "cursor.ai.providers.openai.defaultHeaders": { + "rp-aigw-id": "gw_abc123" + } +} +---- + +See xref:ai-gateway/integrations/cursor-user.adoc[] for complete setup. + +== Best practices + +=== Use environment variables + +Store configuration in environment variables, not hardcoded in code: + +[source,python] +---- +# Good +base_url = os.getenv("REDPANDA_GATEWAY_URL") + +# Bad +base_url = "https://gw.ai.panda.com" # Don't hardcode +---- + +=== Implement retry logic + +Implement exponential backoff for transient errors: + +[source,python] +---- +import time +from openai import OpenAI, OpenAIError + +def make_request_with_retry(client, max_retries=3): + for attempt in range(max_retries): + try: + return client.chat.completions.create( + model="openai/gpt-4o-mini", + messages=[{"role": "user", "content": "Hello"}] + ) + except OpenAIError as e: + if e.status_code >= 500 and attempt < max_retries - 1: + wait_time = 2 ** attempt # Exponential backoff + print(f"Retrying in {wait_time}s...") + time.sleep(wait_time) + else: + raise +---- + +=== Monitor your usage + +Regularly check your usage to avoid unexpected costs: + +[source,python] +---- +# Track tokens in your application +total_tokens = 0 +request_count = 0 + +for request in requests: + response = client.chat.completions.create(...) + total_tokens += response.usage.total_tokens + request_count += 1 + +print(f"Total tokens: {total_tokens} across {request_count} requests") +---- + +// See xref:ai-gateway/builders/monitor-your-usage.adoc[] for detailed monitoring. + +=== Handle rate limits gracefully + +Respect rate limits and implement backoff: + +[source,python] +---- +try: + response = client.chat.completions.create(...) +except OpenAIError as e: + if e.status_code == 429: + # Rate limited - wait and retry + retry_after = int(e.response.headers.get('Retry-After', 60)) + print(f"Rate limited. Waiting {retry_after}s...") + time.sleep(retry_after) + # Retry request +---- + +== Troubleshooting + +=== "Authentication failed" + +Problem: 401 Unauthorized + +Solutions: + +* Verify your API token is correct and not expired +* Check that the token has access to the specified gateway +* Ensure the `Authorization` header is formatted correctly: `Bearer ` + +=== "Model not found" + +Problem: 404 Model not found + +Solutions: + +* Verify the model name uses `vendor/model_id` format +// * Check available models: See xref:ai-gateway/builders/available-models.adoc[] +* Confirm the model is enabled in your gateway (contact administrator) + +=== "Rate limit exceeded" + +Problem: 429 Too Many Requests + +Solutions: + +* Reduce request rate +* Implement exponential backoff +* Contact administrator to review rate limits +* Consider using a different gateway if available + +=== "Connection timeout" + +Problem: Request times out + +Solutions: + +* Check network connectivity to the gateway endpoint +* Verify the gateway endpoint URL is correct +* Check if the gateway is operational (contact administrator) +* Increase client timeout if processing complex requests + +== Next steps + +Now that your agent is connected: + +// * xref:ai-gateway/builders/available-models.adoc[Available Models] - Learn about model selection and routing +// * xref:ai-gateway/builders/use-mcp-tools.adoc[Use MCP Tools] - Access tools from MCP servers (if enabled) +// * xref:ai-gateway/builders/monitor-your-usage.adoc[Monitor Your Usage] - Track requests and costs +* xref:ai-gateway/integrations/index.adoc[Integrations] - Configure specific tools and IDEs diff --git a/modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc b/modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc new file mode 100644 index 000000000..43890fa5f --- /dev/null +++ b/modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc @@ -0,0 +1,299 @@ += Discover Available Gateways +:description: Find which AI Gateways you can access and their configurations. +:page-topic-type: how-to +:personas: app_developer + +As a builder, you need to know which gateways are available to you before integrating your agent or application. This page shows you how to discover accessible gateways, understand their configurations, and verify connectivity. + +After reading this page, you will be able to: + +* List all AI Gateways you have access to and retrieve their endpoints and IDs +* View which models and MCP tools are available through each gateway +* Test gateway connectivity before integration + +== Before you begin + +* You have a Redpanda Cloud account with access to at least one AI Gateway +* You have access to the Redpanda Cloud Console or API credentials + +== List your accessible gateways + +=== Using the Console + +. Navigate to *AI Gateway* in the Redpanda Cloud Console. +. View the *My Gateways* tab (or *Gateways* if you're an administrator). +. Review the list of gateways you can access: ++ +For each gateway, you'll see: ++ +-- +* *Gateway Name*: Human-readable name (for example, `production-gateway`, `team-ml-gateway`) +* *Gateway ID*: Unique identifier used in the `rp-aigw-id` header (for example, `gw_abc123`) +* *Gateway Endpoint*: Base URL for API requests (for example, `https://gw.ai.panda.com`) +* *Status*: Whether the gateway is active and accepting requests +* *Available Models*: Which LLM models you can access +* *MCP Tools*: Whether MCP tool aggregation is enabled +-- + +=== Using the API + +You can also list gateways programmatically: + +[source,bash] +---- +curl https://api.redpanda.com/v1/ai-gateway/gateways \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" +---- + +Response: + +[source,json] +---- +{ + "gateways": [ + { + "id": "gw_abc123", + "name": "production-gateway", + "endpoint": "https://gw.ai.panda.com", + "status": "active", + "workspace_id": "ws_xyz789", + "created_at": "2025-01-15T10:30:00Z" + }, + { + "id": "gw_def456", + "name": "staging-gateway", + "endpoint": "https://gw-staging.ai.panda.com", + "status": "active", + "workspace_id": "ws_xyz789", + "created_at": "2025-01-10T08:15:00Z" + } + ] +} +---- + +== Understand gateway information + +Each gateway provides specific information you'll need for integration: + +=== Gateway ID + +The Gateway ID is a unique identifier that you include in the `rp-aigw-id` header with every request. This tells AI Gateway which gateway configuration to use for routing, policies, and observability. + +Example: +[source,bash] +---- +rp-aigw-id: gw_abc123 +---- + +=== Gateway Endpoint + +The endpoint is the base URL where you send all API requests. This replaces direct provider URLs (like `api.openai.com` or `api.anthropic.com`). + +Example: +[source,bash] +---- +https://gw.ai.panda.com +---- + +Your application configures this as the `base_url` in your SDK client. + +=== Available Models + +Each gateway exposes specific models based on administrator configuration. Models use the `vendor/model_id` format: + +* `openai/gpt-4o` +* `anthropic/claude-sonnet-3.5` +* `openai/gpt-4o-mini` + +To see which models are available through a specific gateway: + +[source,bash] +---- +curl https://{GATEWAY_ENDPOINT}/v1/models \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "rp-aigw-id: ${GATEWAY_ID}" +---- + +Response: + +[source,json] +---- +{ + "object": "list", + "data": [ + { + "id": "openai/gpt-4o", + "object": "model", + "owned_by": "openai" + }, + { + "id": "anthropic/claude-sonnet-3.5", + "object": "model", + "owned_by": "anthropic" + }, + { + "id": "openai/gpt-4o-mini", + "object": "model", + "owned_by": "openai" + } + ] +} +---- + +=== Rate Limits and Quotas + +Each gateway may have configured rate limits and monthly budgets. Check the console or contact your administrator to understand: + +* Requests per minute/hour/day +* Monthly spend limits +* Token usage quotas + +These limits help control costs and ensure fair resource allocation across teams. + +=== MCP Tools + +If MCP aggregation is enabled for your gateway, you can access tools from multiple MCP servers through a single endpoint. + +To discover available MCP tools: + +[source,bash] +---- +curl https://{GATEWAY_ENDPOINT}/mcp/tools \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "rp-aigw-id: ${GATEWAY_ID}" \ + -H "rp-aigw-mcp-deferred: true" +---- + +With deferred loading enabled, you'll receive search and orchestrator tools initially. You can then query for specific tools as needed. + +// See xref:ai-gateway/builders/use-mcp-tools.adoc[] for more details. + +== Check gateway availability + +Before integrating your application, verify that you can successfully connect to the gateway: + +=== Test connectivity + +[source,bash] +---- +curl https://{GATEWAY_ENDPOINT}/v1/models \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "rp-aigw-id: ${GATEWAY_ID}" \ + -v +---- + +Expected result: HTTP 200 response with a list of available models. + +=== Test a simple request + +Send a minimal chat completion request to verify end-to-end functionality: + +[source,bash] +---- +curl https://{GATEWAY_ENDPOINT}/v1/chat/completions \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "rp-aigw-id: ${GATEWAY_ID}" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "openai/gpt-4o-mini", + "messages": [{"role": "user", "content": "Hello"}], + "max_tokens": 10 + }' +---- + +Expected result: HTTP 200 response with a completion. + +=== Troubleshoot connectivity issues + +If you cannot connect to a gateway: + +. *Verify authentication*: Ensure your API token is valid and has not expired +. *Check gateway ID*: Confirm you're using the correct `rp-aigw-id` value +. *Verify endpoint URL*: Check for typos in the gateway endpoint +. *Check permissions*: Confirm with your administrator that you have access to this gateway +. *Review network connectivity*: Ensure your network allows outbound HTTPS connections + +== Choose the right gateway + +If you have access to multiple gateways, consider which one to use based on your needs: + +=== By environment + +Organizations often create separate gateways for different environments: + +* *Production gateway*: Higher rate limits, access to all models, monitoring enabled +* *Staging gateway*: Lower rate limits, restricted models, aggressive cost controls +* *Development gateway*: Minimal limits, all models for experimentation + +Choose the gateway that matches your deployment environment. + +=== By team or project + +Gateways may be organized by team or project for cost tracking and isolation: + +* *team-ml-gateway*: For machine learning team +* *team-product-gateway*: For product team +* *customer-facing-gateway*: For production customer workloads + +Use the gateway designated for your team to ensure proper cost attribution. + +=== By capability + +Different gateways may have different features enabled: + +* *Gateway with MCP tools*: Use if your agent needs to call tools +* *Gateway without MCP*: Use for simple LLM completions +* *Gateway with specific models*: Use if you need access to particular models + +== Example: Complete discovery workflow + +Here's a complete workflow to discover and validate gateway access: + +[source,bash] +---- +#!/bin/bash + +# Set your API token +export REDPANDA_CLOUD_TOKEN="your-token-here" + +# Step 1: List all accessible gateways +echo "=== Discovering gateways ===" +curl -s https://api.redpanda.com/v1/ai-gateway/gateways \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + | jq '.gateways[] | {name: .name, id: .id, endpoint: .endpoint}' + +# Step 2: Select a gateway (example) +export GATEWAY_ID="gw_abc123" +export GATEWAY_ENDPOINT="https://gw.ai.panda.com" + +# Step 3: List available models +echo -e "\n=== Available models ===" +curl -s ${GATEWAY_ENDPOINT}/v1/models \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "rp-aigw-id: ${GATEWAY_ID}" \ + | jq '.data[] | .id' + +# Step 4: Test with a simple request +echo -e "\n=== Testing request ===" +curl -s ${GATEWAY_ENDPOINT}/v1/chat/completions \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "rp-aigw-id: ${GATEWAY_ID}" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "openai/gpt-4o-mini", + "messages": [{"role": "user", "content": "Say hello"}], + "max_tokens": 10 + }' \ + | jq '.choices[0].message.content' + +echo -e "\n=== Gateway validated successfully ===" +---- + +== Next steps + +Now that you've discovered your available gateways: + +* xref:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] - Integrate your application +// * xref:ai-gateway/builders/available-models.adoc[Available Models] - Learn about model selection and routing +// * xref:ai-gateway/builders/use-mcp-tools.adoc[Use MCP Tools] - Access tools from MCP servers +// * xref:ai-gateway/builders/monitor-your-usage.adoc[Monitor Your Usage] - Track requests and costs diff --git a/modules/ai-agents/pages/ai-gateway/index.adoc b/modules/ai-agents/pages/ai-gateway/index.adoc index a84ffbf2a..2a3d3ebca 100644 --- a/modules/ai-agents/pages/ai-gateway/index.adoc +++ b/modules/ai-agents/pages/ai-gateway/index.adoc @@ -1,3 +1,6 @@ = AI Gateway -:description: Learn how to configure the AI Gateway for unified access to multiple LLM providers and MCP servers through a single endpoint. +:description: Unified access layer for LLM providers and AI tools with centralized routing, policy enforcement, cost management, and observability. :page-layout: index +:personas: platform_admin, app_developer, evaluator + +Redpanda AI Gateway provides a unified access layer for LLM providers and AI tools that sits between your applications and the AI services they use. It delivers centralized routing, policy enforcement, cost management, and observability for all your AI traffic. \ No newline at end of file diff --git a/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc new file mode 100644 index 000000000..d884f4a59 --- /dev/null +++ b/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc @@ -0,0 +1,182 @@ += What is an AI Gateway? +:description: Understand what an AI Gateway is, the problems it solves, and how it benefits your AI infrastructure. +:page-topic-type: concept +:personas: app_developer, platform_admin + +NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and later. + +Redpanda AI Gateway is a unified access layer for LLM providers and AI tools that sits between your applications and the AI services they use. It provides centralized routing, policy enforcement, cost management, and observability for all your AI traffic. + +After reading this page, you will be able to: + +* Describe how AI Gateway centralizes LLM provider management and reduces operational complexity +* Identify key features (routing, observability, cost controls) that address common LLM integration challenges +* Determine whether AI Gateway fits your use case based on traffic volume and provider diversity + +== The problem + +Modern AI applications face four critical challenges that increase costs, reduce reliability, and slow down development. + +First, applications typically hardcode provider-specific SDKs. An application using OpenAI's SDK cannot easily switch to Anthropic or Google without code changes and redeployment. This tight coupling makes testing across providers time-consuming and error-prone, and means provider outages directly impact your application availability. + +Second, costs can spiral without visibility into usage patterns. Without a centralized view of token consumption across teams and applications, it's difficult to attribute costs to specific customers, features, or environments. Testing and debugging can generate unexpected bills, and there's no way to enforce budgets or rate limits per team or customer. + +Third, AI agents that use MCP (Model Context Protocol) servers face tool coordination challenges. Managing tool discovery and execution is repetitive across projects, and agents typically load all available tools upfront, which creates high token costs. There's also no centralized governance over which tools agents can access. + +Finally, observability is fragmented across provider dashboards. You cannot reconstruct user sessions that span multiple models, compare latency and costs across providers in a unified view, or efficiently debug issues. Troubleshooting "the AI gave the wrong answer" requires manual log diving across different systems. + +== What AI Gateway solves + +Redpanda AI Gateway addresses these challenges through four core capabilities: + +=== 1. Unified LLM access (single endpoint for all providers) + +AI Gateway provides a single OpenAI-compatible endpoint that routes requests to multiple LLM providers. Instead of integrating with each provider's SDK separately, you configure your application once and switch providers by changing only the model parameter. + +Without AI Gateway, you need different SDKs and patterns for each provider: + +[source,python] +---- +# OpenAI +from openai import OpenAI +client = OpenAI(api_key="sk-...") +response = client.chat.completions.create( + model="gpt-4o", + messages=[{"role": "user", "content": "Hello"}] +) + +# Anthropic (different SDK, different patterns) +from anthropic import Anthropic +client = Anthropic(api_key="sk-ant-...") +response = client.messages.create( + model="claude-sonnet-3.5", + max_tokens=1024, + messages=[{"role": "user", "content": "Hello"}] +) +---- + +With AI Gateway, you use the OpenAI SDK for all providers: + +[source,python] +---- +from openai import OpenAI + +# Single configuration, multiple providers +client = OpenAI( + base_url="https://{GATEWAY_ENDPOINT}", + api_key="your-redpanda-token", + default_headers={"rp-aigw-id": "{GATEWAY_ID}"} +) + +# Route to OpenAI +response = client.chat.completions.create( + model="openai/gpt-4o", + messages=[{"role": "user", "content": "Hello"}] +) + +# Route to Anthropic (same code, different model string) +response = client.chat.completions.create( + model="anthropic/claude-sonnet-3.5", + messages=[{"role": "user", "content": "Hello"}] +) +---- + +To switch providers, you change only the `model` parameter from `openai/gpt-4o` to `anthropic/claude-sonnet-3.5`. No code changes or redeployment needed. + +=== 2. Policy-based routing and cost control + +AI Gateway lets you define routing rules, rate limits, and budgets once, then enforces them automatically for all requests. + +You can route requests to different models based on user attributes. For example, to direct premium users to a more capable model while routing free tier users to a cost-effective option, use a CEL expression: + +[source,cel] +---- +// Route premium users to best model, free users to cost-effective model +request.headers["x-user-tier"] == "premium" + ? "anthropic/claude-opus-4" + : "anthropic/claude-sonnet-3.5" +---- + +You can also set different rate limits and spend limits per environment to prevent staging or development traffic from consuming production budgets. + +For reliability, you can configure provider pools with automatic failover. If you configure OpenAI GPT-4 as your primary model and Anthropic Claude Opus as the fallback, the gateway automatically routes requests to the fallback when it detects rate limits or timeouts from the primary provider. This configuration can achieve 99.9% uptime even during provider outages. + +=== 3. MCP aggregation and orchestration + +AI Gateway aggregates multiple MCP (Model Context Protocol) servers and provides deferred tool loading, which dramatically reduces token costs for AI agents. + +Without AI Gateway, agents typically load all available tools from multiple MCP servers at startup. This approach sends 50+ tool definitions with every request, creating high token costs (thousands of tokens per request), slow agent startup times, and no centralized governance over which tools agents can access. + +With AI Gateway, you configure approved MCP servers once, and the gateway loads only search and orchestrator tools initially. Agents query for specific tools only when needed, which reduces token usage by 80-90% depending on your configuration. You also gain centralized approval and governance over which MCP servers your agents can access. + +For complex workflows, AI Gateway provides a JavaScript-based orchestrator tool that reduces multi-step workflows from multiple round trips to a single call. For example, you can create a workflow that searches a vector database and, if the results are insufficient, falls back to web search—all in one orchestration step. + +=== 4. Unified observability and cost tracking + +AI Gateway provides a single dashboard that tracks all LLM traffic across providers, eliminating the need to switch between multiple provider dashboards. + +The dashboard tracks request volume per gateway, model, and provider, along with token usage for both prompt and completion tokens. You can view estimated spend per model with cross-provider comparisons, latency metrics (p50, p95, p99), and errors broken down by type, provider, and model. + +This unified view helps you answer critical questions such as which model is the most cost-effective for your use case, why a specific user request failed, how much your staging environment costs per week, and what the latency difference is between providers for your workload. + +== Common gateway patterns + +=== Team isolation + +When multiple teams share infrastructure but need separate budgets and policies, create one gateway per team. For example, you might configure Team A's gateway with a $5K/month budget for both staging and production environments, while Team B's gateway has a $10K/month budget with different rate limits. Each team sees only their own traffic in the observability dashboards, providing clear cost attribution and isolation. + +=== Environment separation + +To prevent staging traffic from affecting production metrics, create separate gateways for each environment. Configure the staging gateway with lower rate limits, restricted model access, and aggressive cost controls to prevent runaway expenses. The production gateway can have higher rate limits, access to all models, and alerting configured to detect anomalies. + +=== Primary and fallback for reliability + +To ensure uptime during provider outages, configure provider pools with automatic failover. For example, you can set OpenAI as your primary provider (preferred for quality) and configure Anthropic as the fallback that activates when the gateway detects rate limits or timeouts from OpenAI. Monitor the fallback rate to detect primary provider issues early, before they impact your users. + +=== A/B testing models + +To compare model quality and cost without dual integration, route a percentage of traffic to different models. For example, you can send 80% of traffic to `claude-sonnet-3.5` and 20% to `claude-opus-4`, then compare quality metrics and costs in the observability dashboard before adjusting the split. + +=== Customer-based routing + +For SaaS products with tiered pricing (free, pro, enterprise), use CEL routing based on request headers to match users with appropriate models: + +[source,cel] +---- +request.headers["x-customer-tier"] == "enterprise" ? "anthropic/claude-opus-4" : +request.headers["x-customer-tier"] == "pro" ? "anthropic/claude-sonnet-3.5" : +"anthropic/claude-haiku" +---- + +== When to use AI Gateway + +AI Gateway is ideal for organizations that: + +* Use or plan to use multiple LLM providers +* Need centralized cost tracking and budgeting +* Want to experiment with different models without code changes +* Require high availability during provider outages +* Have multiple teams or customers using AI services +* Build AI agents that need MCP tool aggregation +* Need unified observability across all AI traffic + +AI Gateway may not be necessary if: + +* You only use a single provider with simple requirements +* You have minimal AI traffic (< 1000 requests/day) +* You don't need cost tracking or policy enforcement +* Your application doesn't require provider switching + +== Next steps + +Now that you understand what AI Gateway is and how it can benefit your organization: + +*For Administrators:* + +* xref:ai-gateway/admin/setup-guide.adoc[Setup Guide] - Enable providers, models, and create gateways +* xref:ai-gateway/ai-gateway-overview.adoc[Architecture Deep Dive] - Technical architecture details + +*For Builders:* + +* xref:ai-gateway/builders/discover-gateways.adoc[Discover Available Gateways] - Find which gateways you can access +* xref:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] - Integrate your application diff --git a/modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md b/modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md new file mode 100644 index 000000000..3811c26b5 --- /dev/null +++ b/modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md @@ -0,0 +1,573 @@ +# AI Gateway Content Restructuring Plan +## Persona-Based Reorganization + +**Date:** January 21, 2026 +**Purpose:** Restructure AI Gateway documentation to align with two primary personas (Admins and Builders) and their distinct user journeys. + +--- + +## Executive Summary + +The current AI Gateway documentation is comprehensive but doesn't clearly distinguish between Admin and Builder personas. This plan proposes: + +1. **Restructure the navigation** to create clear persona-based paths +2. **Create new landing/discovery pages** for each persona +3. **Tag existing content** with appropriate personas +4. **Add missing content** to complete user journeys +5. **Reorganize the index** to guide users based on their role + +--- + +## Personas Defined + +### Admin Persona +- **Role:** Platform administrators with broad oversight +- **Responsibilities:** + - Configure system-level parameters + - Enable/disable LLM providers and models + - Set up gateways with policies, routing, and budgets + - Monitor usage across the organization + - Manage access control and security +- **Key Questions:** + - How do I set up and configure AI Gateway for my organization? + - How do I control costs and enforce policies? + - How do I monitor usage across all teams? + +### Builder Persona +- **Role:** Developers/engineers building agents or AI applications +- **Responsibilities:** + - Build agents and AI applications + - Integrate agents with available gateways + - Use MCP tools and services + - Monitor their own usage and costs +- **Key Questions:** + - Which gateways can I use? + - How do I connect my agent to a gateway? + - What tools/models are available to me? + - How much am I spending? + +--- + +## User Journey Mapping + +### Admin User Journey +1. **Understand** → What is an AI gateway? (conceptual) +2. **Set Up** → Enable providers, enable models, create gateways +3. **Configure** → Set up networking, policies, routing, budgets +4. **Monitor** → Track usage, costs, and manage access +5. **Optimize** → Adjust policies, routing, and costs based on metrics + +### Builder User Journey +1. **Discover** → Which gateways can I access? +2. **Connect** → How do I integrate my agent with a gateway? +3. **Build** → Use available models and MCP tools +4. **Test** → Validate my agent's integration +5. **Monitor** → Track my usage and costs + +--- + +## Content Gap Analysis + +### Missing Content +| Content Needed | Persona | Priority | Current Status | +|---------------|---------|----------|----------------| +| Gateway Discovery page | Builder | HIGH | Missing - critical for Builder journey | +| "What is AI Gateway" standalone page | Both | HIGH | Content exists in overview but needs extraction | +| Admin Setup Guide | Admin | HIGH | Scattered across quickstart - needs consolidation | +| Builder Integration Guide | Builder | HIGH | Exists partially in quickstart/integrations | +| Networking Configuration page | Admin | MEDIUM | Mentioned but not detailed | +| Access Management page | Admin | MEDIUM | Missing | + +### Existing Content Gaps +1. **ai-gateway-overview.adoc** - Too dense, mixes Admin and Builder concerns +2. **ai-gateway.adoc (quickstart)** - Conflates Admin setup with Builder usage +3. **index.adoc** - Too minimal, provides no guidance +4. **No discovery mechanism** - Builders don't know which gateways they can use + +--- + +## Recommended Content Structure + +### New Navigation Structure + +``` +AI Gateway/ +├── index.adoc (New: Persona-based landing page) +├── what-is-ai-gateway.adoc (New: Extracted from overview) +│ +├── For Admins/ +│ ├── admin-overview.adoc (New: Admin-focused overview) +│ ├── setup-guide.adoc (New: Complete admin setup) +│ │ ├── enable-providers.adoc (Extracted from quickstart) +│ │ ├── enable-models.adoc (Extracted from quickstart) +│ │ ├── create-gateways.adoc (Extracted from quickstart) +│ │ ├── networking-configuration.adoc (New/Expanded) +│ ├── configure-policies.adoc (Consolidated) +│ │ ├── routing-policies.adoc (Link to CEL cookbook) +│ │ ├── access-controls.adoc (New) +│ │ ├── budgets-and-limits.adoc (Consolidated from quickstart) +│ ├── manage-gateways.adoc (New: List, edit, delete) +│ ├── observability-admin.adoc (Link to metrics dashboard) +│ └── integrations/ (Admin versions) +│ ├── index.adoc +│ ├── claude-code-admin.adoc +│ ├── cursor-admin.adoc +│ └── ... +│ +├── For Builders/ +│ ├── builder-overview.adoc (New: Builder-focused overview) +│ ├── discover-gateways.adoc (NEW - CRITICAL) +│ ├── connect-your-agent.adoc (New: Integration guide) +│ ├── available-models.adoc (New: How to see what's available) +│ ├── use-mcp-tools.adoc (Link to MCP aggregation) +│ ├── test-your-integration.adoc (New: Validation) +│ ├── monitor-your-usage.adoc (Link to observability-logs) +│ └── integrations/ (Builder versions) +│ ├── index.adoc +│ ├── claude-code-user.adoc +│ ├── cursor-user.adoc +│ └── ... +│ +├── Reference/ +│ ├── ai-gateway-overview.adoc (Refactored: Technical deep-dive) +│ ├── cel-routing-cookbook.adoc (Existing) +│ ├── mcp-aggregation-guide.adoc (Existing) +│ ├── observability-logs.adoc (Existing) +│ ├── observability-metrics.adoc (Existing) +│ ├── migration-guide.adoc (Existing) +│ └── quickstart-enhanced.adoc (Existing or remove if redundant) +``` + +--- + +## Detailed Content Recommendations + +### 1. Create New index.adoc (HIGH PRIORITY) + +**Current State:** Minimal landing page with just a description +**Proposed Change:** Transform into a persona-based router + +**Content Structure:** +```asciidoc += AI Gateway +:description: Unified access layer for LLM providers and AI tools +:page-layout: index + +The Redpanda AI Gateway provides centralized routing, policy enforcement, cost management, and observability for all your AI traffic. + +== Choose Your Path + +[.persona-card] +=== I'm an Administrator +You manage AI Gateway infrastructure, configure providers, set policies, and monitor organizational usage. + +* xref:ai-gateway/admin/admin-overview.adoc[Admin Overview] +* xref:ai-gateway/admin/setup-guide.adoc[Setup Guide] +* xref:ai-gateway/admin/manage-gateways.adoc[Manage Gateways] + +[.persona-card] +=== I'm a Builder +You're building AI agents or applications and need to connect to available gateways. + +* xref:ai-gateway/builders/builder-overview.adoc[Builder Overview] +* xref:ai-gateway/builders/discover-gateways.adoc[Discover Available Gateways] +* xref:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] + +== Learn More + +* xref:ai-gateway/what-is-ai-gateway.adoc[What is an AI Gateway?] +* xref:ai-gateway/reference/ai-gateway-overview.adoc[Technical Architecture] +``` + +**Persona Tagging:** Both + +--- + +### 2. Create what-is-ai-gateway.adoc (HIGH PRIORITY) + +**Purpose:** Standalone conceptual page answering "What is an AI gateway?" +**Source:** Extract from ai-gateway-overview.adoc (lines 15-147) + +**Content to Include:** +- The problem AI Gateway solves +- Core capabilities (unified access, routing, MCP aggregation, observability) +- Common gateway patterns +- High-level architecture diagram + +**Remove from Overview:** Keep technical details in overview, move conceptual understanding here + +**Persona Tagging:** Both (Admin and Builder) + +--- + +### 3. Create discover-gateways.adoc (HIGH PRIORITY - NEW) + +**Purpose:** Help Builders find which gateways they have access to +**This is CRITICAL and completely missing from current content** + +**Content Structure:** +```asciidoc += Discover Available Gateways +:description: Find which AI Gateways you can access and their configurations +:page-personas: app_developer + +As a builder, you need to know which gateways are available to you before integrating your agent. + +== List your accessible gateways + +=== Using the Console + +1. Navigate to AI Gateway → My Gateways +2. View all gateways you have access to: + * Gateway Name + * Gateway ID (for `rp-aigw-id` header) + * Endpoint URL + * Available Models + * MCP Tools (if configured) + +=== Using the API + +[source,bash] +---- +curl https://{CLUSTER}.cloud.redpanda.com/api/ai-gateway/v1/gateways \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" +---- + +== Understanding gateway information + +Each gateway shows: + +* **Gateway ID**: Use this in the `rp-aigw-id` header +* **Endpoint URL**: Base URL for API requests +* **Available Models**: Which models you can access (e.g., `openai/gpt-4o`, `anthropic/claude-sonnet-3.5`) +* **Rate Limits**: Your request limits +* **MCP Tools**: Available MCP servers and tools (if enabled) + +== Check gateway availability + +Before integrating, test gateway access: + +[source,bash] +---- +curl https://{GATEWAY_ENDPOINT}/v1/models \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "rp-aigw-id: ${GATEWAY_ID}" +---- + +Expected response: List of available models + +== Next steps + +* xref:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] +* xref:ai-gateway/builders/available-models.adoc[View Available Models] +``` + +**Persona Tagging:** Builder (app_developer) + +--- + +### 4. Refactor ai-gateway.adoc (quickstart) + +**Current Problem:** Mixes Admin setup (Steps 1-3) with Builder usage (Steps 4-5, integrations) + +**Proposed Split:** + +#### Create admin/setup-guide.adoc (Admin path) +- Step 1: Enable providers +- Step 2: Enable models +- Step 3: Create gateways +- Step 4: Configure LLM routing (policies, pools, rate limits) +- Step 5: Configure MCP tools + +#### Create builders/connect-your-agent.adoc (Builder path) +- Prerequisites: Gateway ID and endpoint (from discovery) +- Step 1: Get your gateway credentials +- Step 2: Configure your client SDK +- Step 3: Make your first request +- Step 4: Handle responses +- Step 5: Validate integration + +**Content to Move:** +- Lines 17-89 (Admin steps) → admin/setup-guide.adoc +- Lines 160-337 (Integration examples) → builders/connect-your-agent.adoc +- Lines 106-118 (Observability) → Link to observability pages + +--- + +### 5. Create admin/networking-configuration.adoc (MEDIUM PRIORITY) + +**Purpose:** Dedicated page for networking setup +**Content:** Currently mentioned but not detailed + +**Content Structure:** +```asciidoc += Networking Configuration +:description: Configure networking for AI Gateway including endpoints, private networking, and connectivity +:page-personas: platform_admin + +Configure network access and connectivity for your AI Gateway. + +== Gateway endpoints + +When you create a gateway, you receive: + +* Public endpoint: `https://gw.ai.panda.com` +* Private endpoint (if enabled): `https://gw-internal.ai.panda.com` + +== Public vs private endpoints + +**Public endpoints:** +- Accessible from internet +- Use for external agents, testing +- Standard TLS encryption + +**Private endpoints:** +- Accessible only within your VPC/network +- Use for production workloads +- Enhanced security + +== Configure private networking + +[PLACEHOLDER: Add private networking setup steps] + +== Connectivity requirements + +Outbound connections required: +- To LLM provider APIs (OpenAI, Anthropic, etc.) +- To configured MCP servers (if using MCP aggregation) + +Inbound connections: +- From your agents/applications to gateway endpoint + +== Firewall and security groups + +[PLACEHOLDER: Add security group configuration] + +== Next steps + +* xref:ai-gateway/admin/configure-policies.adoc[Configure Access Policies] +``` + +**Persona Tagging:** Admin (platform_admin) + +--- + +### 6. Create admin/access-controls.adoc (MEDIUM PRIORITY) + +**Purpose:** How Admins control who can access which gateways + +**Content:** +- Gateway-level access control +- API key management +- RBAC configuration (if available) +- Audit logging + +**Persona Tagging:** Admin (platform_admin) + +--- + +### 7. Update Existing Files + +#### ai-gateway-overview.adoc +**Changes:** +- Remove conceptual "What is" content (move to what-is-ai-gateway.adoc) +- Focus on technical architecture deep-dive +- Keep: Architecture details, request lifecycle, advanced patterns +- Update persona tag to: `platform_admin, app_developer` (both, but technical) + +#### cel-routing-cookbook.adoc +**Changes:** +- Add note at top: "This is an advanced reference for Admins configuring routing policies" +- Update persona tag to: `platform_admin` (currently has both) +- No content changes needed + +#### mcp-aggregation-guide.adoc +**Changes:** +- Add section for Builders: "Using MCP tools as a Builder" +- Currently too Admin-focused +- Add discovery section: How Builders see available MCP tools +- Keep persona tag: `app_developer` but clarify sections + +#### observability-logs.adoc +**Changes:** +- Add intro section distinguishing Admin vs Builder use cases: + - Admins: Monitor all traffic, all gateways, org-wide + - Builders: Monitor their own agent's requests +- Update UI paths to reflect persona-based views +- Persona tag is currently correct: `platform_admin, app_developer` + +#### observability-metrics.adoc +**Changes:** +- Similar to logs: Distinguish Admin (org-wide) vs Builder (my usage) views +- Add section: "View your agent's usage" (Builder perspective) +- Persona tag currently: `platform_admin` - should add `app_developer` + +--- + +## Navigation (nav.adoc) Changes + +**Current Structure:** +``` +* AI Gateway +** Overview +** Quickstart +** CEL Routing +** MCP Aggregation +** Observability +** Integrations +``` + +**Proposed Structure:** +``` +* xref:ai-agents:ai-gateway/index.adoc[AI Gateway] +** xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[What is AI Gateway?] +** For Admins +*** xref:ai-agents:ai-gateway/admin/admin-overview.adoc[Admin Overview] +*** xref:ai-agents:ai-gateway/admin/setup-guide.adoc[Setup Guide] +*** xref:ai-agents:ai-gateway/admin/manage-gateways.adoc[Manage Gateways] +*** xref:ai-agents:ai-gateway/admin/networking-configuration.adoc[Networking Configuration] +*** xref:ai-agents:ai-gateway/admin/configure-policies.adoc[Configure Policies] +*** xref:ai-agents:ai-gateway/admin/access-controls.adoc[Access Controls] +*** xref:ai-agents:ai-gateway/admin/observability-admin.adoc[Monitor Usage] +*** xref:ai-agents:ai-gateway/admin/integrations/index.adoc[Integrations (Admin)] +** For Builders +*** xref:ai-agents:ai-gateway/builders/builder-overview.adoc[Builder Overview] +*** xref:ai-agents:ai-gateway/builders/discover-gateways.adoc[Discover Gateways] +*** xref:ai-agents:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] +*** xref:ai-agents:ai-gateway/builders/available-models.adoc[Available Models] +*** xref:ai-agents:ai-gateway/builders/use-mcp-tools.adoc[Use MCP Tools] +*** xref:ai-agents:ai-gateway/builders/monitor-your-usage.adoc[Monitor Your Usage] +*** xref:ai-agents:ai-gateway/builders/integrations/index.adoc[Integrations (Builder)] +** Reference +*** xref:ai-agents:ai-gateway/reference/ai-gateway-overview.adoc[Architecture Deep Dive] +*** xref:ai-agents:ai-gateway/reference/cel-routing-cookbook.adoc[CEL Routing Cookbook] +*** xref:ai-agents:ai-gateway/reference/mcp-aggregation-guide.adoc[MCP Aggregation Guide] +*** xref:ai-agents:ai-gateway/reference/observability-logs.adoc[Request Logs] +*** xref:ai-agents:ai-gateway/reference/observability-metrics.adoc[Metrics and Analytics] +``` + +--- + +## Implementation Priority + +### Phase 1: Critical Path (Do First) +1. **Create index.adoc** - Persona router (HIGH) +2. **Create discover-gateways.adoc** - Critical Builder need (HIGH) +3. **Create what-is-ai-gateway.adoc** - Entry point (HIGH) +4. **Split quickstart** into admin/setup-guide.adoc and builders/connect-your-agent.adoc (HIGH) + +### Phase 2: Complete User Journeys +1. Create admin/manage-gateways.adoc (MEDIUM) +2. Create builders/available-models.adoc (MEDIUM) +3. Create admin/networking-configuration.adoc (MEDIUM) +4. Create admin/access-controls.adoc (MEDIUM) +5. Update observability pages with persona distinctions (MEDIUM) + +### Phase 3: Polish and Optimize +1. Refactor ai-gateway-overview.adoc (MEDIUM) +2. Update mcp-aggregation-guide.adoc with Builder sections (LOW) +3. Create admin/builder overview pages (LOW) +4. Reorganize integrations folders (LOW) +5. Update all cross-references (LOW) + +--- + +## Mapping to User Journey + +### Admin Journey → Content +| Journey Step | Content | +|--------------|---------| +| What is an AI gateway? | what-is-ai-gateway.adoc | +| How do I create, list, and manage gateways? | admin/setup-guide.adoc, admin/manage-gateways.adoc | +| Networking configuration & Gateway creation | admin/networking-configuration.adoc | +| Configure which models are accessible | admin/setup-guide.adoc (enable models section) | +| Configure access and routing policies | admin/configure-policies.adoc, cel-routing-cookbook.adoc | +| Track usage and configure budgeting | admin/setup-guide.adoc (budgets), observability-metrics.adoc | + +### Builder Journey → Content +| Journey Step | Content | +|--------------|---------| +| What is an AI gateway? | what-is-ai-gateway.adoc | +| Discover which AI gateways my agents have access to | **builders/discover-gateways.adoc (NEW)** | +| How do I integrate my agent? | builders/connect-your-agent.adoc | +| What models/tools are available? | builders/available-models.adoc, builders/use-mcp-tools.adoc | +| Test my integration | builders/connect-your-agent.adoc (validation section) | +| Track my usage | builders/monitor-your-usage.adoc → observability-logs.adoc | + +--- + +## Key Principles + +1. **Persona First:** Content should clearly identify which persona it serves +2. **Progressive Disclosure:** Start simple, link to advanced topics +3. **Minimize Duplication:** Use xrefs to avoid maintaining same content twice +4. **Clear Entry Points:** Index page must route users effectively +5. **Discovery is Critical:** Builders MUST be able to find available gateways + +--- + +## Success Metrics + +After implementation, evaluate: +- Can a Builder discover available gateways in <2 minutes? +- Can an Admin complete setup in <15 minutes? +- Do users report clearer distinction between Admin vs Builder tasks? +- Reduced support tickets about "I can't find which gateway to use" + +--- + +## Open Questions + +1. **API for Gateway Discovery:** Does the API support listing accessible gateways per user? +2. **RBAC Model:** How granular is access control (workspace, gateway, model level)? +3. **Private Networking:** What's the detailed setup for private endpoints? +4. **Budgets and Limits:** Can Builders see their own usage/limits, or only Admins? +5. **Integration Folders:** Should we physically split integration files into admin/ and builders/ subdirectories? + +--- + +## Next Steps + +1. **Review this plan** with product and docs team +2. **Validate API capabilities** for gateway discovery +3. **Create Phase 1 content** (index, discover-gateways, what-is, split quickstart) +4. **Test with users** from each persona +5. **Iterate based on feedback** + +--- + +## Appendix: File Operations Summary + +### New Files to Create +- `ai-gateway/index.adoc` (replace existing minimal one) +- `ai-gateway/what-is-ai-gateway.adoc` +- `ai-gateway/admin/admin-overview.adoc` +- `ai-gateway/admin/setup-guide.adoc` +- `ai-gateway/admin/manage-gateways.adoc` +- `ai-gateway/admin/networking-configuration.adoc` +- `ai-gateway/admin/configure-policies.adoc` +- `ai-gateway/admin/access-controls.adoc` +- `ai-gateway/builders/builder-overview.adoc` +- `ai-gateway/builders/discover-gateways.adoc` ⭐ CRITICAL +- `ai-gateway/builders/connect-your-agent.adoc` +- `ai-gateway/builders/available-models.adoc` +- `ai-gateway/builders/use-mcp-tools.adoc` +- `ai-gateway/builders/monitor-your-usage.adoc` + +### Files to Move +- `ai-gateway/ai-gateway-overview.adoc` → `ai-gateway/reference/ai-gateway-overview.adoc` +- `ai-gateway/cel-routing-cookbook.adoc` → `ai-gateway/reference/cel-routing-cookbook.adoc` +- `ai-gateway/mcp-aggregation-guide.adoc` → `ai-gateway/reference/mcp-aggregation-guide.adoc` +- `ai-gateway/observability-*.adoc` → `ai-gateway/reference/observability-*.adoc` + +### Files to Refactor +- `ai-gateway/ai-gateway.adoc` (quickstart) - split content between admin and builder paths +- `ai-gateway/ai-gateway-overview.adoc` - extract conceptual content to what-is page +- `ai-gateway/observability-logs.adoc` - add persona-specific sections +- `ai-gateway/observability-metrics.adoc` - add builder usage section + +### Files to Keep As-Is (Minimal Changes) +- `ai-gateway/integrations/*-admin.adoc` +- `ai-gateway/integrations/*-user.adoc` +- `ai-gateway/migration-guide.adoc` +- `ai-gateway/quickstart-enhanced.adoc` (review if still needed) From 01d61b52cb16215445e9edff12070997e3244493 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Thu, 22 Jan 2026 12:14:32 -0700 Subject: [PATCH 18/50] update nav --- modules/ROOT/nav.adoc | 100 +++++++++++++++++++++--------------------- 1 file changed, 50 insertions(+), 50 deletions(-) diff --git a/modules/ROOT/nav.adoc b/modules/ROOT/nav.adoc index 6b62b9119..0e2c153e4 100644 --- a/modules/ROOT/nav.adoc +++ b/modules/ROOT/nav.adoc @@ -23,52 +23,6 @@ *** xref:get-started:cluster-types/byoc/remote-read-replicas.adoc[] ** xref:get-started:cluster-types/create-dedicated-cloud-cluster.adoc[] -* xref:networking:index.adoc[Networking] -** xref:networking:cloud-security-network.adoc[] -** xref:networking:cidr-ranges.adoc[] -** xref:networking:byoc/index.adoc[BYOC] -*** xref:networking:byoc/aws/index.adoc[AWS] -**** xref:networking:byoc/aws/vpc-peering-aws.adoc[Add a Peering Connection] -**** xref:networking:configure-privatelink-in-cloud-ui.adoc[Configure PrivateLink in the Cloud UI] -**** xref:networking:aws-privatelink.adoc[Configure PrivateLink with the Cloud API] -**** xref:networking:byoc/aws/transit-gateway.adoc[Add a Transit Gateway] -*** xref:networking:byoc/azure/index.adoc[Azure] -**** xref:networking:azure-private-link-in-ui.adoc[] -**** xref:networking:azure-private-link.adoc[] -*** xref:networking:byoc/gcp/index.adoc[GCP] -**** xref:networking:byoc/gcp/vpc-peering-gcp.adoc[Add a Peering Connection] -**** xref:networking:configure-private-service-connect-in-cloud-ui.adoc[Configure Private Service Connect in the Cloud UI] -**** xref:networking:gcp-private-service-connect.adoc[Configure Private Service Connect with the Cloud API] -**** xref:networking:byoc/gcp/enable-global-access.adoc[Enable Global Access] -** xref:networking:dedicated/index.adoc[Dedicated] -*** xref:networking:dedicated/aws/index.adoc[AWS] -**** xref:networking:dedicated/aws/vpc-peering.adoc[Add a Peering Connection] -**** xref:networking:configure-privatelink-in-cloud-ui.adoc[Configure PrivateLink in the Cloud UI] -**** xref:networking:aws-privatelink.adoc[] -*** xref:networking:dedicated/azure/index.adoc[Azure] -**** xref:networking:azure-private-link-in-ui.adoc[] -**** xref:networking:azure-private-link.adoc[] -*** xref:networking:dedicated/gcp/index.adoc[GCP] -**** xref:networking:dedicated/gcp/vpc-peering-gcp.adoc[Add a Peering Connection] -**** xref:networking:dedicated/gcp/configure-psc-in-ui.adoc[Configure Private Service Connect in the Cloud UI] -**** xref:networking:dedicated/gcp/configure-psc-in-api.adoc[Configure Private Service Connect with the Cloud API] - -* xref:security:index.adoc[Security] -** xref:security:cloud-authentication.adoc[Authentication] -** xref:security:authorization/index.adoc[Authorization] -*** xref:security:authorization/cloud-authorization.adoc[Cloud Authorization] -*** xref:security:authorization/rbac/index.adoc[Role-Based Access Control (RBAC)] -**** xref:security:authorization/rbac/rbac.adoc[] -**** xref:security:authorization/rbac/rbac_dp.adoc[] -*** xref:security:authorization/rbac/acl.adoc[Access Control Lists (ACLs)] -*** xref:security:authorization/cloud-iam-policies.adoc[] -*** xref:security:authorization/cloud-iam-policies-gcp.adoc[] -*** xref:security:authorization/cloud-iam-policies-azure.adoc[] -** xref:security:cloud-encryption.adoc[Encryption] -** xref:security:cloud-availability.adoc[Availability] -** xref:security:secrets.adoc[Secrets] -** xref:security:cloud-safety-reliability.adoc[Safety and Reliability] - * xref:ai-agents:index.adoc[Agentic AI] ** xref:ai-agents:ai-gateway/index.adoc[AI Gateway] *** xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[What is AI Gateway?] @@ -101,10 +55,6 @@ ***** xref:ai-agents:ai-gateway/integrations/github-copilot-admin.adoc[Admin Guide] ***** xref:ai-agents:ai-gateway/integrations/github-copilot-user.adoc[User Guide] ** xref:ai-agents:mcp/overview.adoc[MCP Overview] -** xref:ai-agents:mcp/local/index.adoc[Redpanda Cloud Management MCP Server] -*** xref:ai-agents:mcp/local/overview.adoc[Overview] -*** xref:ai-agents:mcp/local/quickstart.adoc[Quickstart] -*** xref:ai-agents:mcp/local/configuration.adoc[Configure] ** xref:ai-agents:mcp/remote/index.adoc[Remote MCP] *** xref:ai-agents:mcp/remote/overview.adoc[Overview] *** xref:ai-agents:mcp/remote/quickstart.adoc[Quickstart] @@ -118,6 +68,10 @@ **** xref:ai-agents:mcp/remote/scale-resources.adoc[Scale Resources] **** xref:ai-agents:mcp/remote/monitor-activity.adoc[Monitor Activity] *** xref:ai-agents:mcp/remote/pipeline-patterns.adoc[MCP Server Patterns] +** xref:ai-agents:mcp/local/index.adoc[Redpanda Cloud Management MCP Server] +*** xref:ai-agents:mcp/local/overview.adoc[Overview] +*** xref:ai-agents:mcp/local/quickstart.adoc[Quickstart] +*** xref:ai-agents:mcp/local/configuration.adoc[Configure] * xref:develop:connect/about.adoc[Redpanda Connect] ** xref:develop:connect/connect-quickstart.adoc[Quickstart] @@ -506,6 +460,52 @@ ** xref:manage:terraform-provider.adoc[] ** xref:manage:monitor-cloud.adoc[] +* xref:networking:index.adoc[Networking] +** xref:networking:cloud-security-network.adoc[] +** xref:networking:cidr-ranges.adoc[] +** xref:networking:byoc/index.adoc[BYOC] +*** xref:networking:byoc/aws/index.adoc[AWS] +**** xref:networking:byoc/aws/vpc-peering-aws.adoc[Add a Peering Connection] +**** xref:networking:configure-privatelink-in-cloud-ui.adoc[Configure PrivateLink in the Cloud UI] +**** xref:networking:aws-privatelink.adoc[Configure PrivateLink with the Cloud API] +**** xref:networking:byoc/aws/transit-gateway.adoc[Add a Transit Gateway] +*** xref:networking:byoc/azure/index.adoc[Azure] +**** xref:networking:azure-private-link-in-ui.adoc[] +**** xref:networking:azure-private-link.adoc[] +*** xref:networking:byoc/gcp/index.adoc[GCP] +**** xref:networking:byoc/gcp/vpc-peering-gcp.adoc[Add a Peering Connection] +**** xref:networking:configure-private-service-connect-in-cloud-ui.adoc[Configure Private Service Connect in the Cloud UI] +**** xref:networking:gcp-private-service-connect.adoc[Configure Private Service Connect with the Cloud API] +**** xref:networking:byoc/gcp/enable-global-access.adoc[Enable Global Access] +** xref:networking:dedicated/index.adoc[Dedicated] +*** xref:networking:dedicated/aws/index.adoc[AWS] +**** xref:networking:dedicated/aws/vpc-peering.adoc[Add a Peering Connection] +**** xref:networking:configure-privatelink-in-cloud-ui.adoc[Configure PrivateLink in the Cloud UI] +**** xref:networking:aws-privatelink.adoc[] +*** xref:networking:dedicated/azure/index.adoc[Azure] +**** xref:networking:azure-private-link-in-ui.adoc[] +**** xref:networking:azure-private-link.adoc[] +*** xref:networking:dedicated/gcp/index.adoc[GCP] +**** xref:networking:dedicated/gcp/vpc-peering-gcp.adoc[Add a Peering Connection] +**** xref:networking:dedicated/gcp/configure-psc-in-ui.adoc[Configure Private Service Connect in the Cloud UI] +**** xref:networking:dedicated/gcp/configure-psc-in-api.adoc[Configure Private Service Connect with the Cloud API] + +* xref:security:index.adoc[Security] +** xref:security:cloud-authentication.adoc[Authentication] +** xref:security:authorization/index.adoc[Authorization] +*** xref:security:authorization/cloud-authorization.adoc[Cloud Authorization] +*** xref:security:authorization/rbac/index.adoc[Role-Based Access Control (RBAC)] +**** xref:security:authorization/rbac/rbac.adoc[] +**** xref:security:authorization/rbac/rbac_dp.adoc[] +*** xref:security:authorization/rbac/acl.adoc[Access Control Lists (ACLs)] +*** xref:security:authorization/cloud-iam-policies.adoc[] +*** xref:security:authorization/cloud-iam-policies-gcp.adoc[] +*** xref:security:authorization/cloud-iam-policies-azure.adoc[] +** xref:security:cloud-encryption.adoc[Encryption] +** xref:security:cloud-availability.adoc[Availability] +** xref:security:secrets.adoc[Secrets] +** xref:security:cloud-safety-reliability.adoc[Safety and Reliability] + * xref:billing:index.adoc[Billing] ** xref:billing:billing.adoc[] ** xref:billing:aws-commit.adoc[AWS: Use Commits] From acbbde86e35bfcf64f64faee8a1196a91f35a015 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Thu, 22 Jan 2026 12:28:36 -0700 Subject: [PATCH 19/50] update nav --- modules/ROOT/nav.adoc | 37 +++++++++++++------------- modules/ai-agents/pages/mcp/index.adoc | 10 +++++++ 2 files changed, 29 insertions(+), 18 deletions(-) create mode 100644 modules/ai-agents/pages/mcp/index.adoc diff --git a/modules/ROOT/nav.adoc b/modules/ROOT/nav.adoc index 0e2c153e4..6e7287438 100644 --- a/modules/ROOT/nav.adoc +++ b/modules/ROOT/nav.adoc @@ -54,24 +54,25 @@ **** GitHub Copilot ***** xref:ai-agents:ai-gateway/integrations/github-copilot-admin.adoc[Admin Guide] ***** xref:ai-agents:ai-gateway/integrations/github-copilot-user.adoc[User Guide] -** xref:ai-agents:mcp/overview.adoc[MCP Overview] -** xref:ai-agents:mcp/remote/index.adoc[Remote MCP] -*** xref:ai-agents:mcp/remote/overview.adoc[Overview] -*** xref:ai-agents:mcp/remote/quickstart.adoc[Quickstart] -*** xref:ai-agents:mcp/remote/concepts.adoc[Concepts] -*** xref:ai-agents:mcp/remote/create-tool.adoc[Create a Tool] -*** xref:ai-agents:mcp/remote/best-practices.adoc[Best Practices] -*** xref:ai-agents:mcp/remote/tool-patterns.adoc[Tool Patterns] -*** xref:ai-agents:mcp/remote/troubleshooting.adoc[Troubleshooting] -*** xref:ai-agents:mcp/remote/admin-guide.adoc[Admin Guide] -**** xref:ai-agents:mcp/remote/manage-servers.adoc[Manage Servers] -**** xref:ai-agents:mcp/remote/scale-resources.adoc[Scale Resources] -**** xref:ai-agents:mcp/remote/monitor-activity.adoc[Monitor Activity] -*** xref:ai-agents:mcp/remote/pipeline-patterns.adoc[MCP Server Patterns] -** xref:ai-agents:mcp/local/index.adoc[Redpanda Cloud Management MCP Server] -*** xref:ai-agents:mcp/local/overview.adoc[Overview] -*** xref:ai-agents:mcp/local/quickstart.adoc[Quickstart] -*** xref:ai-agents:mcp/local/configuration.adoc[Configure] +** xref:ai-agents:mcp/index.adoc[MCP] +*** xref:ai-agents:mcp/overview.adoc[MCP Overview] +*** xref:ai-agents:mcp/remote/index.adoc[Remote MCP] +**** xref:ai-agents:mcp/remote/overview.adoc[Overview] +**** xref:ai-agents:mcp/remote/quickstart.adoc[Quickstart] +**** xref:ai-agents:mcp/remote/concepts.adoc[Concepts] +**** xref:ai-agents:mcp/remote/create-tool.adoc[Create a Tool] +**** xref:ai-agents:mcp/remote/best-practices.adoc[Best Practices] +**** xref:ai-agents:mcp/remote/tool-patterns.adoc[Tool Patterns] +**** xref:ai-agents:mcp/remote/troubleshooting.adoc[Troubleshooting] +**** xref:ai-agents:mcp/remote/admin-guide.adoc[Admin Guide] +***** xref:ai-agents:mcp/remote/manage-servers.adoc[Manage Servers] +***** xref:ai-agents:mcp/remote/scale-resources.adoc[Scale Resources] +***** xref:ai-agents:mcp/remote/monitor-activity.adoc[Monitor Activity] +**** xref:ai-agents:mcp/remote/pipeline-patterns.adoc[MCP Server Patterns] +*** xref:ai-agents:mcp/local/index.adoc[Redpanda Cloud Management MCP Server] +**** xref:ai-agents:mcp/local/overview.adoc[Overview] +**** xref:ai-agents:mcp/local/quickstart.adoc[Quickstart] +**** xref:ai-agents:mcp/local/configuration.adoc[Configure] * xref:develop:connect/about.adoc[Redpanda Connect] ** xref:develop:connect/connect-quickstart.adoc[Quickstart] diff --git a/modules/ai-agents/pages/mcp/index.adoc b/modules/ai-agents/pages/mcp/index.adoc new file mode 100644 index 000000000..6ff198196 --- /dev/null +++ b/modules/ai-agents/pages/mcp/index.adoc @@ -0,0 +1,10 @@ += Model Context Protocol (MCP) +:description: Learn about the Model Context Protocol (MCP) in Redpanda Cloud. +:page-layout: index + +The Model Context Protocol (MCP) provides a standardized way for AI agents to connect with external data sources and tools in Redpanda Cloud. + +Redpanda Cloud offers two complementary MCP options: + +* *Remote MCP*: Deploy MCP servers directly in Redpanda Cloud for scalable, managed AI agent integrations +* *Redpanda Cloud Management MCP Server*: Connect your local AI development environment to manage Redpanda Cloud resources \ No newline at end of file From 492a50fe69d8105e4726a08761ab383cf3c87fc6 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 16:00:51 -0700 Subject: [PATCH 20/50] Refactor AI Gateway documentation for clarity and consistency Major improvements: - Rename ai-gateway.adoc to gateway-quickstart.adoc for clearer naming - Rename ai-gateway-overview.adoc to gateway-architecture.adoc - Consolidate quickstart-enhanced.adoc into gateway-quickstart.adoc - Remove 161 lines of duplicate content between what-is and architecture pages - Add page metadata (:page-topic-type:, :page-personas:, :learning-objective-N:) to all files - Convert learning objectives from bullets to required attribute format - Create BYOC version requirement partial and add to all 23 AI Gateway pages - Restructure navigation with clearer sections (Overview, Quickstart, Architecture, Observability) - Update all cross-references to renamed files This consolidation reduces content duplication, improves metadata consistency, and provides better content organization for users. Co-Authored-By: Claude Sonnet 4.5 --- modules/ROOT/nav.adoc | 15 +- .../pages/ai-gateway/admin/setup-guide.adoc | 2 +- .../pages/ai-gateway/ai-gateway-overview.adoc | 278 --------- .../pages/ai-gateway/ai-gateway.adoc | 345 ----------- .../builders/connect-your-agent.adoc | 2 + .../builders/discover-gateways.adoc | 2 + .../ai-gateway/cel-routing-cookbook.adoc | 12 +- .../ai-gateway/gateway-architecture.adoc | 158 +++++ .../pages/ai-gateway/gateway-quickstart.adoc | 583 ++++++++++++++++++ modules/ai-agents/pages/ai-gateway/index.adoc | 4 +- .../integrations/claude-code-admin.adoc | 4 +- .../integrations/claude-code-user.adoc | 12 +- .../ai-gateway/integrations/cline-admin.adoc | 4 +- .../ai-gateway/integrations/cline-user.adoc | 14 +- .../integrations/continue-admin.adoc | 4 +- .../integrations/continue-user.adoc | 12 +- .../ai-gateway/integrations/cursor-admin.adoc | 4 +- .../ai-gateway/integrations/cursor-user.adoc | 12 +- .../integrations/github-copilot-admin.adoc | 4 +- .../integrations/github-copilot-user.adoc | 12 +- .../pages/ai-gateway/integrations/index.adoc | 2 + .../ai-gateway/mcp-aggregation-guide.adoc | 14 +- .../pages/ai-gateway/migration-guide.adoc | 14 +- .../pages/ai-gateway/observability-logs.adoc | 12 +- .../ai-gateway/observability-metrics.adoc | 16 +- .../pages/ai-gateway/quickstart-enhanced.adoc | 453 -------------- .../pages/ai-gateway/what-is-ai-gateway.adoc | 15 +- .../AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md | 28 +- .../partials/ai-gateway-byoc-note.adoc | 1 + 29 files changed, 865 insertions(+), 1173 deletions(-) delete mode 100644 modules/ai-agents/pages/ai-gateway/ai-gateway-overview.adoc delete mode 100644 modules/ai-agents/pages/ai-gateway/ai-gateway.adoc create mode 100644 modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc create mode 100644 modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc delete mode 100644 modules/ai-agents/pages/ai-gateway/quickstart-enhanced.adoc create mode 100644 modules/ai-agents/partials/ai-gateway-byoc-note.adoc diff --git a/modules/ROOT/nav.adoc b/modules/ROOT/nav.adoc index 10aab64fb..519835c9e 100644 --- a/modules/ROOT/nav.adoc +++ b/modules/ROOT/nav.adoc @@ -25,19 +25,20 @@ * xref:ai-agents:index.adoc[Agentic AI] ** xref:ai-agents:ai-gateway/index.adoc[AI Gateway] -*** xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[AI Gateway Overview] +*** xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[Overview] +*** xref:ai-agents:ai-gateway/gateway-quickstart.adoc[Quickstart] +*** xref:ai-agents:ai-gateway/gateway-architecture.adoc[Architecture] *** For Administrators **** xref:ai-agents:ai-gateway/admin/setup-guide.adoc[Setup Guide] *** For Builders **** xref:ai-agents:ai-gateway/builders/discover-gateways.adoc[Discover Gateways] **** xref:ai-agents:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] -*** Reference -**** xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[Architecture Deep Dive] -**** xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[MCP Aggregation Guide] -**** xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[CEL Routing Cookbook] +**** xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[CEL Routing Patterns] +**** xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[MCP Aggregation] +*** Observability **** xref:ai-agents:ai-gateway/observability-logs.adoc[Request Logs] -**** xref:ai-agents:ai-gateway/observability-metrics.adoc[Metrics and Usage] -**** xref:ai-agents:ai-gateway/migration-guide.adoc[Migration Guide] +**** xref:ai-agents:ai-gateway/observability-metrics.adoc[Metrics and Analytics] +*** xref:ai-agents:ai-gateway/migration-guide.adoc[Migration Guide] *** xref:ai-agents:ai-gateway/integrations/index.adoc[Integrations] **** Claude Code ***** xref:ai-agents:ai-gateway/integrations/claude-code-admin.adoc[Admin Guide] diff --git a/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc b/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc index 150aab357..e3dbdee87 100644 --- a/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc @@ -3,7 +3,7 @@ :page-topic-type: how-to :personas: platform_admin -NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and later. +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] This guide walks administrators through the complete setup process for AI Gateway, from enabling LLM providers to configuring routing policies and MCP tool aggregation. diff --git a/modules/ai-agents/pages/ai-gateway/ai-gateway-overview.adoc b/modules/ai-agents/pages/ai-gateway/ai-gateway-overview.adoc deleted file mode 100644 index dd1ad678a..000000000 --- a/modules/ai-agents/pages/ai-gateway/ai-gateway-overview.adoc +++ /dev/null @@ -1,278 +0,0 @@ -= AI Gateway Overview -:description: Overview of Redpanda AI Gateway, its features, benefits, architecture, supported providers, deployment models, and common usage patterns. -:page-personas: app_developer, platform_admin - -NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and later. - -Redpanda AI Gateway is a unified access layer for LLM providers and AI tools that sits between your applications and the AI services they use. It provides centralized routing, policy enforcement, cost management, and observability for all your AI traffic. - -After reading this page, you will be able to: - -* Explain how AI Gateway centralizes LLM provider management and reduces operational complexity. -* Identify key features (routing, observability, cost controls) that address common LLM integration challenges. -* Determine whether AI Gateway fits your use case based on traffic volume and provider diversity. - -== The problem - -Modern AI applications face four critical challenges that increase costs, reduce reliability, and slow down development. - -First, applications typically hardcode provider-specific SDKs. An application using OpenAI's SDK cannot easily switch to Anthropic or Google without code changes and redeployment. This tight coupling makes testing across providers time-consuming and error-prone, and means provider outages directly impact your application availability. - -Second, costs can spiral without visibility into usage patterns. Without a centralized view of token consumption across teams and applications, it's difficult to attribute costs to specific customers, features, or environments. Testing and debugging can generate unexpected bills, and there's no way to enforce budgets or rate limits per team or customer. - -Third, AI agents that use MCP (Model Context Protocol) servers face tool coordination challenges. Managing tool discovery and execution is repetitive across projects, and agents typically load all available tools upfront, which creates high token costs. There's also no centralized governance over which tools agents can access. - -Finally, observability is fragmented across provider dashboards. You cannot reconstruct user sessions that span multiple models, compare latency and costs across providers in a unified view, or efficiently debug issues. Troubleshooting "the AI gave the wrong answer" requires manual log diving across different systems. - -== What AI Gateway solves - -Redpanda AI Gateway addresses these challenges through four core capabilities: - -=== 1. Unified LLM access (single endpoint for all providers) - -AI Gateway provides a single OpenAI-compatible endpoint that routes requests to multiple LLM providers. Instead of integrating with each provider's SDK separately, you configure your application once and switch providers by changing only the model parameter. - -// PLACEHOLDER: Add architecture diagram showing: -// - Application → AI Gateway → Multiple LLM Providers (OpenAI, Anthropic, etc.) -// - Single baseURL configuration -// - Model routing via vendor/model_id format - -Without AI Gateway, you need different SDKs and patterns for each provider: - -[source,python] ----- -# OpenAI -from openai import OpenAI -client = OpenAI(api_key="sk-...") -response = client.chat.completions.create( - model="gpt-4o", - messages=[{"role": "user", "content": "Hello"}] -) - -# Anthropic (different SDK, different patterns) -from anthropic import Anthropic -client = Anthropic(api_key="sk-ant-...") -response = client.messages.create( - model="claude-sonnet-3.5", - max_tokens=1024, - messages=[{"role": "user", "content": "Hello"}] -) ----- - -With AI Gateway, you use the OpenAI SDK for all providers: - -[source,python] ----- -from openai import OpenAI - -# Single configuration, multiple providers -client = OpenAI( - base_url="https://{GATEWAY_ENDPOINT}", - api_key="your-redpanda-token", - default_headers={"rp-aigw-id": "{GATEWAY_ID}"} -) - -# Route to OpenAI -response = client.chat.completions.create( - model="openai/gpt-4o", - messages=[{"role": "user", "content": "Hello"}] -) - -# Route to Anthropic (same code, different model string) -response = client.chat.completions.create( - model="anthropic/claude-sonnet-3.5", - messages=[{"role": "user", "content": "Hello"}] -) ----- - -To switch providers, you change only the `model` parameter from `openai/gpt-4o` to `anthropic/claude-sonnet-3.5`. No code changes or redeployment needed. - -=== 2. Policy-based routing and cost control - -AI Gateway lets you define routing rules, rate limits, and budgets once, then enforces them automatically for all requests. - -You can route requests to different models based on user attributes. For example, to direct premium users to a more capable model while routing free tier users to a cost-effective option, use a CEL expression: - -[source,cel] ----- -// Route premium users to best model, free users to cost-effective model -request.headers["x-user-tier"] == "premium" - ? "anthropic/claude-opus-4" - : "anthropic/claude-sonnet-3.5" ----- - -You can also set different rate limits and spend limits per environment to prevent staging or development traffic from consuming production budgets: - -// PLACEHOLDER: Confirm exact policy configuration format - -[source,yaml] ----- -rate_limits: - staging: 100 requests/minute - production: 10000 requests/minute - -spend_limits: - staging: $500/month - production: $50000/month ----- - -For reliability, you can configure provider pools with automatic failover. If you configure OpenAI GPT-4 as your primary model and Anthropic Claude Opus as the fallback, the gateway automatically routes requests to the fallback when it detects rate limits or timeouts from the primary provider. This configuration can achieve 99.9% uptime even during provider outages. - -// PLACEHOLDER: Add details on pool configuration and failback behavior - -=== 3. MCP aggregation and orchestration - -AI Gateway aggregates multiple MCP (Model Context Protocol) servers and provides deferred tool loading, which dramatically reduces token costs for AI agents. - -Without AI Gateway, agents typically load all available tools from multiple MCP servers at startup. This approach sends 50+ tool definitions with every request, creating high token costs (thousands of tokens per request), slow agent startup times, and no centralized governance over which tools agents can access. - -With AI Gateway, you configure approved MCP servers once, and the gateway loads only search and orchestrator tools initially. Agents query for specific tools only when needed, which reduces token usage by 80-90% depending on your configuration. You also gain centralized approval and governance over which MCP servers your agents can access. - -For complex workflows, AI Gateway provides a JavaScript-based orchestrator tool that reduces multi-step workflows from multiple round trips to a single call. For example, you can create a workflow that searches a vector database and, if the results are insufficient, falls back to web search—all in one orchestration step. - -=== 4. Unified observability and cost tracking - -AI Gateway provides a single dashboard that tracks all LLM traffic across providers, eliminating the need to switch between multiple provider dashboards. - -// PLACEHOLDER: Add screenshots of: -// - Request logs view -// - Cost breakdown by model/provider -// - Latency histogram -// - Error rate tracking - -The dashboard tracks request volume per gateway, model, and provider, along with token usage for both prompt and completion tokens. You can view estimated spend per model with cross-provider comparisons, latency metrics (p50, p95, p99), and errors broken down by type, provider, and model. - -This unified view helps you answer critical questions such as which model is the most cost-effective for your use case, why a specific user request failed, how much your staging environment costs per week, and what the latency difference is between providers for your workload. - -== Common gateway patterns - -=== Team isolation - -When multiple teams share infrastructure but need separate budgets and policies, create one gateway per team. For example, you might configure Team A's gateway with a $5K/month budget for both staging and production environments, while Team B's gateway has a $10K/month budget with different rate limits. Each team sees only their own traffic in the observability dashboards, providing clear cost attribution and isolation. - -=== Environment separation - -To prevent staging traffic from affecting production metrics, create separate gateways for each environment. Configure the staging gateway with lower rate limits, restricted model access, and aggressive cost controls to prevent runaway expenses. The production gateway can have higher rate limits, access to all models, and alerting configured to detect anomalies. - -=== Primary and fallback for reliability - -To ensure uptime during provider outages, configure provider pools with automatic failover. For example, you can set OpenAI as your primary provider (preferred for quality) and configure Anthropic as the fallback that activates when the gateway detects rate limits or timeouts from OpenAI. Monitor the fallback rate to detect primary provider issues early, before they impact your users. - -=== A/B testing models -To compare model quality and cost without dual integration, route a percentage of traffic to different models. For example, you can send 80% of traffic to `claude-sonnet-3.5` and 20% to `claude-opus-4`, then compare quality metrics and costs in the observability dashboard before adjusting the split. - -// PLACEHOLDER: Confirm if percentage-based routing is supported, or if it's header-based only - -=== Customer-based routing - -For SaaS products with tiered pricing (free, pro, enterprise), use CEL routing based on request headers to match users with appropriate models: - -[source,cel] ----- -request.headers["x-customer-tier"] == "enterprise" ? "anthropic/claude-opus-4" : -request.headers["x-customer-tier"] == "pro" ? "anthropic/claude-sonnet-3.5" : -"anthropic/claude-haiku" ----- - -== What's supported today - -LLM providers - -* OpenAI -* Anthropic -* // PLACEHOLDER: Google, AWS Bedrock, Azure OpenAI, others? - -API compatibility - -* OpenAI-compatible `/v1/chat/completions` endpoint -* // PLACEHOLDER: Streaming support? -* // PLACEHOLDER: Embeddings support? -* // PLACEHOLDER: Other endpoints? - -Policy features - -* CEL-based routing expressions -* Rate limiting (// PLACEHOLDER: per-gateway, per-header, per-tenant?) -* Monthly spend limits (// PLACEHOLDER: per-gateway, per-workspace?) -* Provider pools with automatic failover -* // PLACEHOLDER: Caching support? - -MCP support - -* MCP server aggregation -* Deferred tool loading (80-90% token reduction) -* JavaScript orchestrator for multi-step workflows -* // PLACEHOLDER: Tool execution sandboxing? - -Observability - -* Request logs with full prompt/response history -* Token usage tracking -* Estimated cost per request -* Latency metrics -* // PLACEHOLDER: Metrics export? OpenTelemetry support? - -// What's not supported yet -// PLACEHOLDER: List current limitations, for example: -// - Custom model deployments (Azure OpenAI BYOK, AWS Bedrock custom models) -// - Response caching -// - Prompt templates/versioning -// - Guardrails (PII detection, content moderation) -// - Multi-region active-active deployment -// - Metrics export to external systems -// - Budget alerts/notifications - -== Architecture - -AI Gateway consists of three planes: a control plane for configuration and management, a data plane for request processing and routing, and an observability plane for monitoring and analytics. - -// PLACEHOLDER: Add architecture diagram showing: -// 1. Control Plane: -// - Workspace management -// - Provider/model configuration -// - Gateway creation and policy definition -// - Admin console -// -// 2. Data Plane: -// - Request ingestion -// - Policy evaluation (rate limits → spend limits → routing → execution) -// - Provider pool selection and failover -// - MCP aggregation layer -// - Response logging and metrics -// -// 3. Observability Plane: -// - Request logs storage -// - Metrics aggregation -// - Dashboard UI - -When a request flows through AI Gateway, it passes through several policy and routing stages before reaching the LLM provider. Understanding this lifecycle helps you configure policies effectively and troubleshoot issues: - -. Application sends request to gateway endpoint with `rp-aigw-id` header -. Gateway authenticates request -. Rate limit policy evaluates (allow/deny) -. Spend limit policy evaluates (allow/deny) -. Routing policy evaluates (which model/provider to use) -. Provider pool selects backend (primary/fallback) -. Request forwarded to LLM provider -. Response returned to application -. Request logged with tokens, cost, latency, status - -Each policy evaluation happens synchronously in the request path. If rate limits or spend limits reject the request, the gateway returns an error immediately without calling the LLM provider, which helps you control costs. - -For MCP tool requests, the lifecycle differs slightly to support deferred tool loading: - -. Application discovers tools via `/mcp` endpoint -. Gateway aggregates tools from approved MCP servers -. Application receives search + orchestrator tools (deferred loading) -. Application invokes specific tool -. Gateway routes to appropriate MCP server -. Tool execution result returned -. Request logged with execution time, status - -The gateway only loads and exposes specific tools when requested, which dramatically reduces the token overhead compared to loading all tools upfront. - -== Next steps - -* xref:ai-agents:ai-gateway/ai-gateway.adoc[]: Route your first request through AI Gateway. -* xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Configure MCP server aggregation for AI agents. -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor request logs, token usage, and costs. diff --git a/modules/ai-agents/pages/ai-gateway/ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway/ai-gateway.adoc deleted file mode 100644 index e8b0becb9..000000000 --- a/modules/ai-agents/pages/ai-gateway/ai-gateway.adoc +++ /dev/null @@ -1,345 +0,0 @@ -= AI Gateway Quickstart -:description: Quickstart to configure the AI Gateway for unified access to multiple LLM providers and MCP servers through a single endpoint. - - -NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and later. - -The Redpanda AI Gateway is a production-grade proxy that provides unified access to multiple Large Language Model (LLM) providers and Model Context Protocol (MCP) servers through a single endpoint. MCP servers expose tools that agents can discover and call. An AI Gateway maintains centralized control over routing, rate limiting, cost optimization, security, and observability. - -== Prerequisites - -* Access to the AI Gateway UI (provided by your administrator) -* API key for at least one LLM provider: OpenAI or Anthropic -* Optional: MCP server endpoints if you plan to use tool aggregation - -== Get started - -Before you can create a gateway, an administrator must enable LLM providers and models. - -=== Step 1: Enable a provider - -Providers represent upstream services (Anthropic, OpenAI) and associated credentials. Providers are disabled by default. An administrator must enable them explicitly by adding credentials. - -. In AI Gateways, navigate to *Providers*. -. Select a provider (for example, Anthropic). -. On the *Configuration* tab for the provider, click *Add configuration* and enter your API Key. - -=== Step 2: Enable models - -The model catalog is the set of models made available through the gateway. Models are disabled by default. After enabling a provider, an administrator can enable its models. - -The infrastructure that is serving the model is different based on the provider you select. For example, OpenAI has different reliability and availability metrics than Anthropic. When you consider all the metrics, you can design your gateway to use different providers for different use cases. - -. Navigate to *Models*. -. Enable the models you want exposed through gateways. - -==== Model naming convention - -Model provider requests must use the `vendor/model_id` format in the model property of the request body, and include the `rp-aigw-id` header with the gateway ID the request is being sent to. The following example routes OpenAI API calls through Redpanda's AI Gateway for centralized control. - -[source,python] ----- -# Example: Using the OpenAI Python SDK with AI Gateway -from openai import OpenAI - -client = OpenAI( - base_url="https://gw.ai.panda.com", <1> - api_key="", -) - -# Add header per request -response = client.chat.completions.create( - model="openai/gpt-5", <2> - messages=[{"role": "user", "content": "Hello!"}], - extra_headers={ - "rp-aigw-id": "gateway-abc" # Override for this request - } <3> -) ----- -<1> This redirects the OpenAI client to the AI Gateway endpoint. -<2> The `model` property uses the `vendor/model_id` format as required by the AI Gateway. -<3> Includes the `rp-aigw-id` header to specify which gateway configuration to use. - -=== Step 3: Create a gateway - -A gateway is a logical configuration boundary (policies + routing + observability) on top of a single deployment. It's a "virtual gateway" that you can create per team, environment (staging/production), product, or customer. - -. Navigate to *Gateways*. -. Click *Create Gateway*. -. Choose a name, workspace, and optional metadata. -+ -TIP: A _workspace_ is conceptually similar to a _resource group_ in Redpanda streaming. - -. After creation, copy the *Gateway Endpoint* from the gateway detail page. - -=== Step 4: Configure LLM routing - -On the Gateways page, select the *LLM* tab to configure rate limits, spend limits, routing, and provider pools with fallback options. - -The LLM routing pipeline visually represents the request lifecycle: - -. Rate Limit: For example, global rate limit of 100 requests/second. -. Spend Limit / Monthly Budget: For example, $15K/month with blocking enforcement, so it blocks requests after that budget is exceeded. -. Routing to a primary provider pool with optional fallback provider pools: For example, primary route to Anthropic backend pool, and if that fails, it will fallback to OpenAI pool. - -*Load balancing / multi-provider distribution:* -If a provider pool contains multiple providers, you can distribute traffic (for example, balancing across Anthropic and OpenAI). - -TIP: Provider pool (UI) = Backend pool (API) - -=== Step 5: Configure MCP tools - -On the Gateways page, select the *MCP* tab to configure your MCP tool discovery and tool execution. This MCP proxy is an aggregator of MCP servers, allowing multiple MCP servers behind a single endpoint. Agents can then find tools and call them through the gateway. To configure the MCP proxy, add the following: - -* Display name: When you drag a provider pool, you give it a name. -* Model dropdown: Choose a model from the available models in the catalog. -* Load balancing options: If you have multiple providers, you can load balance requests between them; for example, round robin. - -MCP tools include a data catalog API, the memory store, a vector search service, and an MCP orchestrator. The *MCP orchestrator* is a built-in MCP server that enables programmatic tool calling. Agents can generate code to call multiple tools in a single orchestrated step, which reduces the number of round trips. For example, a workflow requiring 47 file reads can be reduced from 49 round trips to just 1. To add other tools, (for example, Slack), add the Slack MCP server endpoint. - -When many tools are aggregated, listing all tools can consume significant tokens. With *deferred tool loading*, instead of returning all tools, the MCP gateway initially returns a tool search capability and the MCP orchestrator. The agent then searches for the specific tool it needs and retrieves only that subset. That way, the exchange of messages between the MCP gateway and the agent is small. This can reduce token usage significantly when you have many tools configured. - -*REVIEWERS: When/how exactly do you use the orchestrator? Also what happens after they create a gateway? Please provide an example of how to validate end-to-end routing against the gateway endpoint!* - -*REVIEWERS: How do users connect to the ADP catalog + MCP servers exposed through RPCN?* - -== Observability - -After traffic flows through a gateway, you can inspect: - -* Request volume -* Token usage -* Estimated spend -* Latency -* Per-model breakdown - -This is central to governance: You can see and control usage by gateway boundary (for example, by team, environment, customer, or product). - -*REVIEWERS: Where do those metrics appear in the UI, or how does a user validate observability after setup?* - -== CEL routing - -The AI Gateway uses Common Expression Language (CEL) for flexible routing and policy application. CEL expressions let you create sophisticated routing rules based on request properties without code changes. Use CEL to: - -* Route requests to specific providers based on model family -* Apply different rate limits based on user tiers -* Enforce policies based on request content - -The editor in the UI helps you discover available request fields (headers, path, body, and so on). - -=== CEL examples - -Route based on model family: - -[source,cel] ----- -request.body.model.startsWith("anthropic/") ----- - -Apply a rule to all requests: - -[source,cel] ----- -true ----- - -Route based on a header (for example, product tier): - -[source,cel] ----- -request.headers['tier'][0] == "premium" ----- - -Guard for field existence: - -[source,cel] ----- -has(request.body.max_tokens) && request.body.max_tokens > 1000 ----- - -== Integrate with AI agents and tools - -The AI Gateway provides standardized endpoints that work with various AI development tools and agents. This section shows how to configure popular tools to use your AI Gateway endpoints. - -=== MCP server endpoint - -If you've configured MCP tools in your gateway, AI agents can connect to the aggregated MCP endpoint: - -* MCP endpoint URL: `https://gw.ai.panda.com/mcp` - -* Headers required: -** `Authorization: Bearer ` -** `rp-aigw-id: ` - -This endpoint aggregates all MCP servers configured in your gateway, providing a unified interface for tool discovery and execution. - -=== Environment variables - -For consistent configuration across tools, set these environment variables: - -[source,bash] ----- -export REDPANDA_GATEWAY_URL="https://gw.ai.panda.com" -export REDPANDA_GATEWAY_ID="" -export REDPANDA_API_KEY="" ----- - -Many tools and SDKs can automatically use these environment variables when configured appropriately. - -=== Claude Code - -Configure Claude Code to use AI Gateway endpoints using HTTP transport for the MCP connection. - -*For Claude Code CLI:* - -Use the `claude mcp add` command to configure the HTTP transport: - -[source,bash] ----- -claude mcp add --transport http redpanda-aigateway https://gw.ai.panda.com/mcp \ - --header "Authorization: Bearer " \ - --header "rp-aigw-id: " ----- - -*Alternative configuration via config file:* - -Create or edit `~/.claude/config.json`: - -[source,json] ----- -{ - "mcpServers": { - "redpanda-ai-gateway": { - "transport": "http", - "url": "https://gw.ai.panda.com/mcp", - "headers": { - "Authorization": "Bearer ", - "rp-aigw-id": "" - } - } - }, - "apiProviders": { - "redpanda": { - "baseURL": "https://gw.ai.panda.com", - "headers": { - "rp-aigw-id": "" - } - } - } -} ----- - -=== VS Code extensions - -Configure VS Code extensions that support OpenAI-compatible APIs: - -*Continue extension:* - -Edit your Continue config file (`~/.continue/config.json`): - -[source,json] ----- -{ - "models": [ - { - "title": "Redpanda AI Gateway - GPT-4", - "provider": "openai", - "model": "openai/gpt-4", - "apiBase": "https://gw.ai.panda.com", - "apiKey": "", - "requestOptions": { - "headers": { - "rp-aigw-id": "" - } - } - }, - { - "title": "Redpanda AI Gateway - Claude", - "provider": "anthropic", - "model": "anthropic/claude-3-5-sonnet-20241022", - "apiBase": "https://gw.ai.panda.com", - "apiKey": "", - "requestOptions": { - "headers": { - "rp-aigw-id": "" - } - } - } - ] -} ----- - -=== Cursor IDE - -Configure Cursor to route requests through the AI Gateway: - -. Open Cursor Settings (*Cursor* → *Settings* or `Cmd+,`) -. Navigate to *AI* settings -. Add a custom OpenAI-compatible provider: - -[source,json] ----- -{ - "cursor.ai.providers.openai.apiBase": "https://gw.ai.panda.com", - "cursor.ai.providers.openai.defaultHeaders": { - "rp-aigw-id": "" - } -} ----- - -=== Custom applications - -For custom applications using OpenAI or Anthropic SDKs: - -*OpenAI SDK (Python):* - -[source,python] ----- -from openai import OpenAI - -client = OpenAI( - base_url="https://gw.ai.panda.com", - api_key="", - default_headers={ - "rp-aigw-id": "" - } -) ----- - -*Anthropic SDK (Python):* - -[source,python] ----- -from anthropic import Anthropic - -client = Anthropic( - base_url="https://gw.ai.panda.com", - api_key="", - default_headers={ - "rp-aigw-id": "" - } -) ----- - -*Node.js with OpenAI SDK:* - -[source,javascript] ----- -import OpenAI from 'openai'; - -const openai = new OpenAI({ - baseURL: 'https://gw.ai.panda.com', - apiKey: process.env.OPENAI_API_KEY, - defaultHeaders: { - 'rp-aigw-id': '' - } -}); ----- - -== Next steps - -* xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[]: Learn about AI Gateway architecture, deployment models, and common usage patterns. -* xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Explore advanced CEL routing patterns for traffic distribution, cost optimization, and failover. -* xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Configure MCP server aggregation and deferred tool loading for AI agents. -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor request logs, token usage, and costs through the observability dashboard. -* xref:ai-agents:ai-gateway/integrations/index.adoc[]: Connect AI development tools like Claude Code, Cursor, and Continue to your gateway. \ No newline at end of file diff --git a/modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc b/modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc index 44a439d11..7abe34f00 100644 --- a/modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc +++ b/modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc @@ -3,6 +3,8 @@ :page-topic-type: how-to :personas: app_developer +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + This guide shows you how to connect your AI agent or application to a Redpanda AI Gateway. You'll configure your client SDK, make your first request, and validate the integration. After completing this guide, you will be able to: diff --git a/modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc b/modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc index 43890fa5f..01db50d20 100644 --- a/modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc +++ b/modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc @@ -3,6 +3,8 @@ :page-topic-type: how-to :personas: app_developer +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + As a builder, you need to know which gateways are available to you before integrating your agent or application. This page shows you how to discover accessible gateways, understand their configurations, and verify connectivity. After reading this page, you will be able to: diff --git a/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc b/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc index ce12259cc..3d2ee3b0b 100644 --- a/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc +++ b/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc @@ -1,14 +1,14 @@ = CEL Routing Cookbook :description: CEL routing cookbook for Redpanda AI Gateway with common patterns, examples, and best practices. +:page-topic-type: cookbook :page-personas: app_developer, platform_admin +:learning-objective-1: Write CEL expressions to route requests based on user tier or custom headers +:learning-objective-2: Test CEL routing logic using the UI editor or test requests +:learning-objective-3: Troubleshoot common CEL errors using safe patterns -Redpanda AI Gateway uses CEL (Common Expression Language) for dynamic request routing. CEL expressions evaluate request properties (headers, body, context) and determine which model or provider should handle each request. - -After reading this page, you will be able to: +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] -* Write CEL expressions to route requests based on user tier, environment, content complexity, or custom headers. -* Test CEL routing logic using the UI editor or test requests to verify expected model selection. -* Troubleshoot common CEL errors (type mismatches, missing fields, index out of bounds) using safe patterns. +Redpanda AI Gateway uses CEL (Common Expression Language) for dynamic request routing. CEL expressions evaluate request properties (headers, body, context) and determine which model or provider should handle each request. CEL enables: diff --git a/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc b/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc new file mode 100644 index 000000000..99c869948 --- /dev/null +++ b/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc @@ -0,0 +1,158 @@ += AI Gateway Architecture +:description: Technical architecture of Redpanda AI Gateway, including request lifecycle, supported providers, deployment models, and implementation details. +:page-topic-type: concept +:page-personas: app_developer, platform_admin +:learning-objective-1: Describe the three architectural planes of AI Gateway +:learning-objective-2: Explain the request lifecycle through policy evaluation stages +:learning-objective-3: Identify supported providers, features, and current limitations + +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + +This page provides technical details about AI Gateway's architecture, request processing, and capabilities. For an introduction to AI Gateway and the problems it solves, see xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[]. + +== Architecture overview + +AI Gateway consists of three planes: a control plane for configuration and management, a data plane for request processing and routing, and an observability plane for monitoring and analytics. + +// PLACEHOLDER: Add architecture diagram showing: +// 1. Control Plane: +// - Workspace management +// - Provider/model configuration +// - Gateway creation and policy definition +// - Admin console +// +// 2. Data Plane: +// - Request ingestion +// - Policy evaluation (rate limits → spend limits → routing → execution) +// - Provider pool selection and failover +// - MCP aggregation layer +// - Response logging and metrics +// +// 3. Observability Plane: +// - Request logs storage +// - Metrics aggregation +// - Dashboard UI + +=== Control plane + +The control plane manages gateway configuration and policy definition: + +* **Workspace management**: Multi-tenant isolation with separate namespaces for different teams or environments +* **Provider configuration**: Enable and configure LLM providers (OpenAI, Anthropic, etc.) +* **Gateway creation**: Define gateways with specific routing rules, budgets, and rate limits +* **Policy definition**: Create CEL-based routing policies, spend limits, and rate limits +* **MCP server registration**: Configure which MCP servers are available to agents + +=== Data plane + +The data plane handles all runtime request processing: + +* **Request ingestion**: Accept requests via OpenAI-compatible API endpoints +* **Authentication**: Validate API keys and gateway access +* **Policy evaluation**: Apply rate limits, spend limits, and routing policies +* **Provider pool management**: Select primary or fallback providers based on availability +* **MCP aggregation**: Aggregate tools from multiple MCP servers with deferred loading +* **Response transformation**: Normalize provider-specific responses to OpenAI format +* **Metrics collection**: Record token usage, latency, and cost for every request + +=== Observability plane + +The observability plane provides monitoring and analytics: + +* **Request logs**: Store full request/response history with prompt and completion content +* **Metrics aggregation**: Calculate token usage, costs, latency percentiles, and error rates +* **Dashboard UI**: Display real-time and historical analytics per gateway, model, or provider +* **Cost tracking**: Estimate spend based on provider pricing and token consumption + +== Request lifecycle + +When a request flows through AI Gateway, it passes through several policy and routing stages before reaching the LLM provider. Understanding this lifecycle helps you configure policies effectively and troubleshoot issues: + +. Application sends request to gateway endpoint with `rp-aigw-id` header +. Gateway authenticates request +. Rate limit policy evaluates (allow/deny) +. Spend limit policy evaluates (allow/deny) +. Routing policy evaluates (which model/provider to use) +. Provider pool selects backend (primary/fallback) +. Request forwarded to LLM provider +. Response returned to application +. Request logged with tokens, cost, latency, status + +Each policy evaluation happens synchronously in the request path. If rate limits or spend limits reject the request, the gateway returns an error immediately without calling the LLM provider, which helps you control costs. + +=== MCP tool request lifecycle + +For MCP tool requests, the lifecycle differs slightly to support deferred tool loading: + +. Application discovers tools via `/mcp` endpoint +. Gateway aggregates tools from approved MCP servers +. Application receives search + orchestrator tools (deferred loading) +. Application invokes specific tool +. Gateway routes to appropriate MCP server +. Tool execution result returned +. Request logged with execution time, status + +The gateway only loads and exposes specific tools when requested, which dramatically reduces the token overhead compared to loading all tools upfront. + +== Supported features + +=== LLM providers + +* OpenAI +* Anthropic +* // PLACEHOLDER: Google, AWS Bedrock, Azure OpenAI, others? + +=== API compatibility + +* OpenAI-compatible `/v1/chat/completions` endpoint +* // PLACEHOLDER: Streaming support? +* // PLACEHOLDER: Embeddings support? +* // PLACEHOLDER: Other endpoints? + +=== Policy features + +* CEL-based routing expressions +* Rate limiting (// PLACEHOLDER: per-gateway, per-header, per-tenant?) +* Monthly spend limits (// PLACEHOLDER: per-gateway, per-workspace?) +* Provider pools with automatic failover +* // PLACEHOLDER: Caching support? + +=== MCP support + +* MCP server aggregation +* Deferred tool loading (80-90% token reduction) +* JavaScript orchestrator for multi-step workflows +* // PLACEHOLDER: Tool execution sandboxing? + +=== Observability + +* Request logs with full prompt/response history +* Token usage tracking +* Estimated cost per request +* Latency metrics +* // PLACEHOLDER: Metrics export? OpenTelemetry support? + +== Current limitations + +// PLACEHOLDER: List current limitations, for example: +// - Custom model deployments (Azure OpenAI BYOK, AWS Bedrock custom models) +// - Response caching +// - Prompt templates/versioning +// - Guardrails (PII detection, content moderation) +// - Multi-region active-active deployment +// - Metrics export to external systems +// - Budget alerts/notifications + +== Deployment models + +// PLACEHOLDER: Add deployment model details: +// - BYOC deployment requirements +// - Scaling characteristics +// - High availability configuration +// - Regional deployment options + +== Next steps + +* xref:ai-agents:ai-gateway/gateway-quickstart.adoc[]: Route your first request through AI Gateway +* xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Configure MCP server aggregation for AI agents +* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor request logs, token usage, and costs diff --git a/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc b/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc new file mode 100644 index 000000000..dd1505fe1 --- /dev/null +++ b/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc @@ -0,0 +1,583 @@ += AI Gateway Quickstart +:description: Get started with AI Gateway by configuring providers, creating your first gateway, and routing requests through unified LLM endpoints. +:page-topic-type: quickstart +:page-personas: app_developer, platform_admin +:learning-objective-1: Enable an LLM provider and create your first gateway +:learning-objective-2: Route your first request through AI Gateway and verify it works +:learning-objective-3: View request logs and token usage in the observability dashboard + +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + +Redpanda AI Gateway provides unified access to multiple Large Language Model (LLM) providers and Model Context Protocol (MCP) servers through a single endpoint. This quickstart walks you through configuring your first gateway and routing requests through it. + +== Prerequisites + +Before starting, ensure you have: + +* Access to the AI Gateway UI (provided by your administrator) +* Admin permissions to configure providers and gateways +* API key for at least one LLM provider (OpenAI or Anthropic) +* Python 3.8+, Node.js 18+, or cURL (for testing) + +== Step 1: Configure a provider + +Providers represent upstream LLM services (OpenAI, Anthropic) and their associated credentials. Providers are disabled by default and must be enabled explicitly. + +. In AI Gateways, navigate to *Providers*. +. Select a provider (for example, OpenAI or Anthropic). +. On the *Configuration* tab, click *Add configuration* and enter your API Key. +. Verify the provider status shows "Active". + +AI Gateway currently supports: + +* OpenAI +* Anthropic + +== Step 2: Enable models + +After enabling a provider, enable the specific models you want to make available through your gateways. + +. Navigate to *Models*. +. Enable the models you want to use (for example, `gpt-4o`, `gpt-4o-mini`, `claude-sonnet-3.5`). +. Verify the models appear as "Enabled" in the catalog. + +TIP: Different providers have different reliability and cost characteristics. When choosing models, consider your use case requirements for quality, speed, and cost. + +=== Model naming convention + +Requests through AI Gateway must use the `vendor/model_id` format. For example: + +* OpenAI models: `openai/gpt-4o`, `openai/gpt-4o-mini` +* Anthropic models: `anthropic/claude-sonnet-3.5`, `anthropic/claude-opus-4` + +This format allows the gateway to route requests to the correct provider. + +== Step 3: Create a gateway + +A gateway is a logical configuration boundary that defines routing policies, rate limits, spend limits, and observability scope. You can create separate gateways per team, environment (staging/production), or customer. + +. Navigate to *Gateways*. +. Click *Create Gateway*. +. Configure the gateway: ++ +* *Name*: Choose a descriptive name (for example, `my-first-gateway`) +* *Workspace*: Select a workspace (conceptually similar to a resource group) +* *Description*: Optional metadata for documentation + +. After creation, copy the *Gateway Endpoint* and *Gateway ID* from the gateway detail page. You'll need these for sending requests. + +Your gateway endpoint format: +---- +Gateway Endpoint: https://gw.ai.panda.com +Gateway ID: gw_abc123... +---- + +Common gateway patterns: + +* *Environment separation*: Create separate gateways for staging and production +* *Team isolation*: One gateway per team for budget tracking +* *Customer multi-tenancy*: One gateway per customer for isolated policies + +== Step 4: Send your first request + +Now that you've configured a provider and created a gateway, send a test request to verify everything works. + +[tabs] +==== +Python:: ++ +-- +[source,python] +---- +from openai import OpenAI + +# Configure client to use AI Gateway +client = OpenAI( + base_url="https://gw.ai.panda.com", # Your gateway endpoint + api_key="", # Your Redpanda API key + default_headers={ + "rp-aigw-id": "gw_abc123..." # Your gateway ID + } +) + +# Send a request (note the vendor/model_id format) +response = client.chat.completions.create( + model="openai/gpt-4o-mini", # Format: {provider}/{model} + messages=[ + {"role": "user", "content": "Say 'Hello from AI Gateway!'"} + ], + max_tokens=20 +) + +print(response.choices[0].message.content) +# Expected output: Hello from AI Gateway! +---- +-- + +TypeScript/JavaScript:: ++ +-- +[source,typescript] +---- +import OpenAI from 'openai'; + +const client = new OpenAI({ + baseURL: 'https://gw.ai.panda.com', + apiKey: process.env.REDPANDA_API_KEY, + defaultHeaders: { + 'rp-aigw-id': 'gw_abc123...' + } +}); + +const response = await client.chat.completions.create({ + model: 'openai/gpt-4o-mini', + messages: [ + { role: 'user', content: 'Say "Hello from AI Gateway!"' } + ], + max_tokens: 20 +}); + +console.log(response.choices[0].message.content); +// Expected output: Hello from AI Gateway! +---- +-- + +cURL:: ++ +-- +[source,bash] +---- +curl https://gw.ai.panda.com/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer ${REDPANDA_API_KEY}" \ + -H "rp-aigw-id: gw_abc123..." \ + -d '{ + "model": "openai/gpt-4o-mini", + "messages": [ + {"role": "user", "content": "Say \"Hello from AI Gateway!\""} + ], + "max_tokens": 20 + }' +---- + +Expected response: + +[source,json] +---- +{ + "id": "chatcmpl-...", + "object": "chat.completion", + "created": 1704844800, + "model": "openai/gpt-4o-mini", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello from AI Gateway!" + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 8, + "completion_tokens": 5, + "total_tokens": 13 + } +} +---- +-- +==== + +=== Troubleshooting + +If your request fails, check these common issues: + +* *401 Unauthorized*: Verify your API key is valid +* *404 Not Found*: Confirm the base URL matches your gateway endpoint +* *Model not found*: Ensure the model is enabled in Step 2 +* *Missing rp-aigw-id*: Add the gateway ID header to your request + +== Step 5: Verify in observability dashboard + +Confirm your request appears in the AI Gateway observability dashboard. + +// PLACEHOLDER: Add UI navigation path + +. Navigate to the observability dashboard for your gateway. +. Filter by: ++ +* *Gateway*: `my-first-gateway` +* *Model*: `openai/gpt-4o-mini` +* *Time range*: Last 5 minutes + +. Verify the request log shows: ++ +* *Model*: `openai/gpt-4o-mini` +* *Provider*: OpenAI +* *Status*: 200 (success) +* *Prompt tokens*: ~8 +* *Completion tokens*: ~5 +* *Estimated cost*: Based on provider pricing +* *Latency*: Response time in milliseconds + +. Click the request to expand and view: ++ +* Full prompt and response content +* Request headers +* Routing decision details + +If your request doesn't appear: + +* Wait a few seconds for logs to populate (there may be a brief delay) +* Verify the gateway ID in your request matches the gateway you're viewing +* Check that your client received a successful response + +== Step 6: Configure LLM routing (optional) + +Configure rate limits, spend limits, and provider pools with failover. + +On the Gateways page, select the *LLM* tab to configure routing policies. The LLM routing pipeline represents the request lifecycle: + +. *Rate Limit*: Control request throughput (for example, 100 requests/second) +. *Spend Limit*: Set monthly budget caps (for example, $15K/month with blocking enforcement) +. *Provider Pools*: Define primary and fallback providers + +=== Configure provider pool with fallback + +For high availability, configure a fallback provider that activates when the primary fails: + +. Add a second provider (for example, Anthropic) following Step 1. +. In your gateway's *LLM* routing configuration: ++ +* *Primary pool*: OpenAI (preferred for quality) +* *Fallback pool*: Anthropic (activates on rate limits, timeouts, or errors) + +. Save the configuration. + +The gateway automatically routes to the fallback when it detects: + +* Rate limit exceeded +* Request timeout +* 5xx server errors from primary provider + +Monitor the fallback rate in observability to detect primary provider issues early. + +== Step 7: Configure MCP tools (optional) + +If you're using AI agents, configure MCP (Model Context Protocol) tool aggregation. + +On the Gateways page, select the *MCP* tab to configure tool discovery and execution. The MCP proxy aggregates multiple MCP servers behind a single endpoint, allowing agents to discover and call tools through the gateway. + +Configure the MCP settings: + +* *Display name*: Descriptive name for the provider pool +* *Model*: Choose which model handles tool execution +* *Load balancing*: If multiple providers are available, select a strategy (for example, round robin) + +=== Available MCP tools + +The gateway provides these built-in MCP tools: + +* *Data catalog API*: Query your data catalog +* *Memory store*: Persistent storage for agent state +* *Vector search*: Semantic search over embeddings +* *MCP orchestrator*: Built-in tool for programmatic multi-tool workflows + +The *MCP orchestrator* enables agents to generate JavaScript code that calls multiple tools in a single orchestrated step, reducing round trips. For example, a workflow requiring 47 file reads can be reduced from 49 round trips to just 1. + +To add external tools (for example, Slack, GitHub), add their MCP server endpoints to your gateway configuration. + +=== Deferred tool loading + +When many tools are aggregated, listing all tools upfront can consume significant tokens. With *deferred tool loading*, the MCP gateway initially returns only: + +* A tool search capability +* The MCP orchestrator + +Agents then search for specific tools they need, retrieving only that subset. This can reduce token usage by 80-90% when you have many tools configured. + +// REVIEWERS: When/how exactly do you use the orchestrator? Also what happens after they create a gateway? Please provide an example of how to validate end-to-end routing against the gateway endpoint! + +// REVIEWERS: How do users connect to the ADP catalog + MCP servers exposed through RPCN? + +== Step 8: Create CEL routing rule (optional) + +Use CEL (Common Expression Language) expressions to route requests dynamically based on headers, content, or other request properties. + +The AI Gateway uses CEL for flexible routing without code changes. Use CEL to: + +* Route premium users to better models +* Apply different rate limits based on user tiers +* Enforce policies based on request content + +=== Add a routing rule + +In your gateway's routing configuration: + +. Add a CEL expression to route based on user tier: ++ +[source,cel] +---- +# Route based on user tier header +request.headers["x-user-tier"] == "premium" + ? "openai/gpt-4o" + : "openai/gpt-4o-mini" +---- + +. Save the rule. + +The gateway editor helps you discover available request fields (headers, path, body, and so on). + +=== Test the routing rule + +Send requests with different headers to verify routing: + +*Premium user request*: + +[source,python] +---- +response = client.chat.completions.create( + model="openai/gpt-4o", # Will be routed based on CEL rule + messages=[{"role": "user", "content": "Hello"}], + extra_headers={"x-user-tier": "premium"} +) +# Should route to gpt-4o (premium model) +---- + +*Free user request*: + +[source,python] +---- +response = client.chat.completions.create( + model="openai/gpt-4o-mini", + messages=[{"role": "user", "content": "Hello"}], + extra_headers={"x-user-tier": "free"} +) +# Should route to gpt-4o-mini (cost-effective model) +---- + +Check the observability dashboard to verify: + +* The correct model was selected based on the header value +* The routing decision explanation shows which CEL rule matched + +=== Common CEL patterns + +Route based on model family: + +[source,cel] +---- +request.body.model.startsWith("anthropic/") +---- + +Apply a rule to all requests: + +[source,cel] +---- +true +---- + +Guard for field existence: + +[source,cel] +---- +has(request.body.max_tokens) && request.body.max_tokens > 1000 +---- + +For more CEL examples, see xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]. + +== Connect AI tools to your gateway + +The AI Gateway provides standardized endpoints that work with various AI development tools. This section shows how to configure popular tools. + +=== MCP endpoint + +If you've configured MCP tools in your gateway, AI agents can connect to the aggregated MCP endpoint: + +* *MCP endpoint URL*: `https://gw.ai.panda.com/mcp` +* *Required headers*: +** `Authorization: Bearer ` +** `rp-aigw-id: ` + +This endpoint aggregates all MCP servers configured in your gateway. + +=== Environment variables + +For consistent configuration, set these environment variables: + +[source,bash] +---- +export REDPANDA_GATEWAY_URL="https://gw.ai.panda.com" +export REDPANDA_GATEWAY_ID="" +export REDPANDA_API_KEY="" +---- + +=== Claude Code + +Configure Claude Code using HTTP transport for the MCP connection: + +[source,bash] +---- +claude mcp add --transport http redpanda-aigateway https://gw.ai.panda.com/mcp \ + --header "Authorization: Bearer " \ + --header "rp-aigw-id: " +---- + +Alternatively, edit `~/.claude/config.json`: + +[source,json] +---- +{ + "mcpServers": { + "redpanda-ai-gateway": { + "transport": "http", + "url": "https://gw.ai.panda.com/mcp", + "headers": { + "Authorization": "Bearer ", + "rp-aigw-id": "" + } + } + }, + "apiProviders": { + "redpanda": { + "baseURL": "https://gw.ai.panda.com", + "headers": { + "rp-aigw-id": "" + } + } + } +} +---- + +For detailed Claude Code setup, see xref:ai-agents:ai-gateway/integrations/claude-code-user.adoc[]. + +=== Continue.dev + +Edit your Continue config file (`~/.continue/config.json`): + +[source,json] +---- +{ + "models": [ + { + "title": "Redpanda AI Gateway - GPT-4", + "provider": "openai", + "model": "openai/gpt-4", + "apiBase": "https://gw.ai.panda.com", + "apiKey": "", + "requestOptions": { + "headers": { + "rp-aigw-id": "" + } + } + }, + { + "title": "Redpanda AI Gateway - Claude", + "provider": "anthropic", + "model": "anthropic/claude-3-5-sonnet-20241022", + "apiBase": "https://gw.ai.panda.com", + "apiKey": "", + "requestOptions": { + "headers": { + "rp-aigw-id": "" + } + } + } + ] +} +---- + +For detailed Continue setup, see xref:ai-agents:ai-gateway/integrations/continue-user.adoc[]. + +=== Cursor IDE + +Configure Cursor in Settings (*Cursor* → *Settings* or `Cmd+,`): + +[source,json] +---- +{ + "cursor.ai.providers.openai.apiBase": "https://gw.ai.panda.com", + "cursor.ai.providers.openai.defaultHeaders": { + "rp-aigw-id": "" + } +} +---- + +For detailed Cursor setup, see xref:ai-agents:ai-gateway/integrations/cursor-user.adoc[]. + +=== Custom applications + +For custom applications using OpenAI or Anthropic SDKs: + +*Python with OpenAI SDK*: + +[source,python] +---- +from openai import OpenAI + +client = OpenAI( + base_url="https://gw.ai.panda.com", + api_key="", + default_headers={ + "rp-aigw-id": "" + } +) +---- + +*Python with Anthropic SDK*: + +[source,python] +---- +from anthropic import Anthropic + +client = Anthropic( + base_url="https://gw.ai.panda.com", + api_key="", + default_headers={ + "rp-aigw-id": "" + } +) +---- + +*Node.js with OpenAI SDK*: + +[source,javascript] +---- +import OpenAI from 'openai'; + +const openai = new OpenAI({ + baseURL: 'https://gw.ai.panda.com', + apiKey: process.env.REDPANDA_API_KEY, + defaultHeaders: { + 'rp-aigw-id': '' + } +}); +---- + +== What you've accomplished + +After completing this quickstart, you: + +* ✓ Configured an LLM provider and enabled models +* ✓ Created your first AI Gateway +* ✓ Sent requests through the gateway +* ✓ Verified requests in the observability dashboard +* ✓ (Optional) Configured failover with provider pools +* ✓ (Optional) Created CEL routing rules +* ✓ (Optional) Set up MCP tool aggregation + +== Next steps + +Explore advanced AI Gateway features: + +* xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Advanced CEL routing patterns for traffic distribution and cost optimization +* xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Configure MCP server aggregation and deferred tool loading +* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor request logs, token usage, and costs +* xref:ai-agents:ai-gateway/migration-guide.adoc[]: Migrate existing LLM integrations to AI Gateway +* xref:ai-agents:ai-gateway/integrations/index.adoc[]: Connect more AI development tools + +Learn about the architecture: + +* xref:ai-agents:ai-gateway/gateway-architecture.adoc[]: Technical architecture, request lifecycle, and deployment models +* xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[]: Problems AI Gateway solves and common use cases diff --git a/modules/ai-agents/pages/ai-gateway/index.adoc b/modules/ai-agents/pages/ai-gateway/index.adoc index 2a3d3ebca..8c685847b 100644 --- a/modules/ai-agents/pages/ai-gateway/index.adoc +++ b/modules/ai-agents/pages/ai-gateway/index.adoc @@ -1,6 +1,8 @@ = AI Gateway :description: Unified access layer for LLM providers and AI tools with centralized routing, policy enforcement, cost management, and observability. :page-layout: index -:personas: platform_admin, app_developer, evaluator +:page-personas: platform_admin, app_developer, evaluator + +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] Redpanda AI Gateway provides a unified access layer for LLM providers and AI tools that sits between your applications and the AI services they use. It delivers centralized routing, policy enforcement, cost management, and observability for all your AI traffic. \ No newline at end of file diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc index f987cf1f0..3647efca4 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc @@ -6,6 +6,8 @@ :learning-objective-2: Set up authentication and access control for Claude Code clients :learning-objective-3: Deploy MCP tool aggregation for Claude Code tool discovery +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + Configure Redpanda AI Gateway to support Claude Code clients accessing LLM providers and MCP tools through a unified endpoint. After reading this page, you will be able to: @@ -19,7 +21,7 @@ After reading this page, you will be able to: * AI Gateway deployed on a BYOC cluster running Redpanda version 25.3 or later * Administrator access to the AI Gateway UI * At least one LLM provider API key (OpenAI or Anthropic) -* Understanding of xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[AI Gateway concepts] +* Understanding of xref:ai-agents:ai-gateway/gateway-architecture.adoc[AI Gateway concepts] == Architecture overview diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc index 3273a29af..8a6dcc85a 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc @@ -6,7 +6,9 @@ :learning-objective-2: Set up MCP server integration through AI Gateway :learning-objective-3: Verify Claude Code is routing requests through the gateway -After xref:ai-agents:ai-gateway/ai-gateway.adoc[configuring your AI Gateway], set up Claude Code to route LLM requests and access MCP tools through the gateway's unified endpoints. +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + +After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gateway], set up Claude Code to route LLM requests and access MCP tools through the gateway's unified endpoints. After reading this page, you will be able to: @@ -20,8 +22,8 @@ Before configuring Claude Code, ensure you have: * Claude Code CLI installed (download from https://github.com/anthropics/claude-code[Anthropic's GitHub^]) * An active Redpanda AI Gateway with: -** At least one LLM provider enabled (see xref:ai-agents:ai-gateway/ai-gateway.adoc#step-1-enable-a-provider[Enable a provider]) -** A gateway created and configured (see xref:ai-agents:ai-gateway/ai-gateway.adoc#step-3-create-a-gateway[Create a gateway]) +** At least one LLM provider enabled (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc#step-1-enable-a-provider[Enable a provider]) +** A gateway created and configured (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc#step-3-create-a-gateway[Create a gateway]) * Your AI Gateway credentials: ** Gateway endpoint URL (for example, `https://gw.ai.panda.com`) ** Gateway ID (for example, `gateway-abc123`) @@ -480,5 +482,5 @@ chmod 600 ~/.claude/config.json == Related pages -* xref:ai-agents:ai-gateway/ai-gateway.adoc[]: Create and configure your AI Gateway -* xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[]: Learn about AI Gateway architecture and benefits +* xref:ai-agents:ai-gateway/gateway-quickstart.adoc[]: Create and configure your AI Gateway +* xref:ai-agents:ai-gateway/gateway-architecture.adoc[]: Learn about AI Gateway architecture and benefits diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc index 46867c534..376927919 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc @@ -6,6 +6,8 @@ :learning-objective-2: Set up authentication and access control for Cline clients :learning-objective-3: Deploy MCP tool aggregation for Cline tool discovery +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + Configure Redpanda AI Gateway to support Cline (formerly Claude Dev) clients accessing LLM providers and MCP tools through a unified endpoint. After reading this page, you will be able to: @@ -19,7 +21,7 @@ After reading this page, you will be able to: * AI Gateway deployed on a BYOC cluster running Redpanda version 25.3 or later * Administrator access to the AI Gateway UI * At least one LLM provider API key (Anthropic or OpenAI) -* Understanding of xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[AI Gateway concepts] +* Understanding of xref:ai-agents:ai-gateway/gateway-architecture.adoc[AI Gateway concepts] == About Cline diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc index dce1e03c3..5e48edb25 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc @@ -6,7 +6,9 @@ :learning-objective-2: Set up autonomous mode with custom instructions and browser integration :learning-objective-3: Verify Cline routes requests through the gateway and optimize for cost -After xref:ai-agents:ai-gateway/ai-gateway.adoc[configuring your AI Gateway], set up Cline (formerly Claude Dev) to route LLM requests and access MCP tools through the gateway's unified endpoints. +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + +After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gateway], set up Cline (formerly Claude Dev) to route LLM requests and access MCP tools through the gateway's unified endpoints. After reading this page, you will be able to: @@ -20,8 +22,8 @@ Before configuring Cline, ensure you have: * Cline VS Code extension installed (search for "Cline" in VS Code Extensions) * An active Redpanda AI Gateway with: -** At least one LLM provider enabled (see xref:ai-agents:ai-gateway/ai-gateway.adoc#step-1-enable-a-provider[Enable a provider]) -** A gateway created and configured (see xref:ai-agents:ai-gateway/ai-gateway.adoc#step-3-create-a-gateway[Create a gateway]) +** At least one LLM provider enabled (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc#step-1-enable-a-provider[Enable a provider]) +** A gateway created and configured (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc#step-3-create-a-gateway[Create a gateway]) * Your AI Gateway credentials: ** Gateway endpoint URL (for example, `https://gw.ai.panda.com`) ** Gateway ID (for example, `gateway-abc123`) @@ -631,7 +633,7 @@ You may be hitting rate limits. Check the dashboard for rate limit metrics and i . **Provider outage** + -Check the AI Gateway dashboard for provider status. If the primary provider is down, configure failover (see xref:ai-agents:ai-gateway/quickstart-enhanced.adoc#step-6-configure-provider-pool-with-fallback[Configure failover]). +Check the AI Gateway dashboard for provider status. If the primary provider is down, configure failover (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc#configure-provider-pool-with-fallback[Configure failover]). === Settings changes not taking effect @@ -753,6 +755,6 @@ The gateway automatically blocks requests that would exceed the limit. == Related pages -* xref:ai-agents:ai-gateway/ai-gateway.adoc[]: Create and configure your AI Gateway -* xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[]: Learn about AI Gateway architecture and benefits +* xref:ai-agents:ai-gateway/gateway-quickstart.adoc[]: Create and configure your AI Gateway +* xref:ai-agents:ai-gateway/gateway-architecture.adoc[]: Learn about AI Gateway architecture and benefits * xref:ai-agents:ai-gateway/integrations/claude-code-user.adoc[]: Configure Claude Code with AI Gateway diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc index 741e689b1..471128d79 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc @@ -6,6 +6,8 @@ :learning-objective-2: Set up multi-provider backends with native format routing :learning-objective-3: Deploy MCP tool aggregation for Continue.dev tool discovery +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + Configure Redpanda AI Gateway to support Continue.dev clients accessing multiple LLM providers and MCP tools through flexible, native-format endpoints. After reading this page, you will be able to: @@ -19,7 +21,7 @@ After reading this page, you will be able to: * AI Gateway deployed on a BYOC cluster running Redpanda version 25.3 or later * Administrator access to the AI Gateway UI * API keys for at least one LLM provider (Anthropic, OpenAI, or others) -* Understanding of xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[AI Gateway concepts] +* Understanding of xref:ai-agents:ai-gateway/gateway-architecture.adoc[AI Gateway concepts] == About Continue.dev diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc index 0194c8d3d..dac4827fa 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc @@ -6,7 +6,9 @@ :learning-objective-2: Set up MCP server integration through AI Gateway :learning-objective-3: Optimize Continue.dev settings for cost and performance -After xref:ai-agents:ai-gateway/ai-gateway.adoc[configuring your AI Gateway], set up Continue.dev to route LLM requests and access MCP tools through the gateway's unified endpoints. +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + +After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gateway], set up Continue.dev to route LLM requests and access MCP tools through the gateway's unified endpoints. After reading this page, you will be able to: @@ -22,8 +24,8 @@ Before configuring Continue.dev, ensure you have: ** VS Code: Search for "Continue" in Extensions ** JetBrains IDEs: Install from the JetBrains Marketplace * An active Redpanda AI Gateway with: -** At least one LLM provider enabled (see xref:ai-agents:ai-gateway/ai-gateway.adoc#step-1-enable-a-provider[Enable a provider]) -** A gateway created and configured (see xref:ai-agents:ai-gateway/ai-gateway.adoc#step-3-create-a-gateway[Create a gateway]) +** At least one LLM provider enabled (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc#step-1-enable-a-provider[Enable a provider]) +** A gateway created and configured (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc#step-3-create-a-gateway[Create a gateway]) * Your AI Gateway credentials: ** Gateway endpoint URL (for example, `https://gw.ai.panda.com`) ** Gateway ID (for example, `gateway-abc123`) @@ -940,7 +942,7 @@ Autocomplete rarely needs more than 256 tokens, while chat responses can vary. == Related pages -* xref:ai-agents:ai-gateway/ai-gateway.adoc[]: Create and configure your AI Gateway -* xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[]: Learn about AI Gateway architecture and benefits +* xref:ai-agents:ai-gateway/gateway-quickstart.adoc[]: Create and configure your AI Gateway +* xref:ai-agents:ai-gateway/gateway-architecture.adoc[]: Learn about AI Gateway architecture and benefits * xref:ai-agents:ai-gateway/integrations/claude-code-user.adoc[]: Configure Claude Code with AI Gateway * xref:ai-agents:ai-gateway/integrations/cline-user.adoc[]: Configure Cline with AI Gateway diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc index f715d170f..6ad5c0f58 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc @@ -6,6 +6,8 @@ :learning-objective-2: Set up OpenAI-compatible transforms for multi-provider routing :learning-objective-3: Deploy multi-tenant authentication strategies for Cursor clients +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + Configure Redpanda AI Gateway to support Cursor IDE clients accessing multiple LLM providers and MCP tools through OpenAI-compatible endpoints. After reading this page, you will be able to: @@ -19,7 +21,7 @@ After reading this page, you will be able to: * AI Gateway deployed on a BYOC cluster running Redpanda version 25.3 or later * Administrator access to the AI Gateway UI * API keys for at least one LLM provider (Anthropic, OpenAI, or others) -* Understanding of xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[AI Gateway concepts] +* Understanding of xref:ai-agents:ai-gateway/gateway-architecture.adoc[AI Gateway concepts] == About Cursor IDE diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc index 44573a224..5cd74487b 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc @@ -6,7 +6,9 @@ :learning-objective-2: Set up MCP server integration for tool access through the gateway :learning-objective-3: Optimize Cursor settings for multi-tenancy and cost control -After xref:ai-agents:ai-gateway/ai-gateway.adoc[configuring your AI Gateway], set up Cursor IDE to route LLM requests and access MCP tools through the gateway's unified endpoints. +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + +After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gateway], set up Cursor IDE to route LLM requests and access MCP tools through the gateway's unified endpoints. After reading this page, you will be able to: @@ -20,8 +22,8 @@ Before configuring Cursor IDE, ensure you have: * Cursor IDE installed (download from https://cursor.sh[cursor.sh^]) * An active Redpanda AI Gateway with: -** At least one LLM provider enabled (see xref:ai-agents:ai-gateway/ai-gateway.adoc#step-1-enable-a-provider[Enable a provider]) -** A gateway created and configured (see xref:ai-agents:ai-gateway/ai-gateway.adoc#step-3-create-a-gateway[Create a gateway]) +** At least one LLM provider enabled (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc#step-1-enable-a-provider[Enable a provider]) +** A gateway created and configured (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc#step-3-create-a-gateway[Create a gateway]) * Your AI Gateway credentials: ** Gateway endpoint URL (for example, `https://gw.ai.panda.com`) ** Gateway ID (for example, `gateway-abc123`) @@ -830,8 +832,8 @@ This sends only search + orchestrator tools initially, reducing token usage sign == Related pages -* xref:ai-agents:ai-gateway/ai-gateway.adoc[]: Create and configure your AI Gateway -* xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[]: Learn about AI Gateway architecture and benefits +* xref:ai-agents:ai-gateway/gateway-quickstart.adoc[]: Create and configure your AI Gateway +* xref:ai-agents:ai-gateway/gateway-architecture.adoc[]: Learn about AI Gateway architecture and benefits * xref:ai-agents:ai-gateway/integrations/claude-code-user.adoc[]: Configure Claude Code with AI Gateway * xref:ai-agents:ai-gateway/integrations/continue-user.adoc[]: Configure Continue.dev with AI Gateway * xref:ai-agents:ai-gateway/integrations/cline-user.adoc[]: Configure Cline with AI Gateway diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc index ad8aaa1d1..b1e75933d 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc @@ -6,6 +6,8 @@ :learning-objective-2: Deploy multi-tenant authentication strategies for Copilot clients :learning-objective-3: Set up model aliasing and BYOK routing for GitHub Copilot +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + Configure Redpanda AI Gateway to support GitHub Copilot clients accessing multiple LLM providers through OpenAI-compatible endpoints with bring-your-own-key (BYOK) support. After reading this page, you will be able to: @@ -19,7 +21,7 @@ After reading this page, you will be able to: * AI Gateway deployed on a BYOC cluster running Redpanda version 25.3 or later * Administrator access to the AI Gateway UI * API keys for at least one LLM provider (OpenAI, Anthropic, or others) -* Understanding of xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[AI Gateway concepts] +* Understanding of xref:ai-agents:ai-gateway/gateway-architecture.adoc[AI Gateway concepts] * GitHub Copilot Business or Enterprise subscription (for BYOK and custom endpoints) == About GitHub Copilot diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc index d6ad759bf..1896a7544 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc @@ -6,7 +6,9 @@ :learning-objective-2: Set up multi-tenancy with gateway ID headers for cost tracking :learning-objective-3: Configure enterprise BYOK deployments for team-wide Copilot access -After xref:ai-agents:ai-gateway/ai-gateway.adoc[configuring your AI Gateway], set up GitHub Copilot to route LLM requests through the gateway for centralized observability, cost management, and provider flexibility. +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] + +After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gateway], set up GitHub Copilot to route LLM requests through the gateway for centralized observability, cost management, and provider flexibility. After reading this page, you will be able to: @@ -20,8 +22,8 @@ Before configuring GitHub Copilot, ensure you have: * GitHub Copilot subscription (Individual, Business, or Enterprise) * An active Redpanda AI Gateway with: -** At least one LLM provider enabled (see xref:ai-agents:ai-gateway/ai-gateway.adoc#step-1-enable-a-provider[Enable a provider]) -** A gateway created and configured (see xref:ai-agents:ai-gateway/ai-gateway.adoc#step-3-create-a-gateway[Create a gateway]) +** At least one LLM provider enabled (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc#step-1-enable-a-provider[Enable a provider]) +** A gateway created and configured (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc#step-3-create-a-gateway[Create a gateway]) * Your AI Gateway credentials: ** Gateway endpoint URL (for example, `https://gw.ai.panda.com`) ** Gateway ID (for example, `gateway-abc123`) @@ -1003,8 +1005,8 @@ Generate project-specific cost reports from the gateway dashboard. == Related pages -* xref:ai-agents:ai-gateway/ai-gateway.adoc[]: Create and configure your AI Gateway -* xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[]: Learn about AI Gateway architecture and benefits +* xref:ai-agents:ai-gateway/gateway-quickstart.adoc[]: Create and configure your AI Gateway +* xref:ai-agents:ai-gateway/gateway-architecture.adoc[]: Learn about AI Gateway architecture and benefits * xref:ai-agents:ai-gateway/integrations/claude-code-user.adoc[]: Configure Claude Code with AI Gateway * xref:ai-agents:ai-gateway/integrations/continue-user.adoc[]: Configure Continue.dev with AI Gateway * xref:ai-agents:ai-gateway/integrations/cursor-user.adoc[]: Configure Cursor IDE with AI Gateway diff --git a/modules/ai-agents/pages/ai-gateway/integrations/index.adoc b/modules/ai-agents/pages/ai-gateway/integrations/index.adoc index f899d2aca..bf8c6966c 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/index.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/index.adoc @@ -1,3 +1,5 @@ = AI Gateway Integrations :description: Configure AI development tools and IDEs to connect to Redpanda AI Gateway for centralized LLM routing and MCP tool aggregation. :page-layout: index + +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] diff --git a/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc index cb3cbd058..5ed21f841 100644 --- a/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc @@ -1,14 +1,14 @@ = MCP Aggregation and Orchestration Guide :description: Guide to MCP aggregation and orchestration in Redpanda AI Gateway, including architecture, deferred tool loading, orchestrator workflows, administration, observability, security, and integration examples. -:page-personas: app_developer +:page-topic-type: guide +:page-personas: app_developer, platform_admin +:learning-objective-1: Configure MCP aggregation with deferred tool loading to reduce token costs +:learning-objective-2: Write orchestrator workflows to reduce multi-step interactions +:learning-objective-3: Manage approved MCP servers with security controls and audit trails -AI Gateway provides MCP (Model Context Protocol) aggregation, allowing AI agents to access tools from multiple MCP servers through a single unified endpoint. This eliminates the need for agents to manage multiple MCP connections and significantly reduces token costs through deferred tool loading. - -After reading this page, you will be able to: +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] -* Configure MCP aggregation with deferred tool loading to reduce token costs by 80-90%. -* Write orchestrator workflows in JavaScript to reduce multi-step interactions from multiple round trips to a single request. -* Add and manage approved MCP servers with appropriate security controls and audit trails. +AI Gateway provides MCP (Model Context Protocol) aggregation, allowing AI agents to access tools from multiple MCP servers through a single unified endpoint. This eliminates the need for agents to manage multiple MCP connections and significantly reduces token costs through deferred tool loading. MCP aggregation benefits: diff --git a/modules/ai-agents/pages/ai-gateway/migration-guide.adoc b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc index 3ebd7bb98..bb58ba25c 100644 --- a/modules/ai-agents/pages/ai-gateway/migration-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc @@ -1,6 +1,12 @@ = Migrate to AI Gateway :description: Step-by-step migration guide to transition existing applications from direct LLM provider integrations to Redpanda AI Gateway with minimal disruption. -:page-personas: app_developer +:page-topic-type: how-to +:page-personas: app_developer, platform_admin +:learning-objective-1: Migrate LLM integrations to AI Gateway with zero downtime using feature flags +:learning-objective-2: Verify gateway connectivity and compare performance metrics +:learning-objective-3: Roll back to direct integration if issues arise during migration + +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] This guide helps you migrate existing applications from direct LLM provider integrations (OpenAI, Anthropic, and others) to Redpanda AI Gateway. Design the migration to be incremental and reversible, allowing you to test thoroughly before fully committing. @@ -8,12 +14,6 @@ This guide helps you migrate existing applications from direct LLM provider inte **Rollback difficulty:** Easy (feature flag or environment variable) -After completing this migration, you will be able to: - -* Migrate existing LLM integrations to AI Gateway with zero downtime using feature flags and parallel operation. -* Verify gateway connectivity and compare performance metrics between direct and gateway-routed requests. -* Roll back to direct integration immediately if issues arise during migration. - == Prerequisites Before migrating, ensure you have: diff --git a/modules/ai-agents/pages/ai-gateway/observability-logs.adoc b/modules/ai-agents/pages/ai-gateway/observability-logs.adoc index ff82edbb8..01284d90f 100644 --- a/modules/ai-agents/pages/ai-gateway/observability-logs.adoc +++ b/modules/ai-agents/pages/ai-gateway/observability-logs.adoc @@ -1,14 +1,14 @@ = Observability: Logs :description: Guide to AI Gateway request logs, including where to find logs, log fields, filtering, searching, inspecting requests, common analysis tasks, log retention, export options, privacy/security, and troubleshooting. +:page-topic-type: reference :page-personas: platform_admin, app_developer +:learning-objective-1: Locate and filter request logs to debug failures or reconstruct conversations +:learning-objective-2: Interpret log fields to diagnose performance and cost issues +:learning-objective-3: Export logs for compliance auditing or long-term analysis -AI Gateway logs every LLM request that passes through it, capturing the full request/response history, token usage, cost, latency, and routing decisions. This page explains how to find, filter, and interpret request logs. - -After reading this page, you will be able to: +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] -* Locate and filter request logs to debug specific failed requests or reconstruct user conversations. -* Interpret log fields (status codes, token usage, routing decisions) to diagnose performance and cost issues. -* Export logs for compliance auditing or long-term analysis. +AI Gateway logs every LLM request that passes through it, capturing the full request/response history, token usage, cost, latency, and routing decisions. This page explains how to find, filter, and interpret request logs. == Before you begin diff --git a/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc b/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc index 0dc06bc3e..31dea7fe8 100644 --- a/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc +++ b/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc @@ -1,14 +1,14 @@ = Observability: Metrics and Analytics :description: Guide to AI Gateway metrics and analytics, including where to find metrics, key metrics explained, dashboard views, filtering/grouping, alerting, exporting, common analysis tasks, retention, API access, best practices, and troubleshooting. -:page-personas: platform_admin +:page-topic-type: reference +:page-personas: platform_admin, app_developer +:learning-objective-1: Monitor aggregate metrics to track usage patterns and budget adherence +:learning-objective-2: Compare model and provider performance using latency and cost metrics +:learning-objective-3: Configure alerts for budget thresholds and performance degradation -AI Gateway provides aggregate metrics and analytics dashboards to help you understand usage patterns, costs, performance, and errors across all your LLM traffic. - -After reading this page, you will be able to: +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] -* Monitor aggregate metrics (request volume, token usage, estimated spend) to track usage patterns and budget adherence. -* Compare model and provider performance using latency, error rate, and cost metrics. -* Configure alerts for budget thresholds and performance degradation. +AI Gateway provides aggregate metrics and analytics dashboards to help you understand usage patterns, costs, performance, and errors across all your LLM traffic. == Before you begin @@ -859,7 +859,7 @@ Possible causes: Solution: 1. Remove filters, widen time range -2. Send test request (see xref:ai-agents:ai-gateway/quickstart-enhanced.adoc[]) +2. Send test request (see xref:ai-agents:ai-gateway/gateway-quickstart.adoc[]) 3. Check permissions with admin == Next steps diff --git a/modules/ai-agents/pages/ai-gateway/quickstart-enhanced.adoc b/modules/ai-agents/pages/ai-gateway/quickstart-enhanced.adoc deleted file mode 100644 index 6298e7ec3..000000000 --- a/modules/ai-agents/pages/ai-gateway/quickstart-enhanced.adoc +++ /dev/null @@ -1,453 +0,0 @@ -= DRAFT Quickstart enhanced -:description: Get started with AI Gateway by routing your first request, viewing observability data, testing failover and CEL routing. -:page-personas: app_developer - -Get your first request routed through Redpanda AI Gateway. - -After completing this quickstart, you will be able to: - -* Route your first LLM request through AI Gateway using the Cloud UI and verify it in the observability dashboard. -* Configure a provider and gateway with correct authentication and routing policies. -* Test failover behavior and CEL routing rules in a development environment. - -== Prerequisites - -Before starting, ensure you have: - -* Redpanda Cloud account with BYOC -* Admin access to configure providers and gateways -* API keys for at least one LLM provider (OpenAI, Anthropic, etc.) -* Python 3.8+ or Node.js 18+ (for examples) - -== Step 1: Configure a provider - -Providers must be configured before they can be used in gateways. - -// PLACEHOLDER: Add UI navigation path, e.g., "Console → AI Gateway → Providers → Add Provider" - -1. Navigate to *Providers*: - * Open Redpanda Cloud Console - * Go to // PLACEHOLDER: exact menu path - -2. Add provider: -+ ----- -Provider: OpenAI -API Key: sk-... -Enabled Models: gpt-4o, gpt-4o-mini ----- - - // PLACEHOLDER: Add screenshot of provider configuration form - -3. Verify: - - * Provider status shows "Active" - * Models appear in model catalog - -Alternative: CLI (if available) - -[source,bash] ----- -# PLACEHOLDER: CLI command for adding provider -rpk cloud ai-gateway provider create \ - --provider openai \ - --api-key sk-... \ - --models gpt-4o,gpt-4o-mini ----- - - -AI Gateway supports the following LLM providers: - -* OpenAI -* Anthropic -// PLACEHOLDER: Add other supported providers - -== Step 2: Create a gateway - -Gateways define routing policies, rate limits, and observability scope. - -// PLACEHOLDER: Add UI navigation path - -1. Navigate to *Gateways*: - * Go to // PLACEHOLDER: exact menu path - -2. Create gateway: -+ ----- -Name: my-first-gateway -Workspace: default -Description: Quickstart gateway for testing ----- - - // PLACEHOLDER: Add screenshot of gateway creation form - -3. Save gateway ID: -+ -After creation, copy your gateway ID (required for requests): -+ ----- -Gateway ID: gw_abc123... -Gateway Endpoint: https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1 ----- - - // PLACEHOLDER: Confirm exact endpoint format - -When planning your gateway structure, consider these common patterns: - -* One gateway per environment (staging, production) -* One gateway per team (for budget isolation) -* One gateway per customer (for multi-tenant SaaS) - -== Step 3: Send your first request - -Route a request through your gateway. - -[tabs] -==== -Python:: -+ --- -[source,python] ----- -from openai import OpenAI -import os - -# Configure client to use AI Gateway -client = OpenAI( - base_url="https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1", # Gateway endpoint - api_key=os.getenv("REDPANDA_CLOUD_TOKEN"), # Your Redpanda Cloud token - default_headers={ - "rp-aigw-id": "gw_abc123..." # Your gateway ID from Step 2 - } -) - -# Make a request (note the vendor/model_id format) -response = client.chat.completions.create( - model="openai/gpt-4o-mini", # Format: {provider}/{model} - messages=[ - {"role": "user", "content": "Say 'Hello from AI Gateway!'"} - ], - max_tokens=20 -) - -print(response.choices[0].message.content) -# Output: Hello from AI Gateway! ----- --- - -TypeScript/JavaScript:: -+ --- -[source,typescript] ----- -import OpenAI from 'openai'; - -const client = new OpenAI({ - baseURL: 'https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1', - apiKey: process.env.REDPANDA_CLOUD_TOKEN, - defaultHeaders: { - 'rp-aigw-id': 'gw_abc123...' - } -}); - -const response = await client.chat.completions.create({ - model: 'openai/gpt-4o-mini', - messages: [ - { role: 'user', content: 'Say "Hello from AI Gateway!"' } - ], - max_tokens: 20 -}); - -console.log(response.choices[0].message.content); -// Output: Hello from AI Gateway! ----- --- - -cURL:: -+ --- -[source,bash] ----- -curl https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1/chat/completions \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ - -H "rp-aigw-id: gw_abc123..." \ - -d '{ - "model": "openai/gpt-4o-mini", - "messages": [ - {"role": "user", "content": "Say \"Hello from AI Gateway!\""} - ], - "max_tokens": 20 - }' ----- - -Expected response: - -[source,json] ----- -{ - "id": "chatcmpl-...", - "object": "chat.completion", - "created": 1704844800, - "model": "openai/gpt-4o-mini", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "Hello from AI Gateway!" - }, - "finish_reason": "stop" - } - ], - "usage": { - "prompt_tokens": 8, - "completion_tokens": 5, - "total_tokens": 13 - } -} ----- --- -==== - - -If your request fails, check these common issues: - -* `401 Unauthorized` - Verify your `REDPANDA_CLOUD_TOKEN` is valid -* `404 Not Found` - Confirm the `base_url` matches your gateway endpoint -* `Model not found` - Ensure the model is enabled in Step 1 -* `Missing rp-aigw-id` - Add the gateway ID header to your request - -== Step 4: Verify in observability dashboard - -Confirm your request appears in the AI Gateway dashboard. - -// PLACEHOLDER: Add UI navigation path and screenshots - -1. *Navigate to Logs*: - * Go to // PLACEHOLDER: Console → AI Gateway → {Gateway Name} → Logs - -2. *Find your request*: - * Filter by Gateway: `my-first-gateway` - * Filter by Model: `openai/gpt-4o-mini` - * Time range: Last 5 minutes - -3. *Verify fields*: - * Model: `openai/gpt-4o-mini` - * Provider: OpenAI - * Status: 200 - * Prompt tokens: ~8 - * Completion tokens: ~5 - * Estimated cost: // PLACEHOLDER: $X.XXXX - * Latency: // PLACEHOLDER: ~XXXms - -4. *Click Request to Expand*: - * View full prompt and response - * See request headers - * Check routing decision (which provider pool was used) - -If your request doesn't appear in the logs, try these steps: - -* Wait a few seconds for logs to populate (there may be a brief delay) -* Verify the gateway ID in your request matches the gateway you're viewing -* Check that your client received a successful response (no errors) - -== Next steps: Add failover (optional) - -Add automatic failover to a backup provider for reliability. - -=== Step 5: Add second provider - -Add Anthropic as a fallback option: - -// PLACEHOLDER: Add UI path - -1. *Navigate to Providers* → *Add Provider*: -+ ----- -Provider: Anthropic -API Key: sk-ant-... -Enabled Models: claude-sonnet-3.5 ----- - -2. *Verify*: - - * Anthropic provider status: Active - * Models appear in catalog - -=== Step 6: Configure provider pool with fallback - -Update your gateway to use OpenAI as primary, Anthropic as fallback. - -// PLACEHOLDER: Add UI path and configuration format - -1. *Navigate to Gateway Settings*: - - * Go to // PLACEHOLDER: AI Gateway → {Gateway Name} → Routing - -2. *Configure provider pool*: -+ -[source,yaml] ----- -# PLACEHOLDER: Confirm actual configuration format -routing: - primary_pool: - * provider: openai - models: [gpt-4o, gpt-4o-mini] - fallback_pool: - * provider: anthropic - models: [claude-sonnet-3.5] - -fallback_triggers: - * rate_limit_exceeded - * timeout - * 5xx_errors ----- - - // PLACEHOLDER: Add screenshot of routing configuration - -3. *Save configuration* - -=== Step 7: Test failover - -Simulate a provider failure to see fallback in action. - -// PLACEHOLDER: Add method to test failback, or skip if not easily testable - -*Option A: Disable primary provider temporarily* - -1. Disable OpenAI provider in settings -2. Send request with `openai/gpt-4o` model -3. Gateway should automatically route to Anthropic fallback -4. Check logs to confirm fallback was used - -*Option B: Trigger rate limit* - -1. Send many requests rapidly to hit rate limit -2. Gateway should fallback to Anthropic -3. Check logs for "fallback_triggered" indicator - -Verify fallback: - -[source,python] ----- -response = client.chat.completions.create( - model="openai/gpt-4o", # Request OpenAI model - messages=[{"role": "user", "content": "Test fallback"}] -) - -# Check which provider actually handled it -# PLACEHOLDER: How to verify this - response header? Log metadata? ----- - - -Verify the fallback in the observability dashboard. The request log should display: - -* Requested model: `openai/gpt-4o` -* Actual provider: Anthropic (fallback) -* Fallback reason: rate_limit, timeout, or error - - -== Next steps: Add routing rule (optional) - -Use CEL expressions to route requests based on headers or content. - -=== Step 8: Create CEL routing rule - -Route premium users to better models automatically. - -// PLACEHOLDER: Add UI path for CEL configuration - -1. Navigate to *Gateway Settings*: - - * Go to // PLACEHOLDER: AI Gateway → {Gateway Name} → Routing Rules - -2. Add CEL rule: -+ -[source,cel] ----- -# Route based on user tier header -request.headers["x-user-tier"] == "premium" - ? "openai/gpt-4o" - : "openai/gpt-4o-mini" ----- - - // PLACEHOLDER: Add screenshot of CEL editor with syntax highlighting - -3. Test rule (if UI supports testing): - - * Input test headers: `x-user-tier: premium` - * Verify output: `openai/gpt-4o` - * Input test headers: `x-user-tier: free` - * Verify output: `openai/gpt-4o-mini` - -4. Save rule - -=== Step 9: Test routing rule - -Send requests with different headers and verify routing. - -*Premium user request*: - -[source,python] ----- -response = client.chat.completions.create( - model="auto", # PLACEHOLDER: or how to trigger CEL routing - messages=[{"role": "user", "content": "Hello"}], - extra_headers={"x-user-tier": "premium"} -) - -# Should route to gpt-4o (premium model) ----- - - -*Free user request*: - -[source,python] ----- -response = client.chat.completions.create( - model="auto", - messages=[{"role": "user", "content": "Hello"}], - extra_headers={"x-user-tier": "free"} -) - -# Should route to gpt-4o-mini (cost-effective model) ----- - - -To verify the routing rule worked, check the observability dashboard: - -* Open the request logs for your gateway -* Confirm the correct model was selected based on the header value -* Review the routing decision explanation to see which CEL rule matched - -== What's next? - -After completing this quickstart, consider these configurations to optimize your gateway: - -. *Set rate limits* to protect against runaway costs and prevent abuse. -. *Add spend limits* to set monthly budgets per gateway and receive alerts before limits are reached. -. *Configure MCP aggregation* to give agents access to tools and reduce token costs with deferred loading. - -// Explore advanced features -// A/B testing models -// Multi-tenancy patterns -// Cost optimization -// Performance tuning - -Connect your favorite AI development tools to AI Gateway: - -* xref:ai-agents:ai-gateway/integrations/index.adoc[]: Overview of all integrations -* xref:ai-agents:ai-gateway/integrations/claude-code-user.adoc[]: Claude Code -* xref:ai-agents:ai-gateway/integrations/cline-user.adoc[]: Cline -* xref:ai-agents:ai-gateway/integrations/continue-user.adoc[]: Continue.dev -* xref:ai-agents:ai-gateway/integrations/cursor-user.adoc[]: Cursor IDE -* xref:ai-agents:ai-gateway/integrations/github-copilot-user.adoc[]: GitHub Copilot - - -== Related pages - -* xref:ai-agents:ai-gateway/ai-gateway-overview.adoc[] -* xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[] -* xref:ai-agents:ai-gateway/observability-logs.adoc[] -* xref:ai-agents:ai-gateway/migration-guide.adoc[] diff --git a/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc index d884f4a59..da31e4716 100644 --- a/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc +++ b/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc @@ -1,18 +1,15 @@ = What is an AI Gateway? :description: Understand what an AI Gateway is, the problems it solves, and how it benefits your AI infrastructure. :page-topic-type: concept -:personas: app_developer, platform_admin +:page-personas: app_developer, platform_admin +:learning-objective-1: Describe how AI Gateway centralizes LLM provider management and reduces operational complexity +:learning-objective-2: Identify key features that address common LLM integration challenges +:learning-objective-3: Determine whether AI Gateway fits your use case based on traffic volume and provider diversity -NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and later. +include::ai-agents:partial$ai-gateway-byoc-note.adoc[] Redpanda AI Gateway is a unified access layer for LLM providers and AI tools that sits between your applications and the AI services they use. It provides centralized routing, policy enforcement, cost management, and observability for all your AI traffic. -After reading this page, you will be able to: - -* Describe how AI Gateway centralizes LLM provider management and reduces operational complexity -* Identify key features (routing, observability, cost controls) that address common LLM integration challenges -* Determine whether AI Gateway fits your use case based on traffic volume and provider diversity - == The problem Modern AI applications face four critical challenges that increase costs, reduce reliability, and slow down development. @@ -174,7 +171,7 @@ Now that you understand what AI Gateway is and how it can benefit your organizat *For Administrators:* * xref:ai-gateway/admin/setup-guide.adoc[Setup Guide] - Enable providers, models, and create gateways -* xref:ai-gateway/ai-gateway-overview.adoc[Architecture Deep Dive] - Technical architecture details +* xref:ai-gateway/gateway-architecture.adoc[Architecture Deep Dive] - Technical architecture details *For Builders:* diff --git a/modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md b/modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md index 3811c26b5..bc62f7e7c 100644 --- a/modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md +++ b/modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md @@ -79,8 +79,8 @@ The current AI Gateway documentation is comprehensive but doesn't clearly distin | Access Management page | Admin | MEDIUM | Missing | ### Existing Content Gaps -1. **ai-gateway-overview.adoc** - Too dense, mixes Admin and Builder concerns -2. **ai-gateway.adoc (quickstart)** - Conflates Admin setup with Builder usage +1. **gateway-architecture.adoc** - Too dense, mixes Admin and Builder concerns +2. **gateway-quickstart.adoc (quickstart)** - Conflates Admin setup with Builder usage 3. **index.adoc** - Too minimal, provides no guidance 4. **No discovery mechanism** - Builders don't know which gateways they can use @@ -129,13 +129,13 @@ AI Gateway/ │ └── ... │ ├── Reference/ -│ ├── ai-gateway-overview.adoc (Refactored: Technical deep-dive) +│ ├── gateway-architecture.adoc (Refactored: Technical deep-dive) │ ├── cel-routing-cookbook.adoc (Existing) │ ├── mcp-aggregation-guide.adoc (Existing) │ ├── observability-logs.adoc (Existing) │ ├── observability-metrics.adoc (Existing) │ ├── migration-guide.adoc (Existing) -│ └── quickstart-enhanced.adoc (Existing or remove if redundant) +│ └── gateway-quickstart.adoc (Consolidated from ai-gateway.adoc and quickstart-enhanced.adoc) ``` --- @@ -176,7 +176,7 @@ You're building AI agents or applications and need to connect to available gatew == Learn More * xref:ai-gateway/what-is-ai-gateway.adoc[What is an AI Gateway?] -* xref:ai-gateway/reference/ai-gateway-overview.adoc[Technical Architecture] +* xref:ai-gateway/reference/gateway-architecture.adoc[Technical Architecture] ``` **Persona Tagging:** Both @@ -186,7 +186,7 @@ You're building AI agents or applications and need to connect to available gatew ### 2. Create what-is-ai-gateway.adoc (HIGH PRIORITY) **Purpose:** Standalone conceptual page answering "What is an AI gateway?" -**Source:** Extract from ai-gateway-overview.adoc (lines 15-147) +**Source:** Extract from gateway-architecture.adoc (lines 15-147) **Content to Include:** - The problem AI Gateway solves @@ -266,7 +266,7 @@ Expected response: List of available models --- -### 4. Refactor ai-gateway.adoc (quickstart) +### 4. Refactor gateway-quickstart.adoc (quickstart) **Current Problem:** Mixes Admin setup (Steps 1-3) with Builder usage (Steps 4-5, integrations) @@ -368,7 +368,7 @@ Inbound connections: ### 7. Update Existing Files -#### ai-gateway-overview.adoc +#### gateway-architecture.adoc **Changes:** - Remove conceptual "What is" content (move to what-is-ai-gateway.adoc) - Focus on technical architecture deep-dive @@ -439,7 +439,7 @@ Inbound connections: *** xref:ai-agents:ai-gateway/builders/monitor-your-usage.adoc[Monitor Your Usage] *** xref:ai-agents:ai-gateway/builders/integrations/index.adoc[Integrations (Builder)] ** Reference -*** xref:ai-agents:ai-gateway/reference/ai-gateway-overview.adoc[Architecture Deep Dive] +*** xref:ai-agents:ai-gateway/reference/gateway-architecture.adoc[Architecture Deep Dive] *** xref:ai-agents:ai-gateway/reference/cel-routing-cookbook.adoc[CEL Routing Cookbook] *** xref:ai-agents:ai-gateway/reference/mcp-aggregation-guide.adoc[MCP Aggregation Guide] *** xref:ai-agents:ai-gateway/reference/observability-logs.adoc[Request Logs] @@ -464,7 +464,7 @@ Inbound connections: 5. Update observability pages with persona distinctions (MEDIUM) ### Phase 3: Polish and Optimize -1. Refactor ai-gateway-overview.adoc (MEDIUM) +1. Refactor gateway-architecture.adoc (MEDIUM) 2. Update mcp-aggregation-guide.adoc with Builder sections (LOW) 3. Create admin/builder overview pages (LOW) 4. Reorganize integrations folders (LOW) @@ -555,14 +555,14 @@ After implementation, evaluate: - `ai-gateway/builders/monitor-your-usage.adoc` ### Files to Move -- `ai-gateway/ai-gateway-overview.adoc` → `ai-gateway/reference/ai-gateway-overview.adoc` +- `ai-gateway/gateway-architecture.adoc` → `ai-gateway/reference/gateway-architecture.adoc` - `ai-gateway/cel-routing-cookbook.adoc` → `ai-gateway/reference/cel-routing-cookbook.adoc` - `ai-gateway/mcp-aggregation-guide.adoc` → `ai-gateway/reference/mcp-aggregation-guide.adoc` - `ai-gateway/observability-*.adoc` → `ai-gateway/reference/observability-*.adoc` ### Files to Refactor -- `ai-gateway/ai-gateway.adoc` (quickstart) - split content between admin and builder paths -- `ai-gateway/ai-gateway-overview.adoc` - extract conceptual content to what-is page +- `ai-gateway/gateway-quickstart.adoc` (quickstart) - split content between admin and builder paths +- `ai-gateway/gateway-architecture.adoc` - extract conceptual content to what-is page - `ai-gateway/observability-logs.adoc` - add persona-specific sections - `ai-gateway/observability-metrics.adoc` - add builder usage section @@ -570,4 +570,4 @@ After implementation, evaluate: - `ai-gateway/integrations/*-admin.adoc` - `ai-gateway/integrations/*-user.adoc` - `ai-gateway/migration-guide.adoc` -- `ai-gateway/quickstart-enhanced.adoc` (review if still needed) +- `ai-gateway/gateway-quickstart.adoc` (consolidated) diff --git a/modules/ai-agents/partials/ai-gateway-byoc-note.adoc b/modules/ai-agents/partials/ai-gateway-byoc-note.adoc new file mode 100644 index 000000000..5d3b58cf0 --- /dev/null +++ b/modules/ai-agents/partials/ai-gateway-byoc-note.adoc @@ -0,0 +1 @@ +NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and later. From 02bb472b3f8517f266982444688a122c003ca9f6 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 16:32:30 -0700 Subject: [PATCH 21/50] style edits --- .../pages/ai-gateway/admin/setup-guide.adoc | 10 ++--- .../ai-gateway/cel-routing-cookbook.adoc | 31 +++++++------- .../ai-gateway/gateway-architecture.adoc | 26 ++++-------- .../pages/ai-gateway/gateway-quickstart.adoc | 30 ++++---------- .../ai-gateway/mcp-aggregation-guide.adoc | 6 +-- .../pages/ai-gateway/migration-guide.adoc | 41 +++++++++---------- .../ai-gateway/observability-metrics.adoc | 10 ++--- 7 files changed, 66 insertions(+), 88 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc b/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc index e3dbdee87..53363e4b1 100644 --- a/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc @@ -19,7 +19,7 @@ After completing this guide, you will be able to: * API keys for at least one LLM provider (OpenAI or Anthropic) * (Optional) MCP server endpoints if you plan to use tool aggregation -== Step 1: Enable a provider +== Enable a provider Providers represent upstream services (Anthropic, OpenAI) and associated credentials. Providers are disabled by default and must be enabled explicitly by an administrator. @@ -34,7 +34,7 @@ TIP: Store provider API keys securely. Each provider configuration can have mult Repeat this process for each LLM provider you want to make available through AI Gateway. -== Step 2: Enable models +== Enable models The model catalog is the set of models made available through the gateway. Models are disabled by default. After enabling a provider, you can enable its models. @@ -67,7 +67,7 @@ Examples: * `anthropic/claude-sonnet-3.5` * `openai/gpt-4o-mini` -== Step 3: Create a gateway +== Create a gateway A gateway is a logical configuration boundary (policies + routing + observability) on top of a single deployment. It's a "virtual gateway" that you can create per team, environment (staging/production), product, or customer. @@ -96,7 +96,7 @@ TIP: A workspace is conceptually similar to a resource group in Redpanda streami You'll share the Gateway ID and Endpoint with users who need to access this gateway. -== Step 4: Configure LLM routing +== Configure LLM routing On the gateway details page, select the *LLM* tab to configure rate limits, spend limits, routing, and provider pools with fallback options. @@ -196,7 +196,7 @@ If a provider pool contains multiple providers, you can distribute traffic to ba * *Least latency*: Route to fastest provider based on recent performance * *Cost-optimized*: Route to cheapest provider for each model -== Step 5: Configure MCP tools (optional) +== Configure MCP tools (optional) If your users will build AI agents that need access to tools via MCP (Model Context Protocol), configure MCP tool aggregation. diff --git a/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc b/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc index 3d2ee3b0b..352152293 100644 --- a/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc +++ b/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc @@ -179,7 +179,7 @@ Each pattern follows this structure: * Verify: How to test * Cost/performance impact: Implications -=== Pattern 1: Tier-based routing +=== Tier-based routing When to use: Different user tiers (free, pro, enterprise) should get different model quality @@ -229,7 +229,7 @@ Cost impact: Use case: SaaS product with tiered pricing where model quality is a differentiator -=== Pattern 2: Environment-based routing +=== Environment-based routing When to use: Prevent staging from using expensive models @@ -272,9 +272,8 @@ Cost impact: Use case: Protect against runaway staging costs -''' -=== Pattern 3: Content-length guard rails +=== Content-length guard rails When to use: Block or downgrade long prompts to prevent cost spikes @@ -336,7 +335,7 @@ Cost impact: Use case: Staging cost controls, prevent prompt injection attacks that inflate token usage -=== Pattern 4: Topic-based routing +=== Topic-based routing When to use: Route different question types to specialized models @@ -385,7 +384,7 @@ Cost impact: Use case: Multi-purpose chatbot with both coding and general queries -=== Pattern 5: Geographic/regional routing +=== Geographic/regional routing When to use: Route by user region for compliance or latency optimization @@ -422,7 +421,7 @@ Cost impact: Neutral (same model, different region) Use case: GDPR compliance, data residency requirements -=== Pattern 6: Customer-specific routing +=== Customer-specific routing When to use: Different customers have different model access (enterprise features) @@ -462,7 +461,7 @@ Cost impact: Use case: Enterprise contracts with premium model access -=== Pattern 7: a/b testing (percentage-based routing) +=== A/B testing (percentage-based routing) When to use: Test new models with a percentage of traffic @@ -517,7 +516,7 @@ Cost impact: Use case: Evaluate new models in production with real traffic -=== Pattern 8: Complexity-based routing +=== Complexity-based routing When to use: Route simple queries to cheap models, complex queries to expensive models @@ -568,7 +567,7 @@ Cost impact: Use case: FAQ chatbot with mix of simple lookups and complex questions -=== Pattern 9: Time-based routing +=== Time-based routing When to use: Use cheaper models during off-peak hours @@ -598,7 +597,7 @@ Cost impact: Use case: Consumer apps with time-zone-specific usage patterns -=== Pattern 10: Fallback chain (multi-level) +=== Fallback chain (multi-level) When to use: Complex fallback logic beyond simple primary/secondary @@ -629,7 +628,7 @@ Use case: Production systems with SLA requirements == Advanced CEL patterns -=== Pattern: Default values with `has()` +=== Default values with `has()` Problem: Field might not exist in request @@ -645,7 +644,7 @@ has(request.body.max_tokens) && request.body.max_tokens > 2000 What happens: Safely checks if `max_tokens` exists before comparing -=== Pattern: Multiple conditions with parentheses +=== Multiple conditions with parentheses Expression: @@ -661,7 +660,7 @@ request.headers["x-environment"] == "production" What happens: Premium users OR VIP customer, AND production → GPT-4o -=== Pattern: Regex matching +=== Regex matching Expression: @@ -675,7 +674,7 @@ request.body.messages[0].content.matches("(?i)(urgent|asap|emergency)") What happens: Messages containing "urgent", "ASAP", or "emergency" (case-insensitive) → GPT-4o -=== Pattern: String array contains +=== String array contains Expression: @@ -689,7 +688,7 @@ Expression: What happens: Only specific customers get premium model -=== Pattern: Reject invalid requests +=== Reject invalid requests Expression: diff --git a/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc b/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc index 99c869948..916b61691 100644 --- a/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc +++ b/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc @@ -8,7 +8,7 @@ include::ai-agents:partial$ai-gateway-byoc-note.adoc[] -This page provides technical details about AI Gateway's architecture, request processing, and capabilities. For an introduction to AI Gateway and the problems it solves, see xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[]. +This page provides technical details about AI Gateway's architecture, request processing, and capabilities. For an introduction to AI Gateway and the problems it solves, see xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[] == Architecture overview @@ -134,22 +134,14 @@ The gateway only loads and exposes specific tools when requested, which dramatic == Current limitations -// PLACEHOLDER: List current limitations, for example: -// - Custom model deployments (Azure OpenAI BYOK, AWS Bedrock custom models) -// - Response caching -// - Prompt templates/versioning -// - Guardrails (PII detection, content moderation) -// - Multi-region active-active deployment -// - Metrics export to external systems -// - Budget alerts/notifications - -== Deployment models - -// PLACEHOLDER: Add deployment model details: -// - BYOC deployment requirements -// - Scaling characteristics -// - High availability configuration -// - Regional deployment options +* // PLACEHOLDER: List current limitations, for example: +** // - Custom model deployments (Azure OpenAI BYOK, AWS Bedrock custom models) +** // - Response caching +** // - Prompt templates/versioning +** // - Guardrails (PII detection, content moderation) +** // - Multi-region active-active deployment +** // - Metrics export to external systems +** // - Budget alerts/notifications == Next steps diff --git a/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc b/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc index dd1505fe1..7c3eadb7c 100644 --- a/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc +++ b/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc @@ -19,7 +19,7 @@ Before starting, ensure you have: * API key for at least one LLM provider (OpenAI or Anthropic) * Python 3.8+, Node.js 18+, or cURL (for testing) -== Step 1: Configure a provider +== Configure a provider Providers represent upstream LLM services (OpenAI, Anthropic) and their associated credentials. Providers are disabled by default and must be enabled explicitly. @@ -33,7 +33,7 @@ AI Gateway currently supports: * OpenAI * Anthropic -== Step 2: Enable models +== Enable models After enabling a provider, enable the specific models you want to make available through your gateways. @@ -52,7 +52,7 @@ Requests through AI Gateway must use the `vendor/model_id` format. For example: This format allows the gateway to route requests to the correct provider. -== Step 3: Create a gateway +== Create a gateway A gateway is a logical configuration boundary that defines routing policies, rate limits, spend limits, and observability scope. You can create separate gateways per team, environment (staging/production), or customer. @@ -78,7 +78,7 @@ Common gateway patterns: * *Team isolation*: One gateway per team for budget tracking * *Customer multi-tenancy*: One gateway per customer for isolated policies -== Step 4: Send your first request +== Send your first request Now that you've configured a provider and created a gateway, send a test request to verify everything works. @@ -198,7 +198,7 @@ If your request fails, check these common issues: * *Model not found*: Ensure the model is enabled in Step 2 * *Missing rp-aigw-id*: Add the gateway ID header to your request -== Step 5: Verify in observability dashboard +== Verify in observability dashboard Confirm your request appears in the AI Gateway observability dashboard. @@ -233,7 +233,7 @@ If your request doesn't appear: * Verify the gateway ID in your request matches the gateway you're viewing * Check that your client received a successful response -== Step 6: Configure LLM routing (optional) +== Configure LLM routing (optional) Configure rate limits, spend limits, and provider pools with failover. @@ -247,7 +247,7 @@ On the Gateways page, select the *LLM* tab to configure routing policies. The LL For high availability, configure a fallback provider that activates when the primary fails: -. Add a second provider (for example, Anthropic) following Step 1. +. Add a second provider (for example, Anthropic). . In your gateway's *LLM* routing configuration: + * *Primary pool*: OpenAI (preferred for quality) @@ -263,7 +263,7 @@ The gateway automatically routes to the fallback when it detects: Monitor the fallback rate in observability to detect primary provider issues early. -== Step 7: Configure MCP tools (optional) +== Configure MCP tools (optional) If you're using AI agents, configure MCP (Model Context Protocol) tool aggregation. @@ -301,7 +301,7 @@ Agents then search for specific tools they need, retrieving only that subset. Th // REVIEWERS: How do users connect to the ADP catalog + MCP servers exposed through RPCN? -== Step 8: Create CEL routing rule (optional) +== Configure CEL routing rule (optional) Use CEL (Common Expression Language) expressions to route requests dynamically based on headers, content, or other request properties. @@ -555,18 +555,6 @@ const openai = new OpenAI({ }); ---- -== What you've accomplished - -After completing this quickstart, you: - -* ✓ Configured an LLM provider and enabled models -* ✓ Created your first AI Gateway -* ✓ Sent requests through the gateway -* ✓ Verified requests in the observability dashboard -* ✓ (Optional) Configured failover with provider pools -* ✓ (Optional) Created CEL routing rules -* ✓ (Optional) Set up MCP tool aggregation - == Next steps Explore advanced AI Gateway features: diff --git a/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc index 5ed21f841..35fbc5054 100644 --- a/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc @@ -96,7 +96,7 @@ MCP aggregation benefits: == MCP request lifecycle -=== 1. Tool discovery (initial connection) +=== Tool discovery (initial connection) Agent request: @@ -151,7 +151,7 @@ Token savings: * With deferred loading: ~500-1,000 tokens (2 tool definitions) * 80-90% reduction -=== 2. Tool query (when agent needs specific tool) +=== Tool query (when agent needs specific tool) Agent request: @@ -205,7 +205,7 @@ Gateway response: Agent receives only relevant tools based on query. -=== 3. Tool execution +=== Tool execution Agent request: diff --git a/modules/ai-agents/pages/ai-gateway/migration-guide.adoc b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc index bb58ba25c..ce26f9bd9 100644 --- a/modules/ai-agents/pages/ai-gateway/migration-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc @@ -72,7 +72,7 @@ Benefits: == Step-by-step migration -=== Step 1: Add environment variables +=== Add environment variables Add gateway configuration to your environment without removing existing provider keys (yet). @@ -93,7 +93,7 @@ USE_AI_GATEWAY=false ---- -=== Step 2: Update your code +=== Update your code ==== Option A: OpenAI SDK (recommended for most use cases) @@ -303,7 +303,7 @@ else: ---- -=== Step 3: Test gateway connection +=== Test gateway connection Before changing the feature flag, verify gateway connectivity: @@ -355,7 +355,7 @@ Common issues: * `Model not found` → Ensure model is enabled in gateway configuration * No `rp-aigw-id` header → Verify header is set in `default_headers` -=== Step 4: Verify in observability dashboard +=== Verify in observability dashboard After successful test: @@ -371,7 +371,7 @@ After successful test: *If request doesn't appear*: Verify gateway ID and authentication token are correct. -=== Step 5: Enable gateway for subset of traffic +=== Enable gateway for subset of traffic Gradually roll out gateway usage: @@ -422,7 +422,7 @@ use_gateway = feature_flags.is_enabled("ai-gateway", user_context) ---- -=== Step 6: Monitor and compare +=== Monitor and compare During parallel operation, compare metrics: @@ -496,7 +496,7 @@ def call_llm_with_metrics(use_gateway: bool, model: str, messages: list): ---- -=== Step 7: Full cutover +=== Full cutover Once metrics confirm gateway reliability: @@ -724,7 +724,7 @@ Use this checklist to track your migration: == Common migration issues -=== Issue: "Model not found" error +=== "Model not found" error Symptom: [source,text] @@ -745,7 +745,7 @@ Solution: 2. Confirm format: `vendor/model_id` (for example, `openai/gpt-4o`, not `gpt-4o`) 3. Check supported models: // PLACEHOLDER: link to model catalog -=== Issue: Missing `rp-aigw-id` header +=== Missing `rp-aigw-id` header Symptom: @@ -768,7 +768,7 @@ client = OpenAI( ---- -=== Issue: Higher latency than expected +=== Higher latency than expected Expected gateway overhead: // PLACEHOLDER: Xms p50, Yms p99 @@ -781,7 +781,7 @@ If latency is significantly higher: Solution: Review geographic routing and provider pool configuration. -=== Issue: Requests not appearing in dashboard +=== Requests not appearing in dashboard Causes: @@ -791,7 +791,7 @@ Causes: Solution: Verify gateway ID and check for UI delay (logs may take a few seconds to appear). -=== Issue: Different response format +=== Different response format Symptom: Response structure differs between direct and gateway @@ -804,7 +804,7 @@ Solution: == Advanced migration scenarios -=== Scenario: Custom request timeouts +=== Custom request timeouts Before @@ -827,7 +827,7 @@ client = OpenAI( ---- -=== Scenario: Streaming responses +=== Streaming responses // PLACEHOLDER: Verify streaming support @@ -861,7 +861,7 @@ for chunk in stream: ---- -=== Scenario: Custom headers (for example, user tracking) +=== Custom headers (for example, user tracking) Before @@ -896,7 +896,7 @@ NOTE: Gateway may use custom headers for routing (for example, CEL expressions c After successful migration, you gain: -=== 1. Simplified provider management +Simplified provider management [source,python] ---- @@ -904,25 +904,24 @@ After successful migration, you gain: model = "anthropic/claude-sonnet-3.5" # Was openai/gpt-4o ---- - -=== 2. Unified observability +Unified observability * All requests in one dashboard * Cross-provider cost comparison * Session reconstruction across models -=== 3. Automatic failover +Automatic failover * Configure once, benefit everywhere * No application-level retry logic needed -=== 4. Cost controls +Cost controls * Enforce budgets centrally * Rate limit per team/customer * No surprises in cloud bills -=== 5. A/B testing +A/B testing * Test new models without code changes * Compare quality/cost/latency diff --git a/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc b/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc index 31dea7fe8..b9e7d0eb6 100644 --- a/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc +++ b/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc @@ -558,7 +558,7 @@ Supported integrations (if any): == Common analysis tasks -=== Task 1: "Are we staying within budget?" +=== "Are we staying within budget?" 1. View cost breakdown dashboard 2. Check budget utilization widget: @@ -575,7 +575,7 @@ Action: * If approaching limit: Adjust rate limits, optimize models, pause non-prod usage * If well under budget: Opportunity to test more expensive models -=== Task 2: "Which team is using the most resources?" +=== "Which team is using the most resources?" 1. Filter by gateway (assuming one gateway per team) 2. *Sort by Spend* (descending) @@ -606,7 +606,7 @@ Action: Action: Chargeback costs to teams, or investigate high-usage teams -=== Task 3: "Is this model worth the extra cost?" +=== "Is this model worth the extra cost?" 1. *Open Model Comparison Dashboard* 2. Select models to compare: @@ -636,7 +636,7 @@ Action: Chargeback costs to teams, or investigate high-usage teams Decision: If mini's error rate is acceptable, save 10x on costs -=== Task 4: "Why did costs spike yesterday?" +=== "Why did costs spike yesterday?" 1. View cost trend graph 2. Identify spike (e.g., Jan 10th: $500 vs usual $100) @@ -656,7 +656,7 @@ Common causes: * User error (wrong model in config) * Runaway loop in application code -=== Task 5: "Is provider X more reliable than provider Y?" +=== "Is provider X more reliable than provider Y?" 1. Open provider comparison dashboard 2. Compare error rates: From 21601b152746ce764fdb3a463dcba5fb288e3552 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 16:39:15 -0700 Subject: [PATCH 22/50] Convert learning objectives to standard format across all AI Gateway docs Updated 13 files to comply with documentation standards for learning objectives: - Added learning objective attributes to metadata (3 files) - Converted all learning objective bullets to checklist format (* [ ]) Files updated: - admin/setup-guide.adoc - builders/connect-your-agent.adoc - builders/discover-gateways.adoc - All 10 integration guide files (admin and user variants for Claude Code, Cline, Continue, Cursor, GitHub Copilot) All 23 AI Gateway files now have proper learning objective format per team standards. Co-Authored-By: Claude Sonnet 4.5 --- .../ai-agents/pages/ai-gateway/admin/setup-guide.adoc | 9 ++++++--- .../pages/ai-gateway/builders/connect-your-agent.adoc | 9 ++++++--- .../pages/ai-gateway/builders/discover-gateways.adoc | 9 ++++++--- .../pages/ai-gateway/integrations/claude-code-admin.adoc | 6 +++--- .../pages/ai-gateway/integrations/claude-code-user.adoc | 6 +++--- .../pages/ai-gateway/integrations/cline-admin.adoc | 6 +++--- .../pages/ai-gateway/integrations/cline-user.adoc | 6 +++--- .../pages/ai-gateway/integrations/continue-admin.adoc | 6 +++--- .../pages/ai-gateway/integrations/continue-user.adoc | 6 +++--- .../pages/ai-gateway/integrations/cursor-admin.adoc | 6 +++--- .../pages/ai-gateway/integrations/cursor-user.adoc | 6 +++--- .../ai-gateway/integrations/github-copilot-admin.adoc | 6 +++--- .../ai-gateway/integrations/github-copilot-user.adoc | 6 +++--- 13 files changed, 48 insertions(+), 39 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc b/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc index 53363e4b1..0a0a559c7 100644 --- a/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc @@ -2,6 +2,9 @@ :description: Complete setup guide for administrators to enable providers, configure models, create gateways, and set up routing policies. :page-topic-type: how-to :personas: platform_admin +:learning-objective-1: Enable LLM providers and models in the catalog +:learning-objective-2: Create and configure gateways with routing policies, rate limits, and spend limits +:learning-objective-3: Set up MCP tool aggregation for AI agents include::ai-agents:partial$ai-gateway-byoc-note.adoc[] @@ -9,9 +12,9 @@ This guide walks administrators through the complete setup process for AI Gatewa After completing this guide, you will be able to: -* Enable LLM providers and models in the catalog -* Create and configure gateways with routing policies, rate limits, and spend limits -* Set up MCP tool aggregation for AI agents +* [ ] Enable LLM providers and models in the catalog +* [ ] Create and configure gateways with routing policies, rate limits, and spend limits +* [ ] Set up MCP tool aggregation for AI agents == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc b/modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc index 7abe34f00..11c99a0e3 100644 --- a/modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc +++ b/modules/ai-agents/pages/ai-gateway/builders/connect-your-agent.adoc @@ -2,6 +2,9 @@ :description: Integrate your AI agent or application with Redpanda AI Gateway for unified LLM access. :page-topic-type: how-to :personas: app_developer +:learning-objective-1: Configure your application to use AI Gateway with OpenAI-compatible SDKs +:learning-objective-2: Make LLM requests through the gateway and handle responses appropriately +:learning-objective-3: Validate your integration end-to-end include::ai-agents:partial$ai-gateway-byoc-note.adoc[] @@ -9,9 +12,9 @@ This guide shows you how to connect your AI agent or application to a Redpanda A After completing this guide, you will be able to: -* Configure your application to use AI Gateway with OpenAI-compatible SDKs -* Make LLM requests through the gateway and handle responses appropriately -* Validate your integration end-to-end +* [ ] Configure your application to use AI Gateway with OpenAI-compatible SDKs +* [ ] Make LLM requests through the gateway and handle responses appropriately +* [ ] Validate your integration end-to-end == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc b/modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc index 01db50d20..7e280a95e 100644 --- a/modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc +++ b/modules/ai-agents/pages/ai-gateway/builders/discover-gateways.adoc @@ -2,6 +2,9 @@ :description: Find which AI Gateways you can access and their configurations. :page-topic-type: how-to :personas: app_developer +:learning-objective-1: List all AI Gateways you have access to and retrieve their endpoints and IDs +:learning-objective-2: View which models and MCP tools are available through each gateway +:learning-objective-3: Test gateway connectivity before integration include::ai-agents:partial$ai-gateway-byoc-note.adoc[] @@ -9,9 +12,9 @@ As a builder, you need to know which gateways are available to you before integr After reading this page, you will be able to: -* List all AI Gateways you have access to and retrieve their endpoints and IDs -* View which models and MCP tools are available through each gateway -* Test gateway connectivity before integration +* [ ] List all AI Gateways you have access to and retrieve their endpoints and IDs +* [ ] View which models and MCP tools are available through each gateway +* [ ] Test gateway connectivity before integration == Before you begin diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc index 3647efca4..78fe7e29f 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Claude Code clients accessing LLM provi After reading this page, you will be able to: -* Configure AI Gateway endpoints for Claude Code connectivity. -* Set up authentication and access control for Claude Code clients. -* Deploy MCP tool aggregation for Claude Code tool discovery. +* [ ] Configure AI Gateway endpoints for Claude Code connectivity. +* [ ] Set up authentication and access control for Claude Code clients. +* [ ] Deploy MCP tool aggregation for Claude Code tool discovery. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc index 8a6dcc85a..1fe805be9 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* Configure Claude Code to connect to AI Gateway endpoints. -* Set up MCP server integration through AI Gateway. -* Verify Claude Code is routing requests through the gateway. +* [ ] Configure Claude Code to connect to AI Gateway endpoints. +* [ ] Set up MCP server integration through AI Gateway. +* [ ] Verify Claude Code is routing requests through the gateway. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc index 376927919..d66300dfe 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Cline (formerly Claude Dev) clients acc After reading this page, you will be able to: -* Configure AI Gateway endpoints for Cline connectivity. -* Set up authentication and access control for Cline clients. -* Deploy MCP tool aggregation for Cline tool discovery. +* [ ] Configure AI Gateway endpoints for Cline connectivity. +* [ ] Set up authentication and access control for Cline clients. +* [ ] Deploy MCP tool aggregation for Cline tool discovery. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc index 5e48edb25..df3345dc9 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* Configure Cline to connect to AI Gateway for LLM requests and MCP tools. -* Set up autonomous mode with custom instructions and browser integration. -* Verify Cline routes requests through the gateway and optimize for cost. +* [ ] Configure Cline to connect to AI Gateway for LLM requests and MCP tools. +* [ ] Set up autonomous mode with custom instructions and browser integration. +* [ ] Verify Cline routes requests through the gateway and optimize for cost. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc index 471128d79..63ef446c1 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Continue.dev clients accessing multiple After reading this page, you will be able to: -* Configure AI Gateway endpoints for Continue.dev connectivity. -* Set up multi-provider backends with native format routing. -* Deploy MCP tool aggregation for Continue.dev tool discovery. +* [ ] Configure AI Gateway endpoints for Continue.dev connectivity. +* [ ] Set up multi-provider backends with native format routing. +* [ ] Deploy MCP tool aggregation for Continue.dev tool discovery. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc index dac4827fa..315f344af 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* Configure Continue.dev to connect to AI Gateway for chat and autocomplete. -* Set up MCP server integration through AI Gateway. -* Optimize Continue.dev settings for cost and performance. +* [ ] Configure Continue.dev to connect to AI Gateway for chat and autocomplete. +* [ ] Set up MCP server integration through AI Gateway. +* [ ] Optimize Continue.dev settings for cost and performance. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc index 6ad5c0f58..a3e8d2c0c 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Cursor IDE clients accessing multiple L After reading this page, you will be able to: -* Configure AI Gateway endpoints for Cursor IDE connectivity. -* Set up OpenAI-compatible transforms for multi-provider routing. -* Deploy multi-tenant authentication strategies for Cursor clients. +* [ ] Configure AI Gateway endpoints for Cursor IDE connectivity. +* [ ] Set up OpenAI-compatible transforms for multi-provider routing. +* [ ] Deploy multi-tenant authentication strategies for Cursor clients. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc index 5cd74487b..3253affc5 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* Configure Cursor IDE to route LLM requests through AI Gateway. -* Set up MCP server integration for tool access through the gateway. -* Optimize Cursor settings for multi-tenancy and cost control. +* [ ] Configure Cursor IDE to route LLM requests through AI Gateway. +* [ ] Set up MCP server integration for tool access through the gateway. +* [ ] Optimize Cursor settings for multi-tenancy and cost control. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc index b1e75933d..9a27152b5 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support GitHub Copilot clients accessing multip After reading this page, you will be able to: -* Configure AI Gateway endpoints for GitHub Copilot connectivity. -* Deploy multi-tenant authentication strategies for Copilot clients. -* Set up model aliasing and BYOK routing for GitHub Copilot. +* [ ] Configure AI Gateway endpoints for GitHub Copilot connectivity. +* [ ] Deploy multi-tenant authentication strategies for Copilot clients. +* [ ] Set up model aliasing and BYOK routing for GitHub Copilot. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc index 1896a7544..7ba95d61f 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* Configure GitHub Copilot in VS Code and JetBrains IDEs to route requests through AI Gateway. -* Set up multi-tenancy with gateway ID headers for cost tracking. -* Configure enterprise BYOK deployments for team-wide Copilot access. +* [ ] Configure GitHub Copilot in VS Code and JetBrains IDEs to route requests through AI Gateway. +* [ ] Set up multi-tenancy with gateway ID headers for cost tracking. +* [ ] Configure enterprise BYOK deployments for team-wide Copilot access. == Prerequisites From 84e57085a403ce2c253b35efefad95ae1c177f92 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 16:54:27 -0700 Subject: [PATCH 23/50] Fix persona metadata attribute to use correct standard Changed :page-personas: to :personas: across all 19 AI Gateway files. According to the docs-team-standards plugin (content-architecture skill), the correct attribute is :personas: without the page- prefix. Files updated: - All core AI Gateway files (index, overview, architecture, quickstart) - All guide files (CEL routing, MCP aggregation, migration, observability) - All 10 integration files (admin and user variants) Co-Authored-By: Claude Sonnet 4.5 --- modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc | 2 +- modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc | 2 +- modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc | 2 +- modules/ai-agents/pages/ai-gateway/index.adoc | 2 +- .../pages/ai-gateway/integrations/claude-code-admin.adoc | 2 +- .../pages/ai-gateway/integrations/claude-code-user.adoc | 2 +- .../ai-agents/pages/ai-gateway/integrations/cline-admin.adoc | 2 +- modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc | 2 +- .../ai-agents/pages/ai-gateway/integrations/continue-admin.adoc | 2 +- .../ai-agents/pages/ai-gateway/integrations/continue-user.adoc | 2 +- .../ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc | 2 +- .../ai-agents/pages/ai-gateway/integrations/cursor-user.adoc | 2 +- .../pages/ai-gateway/integrations/github-copilot-admin.adoc | 2 +- .../pages/ai-gateway/integrations/github-copilot-user.adoc | 2 +- modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc | 2 +- modules/ai-agents/pages/ai-gateway/migration-guide.adoc | 2 +- modules/ai-agents/pages/ai-gateway/observability-logs.adoc | 2 +- modules/ai-agents/pages/ai-gateway/observability-metrics.adoc | 2 +- modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc | 2 +- 19 files changed, 19 insertions(+), 19 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc b/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc index 352152293..0379595ee 100644 --- a/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc +++ b/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc @@ -1,7 +1,7 @@ = CEL Routing Cookbook :description: CEL routing cookbook for Redpanda AI Gateway with common patterns, examples, and best practices. :page-topic-type: cookbook -:page-personas: app_developer, platform_admin +:personas: app_developer, platform_admin :learning-objective-1: Write CEL expressions to route requests based on user tier or custom headers :learning-objective-2: Test CEL routing logic using the UI editor or test requests :learning-objective-3: Troubleshoot common CEL errors using safe patterns diff --git a/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc b/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc index 916b61691..c0785fccf 100644 --- a/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc +++ b/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc @@ -1,7 +1,7 @@ = AI Gateway Architecture :description: Technical architecture of Redpanda AI Gateway, including request lifecycle, supported providers, deployment models, and implementation details. :page-topic-type: concept -:page-personas: app_developer, platform_admin +:personas: app_developer, platform_admin :learning-objective-1: Describe the three architectural planes of AI Gateway :learning-objective-2: Explain the request lifecycle through policy evaluation stages :learning-objective-3: Identify supported providers, features, and current limitations diff --git a/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc b/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc index 7c3eadb7c..5df0b6b26 100644 --- a/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc +++ b/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc @@ -1,7 +1,7 @@ = AI Gateway Quickstart :description: Get started with AI Gateway by configuring providers, creating your first gateway, and routing requests through unified LLM endpoints. :page-topic-type: quickstart -:page-personas: app_developer, platform_admin +:personas: app_developer, platform_admin :learning-objective-1: Enable an LLM provider and create your first gateway :learning-objective-2: Route your first request through AI Gateway and verify it works :learning-objective-3: View request logs and token usage in the observability dashboard diff --git a/modules/ai-agents/pages/ai-gateway/index.adoc b/modules/ai-agents/pages/ai-gateway/index.adoc index 8c685847b..d8be560a2 100644 --- a/modules/ai-agents/pages/ai-gateway/index.adoc +++ b/modules/ai-agents/pages/ai-gateway/index.adoc @@ -1,7 +1,7 @@ = AI Gateway :description: Unified access layer for LLM providers and AI tools with centralized routing, policy enforcement, cost management, and observability. :page-layout: index -:page-personas: platform_admin, app_developer, evaluator +:personas: platform_admin, app_developer, evaluator include::ai-agents:partial$ai-gateway-byoc-note.adoc[] diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc index 78fe7e29f..396623f96 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc @@ -1,7 +1,7 @@ = Configure AI Gateway for Claude Code :description: Configure Redpanda AI Gateway to support Claude Code clients. :page-topic-type: how-to -:page-personas: platform_admin +:personas: platform_admin :learning-objective-1: Configure AI Gateway endpoints for Claude Code connectivity :learning-objective-2: Set up authentication and access control for Claude Code clients :learning-objective-3: Deploy MCP tool aggregation for Claude Code tool discovery diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc index 1fe805be9..30160c97f 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc @@ -1,7 +1,7 @@ = Configure Claude Code with AI Gateway :description: Configure Claude Code to use Redpanda AI Gateway for unified LLM access and MCP tool aggregation. :page-topic-type: how-to -:page-personas: ai_agent_developer, app_developer +:personas: ai_agent_developer, app_developer :learning-objective-1: Configure Claude Code to connect to AI Gateway endpoints :learning-objective-2: Set up MCP server integration through AI Gateway :learning-objective-3: Verify Claude Code is routing requests through the gateway diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc index d66300dfe..3c1186be6 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc @@ -1,7 +1,7 @@ = Configure AI Gateway for Cline :description: Configure Redpanda AI Gateway to support Cline clients. :page-topic-type: how-to -:page-personas: platform_admin +:personas: platform_admin :learning-objective-1: Configure AI Gateway endpoints for Cline connectivity :learning-objective-2: Set up authentication and access control for Cline clients :learning-objective-3: Deploy MCP tool aggregation for Cline tool discovery diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc index df3345dc9..43b7c244e 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc @@ -1,7 +1,7 @@ = Configure Cline with AI Gateway :description: Configure Cline to use Redpanda AI Gateway for unified LLM access, MCP tool integration, and autonomous coding workflows. :page-topic-type: how-to -:page-personas: ai_agent_developer, app_developer +:personas: ai_agent_developer, app_developer :learning-objective-1: Configure Cline to connect to AI Gateway for LLM requests and MCP tools :learning-objective-2: Set up autonomous mode with custom instructions and browser integration :learning-objective-3: Verify Cline routes requests through the gateway and optimize for cost diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc index 63ef446c1..f4f885d6a 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc @@ -1,7 +1,7 @@ = Configure AI Gateway for Continue.dev :description: Configure Redpanda AI Gateway to support Continue.dev clients. :page-topic-type: how-to -:page-personas: platform_admin +:personas: platform_admin :learning-objective-1: Configure AI Gateway endpoints for Continue.dev connectivity :learning-objective-2: Set up multi-provider backends with native format routing :learning-objective-3: Deploy MCP tool aggregation for Continue.dev tool discovery diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc index 315f344af..2b1022fd5 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc @@ -1,7 +1,7 @@ = Configure Continue.dev with AI Gateway :description: Configure Continue.dev to use Redpanda AI Gateway for unified LLM access, MCP tool integration, and AI-assisted coding. :page-topic-type: how-to -:page-personas: ai_agent_developer, app_developer +:personas: ai_agent_developer, app_developer :learning-objective-1: Configure Continue.dev to connect to AI Gateway for chat and autocomplete :learning-objective-2: Set up MCP server integration through AI Gateway :learning-objective-3: Optimize Continue.dev settings for cost and performance diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc index a3e8d2c0c..55f305500 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc @@ -1,7 +1,7 @@ = Configure AI Gateway for Cursor IDE :description: Configure Redpanda AI Gateway to support Cursor IDE clients. :page-topic-type: how-to -:page-personas: platform_admin +:personas: platform_admin :learning-objective-1: Configure AI Gateway endpoints for Cursor IDE connectivity :learning-objective-2: Set up OpenAI-compatible transforms for multi-provider routing :learning-objective-3: Deploy multi-tenant authentication strategies for Cursor clients diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc index 3253affc5..3b5182176 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc @@ -1,7 +1,7 @@ = Configure Cursor IDE with AI Gateway :description: Configure Cursor IDE to use Redpanda AI Gateway for unified LLM access, MCP tool integration, and AI-assisted coding. :page-topic-type: how-to -:page-personas: ai_agent_developer, app_developer +:personas: ai_agent_developer, app_developer :learning-objective-1: Configure Cursor IDE to route LLM requests through AI Gateway :learning-objective-2: Set up MCP server integration for tool access through the gateway :learning-objective-3: Optimize Cursor settings for multi-tenancy and cost control diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc index 9a27152b5..32e20f144 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc @@ -1,7 +1,7 @@ = Configure AI Gateway for GitHub Copilot :description: Configure Redpanda AI Gateway to support GitHub Copilot clients. :page-topic-type: how-to -:page-personas: platform_admin +:personas: platform_admin :learning-objective-1: Configure AI Gateway endpoints for GitHub Copilot connectivity :learning-objective-2: Deploy multi-tenant authentication strategies for Copilot clients :learning-objective-3: Set up model aliasing and BYOK routing for GitHub Copilot diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc index 7ba95d61f..aa3aa8de4 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc @@ -1,7 +1,7 @@ = Configure GitHub Copilot with AI Gateway :description: Configure GitHub Copilot to use Redpanda AI Gateway for unified LLM access and custom provider management. :page-topic-type: how-to -:page-personas: ai_agent_developer, app_developer +:personas: ai_agent_developer, app_developer :learning-objective-1: Configure GitHub Copilot in VS Code and JetBrains IDEs to route requests through AI Gateway :learning-objective-2: Set up multi-tenancy with gateway ID headers for cost tracking :learning-objective-3: Configure enterprise BYOK deployments for team-wide Copilot access diff --git a/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc index 35fbc5054..75b2b4dcf 100644 --- a/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc @@ -1,7 +1,7 @@ = MCP Aggregation and Orchestration Guide :description: Guide to MCP aggregation and orchestration in Redpanda AI Gateway, including architecture, deferred tool loading, orchestrator workflows, administration, observability, security, and integration examples. :page-topic-type: guide -:page-personas: app_developer, platform_admin +:personas: app_developer, platform_admin :learning-objective-1: Configure MCP aggregation with deferred tool loading to reduce token costs :learning-objective-2: Write orchestrator workflows to reduce multi-step interactions :learning-objective-3: Manage approved MCP servers with security controls and audit trails diff --git a/modules/ai-agents/pages/ai-gateway/migration-guide.adoc b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc index ce26f9bd9..cf87352d5 100644 --- a/modules/ai-agents/pages/ai-gateway/migration-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc @@ -1,7 +1,7 @@ = Migrate to AI Gateway :description: Step-by-step migration guide to transition existing applications from direct LLM provider integrations to Redpanda AI Gateway with minimal disruption. :page-topic-type: how-to -:page-personas: app_developer, platform_admin +:personas: app_developer, platform_admin :learning-objective-1: Migrate LLM integrations to AI Gateway with zero downtime using feature flags :learning-objective-2: Verify gateway connectivity and compare performance metrics :learning-objective-3: Roll back to direct integration if issues arise during migration diff --git a/modules/ai-agents/pages/ai-gateway/observability-logs.adoc b/modules/ai-agents/pages/ai-gateway/observability-logs.adoc index 01284d90f..0d1ad5455 100644 --- a/modules/ai-agents/pages/ai-gateway/observability-logs.adoc +++ b/modules/ai-agents/pages/ai-gateway/observability-logs.adoc @@ -1,7 +1,7 @@ = Observability: Logs :description: Guide to AI Gateway request logs, including where to find logs, log fields, filtering, searching, inspecting requests, common analysis tasks, log retention, export options, privacy/security, and troubleshooting. :page-topic-type: reference -:page-personas: platform_admin, app_developer +:personas: platform_admin, app_developer :learning-objective-1: Locate and filter request logs to debug failures or reconstruct conversations :learning-objective-2: Interpret log fields to diagnose performance and cost issues :learning-objective-3: Export logs for compliance auditing or long-term analysis diff --git a/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc b/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc index b9e7d0eb6..4ce3512c9 100644 --- a/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc +++ b/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc @@ -1,7 +1,7 @@ = Observability: Metrics and Analytics :description: Guide to AI Gateway metrics and analytics, including where to find metrics, key metrics explained, dashboard views, filtering/grouping, alerting, exporting, common analysis tasks, retention, API access, best practices, and troubleshooting. :page-topic-type: reference -:page-personas: platform_admin, app_developer +:personas: platform_admin, app_developer :learning-objective-1: Monitor aggregate metrics to track usage patterns and budget adherence :learning-objective-2: Compare model and provider performance using latency and cost metrics :learning-objective-3: Configure alerts for budget thresholds and performance degradation diff --git a/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc index da31e4716..68a78a7af 100644 --- a/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc +++ b/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc @@ -1,7 +1,7 @@ = What is an AI Gateway? :description: Understand what an AI Gateway is, the problems it solves, and how it benefits your AI infrastructure. :page-topic-type: concept -:page-personas: app_developer, platform_admin +:personas: app_developer, platform_admin :learning-objective-1: Describe how AI Gateway centralizes LLM provider management and reduces operational complexity :learning-objective-2: Identify key features that address common LLM integration challenges :learning-objective-3: Determine whether AI Gateway fits your use case based on traffic volume and provider diversity From 2ab1f1c92e2f8cda3983e7d910f7c2d0a5c3e2d8 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 16:57:13 -0700 Subject: [PATCH 24/50] Fix Anthropic model identifiers in gateway quickstart examples MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Updated short invalid model names to official versioned identifiers: - claude-sonnet-3.5 → claude-3-5-sonnet-20241022 - anthropic/claude-sonnet-3.5 → anthropic/claude-3-5-sonnet-20241022 - anthropic/claude-opus-4 → anthropic/claude-opus-4-1-20250805 These changes ensure the quickstart examples use valid model IDs that match the format already used elsewhere in the file (line 478). Co-Authored-By: Claude Sonnet 4.5 --- modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc b/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc index 5df0b6b26..59292fa1a 100644 --- a/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc +++ b/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc @@ -38,7 +38,7 @@ AI Gateway currently supports: After enabling a provider, enable the specific models you want to make available through your gateways. . Navigate to *Models*. -. Enable the models you want to use (for example, `gpt-4o`, `gpt-4o-mini`, `claude-sonnet-3.5`). +. Enable the models you want to use (for example, `gpt-4o`, `gpt-4o-mini`, `claude-3-5-sonnet-20241022`). . Verify the models appear as "Enabled" in the catalog. TIP: Different providers have different reliability and cost characteristics. When choosing models, consider your use case requirements for quality, speed, and cost. @@ -48,7 +48,7 @@ TIP: Different providers have different reliability and cost characteristics. Wh Requests through AI Gateway must use the `vendor/model_id` format. For example: * OpenAI models: `openai/gpt-4o`, `openai/gpt-4o-mini` -* Anthropic models: `anthropic/claude-sonnet-3.5`, `anthropic/claude-opus-4` +* Anthropic models: `anthropic/claude-3-5-sonnet-20241022`, `anthropic/claude-opus-4-1-20250805` This format allows the gateway to route requests to the correct provider. From c1d3a5e94713ed3ea60084e9d241ced87d65ce62 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 16:57:50 -0700 Subject: [PATCH 25/50] Clarify VS Code settings.json does not support native env var substitution Added IMPORTANT note in the Environment-based configuration section to warn that VS Code's .vscode/settings.json does not natively support environment variable substitution with ${VAR} syntax. The note clarifies that users must either: - Install an extension that provides variable substitution - Replace placeholders manually with actual values - Set environment variables before launching VS Code This prevents confusion about the ${GATEWAY_DEV_URL} and ${GATEWAY_DEV_ID} placeholders shown in the configuration examples. Co-Authored-By: Claude Sonnet 4.5 --- modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc index 43b7c244e..cc66ca5d7 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc @@ -411,6 +411,8 @@ Debug mode shows: Use different gateways for different environments without changing settings manually. +IMPORTANT: VS Code's `.vscode/settings.json` does not natively support environment variable substitution with the `${VAR}` syntax shown below. You must either install an extension that provides variable substitution, replace the placeholders manually with actual values, or set environment variables before launching VS Code. + Create workspace-specific configurations: .Development workspace (.vscode/settings.json) From ad4c882639e754c7b56d3e475e3a3334ec1289ce Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 16:58:43 -0700 Subject: [PATCH 26/50] Change Cursor MCP endpoint to use HTTPS instead of HTTP MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Updated the Node.js one-liner in the MCP configuration to use require('https') instead of require('http') to ensure the bearer token is sent over TLS when connecting to the gateway's MCP endpoint. Changes: - Line 269: require('http') → require('https') - Line 277: Updated description from "HTTP requests" to "HTTPS requests" This ensures secure transmission of the bearer token and other authentication credentials when Cursor connects to the AI Gateway MCP endpoint. Co-Authored-By: Claude Sonnet 4.5 --- .../ai-agents/pages/ai-gateway/integrations/cursor-user.adoc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc index 3b5182176..24460dc55 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc @@ -266,7 +266,7 @@ Edit `settings.json` to add the MCP configuration: "command": "node", "args": [ "-e", - "require('http').request({hostname:'gw.ai.panda.com',path:'/mcp',method:'GET',headers:{'Authorization':'Bearer YOUR_REDPANDA_API_KEY','rp-aigw-id':'GATEWAY_ID'}}).end()" + "require('https').request({hostname:'gw.ai.panda.com',path:'/mcp',method:'GET',headers:{'Authorization':'Bearer YOUR_REDPANDA_API_KEY','rp-aigw-id':'GATEWAY_ID'}}).end()" ] } } @@ -274,7 +274,7 @@ Edit `settings.json` to add the MCP configuration: } ---- -This configuration uses Node.js to make HTTP requests to the gateway's MCP endpoint. The gateway returns tool definitions that Cursor can use. +This configuration uses Node.js to make HTTPS requests to the gateway's MCP endpoint. The gateway returns tool definitions that Cursor can use. Replace placeholder values: From acc0c23a379280bfeebdcab06f295a5e0dab9739 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 16:59:30 -0700 Subject: [PATCH 27/50] Fix inconsistent list markers in GitHub Copilot security section MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Changed mixed list markers in the "Code completion security" section to use consistent bullet markers. Lines 722-723 previously used '.' (ordered list) but were changed to '*' (unordered list) to match the rest of the list starting with "GitHub Copilot sends code context to LLM providers." Changes: - Line 722: '. Proprietary code...' → '* Proprietary code...' - Line 723: '. Configure organization...' → '* Configure organization...' This ensures consistent rendering and improves AsciiDoc compliance. Co-Authored-By: Claude Sonnet 4.5 --- .../pages/ai-gateway/integrations/github-copilot-admin.adoc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc index 32e20f144..fb2cf320f 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc @@ -719,8 +719,8 @@ Review which models GitHub Copilot clients can access: GitHub Copilot sends code context to LLM providers. Ensure: * Users understand what code context is sent with requests -. Proprietary code may be included in prompts -. Configure organization policies to limit code sharing if needed +* Proprietary code may be included in prompts +* Configure organization policies to limit code sharing if needed * Review provider data retention policies * Monitor logs for sensitive information in prompts (if logging includes prompt content) From 8742a94a49a974e5a3c92142ba4db72d711e6c23 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:00:58 -0700 Subject: [PATCH 28/50] Fix SDK examples in migration guide and remove placeholder notes Updated framework integration examples with correct implementations: 1. Anthropic SDK section (lines 238-256): - Replaced incorrect Anthropic base_url override with OpenAI client - Anthropic's base_url only works with native /v1/messages API, not OpenAI-compatible gateways - Preserved use_gateway env var logic and REDPANDA_AI_GATEWAY_* names - Removed PLACEHOLDER note 2. LlamaIndex section (lines 629-638): - Added default_headers alongside additional_kwargs for gateway headers - Removed PLACEHOLDER note about header syntax 3. Vercel AI SDK section (lines 657-671): - Changed from openai() provider to createOpenAI() for custom endpoints - Uses createOpenAI() with baseURL, apiKey, and headers configuration - Removed PLACEHOLDER note about syntax verification Co-Authored-By: Claude Sonnet 4.5 --- .../pages/ai-gateway/migration-guide.adoc | 23 ++++++++----------- 1 file changed, 10 insertions(+), 13 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/migration-guide.adoc b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc index cf87352d5..d80d0c373 100644 --- a/modules/ai-agents/pages/ai-gateway/migration-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc @@ -235,23 +235,22 @@ else: ---- -Alternative: Keep Anthropic SDK with base_url override - -// PLACEHOLDER: Verify if Anthropic SDK supports base_url override for OpenAI-compatible endpoints +Alternative: Use OpenAI client for OpenAI-compatible gateway [source,python] ---- -from anthropic import Anthropic +from openai import OpenAI use_gateway = os.getenv("USE_AI_GATEWAY", "false").lower() == "true" if use_gateway: - client = Anthropic( - base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), # If supported + client = OpenAI( + base_url=os.getenv("REDPANDA_AI_GATEWAY_URL"), api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} ) else: + from anthropic import Anthropic client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY")) ---- @@ -631,13 +630,12 @@ if use_gateway: model="openai/gpt-4o", api_base=os.getenv("REDPANDA_AI_GATEWAY_URL"), api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), - additional_kwargs={"headers": {"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")}} + additional_kwargs={"headers": {"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")}}, + default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} ) else: llm = OpenAI(model="gpt-4o") ---- - -// PLACEHOLDER: Verify LlamaIndex syntax for custom headers -- Vercel AI SDK:: @@ -656,22 +654,21 @@ After [source,typescript] ---- +import { createOpenAI } from '@ai-sdk/openai'; import { openai } from '@ai-sdk/openai'; const useGateway = process.env.USE_AI_GATEWAY === 'true'; const model = useGateway - ? openai('openai/gpt-4o', { + ? createOpenAI({ baseURL: process.env.REDPANDA_AI_GATEWAY_URL, apiKey: process.env.REDPANDA_AI_GATEWAY_TOKEN, headers: { 'rp-aigw-id': process.env.REDPANDA_AI_GATEWAY_ID, }, - }) + })('openai/gpt-4o') : openai('gpt-4o'); ---- - -// PLACEHOLDER: Verify Vercel AI SDK syntax -- ====== From 6457cf40a1847440f1c3057de265ecb14f8e9e23 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:02:11 -0700 Subject: [PATCH 29/50] Soften hard numeric claims with qualifiers across AI Gateway docs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replaced absolute percentage claims with conditional language to present them as typical or potential outcomes rather than guaranteed results: 1. what-is-ai-gateway.adoc: - Line 99: "can achieve 99.9% uptime" → "can significantly improve uptime (potentially up to 99.9% in some configurations)" - Line 107: "reduces token usage by 80-90%" → "often reduces token usage by 80-90% depending on your configuration and the number of tools aggregated" 2. mcp-aggregation-guide.adoc: - Line 16: "80-90% fewer tokens" → "Often 80-90% fewer tokens (depending on configuration)" - Line 152: "80-90% reduction" → "Typically 80-90% reduction" - Line 269: "Still 80-90% lower" → "Often 80-90% lower" - Line 335: "Expected Results: 80-90% reduction" → "Typically 80-90% reduction" 3. gateway-architecture.adoc: - Line 123: "80-90% token reduction" → "often 80-90% token reduction depending on configuration" These changes ensure claims are presented as conditional outcomes that depend on configuration, rather than absolute guarantees. Co-Authored-By: Claude Sonnet 4.5 --- .../ai-agents/pages/ai-gateway/gateway-architecture.adoc | 2 +- .../ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc | 8 ++++---- .../ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc | 4 ++-- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc b/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc index c0785fccf..a25c0d579 100644 --- a/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc +++ b/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc @@ -120,7 +120,7 @@ The gateway only loads and exposes specific tools when requested, which dramatic === MCP support * MCP server aggregation -* Deferred tool loading (80-90% token reduction) +* Deferred tool loading (often 80-90% token reduction depending on configuration) * JavaScript orchestrator for multi-step workflows * // PLACEHOLDER: Tool execution sandboxing? diff --git a/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc index 75b2b4dcf..a5367ad38 100644 --- a/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc @@ -13,7 +13,7 @@ AI Gateway provides MCP (Model Context Protocol) aggregation, allowing AI agents MCP aggregation benefits: * Single endpoint: One MCP endpoint aggregates all approved MCP servers -* Token reduction: 80-90% fewer tokens through deferred tool loading +* Token reduction: Often 80-90% fewer tokens through deferred tool loading (depending on configuration) * Centralized governance: Admin-approved MCP servers only * Orchestration: JavaScript-based orchestrator reduces multi-step round trips * Security: Controlled tool execution environment @@ -149,7 +149,7 @@ Token savings: * Without deferred loading: ~5,000-10,000 tokens (all tool definitions) * With deferred loading: ~500-1,000 tokens (2 tool definitions) -* 80-90% reduction +* Typically 80-90% reduction === Tool query (when agent needs specific tool) @@ -266,7 +266,7 @@ Deferred loading (AI Gateway): * Gateway returns matching tools * Agent calls specific tool (e.g., `execute_sql`) 5. Total token cost: Initial 500-1,000 + per-query ~200-500 - * Still 80-90% lower than loading all tools + * Often 80-90% lower than loading all tools === When to use deferred loading @@ -332,7 +332,7 @@ Compare token usage before/after deferred loading: Savings % = ((Before - After) / Before) × 100 ---- -Expected Results: 80-90% reduction in average tokens per request +Expected Results: Typically 80-90% reduction in average tokens per request == Orchestrator: multi-step workflows diff --git a/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc index 68a78a7af..62e60a4d3 100644 --- a/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc +++ b/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc @@ -96,7 +96,7 @@ request.headers["x-user-tier"] == "premium" You can also set different rate limits and spend limits per environment to prevent staging or development traffic from consuming production budgets. -For reliability, you can configure provider pools with automatic failover. If you configure OpenAI GPT-4 as your primary model and Anthropic Claude Opus as the fallback, the gateway automatically routes requests to the fallback when it detects rate limits or timeouts from the primary provider. This configuration can achieve 99.9% uptime even during provider outages. +For reliability, you can configure provider pools with automatic failover. If you configure OpenAI GPT-4 as your primary model and Anthropic Claude Opus as the fallback, the gateway automatically routes requests to the fallback when it detects rate limits or timeouts from the primary provider. This configuration can significantly improve uptime (potentially up to 99.9% in some configurations) even during provider outages. === 3. MCP aggregation and orchestration @@ -104,7 +104,7 @@ AI Gateway aggregates multiple MCP (Model Context Protocol) servers and provides Without AI Gateway, agents typically load all available tools from multiple MCP servers at startup. This approach sends 50+ tool definitions with every request, creating high token costs (thousands of tokens per request), slow agent startup times, and no centralized governance over which tools agents can access. -With AI Gateway, you configure approved MCP servers once, and the gateway loads only search and orchestrator tools initially. Agents query for specific tools only when needed, which reduces token usage by 80-90% depending on your configuration. You also gain centralized approval and governance over which MCP servers your agents can access. +With AI Gateway, you configure approved MCP servers once, and the gateway loads only search and orchestrator tools initially. Agents query for specific tools only when needed, which often reduces token usage by 80-90% depending on your configuration and the number of tools aggregated. You also gain centralized approval and governance over which MCP servers your agents can access. For complex workflows, AI Gateway provides a JavaScript-based orchestrator tool that reduces multi-step workflows from multiple round trips to a single call. For example, you can create a workflow that searches a vector database and, if the results are insufficient, falls back to web search—all in one orchestration step. From 174199bd2ac100a4f0d8af67c1e9d9679230112c Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:07:01 -0700 Subject: [PATCH 30/50] Convert persona restructuring plan from Markdown to AsciiDoc Converted AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md to proper AsciiDoc format: - Renamed to lowercase: ai-gateway-persona-restructuring-plan.adoc - Converted Markdown headers (# ## ###) to AsciiDoc (= == ===) - Converted Markdown tables to AsciiDoc table format with proper column specs - Converted Markdown code blocks to AsciiDoc [source,text] blocks - Converted Markdown bold (**text**) to AsciiDoc bold (*text*) - Added blank lines before and after all tables and code blocks - Fixed all code blocks to use proper language annotations (text for trees) - Removed old markdown file All tables now have proper blank lines and all fenced code blocks have language annotations, fixing MD058/MD040 violations. Co-Authored-By: Claude Sonnet 4.5 --- .../AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md | 573 ---------------- ...ai-gateway-persona-restructuring-plan.adoc | 634 ++++++++++++++++++ 2 files changed, 634 insertions(+), 573 deletions(-) delete mode 100644 modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md create mode 100644 modules/ai-agents/partials/ai-gateway-persona-restructuring-plan.adoc diff --git a/modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md b/modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md deleted file mode 100644 index bc62f7e7c..000000000 --- a/modules/ai-agents/partials/AI_GATEWAY_PERSONA_RESTRUCTURING_PLAN.md +++ /dev/null @@ -1,573 +0,0 @@ -# AI Gateway Content Restructuring Plan -## Persona-Based Reorganization - -**Date:** January 21, 2026 -**Purpose:** Restructure AI Gateway documentation to align with two primary personas (Admins and Builders) and their distinct user journeys. - ---- - -## Executive Summary - -The current AI Gateway documentation is comprehensive but doesn't clearly distinguish between Admin and Builder personas. This plan proposes: - -1. **Restructure the navigation** to create clear persona-based paths -2. **Create new landing/discovery pages** for each persona -3. **Tag existing content** with appropriate personas -4. **Add missing content** to complete user journeys -5. **Reorganize the index** to guide users based on their role - ---- - -## Personas Defined - -### Admin Persona -- **Role:** Platform administrators with broad oversight -- **Responsibilities:** - - Configure system-level parameters - - Enable/disable LLM providers and models - - Set up gateways with policies, routing, and budgets - - Monitor usage across the organization - - Manage access control and security -- **Key Questions:** - - How do I set up and configure AI Gateway for my organization? - - How do I control costs and enforce policies? - - How do I monitor usage across all teams? - -### Builder Persona -- **Role:** Developers/engineers building agents or AI applications -- **Responsibilities:** - - Build agents and AI applications - - Integrate agents with available gateways - - Use MCP tools and services - - Monitor their own usage and costs -- **Key Questions:** - - Which gateways can I use? - - How do I connect my agent to a gateway? - - What tools/models are available to me? - - How much am I spending? - ---- - -## User Journey Mapping - -### Admin User Journey -1. **Understand** → What is an AI gateway? (conceptual) -2. **Set Up** → Enable providers, enable models, create gateways -3. **Configure** → Set up networking, policies, routing, budgets -4. **Monitor** → Track usage, costs, and manage access -5. **Optimize** → Adjust policies, routing, and costs based on metrics - -### Builder User Journey -1. **Discover** → Which gateways can I access? -2. **Connect** → How do I integrate my agent with a gateway? -3. **Build** → Use available models and MCP tools -4. **Test** → Validate my agent's integration -5. **Monitor** → Track my usage and costs - ---- - -## Content Gap Analysis - -### Missing Content -| Content Needed | Persona | Priority | Current Status | -|---------------|---------|----------|----------------| -| Gateway Discovery page | Builder | HIGH | Missing - critical for Builder journey | -| "What is AI Gateway" standalone page | Both | HIGH | Content exists in overview but needs extraction | -| Admin Setup Guide | Admin | HIGH | Scattered across quickstart - needs consolidation | -| Builder Integration Guide | Builder | HIGH | Exists partially in quickstart/integrations | -| Networking Configuration page | Admin | MEDIUM | Mentioned but not detailed | -| Access Management page | Admin | MEDIUM | Missing | - -### Existing Content Gaps -1. **gateway-architecture.adoc** - Too dense, mixes Admin and Builder concerns -2. **gateway-quickstart.adoc (quickstart)** - Conflates Admin setup with Builder usage -3. **index.adoc** - Too minimal, provides no guidance -4. **No discovery mechanism** - Builders don't know which gateways they can use - ---- - -## Recommended Content Structure - -### New Navigation Structure - -``` -AI Gateway/ -├── index.adoc (New: Persona-based landing page) -├── what-is-ai-gateway.adoc (New: Extracted from overview) -│ -├── For Admins/ -│ ├── admin-overview.adoc (New: Admin-focused overview) -│ ├── setup-guide.adoc (New: Complete admin setup) -│ │ ├── enable-providers.adoc (Extracted from quickstart) -│ │ ├── enable-models.adoc (Extracted from quickstart) -│ │ ├── create-gateways.adoc (Extracted from quickstart) -│ │ ├── networking-configuration.adoc (New/Expanded) -│ ├── configure-policies.adoc (Consolidated) -│ │ ├── routing-policies.adoc (Link to CEL cookbook) -│ │ ├── access-controls.adoc (New) -│ │ ├── budgets-and-limits.adoc (Consolidated from quickstart) -│ ├── manage-gateways.adoc (New: List, edit, delete) -│ ├── observability-admin.adoc (Link to metrics dashboard) -│ └── integrations/ (Admin versions) -│ ├── index.adoc -│ ├── claude-code-admin.adoc -│ ├── cursor-admin.adoc -│ └── ... -│ -├── For Builders/ -│ ├── builder-overview.adoc (New: Builder-focused overview) -│ ├── discover-gateways.adoc (NEW - CRITICAL) -│ ├── connect-your-agent.adoc (New: Integration guide) -│ ├── available-models.adoc (New: How to see what's available) -│ ├── use-mcp-tools.adoc (Link to MCP aggregation) -│ ├── test-your-integration.adoc (New: Validation) -│ ├── monitor-your-usage.adoc (Link to observability-logs) -│ └── integrations/ (Builder versions) -│ ├── index.adoc -│ ├── claude-code-user.adoc -│ ├── cursor-user.adoc -│ └── ... -│ -├── Reference/ -│ ├── gateway-architecture.adoc (Refactored: Technical deep-dive) -│ ├── cel-routing-cookbook.adoc (Existing) -│ ├── mcp-aggregation-guide.adoc (Existing) -│ ├── observability-logs.adoc (Existing) -│ ├── observability-metrics.adoc (Existing) -│ ├── migration-guide.adoc (Existing) -│ └── gateway-quickstart.adoc (Consolidated from ai-gateway.adoc and quickstart-enhanced.adoc) -``` - ---- - -## Detailed Content Recommendations - -### 1. Create New index.adoc (HIGH PRIORITY) - -**Current State:** Minimal landing page with just a description -**Proposed Change:** Transform into a persona-based router - -**Content Structure:** -```asciidoc -= AI Gateway -:description: Unified access layer for LLM providers and AI tools -:page-layout: index - -The Redpanda AI Gateway provides centralized routing, policy enforcement, cost management, and observability for all your AI traffic. - -== Choose Your Path - -[.persona-card] -=== I'm an Administrator -You manage AI Gateway infrastructure, configure providers, set policies, and monitor organizational usage. - -* xref:ai-gateway/admin/admin-overview.adoc[Admin Overview] -* xref:ai-gateway/admin/setup-guide.adoc[Setup Guide] -* xref:ai-gateway/admin/manage-gateways.adoc[Manage Gateways] - -[.persona-card] -=== I'm a Builder -You're building AI agents or applications and need to connect to available gateways. - -* xref:ai-gateway/builders/builder-overview.adoc[Builder Overview] -* xref:ai-gateway/builders/discover-gateways.adoc[Discover Available Gateways] -* xref:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] - -== Learn More - -* xref:ai-gateway/what-is-ai-gateway.adoc[What is an AI Gateway?] -* xref:ai-gateway/reference/gateway-architecture.adoc[Technical Architecture] -``` - -**Persona Tagging:** Both - ---- - -### 2. Create what-is-ai-gateway.adoc (HIGH PRIORITY) - -**Purpose:** Standalone conceptual page answering "What is an AI gateway?" -**Source:** Extract from gateway-architecture.adoc (lines 15-147) - -**Content to Include:** -- The problem AI Gateway solves -- Core capabilities (unified access, routing, MCP aggregation, observability) -- Common gateway patterns -- High-level architecture diagram - -**Remove from Overview:** Keep technical details in overview, move conceptual understanding here - -**Persona Tagging:** Both (Admin and Builder) - ---- - -### 3. Create discover-gateways.adoc (HIGH PRIORITY - NEW) - -**Purpose:** Help Builders find which gateways they have access to -**This is CRITICAL and completely missing from current content** - -**Content Structure:** -```asciidoc -= Discover Available Gateways -:description: Find which AI Gateways you can access and their configurations -:page-personas: app_developer - -As a builder, you need to know which gateways are available to you before integrating your agent. - -== List your accessible gateways - -=== Using the Console - -1. Navigate to AI Gateway → My Gateways -2. View all gateways you have access to: - * Gateway Name - * Gateway ID (for `rp-aigw-id` header) - * Endpoint URL - * Available Models - * MCP Tools (if configured) - -=== Using the API - -[source,bash] ----- -curl https://{CLUSTER}.cloud.redpanda.com/api/ai-gateway/v1/gateways \ - -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" ----- - -== Understanding gateway information - -Each gateway shows: - -* **Gateway ID**: Use this in the `rp-aigw-id` header -* **Endpoint URL**: Base URL for API requests -* **Available Models**: Which models you can access (e.g., `openai/gpt-4o`, `anthropic/claude-sonnet-3.5`) -* **Rate Limits**: Your request limits -* **MCP Tools**: Available MCP servers and tools (if enabled) - -== Check gateway availability - -Before integrating, test gateway access: - -[source,bash] ----- -curl https://{GATEWAY_ENDPOINT}/v1/models \ - -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ - -H "rp-aigw-id: ${GATEWAY_ID}" ----- - -Expected response: List of available models - -== Next steps - -* xref:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] -* xref:ai-gateway/builders/available-models.adoc[View Available Models] -``` - -**Persona Tagging:** Builder (app_developer) - ---- - -### 4. Refactor gateway-quickstart.adoc (quickstart) - -**Current Problem:** Mixes Admin setup (Steps 1-3) with Builder usage (Steps 4-5, integrations) - -**Proposed Split:** - -#### Create admin/setup-guide.adoc (Admin path) -- Step 1: Enable providers -- Step 2: Enable models -- Step 3: Create gateways -- Step 4: Configure LLM routing (policies, pools, rate limits) -- Step 5: Configure MCP tools - -#### Create builders/connect-your-agent.adoc (Builder path) -- Prerequisites: Gateway ID and endpoint (from discovery) -- Step 1: Get your gateway credentials -- Step 2: Configure your client SDK -- Step 3: Make your first request -- Step 4: Handle responses -- Step 5: Validate integration - -**Content to Move:** -- Lines 17-89 (Admin steps) → admin/setup-guide.adoc -- Lines 160-337 (Integration examples) → builders/connect-your-agent.adoc -- Lines 106-118 (Observability) → Link to observability pages - ---- - -### 5. Create admin/networking-configuration.adoc (MEDIUM PRIORITY) - -**Purpose:** Dedicated page for networking setup -**Content:** Currently mentioned but not detailed - -**Content Structure:** -```asciidoc -= Networking Configuration -:description: Configure networking for AI Gateway including endpoints, private networking, and connectivity -:page-personas: platform_admin - -Configure network access and connectivity for your AI Gateway. - -== Gateway endpoints - -When you create a gateway, you receive: - -* Public endpoint: `https://gw.ai.panda.com` -* Private endpoint (if enabled): `https://gw-internal.ai.panda.com` - -== Public vs private endpoints - -**Public endpoints:** -- Accessible from internet -- Use for external agents, testing -- Standard TLS encryption - -**Private endpoints:** -- Accessible only within your VPC/network -- Use for production workloads -- Enhanced security - -== Configure private networking - -[PLACEHOLDER: Add private networking setup steps] - -== Connectivity requirements - -Outbound connections required: -- To LLM provider APIs (OpenAI, Anthropic, etc.) -- To configured MCP servers (if using MCP aggregation) - -Inbound connections: -- From your agents/applications to gateway endpoint - -== Firewall and security groups - -[PLACEHOLDER: Add security group configuration] - -== Next steps - -* xref:ai-gateway/admin/configure-policies.adoc[Configure Access Policies] -``` - -**Persona Tagging:** Admin (platform_admin) - ---- - -### 6. Create admin/access-controls.adoc (MEDIUM PRIORITY) - -**Purpose:** How Admins control who can access which gateways - -**Content:** -- Gateway-level access control -- API key management -- RBAC configuration (if available) -- Audit logging - -**Persona Tagging:** Admin (platform_admin) - ---- - -### 7. Update Existing Files - -#### gateway-architecture.adoc -**Changes:** -- Remove conceptual "What is" content (move to what-is-ai-gateway.adoc) -- Focus on technical architecture deep-dive -- Keep: Architecture details, request lifecycle, advanced patterns -- Update persona tag to: `platform_admin, app_developer` (both, but technical) - -#### cel-routing-cookbook.adoc -**Changes:** -- Add note at top: "This is an advanced reference for Admins configuring routing policies" -- Update persona tag to: `platform_admin` (currently has both) -- No content changes needed - -#### mcp-aggregation-guide.adoc -**Changes:** -- Add section for Builders: "Using MCP tools as a Builder" -- Currently too Admin-focused -- Add discovery section: How Builders see available MCP tools -- Keep persona tag: `app_developer` but clarify sections - -#### observability-logs.adoc -**Changes:** -- Add intro section distinguishing Admin vs Builder use cases: - - Admins: Monitor all traffic, all gateways, org-wide - - Builders: Monitor their own agent's requests -- Update UI paths to reflect persona-based views -- Persona tag is currently correct: `platform_admin, app_developer` - -#### observability-metrics.adoc -**Changes:** -- Similar to logs: Distinguish Admin (org-wide) vs Builder (my usage) views -- Add section: "View your agent's usage" (Builder perspective) -- Persona tag currently: `platform_admin` - should add `app_developer` - ---- - -## Navigation (nav.adoc) Changes - -**Current Structure:** -``` -* AI Gateway -** Overview -** Quickstart -** CEL Routing -** MCP Aggregation -** Observability -** Integrations -``` - -**Proposed Structure:** -``` -* xref:ai-agents:ai-gateway/index.adoc[AI Gateway] -** xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[What is AI Gateway?] -** For Admins -*** xref:ai-agents:ai-gateway/admin/admin-overview.adoc[Admin Overview] -*** xref:ai-agents:ai-gateway/admin/setup-guide.adoc[Setup Guide] -*** xref:ai-agents:ai-gateway/admin/manage-gateways.adoc[Manage Gateways] -*** xref:ai-agents:ai-gateway/admin/networking-configuration.adoc[Networking Configuration] -*** xref:ai-agents:ai-gateway/admin/configure-policies.adoc[Configure Policies] -*** xref:ai-agents:ai-gateway/admin/access-controls.adoc[Access Controls] -*** xref:ai-agents:ai-gateway/admin/observability-admin.adoc[Monitor Usage] -*** xref:ai-agents:ai-gateway/admin/integrations/index.adoc[Integrations (Admin)] -** For Builders -*** xref:ai-agents:ai-gateway/builders/builder-overview.adoc[Builder Overview] -*** xref:ai-agents:ai-gateway/builders/discover-gateways.adoc[Discover Gateways] -*** xref:ai-agents:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] -*** xref:ai-agents:ai-gateway/builders/available-models.adoc[Available Models] -*** xref:ai-agents:ai-gateway/builders/use-mcp-tools.adoc[Use MCP Tools] -*** xref:ai-agents:ai-gateway/builders/monitor-your-usage.adoc[Monitor Your Usage] -*** xref:ai-agents:ai-gateway/builders/integrations/index.adoc[Integrations (Builder)] -** Reference -*** xref:ai-agents:ai-gateway/reference/gateway-architecture.adoc[Architecture Deep Dive] -*** xref:ai-agents:ai-gateway/reference/cel-routing-cookbook.adoc[CEL Routing Cookbook] -*** xref:ai-agents:ai-gateway/reference/mcp-aggregation-guide.adoc[MCP Aggregation Guide] -*** xref:ai-agents:ai-gateway/reference/observability-logs.adoc[Request Logs] -*** xref:ai-agents:ai-gateway/reference/observability-metrics.adoc[Metrics and Analytics] -``` - ---- - -## Implementation Priority - -### Phase 1: Critical Path (Do First) -1. **Create index.adoc** - Persona router (HIGH) -2. **Create discover-gateways.adoc** - Critical Builder need (HIGH) -3. **Create what-is-ai-gateway.adoc** - Entry point (HIGH) -4. **Split quickstart** into admin/setup-guide.adoc and builders/connect-your-agent.adoc (HIGH) - -### Phase 2: Complete User Journeys -1. Create admin/manage-gateways.adoc (MEDIUM) -2. Create builders/available-models.adoc (MEDIUM) -3. Create admin/networking-configuration.adoc (MEDIUM) -4. Create admin/access-controls.adoc (MEDIUM) -5. Update observability pages with persona distinctions (MEDIUM) - -### Phase 3: Polish and Optimize -1. Refactor gateway-architecture.adoc (MEDIUM) -2. Update mcp-aggregation-guide.adoc with Builder sections (LOW) -3. Create admin/builder overview pages (LOW) -4. Reorganize integrations folders (LOW) -5. Update all cross-references (LOW) - ---- - -## Mapping to User Journey - -### Admin Journey → Content -| Journey Step | Content | -|--------------|---------| -| What is an AI gateway? | what-is-ai-gateway.adoc | -| How do I create, list, and manage gateways? | admin/setup-guide.adoc, admin/manage-gateways.adoc | -| Networking configuration & Gateway creation | admin/networking-configuration.adoc | -| Configure which models are accessible | admin/setup-guide.adoc (enable models section) | -| Configure access and routing policies | admin/configure-policies.adoc, cel-routing-cookbook.adoc | -| Track usage and configure budgeting | admin/setup-guide.adoc (budgets), observability-metrics.adoc | - -### Builder Journey → Content -| Journey Step | Content | -|--------------|---------| -| What is an AI gateway? | what-is-ai-gateway.adoc | -| Discover which AI gateways my agents have access to | **builders/discover-gateways.adoc (NEW)** | -| How do I integrate my agent? | builders/connect-your-agent.adoc | -| What models/tools are available? | builders/available-models.adoc, builders/use-mcp-tools.adoc | -| Test my integration | builders/connect-your-agent.adoc (validation section) | -| Track my usage | builders/monitor-your-usage.adoc → observability-logs.adoc | - ---- - -## Key Principles - -1. **Persona First:** Content should clearly identify which persona it serves -2. **Progressive Disclosure:** Start simple, link to advanced topics -3. **Minimize Duplication:** Use xrefs to avoid maintaining same content twice -4. **Clear Entry Points:** Index page must route users effectively -5. **Discovery is Critical:** Builders MUST be able to find available gateways - ---- - -## Success Metrics - -After implementation, evaluate: -- Can a Builder discover available gateways in <2 minutes? -- Can an Admin complete setup in <15 minutes? -- Do users report clearer distinction between Admin vs Builder tasks? -- Reduced support tickets about "I can't find which gateway to use" - ---- - -## Open Questions - -1. **API for Gateway Discovery:** Does the API support listing accessible gateways per user? -2. **RBAC Model:** How granular is access control (workspace, gateway, model level)? -3. **Private Networking:** What's the detailed setup for private endpoints? -4. **Budgets and Limits:** Can Builders see their own usage/limits, or only Admins? -5. **Integration Folders:** Should we physically split integration files into admin/ and builders/ subdirectories? - ---- - -## Next Steps - -1. **Review this plan** with product and docs team -2. **Validate API capabilities** for gateway discovery -3. **Create Phase 1 content** (index, discover-gateways, what-is, split quickstart) -4. **Test with users** from each persona -5. **Iterate based on feedback** - ---- - -## Appendix: File Operations Summary - -### New Files to Create -- `ai-gateway/index.adoc` (replace existing minimal one) -- `ai-gateway/what-is-ai-gateway.adoc` -- `ai-gateway/admin/admin-overview.adoc` -- `ai-gateway/admin/setup-guide.adoc` -- `ai-gateway/admin/manage-gateways.adoc` -- `ai-gateway/admin/networking-configuration.adoc` -- `ai-gateway/admin/configure-policies.adoc` -- `ai-gateway/admin/access-controls.adoc` -- `ai-gateway/builders/builder-overview.adoc` -- `ai-gateway/builders/discover-gateways.adoc` ⭐ CRITICAL -- `ai-gateway/builders/connect-your-agent.adoc` -- `ai-gateway/builders/available-models.adoc` -- `ai-gateway/builders/use-mcp-tools.adoc` -- `ai-gateway/builders/monitor-your-usage.adoc` - -### Files to Move -- `ai-gateway/gateway-architecture.adoc` → `ai-gateway/reference/gateway-architecture.adoc` -- `ai-gateway/cel-routing-cookbook.adoc` → `ai-gateway/reference/cel-routing-cookbook.adoc` -- `ai-gateway/mcp-aggregation-guide.adoc` → `ai-gateway/reference/mcp-aggregation-guide.adoc` -- `ai-gateway/observability-*.adoc` → `ai-gateway/reference/observability-*.adoc` - -### Files to Refactor -- `ai-gateway/gateway-quickstart.adoc` (quickstart) - split content between admin and builder paths -- `ai-gateway/gateway-architecture.adoc` - extract conceptual content to what-is page -- `ai-gateway/observability-logs.adoc` - add persona-specific sections -- `ai-gateway/observability-metrics.adoc` - add builder usage section - -### Files to Keep As-Is (Minimal Changes) -- `ai-gateway/integrations/*-admin.adoc` -- `ai-gateway/integrations/*-user.adoc` -- `ai-gateway/migration-guide.adoc` -- `ai-gateway/gateway-quickstart.adoc` (consolidated) diff --git a/modules/ai-agents/partials/ai-gateway-persona-restructuring-plan.adoc b/modules/ai-agents/partials/ai-gateway-persona-restructuring-plan.adoc new file mode 100644 index 000000000..f1062254b --- /dev/null +++ b/modules/ai-agents/partials/ai-gateway-persona-restructuring-plan.adoc @@ -0,0 +1,634 @@ += AI Gateway Content Restructuring Plan +:description: Persona-based reorganization of AI Gateway documentation + +*Date:* January 21, 2026 + +*Purpose:* Restructure AI Gateway documentation to align with two primary personas (Admins and Builders) and their distinct user journeys. + +== Executive Summary + +The current AI Gateway documentation is comprehensive but doesn't clearly distinguish between Admin and Builder personas. This plan proposes: + +. *Restructure the navigation* to create clear persona-based paths +. *Create new landing/discovery pages* for each persona +. *Tag existing content* with appropriate personas +. *Add missing content* to complete user journeys +. *Reorganize the index* to guide users based on their role + +== Personas Defined + +=== Admin Persona + +* *Role:* Platform administrators with broad oversight +* *Responsibilities:* +** Configure system-level parameters +** Enable/disable LLM providers and models +** Set up gateways with policies, routing, and budgets +** Monitor usage across the organization +** Manage access control and security +* *Key Questions:* +** How do I set up and configure AI Gateway for my organization? +** How do I control costs and enforce policies? +** How do I monitor usage across all teams? + +=== Builder Persona + +* *Role:* Developers/engineers building agents or AI applications +* *Responsibilities:* +** Build agents and AI applications +** Integrate agents with available gateways +** Use MCP tools and services +** Monitor their own usage and costs +* *Key Questions:* +** Which gateways can I use? +** How do I connect my agent to a gateway? +** What tools/models are available to me? +** How much am I spending? + +== User Journey Mapping + +=== Admin User Journey + +. *Understand* → What is an AI gateway? (conceptual) +. *Set Up* → Enable providers, enable models, create gateways +. *Configure* → Set up networking, policies, routing, budgets +. *Monitor* → Track usage, costs, and manage access +. *Optimize* → Adjust policies, routing, and costs based on metrics + +=== Builder User Journey + +. *Discover* → Which gateways can I access? +. *Connect* → How do I integrate my agent with a gateway? +. *Build* → Use available models and MCP tools +. *Test* → Validate my agent's integration +. *Monitor* → Track my usage and costs + +== Content Gap Analysis + +=== Missing Content + +[cols="1,1,1,2"] +|=== +| Content Needed | Persona | Priority | Current Status + +| Gateway Discovery page +| Builder +| HIGH +| Missing - critical for Builder journey + +| "What is AI Gateway" standalone page +| Both +| HIGH +| Content exists in overview but needs extraction + +| Admin Setup Guide +| Admin +| HIGH +| Scattered across quickstart - needs consolidation + +| Builder Integration Guide +| Builder +| HIGH +| Exists partially in quickstart/integrations + +| Networking Configuration page +| Admin +| MEDIUM +| Mentioned but not detailed + +| Access Management page +| Admin +| MEDIUM +| Missing +|=== + +=== Existing Content Gaps + +. *gateway-architecture.adoc* - Too dense, mixes Admin and Builder concerns +. *gateway-quickstart.adoc (quickstart)* - Conflates Admin setup with Builder usage +. *index.adoc* - Too minimal, provides no guidance +. *No discovery mechanism* - Builders don't know which gateways they can use + +== Recommended Content Structure + +=== New Navigation Structure + +[source,text] +---- +AI Gateway/ +├── index.adoc (New: Persona-based landing page) +├── what-is-ai-gateway.adoc (New: Extracted from overview) +│ +├── For Admins/ +│ ├── admin-overview.adoc (New: Admin-focused overview) +│ ├── setup-guide.adoc (New: Complete admin setup) +│ │ ├── enable-providers.adoc (Extracted from quickstart) +│ │ ├── enable-models.adoc (Extracted from quickstart) +│ │ ├── create-gateways.adoc (Extracted from quickstart) +│ │ ├── networking-configuration.adoc (New/Expanded) +│ ├── configure-policies.adoc (Consolidated) +│ │ ├── routing-policies.adoc (Link to CEL cookbook) +│ │ ├── access-controls.adoc (New) +│ │ ├── budgets-and-limits.adoc (Consolidated from quickstart) +│ ├── manage-gateways.adoc (New: List, edit, delete) +│ ├── observability-admin.adoc (Link to metrics dashboard) +│ └── integrations/ (Admin versions) +│ ├── index.adoc +│ ├── claude-code-admin.adoc +│ ├── cursor-admin.adoc +│ └── ... +│ +├── For Builders/ +│ ├── builder-overview.adoc (New: Builder-focused overview) +│ ├── discover-gateways.adoc (NEW - CRITICAL) +│ ├── connect-your-agent.adoc (New: Integration guide) +│ ├── available-models.adoc (New: How to see what's available) +│ ├── use-mcp-tools.adoc (Link to MCP aggregation) +│ ├── test-your-integration.adoc (New: Validation) +│ ├── monitor-your-usage.adoc (Link to observability-logs) +│ └── integrations/ (Builder versions) +│ ├── index.adoc +│ ├── claude-code-user.adoc +│ ├── cursor-user.adoc +│ └── ... +│ +├── Reference/ +│ ├── gateway-architecture.adoc (Refactored: Technical deep-dive) +│ ├── cel-routing-cookbook.adoc (Existing) +│ ├── mcp-aggregation-guide.adoc (Existing) +│ ├── observability-logs.adoc (Existing) +│ ├── observability-metrics.adoc (Existing) +│ ├── migration-guide.adoc (Existing) +│ └── gateway-quickstart.adoc (Consolidated from ai-gateway.adoc and quickstart-enhanced.adoc) +---- + +== Detailed Content Recommendations + +=== 1. Create New index.adoc (HIGH PRIORITY) + +*Current State:* Minimal landing page with just a description + +*Proposed Change:* Transform into a persona-based router + +*Content Structure:* + +[source,text] +---- += AI Gateway +:description: Unified access layer for LLM providers and AI tools +:page-layout: index + +The Redpanda AI Gateway provides centralized routing, policy enforcement, cost management, and observability for all your AI traffic. + +== Choose Your Path + +[.persona-card] +=== I'm an Administrator +You manage AI Gateway infrastructure, configure providers, set policies, and monitor organizational usage. + +* xref:ai-gateway/admin/admin-overview.adoc[Admin Overview] +* xref:ai-gateway/admin/setup-guide.adoc[Setup Guide] +* xref:ai-gateway/admin/manage-gateways.adoc[Manage Gateways] + +[.persona-card] +=== I'm a Builder +You're building AI agents or applications and need to connect to available gateways. + +* xref:ai-gateway/builders/builder-overview.adoc[Builder Overview] +* xref:ai-gateway/builders/discover-gateways.adoc[Discover Available Gateways] +* xref:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] + +== Learn More + +* xref:ai-gateway/what-is-ai-gateway.adoc[What is an AI Gateway?] +* xref:ai-gateway/reference/gateway-architecture.adoc[Technical Architecture] +---- + +*Persona Tagging:* Both + +=== 2. Create what-is-ai-gateway.adoc (HIGH PRIORITY) + +*Purpose:* Standalone conceptual page answering "What is an AI gateway?" + +*Source:* Extract from gateway-architecture.adoc (lines 15-147) + +*Content to Include:* + +* The problem AI Gateway solves +* Core capabilities (unified access, routing, MCP aggregation, observability) +* Common gateway patterns +* High-level architecture diagram + +*Remove from Overview:* Keep technical details in overview, move conceptual understanding here + +*Persona Tagging:* Both (Admin and Builder) + +=== 3. Create discover-gateways.adoc (HIGH PRIORITY - NEW) + +*Purpose:* Help Builders find which gateways they have access to + +*This is CRITICAL and completely missing from current content* + +*Content Structure:* + +[source,text] +---- += Discover Available Gateways +:description: Find which AI Gateways you can access and their configurations +:page-personas: app_developer + +As a builder, you need to know which gateways are available to you before integrating your agent. + +== List your accessible gateways + +=== Using the Console + +1. Navigate to AI Gateway → My Gateways +2. View all gateways you have access to: + * Gateway Name + * Gateway ID (for `rp-aigw-id` header) + * Endpoint URL + * Available Models + * MCP Tools (if configured) + +=== Using the API + +[source,bash] +---- +curl https://{CLUSTER}.cloud.redpanda.com/api/ai-gateway/v1/gateways \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" +---- + +== Understanding gateway information + +Each gateway shows: + +* *Gateway ID*: Use this in the `rp-aigw-id` header +* *Endpoint URL*: Base URL for API requests +* *Available Models*: Which models you can access (e.g., `openai/gpt-4o`, `anthropic/claude-sonnet-3.5`) +* *Rate Limits*: Your request limits +* *MCP Tools*: Available MCP servers and tools (if enabled) + +== Check gateway availability + +Before integrating, test gateway access: + +[source,bash] +---- +curl https://{GATEWAY_ENDPOINT}/v1/models \ + -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ + -H "rp-aigw-id: ${GATEWAY_ID}" +---- + +Expected response: List of available models + +== Next steps + +* xref:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] +* xref:ai-gateway/builders/available-models.adoc[View Available Models] +---- + +*Persona Tagging:* Builder (app_developer) + +=== 4. Refactor gateway-quickstart.adoc (quickstart) + +*Current Problem:* Mixes Admin setup (Steps 1-3) with Builder usage (Steps 4-5, integrations) + +*Proposed Split:* + +==== Create admin/setup-guide.adoc (Admin path) + +* Step 1: Enable providers +* Step 2: Enable models +* Step 3: Create gateways +* Step 4: Configure LLM routing (policies, pools, rate limits) +* Step 5: Configure MCP tools + +==== Create builders/connect-your-agent.adoc (Builder path) + +* Prerequisites: Gateway ID and endpoint (from discovery) +* Step 1: Get your gateway credentials +* Step 2: Configure your client SDK +* Step 3: Make your first request +* Step 4: Handle responses +* Step 5: Validate integration + +*Content to Move:* + +* Lines 17-89 (Admin steps) → admin/setup-guide.adoc +* Lines 160-337 (Integration examples) → builders/connect-your-agent.adoc +* Lines 106-118 (Observability) → Link to observability pages + +=== 5. Create admin/networking-configuration.adoc (MEDIUM PRIORITY) + +*Purpose:* Dedicated page for networking setup + +*Content:* Currently mentioned but not detailed + +*Content Structure:* + +[source,text] +---- += Networking Configuration +:description: Configure networking for AI Gateway including endpoints, private networking, and connectivity +:page-personas: platform_admin + +Configure network access and connectivity for your AI Gateway. + +== Gateway endpoints + +When you create a gateway, you receive: + +* Public endpoint: `https://gw.ai.panda.com` +* Private endpoint (if enabled): `https://gw-internal.ai.panda.com` + +== Public vs private endpoints + +*Public endpoints:* +- Accessible from internet +- Use for external agents, testing +- Standard TLS encryption + +*Private endpoints:* +- Accessible only within your VPC/network +- Use for production workloads +- Enhanced security + +== Configure private networking + +[PLACEHOLDER: Add private networking setup steps] + +== Connectivity requirements + +Outbound connections required: +- To LLM provider APIs (OpenAI, Anthropic, etc.) +- To configured MCP servers (if using MCP aggregation) + +Inbound connections: +- From your agents/applications to gateway endpoint + +== Firewall and security groups + +[PLACEHOLDER: Add security group configuration] + +== Next steps + +* xref:ai-gateway/admin/configure-policies.adoc[Configure Access Policies] +---- + +*Persona Tagging:* Admin (platform_admin) + +=== 6. Create admin/access-controls.adoc (MEDIUM PRIORITY) + +*Purpose:* How Admins control who can access which gateways + +*Content:* + +* Gateway-level access control +* API key management +* RBAC configuration (if available) +* Audit logging + +*Persona Tagging:* Admin (platform_admin) + +=== 7. Update Existing Files + +==== gateway-architecture.adoc + +*Changes:* + +* Remove conceptual "What is" content (move to what-is-ai-gateway.adoc) +* Focus on technical architecture deep-dive +* Keep: Architecture details, request lifecycle, advanced patterns +* Update persona tag to: `platform_admin, app_developer` (both, but technical) + +==== cel-routing-cookbook.adoc + +*Changes:* + +* Add note at top: "This is an advanced reference for Admins configuring routing policies" +* Update persona tag to: `platform_admin` (currently has both) +* No content changes needed + +==== mcp-aggregation-guide.adoc + +*Changes:* + +* Add section for Builders: "Using MCP tools as a Builder" +* Currently too Admin-focused +* Add discovery section: How Builders see available MCP tools +* Keep persona tag: `app_developer` but clarify sections + +==== observability-logs.adoc + +*Changes:* + +* Add intro section distinguishing Admin vs Builder use cases: +** Admins: Monitor all traffic, all gateways, org-wide +** Builders: Monitor their own agent's requests +* Update UI paths to reflect persona-based views +* Persona tag is currently correct: `platform_admin, app_developer` + +==== observability-metrics.adoc + +*Changes:* + +* Similar to logs: Distinguish Admin (org-wide) vs Builder (my usage) views +* Add section: "View your agent's usage" (Builder perspective) +* Persona tag currently: `platform_admin` - should add `app_developer` + +== Navigation (nav.adoc) Changes + +*Current Structure:* + +[source,text] +---- +* AI Gateway +** Overview +** Quickstart +** CEL Routing +** MCP Aggregation +** Observability +** Integrations +---- + +*Proposed Structure:* + +[source,text] +---- +* xref:ai-agents:ai-gateway/index.adoc[AI Gateway] +** xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[What is AI Gateway?] +** For Admins +*** xref:ai-agents:ai-gateway/admin/admin-overview.adoc[Admin Overview] +*** xref:ai-agents:ai-gateway/admin/setup-guide.adoc[Setup Guide] +*** xref:ai-agents:ai-gateway/admin/manage-gateways.adoc[Manage Gateways] +*** xref:ai-agents:ai-gateway/admin/networking-configuration.adoc[Networking Configuration] +*** xref:ai-agents:ai-gateway/admin/configure-policies.adoc[Configure Policies] +*** xref:ai-agents:ai-gateway/admin/access-controls.adoc[Access Controls] +*** xref:ai-agents:ai-gateway/admin/observability-admin.adoc[Monitor Usage] +*** xref:ai-agents:ai-gateway/admin/integrations/index.adoc[Integrations (Admin)] +** For Builders +*** xref:ai-agents:ai-gateway/builders/builder-overview.adoc[Builder Overview] +*** xref:ai-agents:ai-gateway/builders/discover-gateways.adoc[Discover Gateways] +*** xref:ai-agents:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] +*** xref:ai-agents:ai-gateway/builders/available-models.adoc[Available Models] +*** xref:ai-agents:ai-gateway/builders/use-mcp-tools.adoc[Use MCP Tools] +*** xref:ai-agents:ai-gateway/builders/monitor-your-usage.adoc[Monitor Your Usage] +*** xref:ai-agents:ai-gateway/builders/integrations/index.adoc[Integrations (Builder)] +** Reference +*** xref:ai-agents:ai-gateway/reference/gateway-architecture.adoc[Architecture Deep Dive] +*** xref:ai-agents:ai-gateway/reference/cel-routing-cookbook.adoc[CEL Routing Cookbook] +*** xref:ai-agents:ai-gateway/reference/mcp-aggregation-guide.adoc[MCP Aggregation Guide] +*** xref:ai-agents:ai-gateway/reference/observability-logs.adoc[Request Logs] +*** xref:ai-agents:ai-gateway/reference/observability-metrics.adoc[Metrics and Analytics] +---- + +== Implementation Priority + +=== Phase 1: Critical Path (Do First) + +. *Create index.adoc* - Persona router (HIGH) +. *Create discover-gateways.adoc* - Critical Builder need (HIGH) +. *Create what-is-ai-gateway.adoc* - Entry point (HIGH) +. *Split quickstart* into admin/setup-guide.adoc and builders/connect-your-agent.adoc (HIGH) + +=== Phase 2: Complete User Journeys + +. Create admin/manage-gateways.adoc (MEDIUM) +. Create builders/available-models.adoc (MEDIUM) +. Create admin/networking-configuration.adoc (MEDIUM) +. Create admin/access-controls.adoc (MEDIUM) +. Update observability pages with persona distinctions (MEDIUM) + +=== Phase 3: Polish and Optimize + +. Refactor gateway-architecture.adoc (MEDIUM) +. Update mcp-aggregation-guide.adoc with Builder sections (LOW) +. Create admin/builder overview pages (LOW) +. Reorganize integrations folders (LOW) +. Update all cross-references (LOW) + +== Mapping to User Journey + +=== Admin Journey → Content + +[cols="1,2"] +|=== +| Journey Step | Content + +| What is an AI gateway? +| what-is-ai-gateway.adoc + +| How do I create, list, and manage gateways? +| admin/setup-guide.adoc, admin/manage-gateways.adoc + +| Networking configuration & Gateway creation +| admin/networking-configuration.adoc + +| Configure which models are accessible +| admin/setup-guide.adoc (enable models section) + +| Configure access and routing policies +| admin/configure-policies.adoc, cel-routing-cookbook.adoc + +| Track usage and configure budgeting +| admin/setup-guide.adoc (budgets), observability-metrics.adoc +|=== + +=== Builder Journey → Content + +[cols="1,2"] +|=== +| Journey Step | Content + +| What is an AI gateway? +| what-is-ai-gateway.adoc + +| Discover which AI gateways my agents have access to +| *builders/discover-gateways.adoc (NEW)* + +| How do I integrate my agent? +| builders/connect-your-agent.adoc + +| What models/tools are available? +| builders/available-models.adoc, builders/use-mcp-tools.adoc + +| Test my integration +| builders/connect-your-agent.adoc (validation section) + +| Track my usage +| builders/monitor-your-usage.adoc → observability-logs.adoc +|=== + +== Key Principles + +. *Persona First:* Content should clearly identify which persona it serves +. *Progressive Disclosure:* Start simple, link to advanced topics +. *Minimize Duplication:* Use xrefs to avoid maintaining same content twice +. *Clear Entry Points:* Index page must route users effectively +. *Discovery is Critical:* Builders MUST be able to find available gateways + +== Success Metrics + +After implementation, evaluate: + +* Can a Builder discover available gateways in <2 minutes? +* Can an Admin complete setup in <15 minutes? +* Do users report clearer distinction between Admin vs Builder tasks? +* Reduced support tickets about "I can't find which gateway to use" + +== Open Questions + +. *API for Gateway Discovery:* Does the API support listing accessible gateways per user? +. *RBAC Model:* How granular is access control (workspace, gateway, model level)? +. *Private Networking:* What's the detailed setup for private endpoints? +. *Budgets and Limits:* Can Builders see their own usage/limits, or only Admins? +. *Integration Folders:* Should we physically split integration files into admin/ and builders/ subdirectories? + +== Next Steps + +. *Review this plan* with product and docs team +. *Validate API capabilities* for gateway discovery +. *Create Phase 1 content* (index, discover-gateways, what-is, split quickstart) +. *Test with users* from each persona +. *Iterate based on feedback* + +== Appendix: File Operations Summary + +=== New Files to Create + +* `ai-gateway/index.adoc` (replace existing minimal one) +* `ai-gateway/what-is-ai-gateway.adoc` +* `ai-gateway/admin/admin-overview.adoc` +* `ai-gateway/admin/setup-guide.adoc` +* `ai-gateway/admin/manage-gateways.adoc` +* `ai-gateway/admin/networking-configuration.adoc` +* `ai-gateway/admin/configure-policies.adoc` +* `ai-gateway/admin/access-controls.adoc` +* `ai-gateway/builders/builder-overview.adoc` +* `ai-gateway/builders/discover-gateways.adoc` ⭐ CRITICAL +* `ai-gateway/builders/connect-your-agent.adoc` +* `ai-gateway/builders/available-models.adoc` +* `ai-gateway/builders/use-mcp-tools.adoc` +* `ai-gateway/builders/monitor-your-usage.adoc` + +=== Files to Move + +* `ai-gateway/gateway-architecture.adoc` → `ai-gateway/reference/gateway-architecture.adoc` +* `ai-gateway/cel-routing-cookbook.adoc` → `ai-gateway/reference/cel-routing-cookbook.adoc` +* `ai-gateway/mcp-aggregation-guide.adoc` → `ai-gateway/reference/mcp-aggregation-guide.adoc` +* `ai-gateway/observability-*.adoc` → `ai-gateway/reference/observability-*.adoc` + +=== Files to Refactor + +* `ai-gateway/gateway-quickstart.adoc` (quickstart) - split content between admin and builder paths +* `ai-gateway/gateway-architecture.adoc` - extract conceptual content to what-is page +* `ai-gateway/observability-logs.adoc` - add persona-specific sections +* `ai-gateway/observability-metrics.adoc` - add builder usage section + +=== Files to Keep As-Is (Minimal Changes) + +* `ai-gateway/integrations/*-admin.adoc` +* `ai-gateway/integrations/*-user.adoc` +* `ai-gateway/migration-guide.adoc` +* `ai-gateway/gateway-quickstart.adoc` (consolidated) From 7621fdbbc4b9a4acd66fc8965c5e499031414af1 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:07:52 -0700 Subject: [PATCH 31/50] Remove unused ai-gateway.png image file Deleted modules/shared/partials/ai-gateway.png as it's no longer needed. 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ecF%f1Yj+-L=ACGAt=<$8Z0B)S*gPZGyZ;A+oa)d3 From 273efa9b200886d0171eaf488d12c94d33794561 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:10:10 -0700 Subject: [PATCH 32/50] Remove persona restructuring planning document Deleted ai-gateway-persona-restructuring-plan.adoc as it was an internal planning document that isn't referenced anywhere in the published docs. Planning documents don't need to be in the documentation tree. Co-Authored-By: Claude Sonnet 4.5 --- ...ai-gateway-persona-restructuring-plan.adoc | 634 ------------------ 1 file changed, 634 deletions(-) delete mode 100644 modules/ai-agents/partials/ai-gateway-persona-restructuring-plan.adoc diff --git a/modules/ai-agents/partials/ai-gateway-persona-restructuring-plan.adoc b/modules/ai-agents/partials/ai-gateway-persona-restructuring-plan.adoc deleted file mode 100644 index f1062254b..000000000 --- a/modules/ai-agents/partials/ai-gateway-persona-restructuring-plan.adoc +++ /dev/null @@ -1,634 +0,0 @@ -= AI Gateway Content Restructuring Plan -:description: Persona-based reorganization of AI Gateway documentation - -*Date:* January 21, 2026 - -*Purpose:* Restructure AI Gateway documentation to align with two primary personas (Admins and Builders) and their distinct user journeys. - -== Executive Summary - -The current AI Gateway documentation is comprehensive but doesn't clearly distinguish between Admin and Builder personas. This plan proposes: - -. *Restructure the navigation* to create clear persona-based paths -. *Create new landing/discovery pages* for each persona -. *Tag existing content* with appropriate personas -. *Add missing content* to complete user journeys -. *Reorganize the index* to guide users based on their role - -== Personas Defined - -=== Admin Persona - -* *Role:* Platform administrators with broad oversight -* *Responsibilities:* -** Configure system-level parameters -** Enable/disable LLM providers and models -** Set up gateways with policies, routing, and budgets -** Monitor usage across the organization -** Manage access control and security -* *Key Questions:* -** How do I set up and configure AI Gateway for my organization? -** How do I control costs and enforce policies? -** How do I monitor usage across all teams? - -=== Builder Persona - -* *Role:* Developers/engineers building agents or AI applications -* *Responsibilities:* -** Build agents and AI applications -** Integrate agents with available gateways -** Use MCP tools and services -** Monitor their own usage and costs -* *Key Questions:* -** Which gateways can I use? -** How do I connect my agent to a gateway? -** What tools/models are available to me? -** How much am I spending? - -== User Journey Mapping - -=== Admin User Journey - -. *Understand* → What is an AI gateway? (conceptual) -. *Set Up* → Enable providers, enable models, create gateways -. *Configure* → Set up networking, policies, routing, budgets -. *Monitor* → Track usage, costs, and manage access -. *Optimize* → Adjust policies, routing, and costs based on metrics - -=== Builder User Journey - -. *Discover* → Which gateways can I access? -. *Connect* → How do I integrate my agent with a gateway? -. *Build* → Use available models and MCP tools -. *Test* → Validate my agent's integration -. *Monitor* → Track my usage and costs - -== Content Gap Analysis - -=== Missing Content - -[cols="1,1,1,2"] -|=== -| Content Needed | Persona | Priority | Current Status - -| Gateway Discovery page -| Builder -| HIGH -| Missing - critical for Builder journey - -| "What is AI Gateway" standalone page -| Both -| HIGH -| Content exists in overview but needs extraction - -| Admin Setup Guide -| Admin -| HIGH -| Scattered across quickstart - needs consolidation - -| Builder Integration Guide -| Builder -| HIGH -| Exists partially in quickstart/integrations - -| Networking Configuration page -| Admin -| MEDIUM -| Mentioned but not detailed - -| Access Management page -| Admin -| MEDIUM -| Missing -|=== - -=== Existing Content Gaps - -. *gateway-architecture.adoc* - Too dense, mixes Admin and Builder concerns -. *gateway-quickstart.adoc (quickstart)* - Conflates Admin setup with Builder usage -. *index.adoc* - Too minimal, provides no guidance -. *No discovery mechanism* - Builders don't know which gateways they can use - -== Recommended Content Structure - -=== New Navigation Structure - -[source,text] ----- -AI Gateway/ -├── index.adoc (New: Persona-based landing page) -├── what-is-ai-gateway.adoc (New: Extracted from overview) -│ -├── For Admins/ -│ ├── admin-overview.adoc (New: Admin-focused overview) -│ ├── setup-guide.adoc (New: Complete admin setup) -│ │ ├── enable-providers.adoc (Extracted from quickstart) -│ │ ├── enable-models.adoc (Extracted from quickstart) -│ │ ├── create-gateways.adoc (Extracted from quickstart) -│ │ ├── networking-configuration.adoc (New/Expanded) -│ ├── configure-policies.adoc (Consolidated) -│ │ ├── routing-policies.adoc (Link to CEL cookbook) -│ │ ├── access-controls.adoc (New) -│ │ ├── budgets-and-limits.adoc (Consolidated from quickstart) -│ ├── manage-gateways.adoc (New: List, edit, delete) -│ ├── observability-admin.adoc (Link to metrics dashboard) -│ └── integrations/ (Admin versions) -│ ├── index.adoc -│ ├── claude-code-admin.adoc -│ ├── cursor-admin.adoc -│ └── ... -│ -├── For Builders/ -│ ├── builder-overview.adoc (New: Builder-focused overview) -│ ├── discover-gateways.adoc (NEW - CRITICAL) -│ ├── connect-your-agent.adoc (New: Integration guide) -│ ├── available-models.adoc (New: How to see what's available) -│ ├── use-mcp-tools.adoc (Link to MCP aggregation) -│ ├── test-your-integration.adoc (New: Validation) -│ ├── monitor-your-usage.adoc (Link to observability-logs) -│ └── integrations/ (Builder versions) -│ ├── index.adoc -│ ├── claude-code-user.adoc -│ ├── cursor-user.adoc -│ └── ... -│ -├── Reference/ -│ ├── gateway-architecture.adoc (Refactored: Technical deep-dive) -│ ├── cel-routing-cookbook.adoc (Existing) -│ ├── mcp-aggregation-guide.adoc (Existing) -│ ├── observability-logs.adoc (Existing) -│ ├── observability-metrics.adoc (Existing) -│ ├── migration-guide.adoc (Existing) -│ └── gateway-quickstart.adoc (Consolidated from ai-gateway.adoc and quickstart-enhanced.adoc) ----- - -== Detailed Content Recommendations - -=== 1. Create New index.adoc (HIGH PRIORITY) - -*Current State:* Minimal landing page with just a description - -*Proposed Change:* Transform into a persona-based router - -*Content Structure:* - -[source,text] ----- -= AI Gateway -:description: Unified access layer for LLM providers and AI tools -:page-layout: index - -The Redpanda AI Gateway provides centralized routing, policy enforcement, cost management, and observability for all your AI traffic. - -== Choose Your Path - -[.persona-card] -=== I'm an Administrator -You manage AI Gateway infrastructure, configure providers, set policies, and monitor organizational usage. - -* xref:ai-gateway/admin/admin-overview.adoc[Admin Overview] -* xref:ai-gateway/admin/setup-guide.adoc[Setup Guide] -* xref:ai-gateway/admin/manage-gateways.adoc[Manage Gateways] - -[.persona-card] -=== I'm a Builder -You're building AI agents or applications and need to connect to available gateways. - -* xref:ai-gateway/builders/builder-overview.adoc[Builder Overview] -* xref:ai-gateway/builders/discover-gateways.adoc[Discover Available Gateways] -* xref:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] - -== Learn More - -* xref:ai-gateway/what-is-ai-gateway.adoc[What is an AI Gateway?] -* xref:ai-gateway/reference/gateway-architecture.adoc[Technical Architecture] ----- - -*Persona Tagging:* Both - -=== 2. Create what-is-ai-gateway.adoc (HIGH PRIORITY) - -*Purpose:* Standalone conceptual page answering "What is an AI gateway?" - -*Source:* Extract from gateway-architecture.adoc (lines 15-147) - -*Content to Include:* - -* The problem AI Gateway solves -* Core capabilities (unified access, routing, MCP aggregation, observability) -* Common gateway patterns -* High-level architecture diagram - -*Remove from Overview:* Keep technical details in overview, move conceptual understanding here - -*Persona Tagging:* Both (Admin and Builder) - -=== 3. Create discover-gateways.adoc (HIGH PRIORITY - NEW) - -*Purpose:* Help Builders find which gateways they have access to - -*This is CRITICAL and completely missing from current content* - -*Content Structure:* - -[source,text] ----- -= Discover Available Gateways -:description: Find which AI Gateways you can access and their configurations -:page-personas: app_developer - -As a builder, you need to know which gateways are available to you before integrating your agent. - -== List your accessible gateways - -=== Using the Console - -1. Navigate to AI Gateway → My Gateways -2. View all gateways you have access to: - * Gateway Name - * Gateway ID (for `rp-aigw-id` header) - * Endpoint URL - * Available Models - * MCP Tools (if configured) - -=== Using the API - -[source,bash] ----- -curl https://{CLUSTER}.cloud.redpanda.com/api/ai-gateway/v1/gateways \ - -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" ----- - -== Understanding gateway information - -Each gateway shows: - -* *Gateway ID*: Use this in the `rp-aigw-id` header -* *Endpoint URL*: Base URL for API requests -* *Available Models*: Which models you can access (e.g., `openai/gpt-4o`, `anthropic/claude-sonnet-3.5`) -* *Rate Limits*: Your request limits -* *MCP Tools*: Available MCP servers and tools (if enabled) - -== Check gateway availability - -Before integrating, test gateway access: - -[source,bash] ----- -curl https://{GATEWAY_ENDPOINT}/v1/models \ - -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \ - -H "rp-aigw-id: ${GATEWAY_ID}" ----- - -Expected response: List of available models - -== Next steps - -* xref:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] -* xref:ai-gateway/builders/available-models.adoc[View Available Models] ----- - -*Persona Tagging:* Builder (app_developer) - -=== 4. Refactor gateway-quickstart.adoc (quickstart) - -*Current Problem:* Mixes Admin setup (Steps 1-3) with Builder usage (Steps 4-5, integrations) - -*Proposed Split:* - -==== Create admin/setup-guide.adoc (Admin path) - -* Step 1: Enable providers -* Step 2: Enable models -* Step 3: Create gateways -* Step 4: Configure LLM routing (policies, pools, rate limits) -* Step 5: Configure MCP tools - -==== Create builders/connect-your-agent.adoc (Builder path) - -* Prerequisites: Gateway ID and endpoint (from discovery) -* Step 1: Get your gateway credentials -* Step 2: Configure your client SDK -* Step 3: Make your first request -* Step 4: Handle responses -* Step 5: Validate integration - -*Content to Move:* - -* Lines 17-89 (Admin steps) → admin/setup-guide.adoc -* Lines 160-337 (Integration examples) → builders/connect-your-agent.adoc -* Lines 106-118 (Observability) → Link to observability pages - -=== 5. Create admin/networking-configuration.adoc (MEDIUM PRIORITY) - -*Purpose:* Dedicated page for networking setup - -*Content:* Currently mentioned but not detailed - -*Content Structure:* - -[source,text] ----- -= Networking Configuration -:description: Configure networking for AI Gateway including endpoints, private networking, and connectivity -:page-personas: platform_admin - -Configure network access and connectivity for your AI Gateway. - -== Gateway endpoints - -When you create a gateway, you receive: - -* Public endpoint: `https://gw.ai.panda.com` -* Private endpoint (if enabled): `https://gw-internal.ai.panda.com` - -== Public vs private endpoints - -*Public endpoints:* -- Accessible from internet -- Use for external agents, testing -- Standard TLS encryption - -*Private endpoints:* -- Accessible only within your VPC/network -- Use for production workloads -- Enhanced security - -== Configure private networking - -[PLACEHOLDER: Add private networking setup steps] - -== Connectivity requirements - -Outbound connections required: -- To LLM provider APIs (OpenAI, Anthropic, etc.) -- To configured MCP servers (if using MCP aggregation) - -Inbound connections: -- From your agents/applications to gateway endpoint - -== Firewall and security groups - -[PLACEHOLDER: Add security group configuration] - -== Next steps - -* xref:ai-gateway/admin/configure-policies.adoc[Configure Access Policies] ----- - -*Persona Tagging:* Admin (platform_admin) - -=== 6. Create admin/access-controls.adoc (MEDIUM PRIORITY) - -*Purpose:* How Admins control who can access which gateways - -*Content:* - -* Gateway-level access control -* API key management -* RBAC configuration (if available) -* Audit logging - -*Persona Tagging:* Admin (platform_admin) - -=== 7. Update Existing Files - -==== gateway-architecture.adoc - -*Changes:* - -* Remove conceptual "What is" content (move to what-is-ai-gateway.adoc) -* Focus on technical architecture deep-dive -* Keep: Architecture details, request lifecycle, advanced patterns -* Update persona tag to: `platform_admin, app_developer` (both, but technical) - -==== cel-routing-cookbook.adoc - -*Changes:* - -* Add note at top: "This is an advanced reference for Admins configuring routing policies" -* Update persona tag to: `platform_admin` (currently has both) -* No content changes needed - -==== mcp-aggregation-guide.adoc - -*Changes:* - -* Add section for Builders: "Using MCP tools as a Builder" -* Currently too Admin-focused -* Add discovery section: How Builders see available MCP tools -* Keep persona tag: `app_developer` but clarify sections - -==== observability-logs.adoc - -*Changes:* - -* Add intro section distinguishing Admin vs Builder use cases: -** Admins: Monitor all traffic, all gateways, org-wide -** Builders: Monitor their own agent's requests -* Update UI paths to reflect persona-based views -* Persona tag is currently correct: `platform_admin, app_developer` - -==== observability-metrics.adoc - -*Changes:* - -* Similar to logs: Distinguish Admin (org-wide) vs Builder (my usage) views -* Add section: "View your agent's usage" (Builder perspective) -* Persona tag currently: `platform_admin` - should add `app_developer` - -== Navigation (nav.adoc) Changes - -*Current Structure:* - -[source,text] ----- -* AI Gateway -** Overview -** Quickstart -** CEL Routing -** MCP Aggregation -** Observability -** Integrations ----- - -*Proposed Structure:* - -[source,text] ----- -* xref:ai-agents:ai-gateway/index.adoc[AI Gateway] -** xref:ai-agents:ai-gateway/what-is-ai-gateway.adoc[What is AI Gateway?] -** For Admins -*** xref:ai-agents:ai-gateway/admin/admin-overview.adoc[Admin Overview] -*** xref:ai-agents:ai-gateway/admin/setup-guide.adoc[Setup Guide] -*** xref:ai-agents:ai-gateway/admin/manage-gateways.adoc[Manage Gateways] -*** xref:ai-agents:ai-gateway/admin/networking-configuration.adoc[Networking Configuration] -*** xref:ai-agents:ai-gateway/admin/configure-policies.adoc[Configure Policies] -*** xref:ai-agents:ai-gateway/admin/access-controls.adoc[Access Controls] -*** xref:ai-agents:ai-gateway/admin/observability-admin.adoc[Monitor Usage] -*** xref:ai-agents:ai-gateway/admin/integrations/index.adoc[Integrations (Admin)] -** For Builders -*** xref:ai-agents:ai-gateway/builders/builder-overview.adoc[Builder Overview] -*** xref:ai-agents:ai-gateway/builders/discover-gateways.adoc[Discover Gateways] -*** xref:ai-agents:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] -*** xref:ai-agents:ai-gateway/builders/available-models.adoc[Available Models] -*** xref:ai-agents:ai-gateway/builders/use-mcp-tools.adoc[Use MCP Tools] -*** xref:ai-agents:ai-gateway/builders/monitor-your-usage.adoc[Monitor Your Usage] -*** xref:ai-agents:ai-gateway/builders/integrations/index.adoc[Integrations (Builder)] -** Reference -*** xref:ai-agents:ai-gateway/reference/gateway-architecture.adoc[Architecture Deep Dive] -*** xref:ai-agents:ai-gateway/reference/cel-routing-cookbook.adoc[CEL Routing Cookbook] -*** xref:ai-agents:ai-gateway/reference/mcp-aggregation-guide.adoc[MCP Aggregation Guide] -*** xref:ai-agents:ai-gateway/reference/observability-logs.adoc[Request Logs] -*** xref:ai-agents:ai-gateway/reference/observability-metrics.adoc[Metrics and Analytics] ----- - -== Implementation Priority - -=== Phase 1: Critical Path (Do First) - -. *Create index.adoc* - Persona router (HIGH) -. *Create discover-gateways.adoc* - Critical Builder need (HIGH) -. *Create what-is-ai-gateway.adoc* - Entry point (HIGH) -. *Split quickstart* into admin/setup-guide.adoc and builders/connect-your-agent.adoc (HIGH) - -=== Phase 2: Complete User Journeys - -. Create admin/manage-gateways.adoc (MEDIUM) -. Create builders/available-models.adoc (MEDIUM) -. Create admin/networking-configuration.adoc (MEDIUM) -. Create admin/access-controls.adoc (MEDIUM) -. Update observability pages with persona distinctions (MEDIUM) - -=== Phase 3: Polish and Optimize - -. Refactor gateway-architecture.adoc (MEDIUM) -. Update mcp-aggregation-guide.adoc with Builder sections (LOW) -. Create admin/builder overview pages (LOW) -. Reorganize integrations folders (LOW) -. Update all cross-references (LOW) - -== Mapping to User Journey - -=== Admin Journey → Content - -[cols="1,2"] -|=== -| Journey Step | Content - -| What is an AI gateway? -| what-is-ai-gateway.adoc - -| How do I create, list, and manage gateways? -| admin/setup-guide.adoc, admin/manage-gateways.adoc - -| Networking configuration & Gateway creation -| admin/networking-configuration.adoc - -| Configure which models are accessible -| admin/setup-guide.adoc (enable models section) - -| Configure access and routing policies -| admin/configure-policies.adoc, cel-routing-cookbook.adoc - -| Track usage and configure budgeting -| admin/setup-guide.adoc (budgets), observability-metrics.adoc -|=== - -=== Builder Journey → Content - -[cols="1,2"] -|=== -| Journey Step | Content - -| What is an AI gateway? -| what-is-ai-gateway.adoc - -| Discover which AI gateways my agents have access to -| *builders/discover-gateways.adoc (NEW)* - -| How do I integrate my agent? -| builders/connect-your-agent.adoc - -| What models/tools are available? -| builders/available-models.adoc, builders/use-mcp-tools.adoc - -| Test my integration -| builders/connect-your-agent.adoc (validation section) - -| Track my usage -| builders/monitor-your-usage.adoc → observability-logs.adoc -|=== - -== Key Principles - -. *Persona First:* Content should clearly identify which persona it serves -. *Progressive Disclosure:* Start simple, link to advanced topics -. *Minimize Duplication:* Use xrefs to avoid maintaining same content twice -. *Clear Entry Points:* Index page must route users effectively -. *Discovery is Critical:* Builders MUST be able to find available gateways - -== Success Metrics - -After implementation, evaluate: - -* Can a Builder discover available gateways in <2 minutes? -* Can an Admin complete setup in <15 minutes? -* Do users report clearer distinction between Admin vs Builder tasks? -* Reduced support tickets about "I can't find which gateway to use" - -== Open Questions - -. *API for Gateway Discovery:* Does the API support listing accessible gateways per user? -. *RBAC Model:* How granular is access control (workspace, gateway, model level)? -. *Private Networking:* What's the detailed setup for private endpoints? -. *Budgets and Limits:* Can Builders see their own usage/limits, or only Admins? -. *Integration Folders:* Should we physically split integration files into admin/ and builders/ subdirectories? - -== Next Steps - -. *Review this plan* with product and docs team -. *Validate API capabilities* for gateway discovery -. *Create Phase 1 content* (index, discover-gateways, what-is, split quickstart) -. *Test with users* from each persona -. *Iterate based on feedback* - -== Appendix: File Operations Summary - -=== New Files to Create - -* `ai-gateway/index.adoc` (replace existing minimal one) -* `ai-gateway/what-is-ai-gateway.adoc` -* `ai-gateway/admin/admin-overview.adoc` -* `ai-gateway/admin/setup-guide.adoc` -* `ai-gateway/admin/manage-gateways.adoc` -* `ai-gateway/admin/networking-configuration.adoc` -* `ai-gateway/admin/configure-policies.adoc` -* `ai-gateway/admin/access-controls.adoc` -* `ai-gateway/builders/builder-overview.adoc` -* `ai-gateway/builders/discover-gateways.adoc` ⭐ CRITICAL -* `ai-gateway/builders/connect-your-agent.adoc` -* `ai-gateway/builders/available-models.adoc` -* `ai-gateway/builders/use-mcp-tools.adoc` -* `ai-gateway/builders/monitor-your-usage.adoc` - -=== Files to Move - -* `ai-gateway/gateway-architecture.adoc` → `ai-gateway/reference/gateway-architecture.adoc` -* `ai-gateway/cel-routing-cookbook.adoc` → `ai-gateway/reference/cel-routing-cookbook.adoc` -* `ai-gateway/mcp-aggregation-guide.adoc` → `ai-gateway/reference/mcp-aggregation-guide.adoc` -* `ai-gateway/observability-*.adoc` → `ai-gateway/reference/observability-*.adoc` - -=== Files to Refactor - -* `ai-gateway/gateway-quickstart.adoc` (quickstart) - split content between admin and builder paths -* `ai-gateway/gateway-architecture.adoc` - extract conceptual content to what-is page -* `ai-gateway/observability-logs.adoc` - add persona-specific sections -* `ai-gateway/observability-metrics.adoc` - add builder usage section - -=== Files to Keep As-Is (Minimal Changes) - -* `ai-gateway/integrations/*-admin.adoc` -* `ai-gateway/integrations/*-user.adoc` -* `ai-gateway/migration-guide.adoc` -* `ai-gateway/gateway-quickstart.adoc` (consolidated) From 3de2abf1f401f046f05e8058c8f9987ea473a310 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:30:15 -0700 Subject: [PATCH 33/50] Update Claude Code config to match Anthropic MCP schema - Change config file path from ~/.claude/config.json to ~/.claude.json - Add mention of project-level .mcp.json alternative - Replace "transport": "http" with "type": "http" per MCP spec - Remove undocumented apiProviders sections from all examples - Remove request retry configuration section (used apiProviders) - Remove troubleshooting item about missing apiProviders - Update all troubleshooting commands to use correct file path Files updated: - claude-code-admin.adoc - claude-code-user.adoc Co-Authored-By: Claude Sonnet 4.5 --- .../integrations/claude-code-admin.adoc | 13 +-- .../integrations/claude-code-user.adoc | 94 +++---------------- 2 files changed, 16 insertions(+), 91 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc index 396623f96..5bbb2e844 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc @@ -328,28 +328,20 @@ Replace: === Configuration file -Alternatively, users can edit `~/.claude/config.json`: +Alternatively, users can edit `~/.claude.json` (user-level) or `.mcp.json` (project-level): [source,json] ---- { "mcpServers": { "redpanda-ai-gateway": { - "transport": "http", + "type": "http", "url": "https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/mcp", "headers": { "Authorization": "Bearer YOUR_API_TOKEN", "rp-aigw-id": "GATEWAY_ID" } } - }, - "apiProviders": { - "redpanda": { - "baseURL": "https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1", - "headers": { - "rp-aigw-id": "GATEWAY_ID" - } - } } } ---- @@ -357,7 +349,6 @@ Alternatively, users can edit `~/.claude/config.json`: This configuration: * Connects Claude Code to the aggregated MCP endpoint -* Routes LLM requests through the AI Gateway * Includes authentication and gateway identification headers == Monitor Claude Code usage diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc index 30160c97f..41132c491 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc @@ -94,40 +94,25 @@ For more complex configurations or when managing multiple gateways, edit the Cla Claude Code stores configuration in: -* macOS/Linux: `~/.claude/config.json` -* Windows: `%USERPROFILE%\.claude\config.json` - -Create the directory if it doesn't exist: - -[,bash] ----- -mkdir -p ~/.claude ----- +* macOS/Linux: `~/.claude.json` (user-level) or `.mcp.json` (project-level) +* Windows: `%USERPROFILE%\.claude.json` === Basic configuration -Create or edit `~/.claude/config.json` with the following structure: +Create or edit `~/.claude.json` with the following structure: [,json] ---- { "mcpServers": { "redpanda-ai-gateway": { - "transport": "http", + "type": "http", "url": "https://gw.ai.panda.com/mcp", "headers": { "Authorization": "Bearer YOUR_API_KEY", "rp-aigw-id": "GATEWAY_ID" } } - }, - "apiProviders": { - "redpanda": { - "baseURL": "https://gw.ai.panda.com", - "headers": { - "rp-aigw-id": "GATEWAY_ID" - } - } } } ---- @@ -146,7 +131,7 @@ To configure different gateways for development and production: { "mcpServers": { "redpanda-staging": { - "transport": "http", + "type": "http", "url": "https://gw.staging.ai.panda.com/mcp", "headers": { "Authorization": "Bearer STAGING_API_KEY", @@ -154,32 +139,18 @@ To configure different gateways for development and production: } }, "redpanda-production": { - "transport": "http", + "type": "http", "url": "https://gw.ai.panda.com/mcp", "headers": { "Authorization": "Bearer PROD_API_KEY", "rp-aigw-id": "prod-gateway-456" } } - }, - "apiProviders": { - "redpanda-staging": { - "baseURL": "https://gw.staging.ai.panda.com", - "headers": { - "rp-aigw-id": "staging-gateway-123" - } - }, - "redpanda-production": { - "baseURL": "https://gw.ai.panda.com", - "headers": { - "rp-aigw-id": "prod-gateway-456" - } - } } } ---- -Switch between gateways by selecting the appropriate MCP server or API provider when using Claude Code. +Switch between gateways by selecting the appropriate MCP server when using Claude Code. === Configuration with environment variables @@ -190,21 +161,13 @@ For sensitive credentials, use environment variables instead of hardcoding value { "mcpServers": { "redpanda-ai-gateway": { - "transport": "http", + "type": "http", "url": "${REDPANDA_GATEWAY_URL}/mcp", "headers": { "Authorization": "Bearer ${REDPANDA_API_KEY}", "rp-aigw-id": "${REDPANDA_GATEWAY_ID}" } } - }, - "apiProviders": { - "redpanda": { - "baseURL": "${REDPANDA_GATEWAY_URL}", - "headers": { - "rp-aigw-id": "${REDPANDA_GATEWAY_ID}" - } - } } } ---- @@ -280,7 +243,7 @@ Configure timeout for MCP requests in the configuration file: { "mcpServers": { "redpanda-ai-gateway": { - "transport": "http", + "type": "http", "url": "https://gw.ai.panda.com/mcp", "headers": { "Authorization": "Bearer YOUR_API_KEY", @@ -294,31 +257,6 @@ Configure timeout for MCP requests in the configuration file: The `timeout` value is in milliseconds. Default is 10000 (10 seconds). Increase this for MCP tools that perform long-running operations. -=== Request retry configuration - -Configure retry behavior for transient failures: - -[,json] ----- -{ - "apiProviders": { - "redpanda": { - "baseURL": "https://gw.ai.panda.com", - "headers": { - "rp-aigw-id": "GATEWAY_ID" - }, - "retry": { - "maxRetries": 3, - "retryDelay": 1000, - "retryCondition": ["5xx", "timeout"] - } - } - } -} ----- - -This configuration retries requests up to 3 times on server errors (5xx status codes) or timeouts, with a 1-second delay between retries. - === Debug mode Enable debug logging to troubleshoot connection issues: @@ -396,10 +334,6 @@ If this times out, check your network configuration, firewall rules, or VPN conn + Verify that the `rp-aigw-id` header in your configuration matches the gateway you're viewing in the dashboard. -. **Using direct Anthropic API** -+ -If you didn't configure the `apiProviders` section, Claude Code may be routing directly to Anthropic instead of through your gateway. Verify the `apiProviders` section exists in your config file. - . **Log ingestion delay** + Gateway logs can take 5-10 seconds to appear in the dashboard. Wait briefly and refresh. @@ -432,7 +366,7 @@ If you're hitting rate limits, the gateway may be queuing requests. Check the ob === Configuration file not loading -**Symptom**: Changes to `config.json` don't take effect. +**Symptom**: Changes to `.claude.json` don't take effect. **Solutions**: @@ -451,11 +385,11 @@ claude . **Validate JSON syntax** + -Ensure your `config.json` is valid JSON. Use a JSON validator: +Ensure your `.claude.json` is valid JSON. Use a JSON validator: + [,bash] ---- -python3 -m json.tool ~/.claude/config.json +python3 -m json.tool ~/.claude.json ---- . **Check file permissions** @@ -464,14 +398,14 @@ Verify Claude Code can read the configuration file: + [,bash] ---- -ls -la ~/.claude/config.json +ls -la ~/.claude.json ---- + The file should be readable by your user. If not, fix permissions: + [,bash] ---- -chmod 600 ~/.claude/config.json +chmod 600 ~/.claude.json ---- == Next steps From 2d3c01878c7d7e7a90ffd367be06dbcae6efea46 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:46:11 -0700 Subject: [PATCH 34/50] Add note about environment variable interpolation in mcpServers Clarify that Claude Code supports ${VAR} syntax for environment variable interpolation in the mcpServers section, specifying which variables will be resolved at runtime (REDPANDA_GATEWAY_URL, REDPANDA_GATEWAY_ID, REDPANDA_API_KEY). Co-Authored-By: Claude Sonnet 4.5 --- .../pages/ai-gateway/integrations/claude-code-user.adoc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc index 41132c491..7fb085124 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc @@ -172,6 +172,8 @@ For sensitive credentials, use environment variables instead of hardcoding value } ---- +NOTE: Claude Code supports `${VAR}` interpolation syntax in the `mcpServers` section. The variables `REDPANDA_GATEWAY_URL`, `REDPANDA_GATEWAY_ID`, and `REDPANDA_API_KEY` will be resolved from environment variables at runtime. + Set environment variables before launching Claude Code: [,bash] From 6c2b01c289146cb5df904c4b8a295bc82166df9f Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:47:55 -0700 Subject: [PATCH 35/50] Update Cline config to use official extension settings Remove unofficial VS Code settings keys (cline.apiProvider, cline.apiBaseUrl, cline.customHeaders) and document that API/provider configuration is managed via Cline extension global state, not settings.json. Replace MCP configuration to use cline_mcp_settings.json with official schema: - Change "transport": "http" to "type": "streamableHttp" - Remove "cline.mcpServers" wrapper (use "mcpServers" directly) - Add "Cline > Mcp: Mode" toggle for MCP enablement - Document Cline UI and cline_mcp_settings.json as config methods Update configuration scope section to reflect extension global state storage model instead of VS Code settings.json levels. Co-Authored-By: Claude Sonnet 4.5 --- .../ai-gateway/integrations/cline-admin.adoc | 87 +++++++++---------- 1 file changed, 41 insertions(+), 46 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc index 3c1186be6..64fb52b7c 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc @@ -327,35 +327,22 @@ Implement token rotation for security: Provide these instructions to users configuring Cline in VS Code. -=== VS Code settings configuration +=== API provider configuration -Users configure Cline through VS Code settings (either UI or `settings.json`). +Users configure Cline's API provider and credentials through the Cline extension interface. -==== Using VS Code Settings UI +IMPORTANT: API provider configuration (API keys, base URLs, custom headers) is managed via Cline's extension global state, not VS Code `settings.json`. These settings are stored in the extension's internal state and must be configured through the Cline UI. -. Open VS Code Settings (Cmd/Ctrl + ,) -. Search for "Cline" -. Configure the following settings: -+ -* *Cline: API Provider*: Select "Custom" or "Anthropic" -* *Cline: API Base URL*: `https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1` -* *Cline: API Key*: The API token generated earlier - -==== Using settings.json - -Alternatively, users can edit `.vscode/settings.json` in their workspace: +==== Configure via Cline UI -[source,json] ----- -{ - "cline.apiProvider": "custom", - "cline.apiBaseUrl": "https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1", - "cline.apiKey": "YOUR_API_TOKEN", - "cline.customHeaders": { - "rp-aigw-id": "GATEWAY_ID" - } -} ----- +. Open the Cline extension panel in VS Code +. Click the settings icon or gear menu +. Configure the API connection: ++ +* *API Provider*: Select "Custom" or "Anthropic" +* *API Base URL*: `https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1` +* *API Key*: The API token generated earlier +* *Custom Headers*: Add `rp-aigw-id` with value `GATEWAY_ID` Replace: @@ -365,14 +352,30 @@ Replace: === MCP server configuration -Configure Cline to connect to the aggregated MCP endpoint: +Configure Cline to connect to the aggregated MCP endpoint through the Cline UI or by editing `cline_mcp_settings.json`. + +==== Enable MCP mode + +. Open VS Code Settings (Cmd/Ctrl + ,) +. Search for "Cline > Mcp: Mode" +. Enable the MCP mode toggle + +==== Configure MCP server via Cline UI + +. Open the Cline extension panel in VS Code +. Navigate to MCP server settings +. Add the Redpanda AI Gateway MCP server with the connection details + +==== Configure via cline_mcp_settings.json + +Alternatively, edit `cline_mcp_settings.json` (located in the Cline extension storage directory): [source,json] ---- { - "cline.mcpServers": { + "mcpServers": { "redpanda-ai-gateway": { - "transport": "http", + "type": "streamableHttp", "url": "https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/mcp", "headers": { "Authorization": "Bearer YOUR_API_TOKEN", @@ -383,30 +386,22 @@ Configure Cline to connect to the aggregated MCP endpoint: } ---- -This configuration: - -* Connects Cline to the aggregated MCP endpoint -* Routes LLM requests through the AI Gateway -* Includes authentication and gateway identification headers +Replace: -=== User settings vs workspace settings +* `{CLUSTER_ID}`: Your Redpanda cluster ID +* `YOUR_API_TOKEN`: The API token generated earlier +* `GATEWAY_ID`: The gateway ID from gateway creation -Cline settings can be configured at two levels: +This configuration connects Cline to the aggregated MCP endpoint with authentication and gateway identification headers. -[cols="1,2,2"] -|=== -|Level |Location |Use Case +=== Configuration scope -|User settings -|`~/.vscode/settings.json` -|Personal API token, default gateway for all projects +Cline stores configuration in the extension's global state: -|Workspace settings -|`.vscode/settings.json` in project -|Project-specific gateway, shared team configuration -|=== +* *API Provider settings*: Stored globally per VS Code instance, applies to all workspaces +* *MCP server settings*: Can be configured per workspace using `cline_mcp_settings.json` -Use workspace settings when different projects require different gateways (for example, development vs production environments). +For project-specific MCP server configurations (for example, development vs production gateways), place `cline_mcp_settings.json` in the workspace directory and configure different MCP servers per project. == Monitor Cline usage From d73e5721aa07a675399effa7bde524a6bbfc240a Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:49:27 -0700 Subject: [PATCH 36/50] Replace hardcoded token with env var in curl example Replace "YOUR_API_TOKEN" with "${REDPANDA_API_TOKEN}" environment variable in the curl Authorization header to avoid secret scanner false positives. Add inline comment and NOTE block to inform users they must set the environment variable before running the command. Co-Authored-By: Claude Sonnet 4.5 --- .../ai-agents/pages/ai-gateway/integrations/cline-admin.adoc | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc index 64fb52b7c..8552199c4 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc @@ -455,8 +455,9 @@ Programmatically access logs for integration with monitoring systems: [source,bash] ---- +# Set REDPANDA_API_TOKEN environment variable before running curl https://{CLUSTER_ID}.cloud.redpanda.com/api/ai-gateway/logs \ - -H "Authorization: Bearer YOUR_API_TOKEN" \ + -H "Authorization: Bearer ${REDPANDA_API_TOKEN}" \ -H "Content-Type: application/json" \ -d '{ "gateway_id": "GATEWAY_ID", @@ -466,6 +467,8 @@ curl https://{CLUSTER_ID}.cloud.redpanda.com/api/ai-gateway/logs \ }' ---- +NOTE: Set the `REDPANDA_API_TOKEN` environment variable to your API token before running this command. + == Security considerations Apply these security best practices for Cline deployments. From 1b32ae01fe02f3a32dfc1ef5b3c49b82d250ad65 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:51:03 -0700 Subject: [PATCH 37/50] Update Continue.dev config to current YAML standard Convert configuration from JSON to YAML format: - Change file name from ~/.continue/config.json to ~/.continue/config.yaml - Convert all JSON examples to YAML syntax - Update models, tabAutocompleteModel, apiBase, apiKey references Update MCP configuration to official schema: - Change modelContextProtocolServers (plural) to modelContextProtocolServer (singular) - Replace transport type "http" with "streamable-http" (valid value) - Add recommended directory-based configuration using ~/.continue/mcpServers/ - Provide alternative inline configuration in config.yaml Document that Continue.dev automatically discovers MCP server configurations in the mcpServers/ directory, which is the preferred configuration method. Co-Authored-By: Claude Sonnet 4.5 --- .../integrations/continue-admin.adoc | 158 +++++++++--------- 1 file changed, 80 insertions(+), 78 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc index f4f885d6a..015e07862 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc @@ -421,69 +421,54 @@ Provide these instructions to users configuring Continue.dev in their IDE. === Configuration file location -Continue.dev uses a JSON configuration file: +Continue.dev uses a YAML configuration file: -* VS Code: `~/.continue/config.json` -* JetBrains: `~/.continue/config.json` +* VS Code: `~/.continue/config.yaml` +* JetBrains: `~/.continue/config.yaml` === Multi-provider configuration Users configure Continue.dev with separate provider entries for each backend: -[source,json] +[source,yaml] ---- -{ - "models": [ - { - "title": "Claude Sonnet (Redpanda)", - "provider": "anthropic", - "model": "claude-sonnet-4-5", - "apiBase": "https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1/anthropic", - "apiKey": "YOUR_API_TOKEN", - "requestOptions": { - "headers": { - "rp-aigw-id": "GATEWAY_ID" - } - } - }, - { - "title": "GPT-4o (Redpanda)", - "provider": "openai", - "model": "gpt-4o", - "apiBase": "https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1/openai", - "apiKey": "YOUR_API_TOKEN", - "requestOptions": { - "headers": { - "rp-aigw-id": "GATEWAY_ID" - } - } - }, - { - "title": "GPT-4o-mini (Autocomplete)", - "provider": "openai", - "model": "gpt-4o-mini", - "apiBase": "https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1/openai", - "apiKey": "YOUR_API_TOKEN", - "requestOptions": { - "headers": { - "rp-aigw-id": "GATEWAY_ID" - } - } - } - ], - "tabAutocompleteModel": { - "title": "GPT-4o-mini (Autocomplete)", - "provider": "openai", - "model": "gpt-4o-mini", - "apiBase": "https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1/openai", - "apiKey": "YOUR_API_TOKEN", - "requestOptions": { - "headers": { - "rp-aigw-id": "GATEWAY_ID" - } - } - } -} +models: + - title: Claude Sonnet (Redpanda) + provider: anthropic + model: claude-sonnet-4-5 + apiBase: https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1/anthropic + apiKey: YOUR_API_TOKEN + requestOptions: + headers: + rp-aigw-id: GATEWAY_ID + + - title: GPT-4o (Redpanda) + provider: openai + model: gpt-4o + apiBase: https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1/openai + apiKey: YOUR_API_TOKEN + requestOptions: + headers: + rp-aigw-id: GATEWAY_ID + + - title: GPT-4o-mini (Autocomplete) + provider: openai + model: gpt-4o-mini + apiBase: https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1/openai + apiKey: YOUR_API_TOKEN + requestOptions: + headers: + rp-aigw-id: GATEWAY_ID + +tabAutocompleteModel: + title: GPT-4o-mini (Autocomplete) + provider: openai + model: gpt-4o-mini + apiBase: https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/v1/openai + apiKey: YOUR_API_TOKEN + requestOptions: + headers: + rp-aigw-id: GATEWAY_ID ---- Replace: @@ -494,33 +479,50 @@ Replace: === MCP server configuration -Configure Continue.dev to connect to the aggregated MCP endpoint: +Configure Continue.dev to connect to the aggregated MCP endpoint. -[source,json] +==== Recommended: Directory-based configuration + +The preferred method is to create MCP server configuration files in the `~/.continue/mcpServers/` directory: + +. Create the directory: `mkdir -p ~/.continue/mcpServers` +. Create `~/.continue/mcpServers/redpanda-ai-gateway.yaml`: ++ +[source,yaml] +---- +transport: + type: streamable-http + url: https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/mcp + headers: + Authorization: Bearer YOUR_API_TOKEN + rp-aigw-id: GATEWAY_ID +---- + +Continue.dev automatically discovers MCP server configurations in this directory. + +==== Alternative: Inline configuration + +Alternatively, embed MCP server configuration in `~/.continue/config.yaml`: + +[source,yaml] ---- -{ - "experimental": { - "modelContextProtocolServers": [ - { - "transport": { - "type": "http", - "url": "https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/mcp", - "headers": { - "Authorization": "Bearer YOUR_API_TOKEN", - "rp-aigw-id": "GATEWAY_ID" - } - } - } - ] - } -} +experimental: + modelContextProtocolServer: + transport: + type: streamable-http + url: https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/mcp + headers: + Authorization: Bearer YOUR_API_TOKEN + rp-aigw-id: GATEWAY_ID ---- -This configuration: +Replace: + +* `{CLUSTER_ID}`: Your Redpanda cluster ID +* `YOUR_API_TOKEN`: The API token generated earlier +* `GATEWAY_ID`: The gateway ID from gateway creation -* Connects Continue.dev to the aggregated MCP endpoint -* Routes LLM requests through provider-specific backends -* Includes authentication and gateway identification headers +This configuration connects Continue.dev to the aggregated MCP endpoint with authentication and gateway identification headers. === Model selection strategy From c34b122107eb7f165f5e328c4d1e61d480c68aff Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:52:26 -0700 Subject: [PATCH 38/50] Fix Continue.dev environment variable interpolation syntax Replace legacy JSON config with YAML using correct secrets interpolation: - Change from ${VAR} to ${{ secrets.VAR }} syntax - Update models -> apiKey, apiBase with secrets.REDPANDA_API_KEY and secrets.REDPANDA_GATEWAY_URL - Update requestOptions.headers.rp-aigw-id with secrets.REDPANDA_GATEWAY_ID - Update experimental.modelContextProtocolServer fields (changed from plural to singular per schema) - Change transport type from "http" to "streamable-http" - Replace transport.url, Authorization header, and rp-aigw-id header with secrets interpolation Add IMPORTANT note that ${VAR} syntax from config.json is not supported and will be treated as literal strings in Continue.dev. Replace shell environment variable exports with instructions to set secrets in Continue.dev settings UI. Co-Authored-By: Claude Sonnet 4.5 --- .../integrations/continue-user.adoc | 76 +++++++------------ 1 file changed, 29 insertions(+), 47 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc index 2b1022fd5..577b7250d 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc @@ -268,59 +268,41 @@ If using deferred tool loading in your gateway, you'll see a search tool and MCP == Configure with environment variables -For sensitive credentials or multi-environment setups, use environment variables: +For sensitive credentials or multi-environment setups, use Continue.dev's secrets interpolation in `config.yaml`: -[,json] +[,yaml] ---- -{ - "models": [ - { - "title": "Gateway - Claude Sonnet", - "provider": "anthropic", - "model": "claude-sonnet-4-5", - "apiKey": "${REDPANDA_API_KEY}", - "apiBase": "${REDPANDA_GATEWAY_URL}", - "requestOptions": { - "headers": { - "rp-aigw-id": "${REDPANDA_GATEWAY_ID}" - } - } - } - ], - "experimental": { - "modelContextProtocolServers": [ - { - "transport": { - "type": "http", - "url": "${REDPANDA_GATEWAY_URL}/mcp", - "headers": { - "Authorization": "Bearer ${REDPANDA_API_KEY}", - "rp-aigw-id": "${REDPANDA_GATEWAY_ID}" - } - } - } - ] - } -} ----- - -Set environment variables before launching your editor: +models: + - title: Gateway - Claude Sonnet + provider: anthropic + model: claude-sonnet-4-5 + apiKey: ${{ secrets.REDPANDA_API_KEY }} + apiBase: ${{ secrets.REDPANDA_GATEWAY_URL }} + requestOptions: + headers: + rp-aigw-id: ${{ secrets.REDPANDA_GATEWAY_ID }} -[,bash] ----- -export REDPANDA_GATEWAY_URL="https://gw.ai.panda.com" -export REDPANDA_GATEWAY_ID="gateway-abc123" -export REDPANDA_API_KEY="your-api-key" +experimental: + modelContextProtocolServer: + transport: + type: streamable-http + url: ${{ secrets.REDPANDA_GATEWAY_URL }}/mcp + headers: + Authorization: Bearer ${{ secrets.REDPANDA_API_KEY }} + rp-aigw-id: ${{ secrets.REDPANDA_GATEWAY_ID }} ---- -On Windows (PowerShell): +IMPORTANT: Continue.dev uses the `${{ secrets.* }}` syntax for interpolation in `config.yaml`. The legacy `${VAR}` syntax from `config.json` is not supported and will be treated as a literal string. -[,powershell] ----- -$env:REDPANDA_GATEWAY_URL = "https://gw.ai.panda.com" -$env:REDPANDA_GATEWAY_ID = "gateway-abc123" -$env:REDPANDA_API_KEY = "your-api-key" ----- +Set secrets in Continue.dev settings: + +. Open Continue.dev settings in your IDE +. Navigate to the "Secrets" section +. Add the following secrets: ++ +* `REDPANDA_GATEWAY_URL`: `https://gw.ai.panda.com` +* `REDPANDA_GATEWAY_ID`: `gateway-abc123` +* `REDPANDA_API_KEY`: `your-api-key` == Project-level configuration From 48a108b5db9f281289271c7c53747af49dc936c9 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:53:27 -0700 Subject: [PATCH 39/50] Fix Cursor settings.json environment variable handling Add IMPORTANT note that VS Code settings.json does not support ${VAR} interpolation - such placeholders are treated as literal strings. Provide two alternatives for handling sensitive credentials: Option 1: Script-based generation - Add bash script that reads environment variables and generates settings.json with actual values for cursor.overrideOpenAIBaseUrl, cursor.overrideOpenAIApiKey, and openai.additionalHeaders - Add PowerShell script for Windows users with ConvertTo-Json - Include instructions to run script before launching Cursor Option 2: Manual value replacement - Show example with concrete placeholder values users must replace - Add security note about file permissions (chmod 600) Remove misleading instructions to set environment variables and launch from terminal, as this does not enable interpolation in settings.json. Co-Authored-By: Claude Sonnet 4.5 --- .../ai-gateway/integrations/cursor-user.adoc | 70 +++++++++++++++---- 1 file changed, 56 insertions(+), 14 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc index 24460dc55..45041ea5d 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc @@ -192,12 +192,26 @@ Workspace settings override global settings. Use this to: === Configuration with environment variables -For sensitive credentials, use environment variables instead of hardcoding values. +For sensitive credentials, avoid hardcoding values in `settings.json`. -In `settings.json`: +IMPORTANT: VS Code `settings.json` does not support `${VAR}` interpolation - such placeholders will be treated as literal strings. To use environment variables, generate the settings file dynamically with a script. -[,json] +==== Option 1: Generate settings.json with a script + +Create a setup script that reads environment variables and writes the actual values to `settings.json`: + +[,bash] ---- +#!/bin/bash +# setup-cursor-config.sh + +# Set your credentials +export REDPANDA_GATEWAY_URL="https://gw.ai.panda.com" +export REDPANDA_GATEWAY_ID="gateway-abc123" +export REDPANDA_API_KEY="your-api-key" + +# Generate settings.json +cat > ~/.cursor/settings.json < Date: Fri, 23 Jan 2026 17:54:51 -0700 Subject: [PATCH 40/50] Fix GitHub Copilot configuration settings and flow MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Update Option 1 (built-in custom models): - Change github.copilot.advanced.customModels to correct setting: github.copilot.chat.customOAIModels (experimental chat OAI models) - Restructure configuration as array instead of nested object - Add IMPORTANT note that API keys/headers must be configured via Copilot Chat UI, not settings.json - Replace environment variable instructions with Copilot Chat → Manage Models UI flow: Add Model → Select provider → Enter API key and custom headers (rp-aigw-id) - Remove references to non-working environment variable approach Update Option 2 (OAI Compatible Provider extension): - Replace incorrect oai.provider.* settings with actual extension settings: oaicopilot.baseUrl and oaicopilot.models - Remove non-existent useOAIProvider setting from configuration - Add IMPORTANT note that API keys/headers are NOT set in settings.json - Document correct flow: Copilot Chat → Manage Models → Configure provider → Enter API key and custom headers - Remove environment variable instructions that don't apply - Remove unnecessary window reload step Both options now correctly document that users must use the Copilot Chat → Manage Models UI to configure API keys and custom headers. Co-Authored-By: Claude Sonnet 4.5 --- .../integrations/github-copilot-user.adoc | 162 +++++++----------- 1 file changed, 58 insertions(+), 104 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc index aa3aa8de4..b52f79d3b 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc @@ -86,81 +86,58 @@ This approach configures VS Code to recognize your AI Gateway as a custom OpenAI . Open VS Code Settings: ** macOS: `Cmd+,` ** Windows/Linux: `Ctrl+,` -. Search for `github.copilot.advanced` +. Search for `github.copilot.chat.customOAIModels` . Click *Edit in settings.json* . Add the following configuration: [,json] ---- { - "github.copilot.advanced": { - "customModels": { - "redpanda-gateway": { - "endpoint": "https://gw.ai.panda.com/v1", - "apiKey": "${env:REDPANDA_API_KEY}", - "models": [ - { - "id": "anthropic/claude-sonnet-4-5", - "name": "Claude Sonnet 4.5 (Gateway)", - "type": "chat" - }, - { - "id": "openai/gpt-4o", - "name": "GPT-4o (Gateway)", - "type": "chat" - }, - { - "id": "openai/gpt-4o-mini", - "name": "GPT-4o Mini (Gateway)", - "type": "completion" - } - ] - } + "github.copilot.chat.customOAIModels": [ + { + "id": "anthropic/claude-sonnet-4-5", + "name": "Claude Sonnet 4.5 (Gateway)", + "endpoint": "https://gw.ai.panda.com/v1", + "provider": "redpanda-gateway" + }, + { + "id": "openai/gpt-4o", + "name": "GPT-4o (Gateway)", + "endpoint": "https://gw.ai.panda.com/v1", + "provider": "redpanda-gateway" } - } + ] } ---- Replace `https://gw.ai.panda.com/v1` with your gateway endpoint. -==== Add gateway ID header - -The custom models configuration doesn't support custom headers directly. To add the `rp-aigw-id` header, use one of these approaches: +IMPORTANT: This experimental feature requires configuring API keys and custom headers through the Copilot Chat UI, not in `settings.json`. -**Approach A: Use OAI Compatible Provider extension** (recommended, see Option 2 below) +==== Configure API key and headers via Copilot Chat UI -**Approach B: Configure gateway to use API key for routing** (if your gateway supports this) - -Check your AI Gateway documentation to see if you can embed the gateway ID in the API key or use a different authentication method that doesn't require custom headers. - -==== Set environment variable - -Set the API key as an environment variable before launching VS Code: - -[,bash] ----- -export REDPANDA_API_KEY="your-api-key" -code . ----- - -On Windows (PowerShell): - -[,powershell] ----- -$env:REDPANDA_API_KEY = "your-api-key" -code . ----- +. Open Copilot Chat in VS Code (`Cmd+I` or `Ctrl+I`) +. Click the model selector dropdown +. Click *Manage Models* at the bottom of the dropdown +. Click *Add Model* +. Select your configured provider ("redpanda-gateway") +. Enter the connection details: +** *Base URL*: `https://gw.ai.panda.com/v1` (should match your settings.json endpoint) +** *API Key*: Your Redpanda API key +** *Custom Headers*: Click *Add Header* +*** Header name: `rp-aigw-id` +*** Header value: `gateway-abc123` (your gateway ID) +. Click *Save* ==== Select model -. Open a file in VS Code . Open Copilot chat with `Cmd+I` (macOS) or `Ctrl+I` (Windows/Linux) . Click the model selector dropdown . Choose a model from the "redpanda-gateway" provider === Option 2: OAI Compatible Provider extension -The OAI Compatible Provider extension simplifies custom provider configuration and supports custom headers. +The OAI Compatible Provider extension provides enhanced support for OpenAI-compatible endpoints with custom headers. ==== Install extension @@ -168,73 +145,50 @@ The OAI Compatible Provider extension simplifies custom provider configuration a . Search for "OAI Compatible Provider" . Click *Install* -==== Configure provider +==== Configure base URL in settings + +Add the base URL configuration in VS Code settings: . Open VS Code Settings (`Cmd+,` or `Ctrl+,`) -. Search for `oai.provider` -. Configure the following settings: +. Search for `oaicopilot` +. Click *Edit in settings.json* +. Add the following: [,json] ---- { - "oai.provider.endpoint": "https://gw.ai.panda.com/v1", - "oai.provider.apiKey": "${env:REDPANDA_API_KEY}", - "oai.provider.headers": { - "rp-aigw-id": "gateway-abc123" - }, - "oai.provider.models": [ - { - "id": "anthropic/claude-sonnet-4-5", - "name": "Claude Sonnet 4.5", - "type": "chat" - }, - { - "id": "openai/gpt-4o-mini", - "name": "GPT-4o Mini", - "type": "completion" - } + "oaicopilot.baseUrl": "https://gw.ai.panda.com/v1", + "oaicopilot.models": [ + "anthropic/claude-sonnet-4-5", + "openai/gpt-4o", + "openai/gpt-4o-mini" ] } ---- -Replace placeholder values: - -* `https://gw.ai.panda.com/v1` - Your gateway endpoint -* `gateway-abc123` - Your gateway ID - -==== Enable for Copilot - -. Search for `github.copilot.advanced` in settings -. Add the following: - -[,json] ----- -{ - "github.copilot.advanced": { - "useOAIProvider": true - } -} ----- +Replace `https://gw.ai.panda.com/v1` with your gateway endpoint. -. Reload VS Code window: -** Command Palette (`Cmd+Shift+P` or `Ctrl+Shift+P`) -** Type "Developer: Reload Window" +==== Configure API key and headers via Copilot Chat UI -==== Set environment variable +IMPORTANT: Do not configure API keys or custom headers in `settings.json`. Use the Copilot Chat UI instead. -[,bash] ----- -export REDPANDA_API_KEY="your-api-key" -code . ----- +. Open Copilot Chat in VS Code (`Cmd+I` or `Ctrl+I`) +. Click the model selector dropdown +. Click *Manage Models* +. Find the OAI Compatible Provider in the list +. Click *Configure* or *Edit* +. Enter the connection details: +** *API Key*: Your Redpanda API key +** *Custom Headers*: Add the `rp-aigw-id` header +*** Header name: `rp-aigw-id` +*** Header value: `gateway-abc123` (your gateway ID) +. Click *Save* -On Windows (PowerShell): +==== Select model -[,powershell] ----- -$env:REDPANDA_API_KEY = "your-api-key" -code . ----- +. Open Copilot chat with `Cmd+I` (macOS) or `Ctrl+I` (Windows/Linux) +. Click the model selector dropdown +. Choose a model from the OAI Compatible Provider == Configure in JetBrains IDEs From a034c9c2fb6f0504505932f35622287870b8bb3c Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 17:55:31 -0700 Subject: [PATCH 41/50] Remove duplicate rp-aigw-id from LlamaIndex example Remove rp-aigw-id from additional_kwargs in the LlamaIndex OpenAI client configuration. The header was being set twice - once in additional_kwargs (which goes in the request body) and once in default_headers (which sets HTTP headers). Keep only the default_headers configuration to correctly set the rp-aigw-id as an HTTP header. Co-Authored-By: Claude Sonnet 4.5 --- modules/ai-agents/pages/ai-gateway/migration-guide.adoc | 1 - 1 file changed, 1 deletion(-) diff --git a/modules/ai-agents/pages/ai-gateway/migration-guide.adoc b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc index d80d0c373..3c5c228d4 100644 --- a/modules/ai-agents/pages/ai-gateway/migration-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/migration-guide.adoc @@ -630,7 +630,6 @@ if use_gateway: model="openai/gpt-4o", api_base=os.getenv("REDPANDA_AI_GATEWAY_URL"), api_key=os.getenv("REDPANDA_AI_GATEWAY_TOKEN"), - additional_kwargs={"headers": {"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")}}, default_headers={"rp-aigw-id": os.getenv("REDPANDA_AI_GATEWAY_ID")} ) else: From 930b261783f5021d34eb527663156c94cc1aa90e Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 18:29:54 -0700 Subject: [PATCH 42/50] Fix Continue.dev config file references to use YAML Update security guidance section to reference config.yaml instead of config.json: - Change "stores the API token in plain text in config.json" to config.yaml - Update version control warning to reference config.yaml - Update chmod example from ~/.continue/config.json to ~/.continue/config.yaml Keep guidance points the same: never commit the config file, restrict filesystem permissions, and rotate tokens if compromised. Co-Authored-By: Claude Sonnet 4.5 --- .../pages/ai-gateway/integrations/continue-admin.adoc | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc index 015e07862..7b69cf291 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc @@ -656,10 +656,10 @@ Review which MCP tools Continue.dev clients can access: === Protect API keys in configuration -Continue.dev stores the API token in plain text in `config.json`. Remind users to: +Continue.dev stores the API token in plain text in `config.yaml`. Remind users to: -* Never commit `config.json` to version control -* Use file system permissions to restrict access (for example, `chmod 600 ~/.continue/config.json`) +* Never commit `config.yaml` to version control +* Use file system permissions to restrict access (for example, `chmod 600 ~/.continue/config.yaml`) * Rotate tokens if they suspect compromise == Troubleshooting From 3a4d0267e0835497ea514791f6099bde36203dab Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 19:27:49 -0700 Subject: [PATCH 43/50] Fix Continue.dev MCP server schema in config.yaml examples Update Continue.dev documentation to use the correct MCP server configuration schema: - Change from experimental.modelContextProtocolServer (singular) to mcpServers (plural) at root level - Add array syntax with dash (-) before transport configuration - Update continue-user.adoc inline config.yaml examples (2 instances) - Update continue-admin.adoc inline config.yaml example - Fix troubleshooting references to use new mcpServers schema - Convert JSON config example to YAML with new schema for consistency The new schema allows multiple MCP servers to be configured and aligns with Continue.dev's current config.yaml structure. Directory-based configuration in ~/.continue/mcpServers/ already uses correct schema. Co-Authored-By: Claude Sonnet 4.5 --- .../integrations/continue-admin.adoc | 7 +-- .../integrations/continue-user.adoc | 58 +++++++------------ 2 files changed, 25 insertions(+), 40 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc index 7b69cf291..8585ab2d9 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc @@ -506,9 +506,8 @@ Alternatively, embed MCP server configuration in `~/.continue/config.yaml`: [source,yaml] ---- -experimental: - modelContextProtocolServer: - transport: +mcpServers: + - transport: type: streamable-http url: https://{CLUSTER_ID}.cloud.redpanda.com/ai-gateway/mcp headers: @@ -715,7 +714,7 @@ Symptom: Continue.dev does not discover MCP tools. Causes and solutions: -* **MCP configuration missing**: Ensure `experimental.modelContextProtocolServers` is configured +* **MCP configuration missing**: Ensure `mcpServers` is configured * **MCP servers not configured in gateway**: Add MCP server endpoints in the gateway's MCP tab * **Deferred loading enabled but search failing**: Check that the search tool is correctly configured * **MCP server authentication failing**: Verify MCP server authentication credentials in the gateway configuration diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc index 577b7250d..930db05c5 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc @@ -222,40 +222,27 @@ When using OpenAI provider format: Connect Continue.dev to your AI Gateway's MCP endpoint to aggregate tools from multiple MCP servers. -Add the `experimental` section to `config.json`: +Add the MCP configuration to `config.yaml`: -[,json] +[,yaml] ---- -{ - "models": [ - { - "title": "Gateway - Claude Sonnet", - "provider": "anthropic", - "model": "claude-sonnet-4-5", - "apiKey": "YOUR_REDPANDA_API_KEY", - "apiBase": "https://gw.ai.panda.com", - "requestOptions": { - "headers": { - "rp-aigw-id": "GATEWAY_ID" - } - } - } - ], - "experimental": { - "modelContextProtocolServers": [ - { - "transport": { - "type": "http", - "url": "https://gw.ai.panda.com/mcp", - "headers": { - "Authorization": "Bearer YOUR_REDPANDA_API_KEY", - "rp-aigw-id": "GATEWAY_ID" - } - } - } - ] - } -} +models: + - title: Gateway - Claude Sonnet + provider: anthropic + model: claude-sonnet-4-5 + apiKey: YOUR_REDPANDA_API_KEY + apiBase: https://gw.ai.panda.com + requestOptions: + headers: + rp-aigw-id: GATEWAY_ID + +mcpServers: + - transport: + type: streamable-http + url: https://gw.ai.panda.com/mcp + headers: + Authorization: Bearer YOUR_REDPANDA_API_KEY + rp-aigw-id: GATEWAY_ID ---- After adding this configuration: @@ -282,9 +269,8 @@ models: headers: rp-aigw-id: ${{ secrets.REDPANDA_GATEWAY_ID }} -experimental: - modelContextProtocolServer: - transport: +mcpServers: + - transport: type: streamable-http url: ${{ secrets.REDPANDA_GATEWAY_URL }}/mcp headers: @@ -667,7 +653,7 @@ Check Continue.dev settings in your editor: . **MCP configuration missing** + -Verify the `experimental.modelContextProtocolServers` section exists in `config.json`. +Verify the `mcpServers` section exists in `config.yaml`. . **Incorrect MCP endpoint** + From d0fdb50fa9e8515490fef4eb6fb72579142f5eba Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 19:28:52 -0700 Subject: [PATCH 44/50] Fix unsupported ${VAR} interpolation in Continue.dev project config Replace misleading ${VAR} syntax in .continuerc.json example with explicit placeholder text (your_project_api_key_here, etc.) to avoid confusion. Add IMPORTANT note explaining that .continuerc.json does not support environment variable interpolation and must use hardcoded values. Direct users to config.yaml with ${{ secrets.* }} syntax or config.ts for programmatic environment access as alternatives. Co-Authored-By: Claude Sonnet 4.5 --- .../pages/ai-gateway/integrations/continue-user.adoc | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc index 930db05c5..b58df3acd 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc @@ -302,11 +302,11 @@ Override global settings for specific projects by creating `.continuerc.json` in "title": "Project Gateway - Claude Haiku", "provider": "anthropic", "model": "claude-haiku", - "apiKey": "${PROJECT_API_KEY}", + "apiKey": "your_project_api_key_here", "apiBase": "https://gw.project.ai.panda.com", "requestOptions": { "headers": { - "rp-aigw-id": "${PROJECT_GATEWAY_ID}" + "rp-aigw-id": "your_project_gateway_id_here" } } } @@ -314,6 +314,8 @@ Override global settings for specific projects by creating `.continuerc.json` in } ---- +IMPORTANT: `.continuerc.json` does not support environment variable interpolation. You must hardcode values in this file. For dynamic configuration, use `~/.continue/config.yaml` with `${{ secrets.* }}` syntax (see <>) or create a `~/.continue/config.ts` file for programmatic environment access. + Project-level configuration takes precedence over global configuration. Use this to: * Route different projects through different gateways From ac4d51db6142ba2883cd5fe17a45e08227cd9034 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 19:39:43 -0700 Subject: [PATCH 45/50] Improve Continue.dev documentation clarity and compliance Address critical documentation issues identified in audit: 1. Remove checkboxes from learning objectives in both user and admin guides per content architecture standards 2. Add config file format clarification section explaining JSON vs YAML, when each is required/recommended, helping users understand which format to choose 3. Fix environment variable syntax documentation: - Add explicit anchor [[configure-env-vars]] for cross-references - Clarify ${{ secrets.* }} is YAML-only - Explain config.json does not support any variable interpolation - Clarify ${VAR} syntax is treated as literal string 4. Add security warning in admin guide about hardcoded tokens with cross-reference to environment variable interpolation best practices 5. Update admin guide to acknowledge both JSON and YAML formats are supported, explaining why YAML is recommended for MCP features These changes eliminate confusion about config file formats and improve security guidance for production deployments. Co-Authored-By: Claude Sonnet 4.5 --- .../integrations/continue-admin.adoc | 12 ++++-- .../integrations/continue-user.adoc | 43 +++++++++++++++---- 2 files changed, 42 insertions(+), 13 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc index 8585ab2d9..defbbd05e 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Continue.dev clients accessing multiple After reading this page, you will be able to: -* [ ] Configure AI Gateway endpoints for Continue.dev connectivity. -* [ ] Set up multi-provider backends with native format routing. -* [ ] Deploy MCP tool aggregation for Continue.dev tool discovery. +* Configure AI Gateway endpoints for Continue.dev connectivity +* Set up multi-provider backends with native format routing +* Deploy MCP tool aggregation for Continue.dev tool discovery == Prerequisites @@ -421,11 +421,13 @@ Provide these instructions to users configuring Continue.dev in their IDE. === Configuration file location -Continue.dev uses a YAML configuration file: +Continue.dev supports both JSON and YAML configuration formats. This guide uses YAML (`config.yaml`) because it supports MCP server configuration and environment variable interpolation: * VS Code: `~/.continue/config.yaml` * JetBrains: `~/.continue/config.yaml` +NOTE: While `config.json` is still supported for basic LLM configuration, `config.yaml` is required for MCP server integration. + === Multi-provider configuration Users configure Continue.dev with separate provider entries for each backend: @@ -497,6 +499,8 @@ transport: Authorization: Bearer YOUR_API_TOKEN rp-aigw-id: GATEWAY_ID ---- ++ +IMPORTANT: For production deployments, use environment variable interpolation with `${{ secrets.VARIABLE }}` syntax instead of hardcoding tokens. See xref:ai-agents:ai-gateway/integrations/continue-user.adoc#configure-env-vars[Configure with environment variables] in the user guide for details. Continue.dev automatically discovers MCP server configurations in this directory. diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc index b58df3acd..ec844ce93 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* [ ] Configure Continue.dev to connect to AI Gateway for chat and autocomplete. -* [ ] Set up MCP server integration through AI Gateway. -* [ ] Optimize Continue.dev settings for cost and performance. +* Configure Continue.dev to connect to AI Gateway for chat and autocomplete +* Set up MCP server integration through AI Gateway +* Optimize Continue.dev settings for cost and performance == Prerequisites @@ -43,12 +43,17 @@ Continue.dev is an open-source AI coding assistant that integrates with VS Code By routing Continue.dev through AI Gateway, you gain centralized observability, cost controls, and the ability to aggregate multiple MCP servers into a single interface. -== Configuration file location +== Configuration files -Continue.dev stores configuration in `config.json`: +Continue.dev supports two configuration file formats: -* VS Code: `~/.continue/config.json` -* JetBrains: `~/.continue/config.json` (same location) +* `config.json` (legacy format) +* `config.yaml` (recommended format) + +Both files are stored in the same location: + +* VS Code: `~/.continue/` +* JetBrains: `~/.continue/` Create the directory if it doesn't exist: @@ -57,6 +62,23 @@ Create the directory if it doesn't exist: mkdir -p ~/.continue ---- +=== Choose a configuration format + +[cols="1,2,2"] +|=== +|Format |Use when |Limitations + +|`config.json` +|You need basic LLM configuration without MCP servers +|Does not support MCP server configuration or environment variable interpolation + +|`config.yaml` +|You need MCP server integration or environment variable interpolation +|Requires Continue.dev version that supports YAML (recent versions) +|=== + +TIP: Use `config.yaml` for new setups to take advantage of MCP server integration and the `${{ secrets.* }}` environment variable syntax. + == Basic configuration Create or edit `~/.continue/config.json` with the following structure to connect to AI Gateway: @@ -253,9 +275,12 @@ After adding this configuration: If using deferred tool loading in your gateway, you'll see a search tool and MCP orchestrator tool instead of all tools upfront. +[[configure-env-vars]] == Configure with environment variables -For sensitive credentials or multi-environment setups, use Continue.dev's secrets interpolation in `config.yaml`: +For sensitive credentials or multi-environment setups, use Continue.dev's secrets interpolation in `config.yaml`. + +IMPORTANT: Environment variable interpolation is only supported in `config.yaml` files. The `config.json` format does not support any form of variable substitution - all values must be hardcoded. [,yaml] ---- @@ -278,7 +303,7 @@ mcpServers: rp-aigw-id: ${{ secrets.REDPANDA_GATEWAY_ID }} ---- -IMPORTANT: Continue.dev uses the `${{ secrets.* }}` syntax for interpolation in `config.yaml`. The legacy `${VAR}` syntax from `config.json` is not supported and will be treated as a literal string. +IMPORTANT: Continue.dev uses the `${{ secrets.* }}` syntax for interpolation in `config.yaml`. Do not use the `${VAR}` shell syntax - Continue.dev treats it as a literal string rather than performing substitution. Set secrets in Continue.dev settings: From f754065d0a9eb4673f5408dc7b5f1658cb69a0f6 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 19:41:00 -0700 Subject: [PATCH 46/50] Remove checkboxes and periods from learning objectives across all integration files Apply content architecture standards to all AI Gateway integration documentation: - Remove [ ] checkbox formatting from learning objectives - Remove trailing periods from objective fragments Files updated: - cursor-user.adoc, cursor-admin.adoc - github-copilot-user.adoc, github-copilot-admin.adoc - claude-code-user.adoc, claude-code-admin.adoc - cline-user.adoc, cline-admin.adoc Learning objectives should be simple list items without interactive elements or punctuation for fragments. Co-Authored-By: Claude Sonnet 4.5 --- .../pages/ai-gateway/integrations/claude-code-admin.adoc | 6 +++--- .../pages/ai-gateway/integrations/claude-code-user.adoc | 6 +++--- .../pages/ai-gateway/integrations/cline-admin.adoc | 6 +++--- .../ai-agents/pages/ai-gateway/integrations/cline-user.adoc | 6 +++--- .../pages/ai-gateway/integrations/cursor-admin.adoc | 6 +++--- .../pages/ai-gateway/integrations/cursor-user.adoc | 6 +++--- .../pages/ai-gateway/integrations/github-copilot-admin.adoc | 6 +++--- .../pages/ai-gateway/integrations/github-copilot-user.adoc | 6 +++--- 8 files changed, 24 insertions(+), 24 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc index 5bbb2e844..8c8272f10 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Claude Code clients accessing LLM provi After reading this page, you will be able to: -* [ ] Configure AI Gateway endpoints for Claude Code connectivity. -* [ ] Set up authentication and access control for Claude Code clients. -* [ ] Deploy MCP tool aggregation for Claude Code tool discovery. +* Configure AI Gateway endpoints for Claude Code connectivity +* Set up authentication and access control for Claude Code clients +* Deploy MCP tool aggregation for Claude Code tool discovery == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc index 7fb085124..41abbf2b6 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* [ ] Configure Claude Code to connect to AI Gateway endpoints. -* [ ] Set up MCP server integration through AI Gateway. -* [ ] Verify Claude Code is routing requests through the gateway. +* Configure Claude Code to connect to AI Gateway endpoints +* Set up MCP server integration through AI Gateway +* Verify Claude Code is routing requests through the gateway == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc index 8552199c4..d3f6464d0 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Cline (formerly Claude Dev) clients acc After reading this page, you will be able to: -* [ ] Configure AI Gateway endpoints for Cline connectivity. -* [ ] Set up authentication and access control for Cline clients. -* [ ] Deploy MCP tool aggregation for Cline tool discovery. +* Configure AI Gateway endpoints for Cline connectivity +* Set up authentication and access control for Cline clients +* Deploy MCP tool aggregation for Cline tool discovery == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc index cc66ca5d7..db1bba90a 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* [ ] Configure Cline to connect to AI Gateway for LLM requests and MCP tools. -* [ ] Set up autonomous mode with custom instructions and browser integration. -* [ ] Verify Cline routes requests through the gateway and optimize for cost. +* Configure Cline to connect to AI Gateway for LLM requests and MCP tools +* Set up autonomous mode with custom instructions and browser integration +* Verify Cline routes requests through the gateway and optimize for cost == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc index 55f305500..797b71c10 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Cursor IDE clients accessing multiple L After reading this page, you will be able to: -* [ ] Configure AI Gateway endpoints for Cursor IDE connectivity. -* [ ] Set up OpenAI-compatible transforms for multi-provider routing. -* [ ] Deploy multi-tenant authentication strategies for Cursor clients. +* Configure AI Gateway endpoints for Cursor IDE connectivity +* Set up OpenAI-compatible transforms for multi-provider routing +* Deploy multi-tenant authentication strategies for Cursor clients == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc index 45041ea5d..c3fc57643 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* [ ] Configure Cursor IDE to route LLM requests through AI Gateway. -* [ ] Set up MCP server integration for tool access through the gateway. -* [ ] Optimize Cursor settings for multi-tenancy and cost control. +* Configure Cursor IDE to route LLM requests through AI Gateway +* Set up MCP server integration for tool access through the gateway +* Optimize Cursor settings for multi-tenancy and cost control == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc index fb2cf320f..126378e29 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support GitHub Copilot clients accessing multip After reading this page, you will be able to: -* [ ] Configure AI Gateway endpoints for GitHub Copilot connectivity. -* [ ] Deploy multi-tenant authentication strategies for Copilot clients. -* [ ] Set up model aliasing and BYOK routing for GitHub Copilot. +* Configure AI Gateway endpoints for GitHub Copilot connectivity +* Deploy multi-tenant authentication strategies for Copilot clients +* Set up model aliasing and BYOK routing for GitHub Copilot == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc index b52f79d3b..3f3db4c13 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* [ ] Configure GitHub Copilot in VS Code and JetBrains IDEs to route requests through AI Gateway. -* [ ] Set up multi-tenancy with gateway ID headers for cost tracking. -* [ ] Configure enterprise BYOK deployments for team-wide Copilot access. +* Configure GitHub Copilot in VS Code and JetBrains IDEs to route requests through AI Gateway +* Set up multi-tenancy with gateway ID headers for cost tracking +* Configure enterprise BYOK deployments for team-wide Copilot access == Prerequisites From 8ce3de86693570ce21289093abb548b7443f3fea Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 19:43:32 -0700 Subject: [PATCH 47/50] Revert "Remove checkboxes and periods from learning objectives across all integration files" This reverts commit f754065. Checkboxes [ ] ARE the doc team standard for learning objectives in this repository, as evidenced by the MCP documentation pages and other content. The audit agent was incorrect in recommending their removal. Restored checkboxes and periods to learning objectives in all integration files to match the established pattern: * [ ] Learning objective text. Examples of this pattern in the repo: - modules/ai-agents/pages/mcp/remote/quickstart.adoc - modules/ai-agents/pages/mcp/remote/concepts.adoc - modules/ai-agents/pages/mcp/local/configuration.adoc --- .../pages/ai-gateway/integrations/claude-code-admin.adoc | 6 +++--- .../pages/ai-gateway/integrations/claude-code-user.adoc | 6 +++--- .../pages/ai-gateway/integrations/cline-admin.adoc | 6 +++--- .../ai-agents/pages/ai-gateway/integrations/cline-user.adoc | 6 +++--- .../pages/ai-gateway/integrations/continue-admin.adoc | 6 +++--- .../pages/ai-gateway/integrations/continue-user.adoc | 6 +++--- .../pages/ai-gateway/integrations/cursor-admin.adoc | 6 +++--- .../pages/ai-gateway/integrations/cursor-user.adoc | 6 +++--- .../pages/ai-gateway/integrations/github-copilot-admin.adoc | 6 +++--- .../pages/ai-gateway/integrations/github-copilot-user.adoc | 6 +++--- 10 files changed, 30 insertions(+), 30 deletions(-) diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc index 8c8272f10..5bbb2e844 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Claude Code clients accessing LLM provi After reading this page, you will be able to: -* Configure AI Gateway endpoints for Claude Code connectivity -* Set up authentication and access control for Claude Code clients -* Deploy MCP tool aggregation for Claude Code tool discovery +* [ ] Configure AI Gateway endpoints for Claude Code connectivity. +* [ ] Set up authentication and access control for Claude Code clients. +* [ ] Deploy MCP tool aggregation for Claude Code tool discovery. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc index 41abbf2b6..7fb085124 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* Configure Claude Code to connect to AI Gateway endpoints -* Set up MCP server integration through AI Gateway -* Verify Claude Code is routing requests through the gateway +* [ ] Configure Claude Code to connect to AI Gateway endpoints. +* [ ] Set up MCP server integration through AI Gateway. +* [ ] Verify Claude Code is routing requests through the gateway. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc index d3f6464d0..8552199c4 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Cline (formerly Claude Dev) clients acc After reading this page, you will be able to: -* Configure AI Gateway endpoints for Cline connectivity -* Set up authentication and access control for Cline clients -* Deploy MCP tool aggregation for Cline tool discovery +* [ ] Configure AI Gateway endpoints for Cline connectivity. +* [ ] Set up authentication and access control for Cline clients. +* [ ] Deploy MCP tool aggregation for Cline tool discovery. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc index db1bba90a..cc66ca5d7 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* Configure Cline to connect to AI Gateway for LLM requests and MCP tools -* Set up autonomous mode with custom instructions and browser integration -* Verify Cline routes requests through the gateway and optimize for cost +* [ ] Configure Cline to connect to AI Gateway for LLM requests and MCP tools. +* [ ] Set up autonomous mode with custom instructions and browser integration. +* [ ] Verify Cline routes requests through the gateway and optimize for cost. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc index defbbd05e..ce75b6306 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Continue.dev clients accessing multiple After reading this page, you will be able to: -* Configure AI Gateway endpoints for Continue.dev connectivity -* Set up multi-provider backends with native format routing -* Deploy MCP tool aggregation for Continue.dev tool discovery +* [ ] Configure AI Gateway endpoints for Continue.dev connectivity. +* [ ] Set up multi-provider backends with native format routing. +* [ ] Deploy MCP tool aggregation for Continue.dev tool discovery. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc index ec844ce93..5dcb3094c 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* Configure Continue.dev to connect to AI Gateway for chat and autocomplete -* Set up MCP server integration through AI Gateway -* Optimize Continue.dev settings for cost and performance +* [ ] Configure Continue.dev to connect to AI Gateway for chat and autocomplete. +* [ ] Set up MCP server integration through AI Gateway. +* [ ] Optimize Continue.dev settings for cost and performance. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc index 797b71c10..55f305500 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support Cursor IDE clients accessing multiple L After reading this page, you will be able to: -* Configure AI Gateway endpoints for Cursor IDE connectivity -* Set up OpenAI-compatible transforms for multi-provider routing -* Deploy multi-tenant authentication strategies for Cursor clients +* [ ] Configure AI Gateway endpoints for Cursor IDE connectivity. +* [ ] Set up OpenAI-compatible transforms for multi-provider routing. +* [ ] Deploy multi-tenant authentication strategies for Cursor clients. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc index c3fc57643..45041ea5d 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* Configure Cursor IDE to route LLM requests through AI Gateway -* Set up MCP server integration for tool access through the gateway -* Optimize Cursor settings for multi-tenancy and cost control +* [ ] Configure Cursor IDE to route LLM requests through AI Gateway. +* [ ] Set up MCP server integration for tool access through the gateway. +* [ ] Optimize Cursor settings for multi-tenancy and cost control. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc index 126378e29..fb2cf320f 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc @@ -12,9 +12,9 @@ Configure Redpanda AI Gateway to support GitHub Copilot clients accessing multip After reading this page, you will be able to: -* Configure AI Gateway endpoints for GitHub Copilot connectivity -* Deploy multi-tenant authentication strategies for Copilot clients -* Set up model aliasing and BYOK routing for GitHub Copilot +* [ ] Configure AI Gateway endpoints for GitHub Copilot connectivity. +* [ ] Deploy multi-tenant authentication strategies for Copilot clients. +* [ ] Set up model aliasing and BYOK routing for GitHub Copilot. == Prerequisites diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc index 3f3db4c13..b52f79d3b 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc @@ -12,9 +12,9 @@ After xref:ai-agents:ai-gateway/gateway-quickstart.adoc[configuring your AI Gate After reading this page, you will be able to: -* Configure GitHub Copilot in VS Code and JetBrains IDEs to route requests through AI Gateway -* Set up multi-tenancy with gateway ID headers for cost tracking -* Configure enterprise BYOK deployments for team-wide Copilot access +* [ ] Configure GitHub Copilot in VS Code and JetBrains IDEs to route requests through AI Gateway. +* [ ] Set up multi-tenancy with gateway ID headers for cost tracking. +* [ ] Configure enterprise BYOK deployments for team-wide Copilot access. == Prerequisites From 44c45285f7e8d24329dac59dd2a0078a5c203d10 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Fri, 23 Jan 2026 20:02:30 -0700 Subject: [PATCH 48/50] update nav --- modules/ROOT/nav.adoc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ROOT/nav.adoc b/modules/ROOT/nav.adoc index 519835c9e..fc1b92a15 100644 --- a/modules/ROOT/nav.adoc +++ b/modules/ROOT/nav.adoc @@ -38,7 +38,7 @@ *** Observability **** xref:ai-agents:ai-gateway/observability-logs.adoc[Request Logs] **** xref:ai-agents:ai-gateway/observability-metrics.adoc[Metrics and Analytics] -*** xref:ai-agents:ai-gateway/migration-guide.adoc[Migration Guide] +*** xref:ai-agents:ai-gateway/migration-guide.adoc[Migrate] *** xref:ai-agents:ai-gateway/integrations/index.adoc[Integrations] **** Claude Code ***** xref:ai-agents:ai-gateway/integrations/claude-code-admin.adoc[Admin Guide] From 000b819d8af81ca57e7922e68c1af8ca001fdae7 Mon Sep 17 00:00:00 2001 From: micheleRP Date: Thu, 29 Jan 2026 10:36:27 -0700 Subject: [PATCH 49/50] AWS only --- modules/ai-agents/partials/ai-gateway-byoc-note.adoc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ai-agents/partials/ai-gateway-byoc-note.adoc b/modules/ai-agents/partials/ai-gateway-byoc-note.adoc index 5d3b58cf0..86fdf86a6 100644 --- a/modules/ai-agents/partials/ai-gateway-byoc-note.adoc +++ b/modules/ai-agents/partials/ai-gateway-byoc-note.adoc @@ -1 +1 @@ -NOTE: AI Gateway is supported on BYOC clusters running Redpanda version 25.3 and later. +NOTE: The Agentic Data Plane is supported on BYOC clusters running with AWS and Redpanda version 25.3 and later. From ca6f6dc8f75f8e73b94717e476ab5802ac36810e Mon Sep 17 00:00:00 2001 From: micheleRP Date: Mon, 2 Feb 2026 22:34:21 -0700 Subject: [PATCH 50/50] conditionalize out content not for package 1 --- modules/ROOT/nav.adoc | 8 ++++---- .../ai-agents/pages/ai-gateway/admin/setup-guide.adoc | 6 ++---- .../pages/ai-gateway/cel-routing-cookbook.adoc | 1 - .../pages/ai-gateway/gateway-architecture.adoc | 1 - .../pages/ai-gateway/gateway-quickstart.adoc | 2 -- .../ai-gateway/integrations/claude-code-admin.adoc | 5 +---- .../ai-gateway/integrations/claude-code-user.adoc | 1 - .../pages/ai-gateway/integrations/cline-admin.adoc | 5 +---- .../pages/ai-gateway/integrations/cline-user.adoc | 1 - .../pages/ai-gateway/integrations/continue-admin.adoc | 5 +---- .../pages/ai-gateway/integrations/continue-user.adoc | 1 - .../pages/ai-gateway/integrations/cursor-admin.adoc | 5 +---- .../pages/ai-gateway/integrations/cursor-user.adoc | 1 - .../ai-gateway/integrations/github-copilot-admin.adoc | 7 ++----- .../ai-gateway/integrations/github-copilot-user.adoc | 1 - .../pages/ai-gateway/mcp-aggregation-guide.adoc | 2 -- .../pages/ai-gateway/what-is-ai-gateway.adoc | 11 +++++++---- .../ai-gateway => partials}/migration-guide.adoc | 0 .../ai-gateway => partials}/observability-logs.adoc | 0 .../observability-metrics.adoc | 0 20 files changed, 19 insertions(+), 44 deletions(-) rename modules/ai-agents/{pages/ai-gateway => partials}/migration-guide.adoc (100%) rename modules/ai-agents/{pages/ai-gateway => partials}/observability-logs.adoc (100%) rename modules/ai-agents/{pages/ai-gateway => partials}/observability-metrics.adoc (100%) diff --git a/modules/ROOT/nav.adoc b/modules/ROOT/nav.adoc index fc1b92a15..05771a00c 100644 --- a/modules/ROOT/nav.adoc +++ b/modules/ROOT/nav.adoc @@ -35,10 +35,10 @@ **** xref:ai-agents:ai-gateway/builders/connect-your-agent.adoc[Connect Your Agent] **** xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[CEL Routing Patterns] **** xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[MCP Aggregation] -*** Observability -**** xref:ai-agents:ai-gateway/observability-logs.adoc[Request Logs] -**** xref:ai-agents:ai-gateway/observability-metrics.adoc[Metrics and Analytics] -*** xref:ai-agents:ai-gateway/migration-guide.adoc[Migrate] +//*** Observability +//**** xref:ai-agents:ai-gateway/observability-logs.adoc[Request Logs] +//**** xref:ai-agents:ai-gateway/observability-metrics.adoc[Metrics and Analytics] +//*** xref:ai-agents:ai-gateway/migration-guide.adoc[Migrate] *** xref:ai-agents:ai-gateway/integrations/index.adoc[Integrations] **** Claude Code ***** xref:ai-agents:ai-gateway/integrations/claude-code-admin.adoc[Admin Guide] diff --git a/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc b/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc index 0a0a559c7..c3e20e1b2 100644 --- a/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/admin/setup-guide.adoc @@ -316,10 +316,8 @@ Users can then discover and connect to the gateway using the information provide * xref:ai-gateway/cel-routing-cookbook.adoc[CEL Routing Cookbook] - Advanced routing patterns // * xref:ai-gateway/admin/networking-configuration.adoc[Networking Configuration] - Configure private endpoints and connectivity -*Monitor and observe:* - -* xref:ai-gateway/observability-metrics.adoc[Monitor Usage] - Track costs and usage across all gateways -* xref:ai-gateway/observability-logs.adoc[Request Logs] - View and filter request logs +//*Monitor and observe:* +// *Integrate tools:* diff --git a/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc b/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc index 0379595ee..57d997342 100644 --- a/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc +++ b/modules/ai-agents/pages/ai-gateway/cel-routing-cookbook.adoc @@ -951,4 +951,3 @@ Each request evaluates CEL expression once. Total latency impact: == Next steps * *Apply CEL routing*: See the gateway configuration options available in the Redpanda Cloud console. -* *Monitor routing decisions*: xref:ai-agents:ai-gateway/observability-logs.adoc[] diff --git a/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc b/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc index a25c0d579..5295f2134 100644 --- a/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc +++ b/modules/ai-agents/pages/ai-gateway/gateway-architecture.adoc @@ -147,4 +147,3 @@ The gateway only loads and exposes specific tools when requested, which dramatic * xref:ai-agents:ai-gateway/gateway-quickstart.adoc[]: Route your first request through AI Gateway * xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Configure MCP server aggregation for AI agents -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor request logs, token usage, and costs diff --git a/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc b/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc index 59292fa1a..ffcf58aeb 100644 --- a/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc +++ b/modules/ai-agents/pages/ai-gateway/gateway-quickstart.adoc @@ -561,8 +561,6 @@ Explore advanced AI Gateway features: * xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Advanced CEL routing patterns for traffic distribution and cost optimization * xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Configure MCP server aggregation and deferred tool loading -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor request logs, token usage, and costs -* xref:ai-agents:ai-gateway/migration-guide.adoc[]: Migrate existing LLM integrations to AI Gateway * xref:ai-agents:ai-gateway/integrations/index.adoc[]: Connect more AI development tools Learn about the architecture: diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc index 5bbb2e844..08ab8c01d 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-admin.adoc @@ -299,7 +299,7 @@ Implement token rotation for security: . Create a new token before the existing token expires . Distribute the new token to users -. Monitor usage of the old token in xref:ai-agents:ai-gateway/observability-logs.adoc[request logs] +. Monitor usage of the old token in (observability dashboard) . Revoke the old token after all users have migrated == Configure Claude Code clients @@ -393,7 +393,6 @@ Track Claude Code activity through gateway observability features. |Identify failing requests or misconfigured clients |=== -For detailed metrics configuration, see xref:ai-agents:ai-gateway/observability-metrics.adoc[]. === Query logs via API @@ -498,7 +497,5 @@ Causes and solutions: == Next steps -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Analyze detailed request logs -* xref:ai-agents:ai-gateway/observability-metrics.adoc[]: Set up metrics dashboards * xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Implement advanced routing rules * xref:ai-agents:mcp/remote/overview.adoc[]: Deploy Remote MCP servers for custom tools diff --git a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc index 7fb085124..3cc98eae8 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/claude-code-user.adoc @@ -413,7 +413,6 @@ chmod 600 ~/.claude.json == Next steps * xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Configure deferred tool loading to reduce token costs -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor Claude Code requests in the gateway dashboard * xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Use CEL expressions to route Claude Code requests based on context == Related pages diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc index 8552199c4..3092e84e2 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-admin.adoc @@ -320,7 +320,7 @@ Implement token rotation for security: . Create a new token before the existing token expires . Distribute the new token to users -. Monitor usage of the old token in xref:ai-agents:ai-gateway/observability-logs.adoc[request logs] +. Monitor usage of the old token in (observability dashboard) . Revoke the old token after all users have migrated == Configure Cline clients @@ -447,7 +447,6 @@ Cline autonomous operations may generate request sequences. Look for patterns to |Identify failing requests or misconfigured clients |=== -For detailed metrics configuration, see xref:ai-agents:ai-gateway/observability-metrics.adoc[]. === Query logs via API @@ -584,7 +583,5 @@ Causes and solutions: == Next steps -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Analyze detailed request logs -* xref:ai-agents:ai-gateway/observability-metrics.adoc[]: Set up metrics dashboards * xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Implement advanced routing rules * xref:ai-agents:mcp/remote/overview.adoc[]: Deploy Remote MCP servers for custom tools diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc index cc66ca5d7..795a638eb 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cline-user.adoc @@ -752,7 +752,6 @@ The gateway automatically blocks requests that would exceed the limit. == Next steps * xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Configure deferred tool loading to reduce token costs -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor Cline requests in the gateway dashboard * xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Use CEL expressions to route Cline requests based on task complexity == Related pages diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc index ce75b6306..6e160c87c 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-admin.adoc @@ -412,7 +412,7 @@ Implement token rotation for security: . Create a new token before the existing token expires . Distribute the new token to users -. Monitor usage of the old token in xref:ai-agents:ai-gateway/observability-logs.adoc[request logs] +. Monitor usage of the old token in (observability dashboard) . Revoke the old token after all users have migrated == Configure Continue.dev clients @@ -600,7 +600,6 @@ Continue.dev generates different request patterns: |Identify failing providers or misconfigured backends |=== -For detailed metrics configuration, see xref:ai-agents:ai-gateway/observability-metrics.adoc[]. === Query logs via API @@ -757,7 +756,5 @@ This is expected behavior, not a configuration issue: == Next steps -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Analyze detailed request logs -* xref:ai-agents:ai-gateway/observability-metrics.adoc[]: Set up metrics dashboards * xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Implement advanced routing rules * xref:ai-agents:mcp/remote/overview.adoc[]: Deploy Remote MCP servers for custom tools diff --git a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc index 5dcb3094c..b8f282021 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/continue-user.adoc @@ -932,7 +932,6 @@ Autocomplete rarely needs more than 256 tokens, while chat responses can vary. == Next steps * xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Configure deferred tool loading to reduce token costs -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor Continue.dev requests in the gateway dashboard * xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Use CEL expressions to route Continue.dev requests based on context == Related pages diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc index 55f305500..cfaa68595 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-admin.adoc @@ -373,7 +373,7 @@ Implement token rotation for security: . Create a new token before the existing token expires . Distribute the new token to users -. Monitor usage of the old token in xref:ai-agents:ai-gateway/observability-logs.adoc[request logs] +. Monitor usage of the old token in (observability dashboard) . Revoke the old token after all users have migrated == Multi-tenant deployment strategies @@ -647,7 +647,6 @@ Cursor generates different request patterns: |Monitor OpenAI-to-provider format conversion success |=== -For detailed metrics configuration, see xref:ai-agents:ai-gateway/observability-metrics.adoc[]. === Query logs via API @@ -811,7 +810,5 @@ Causes and solutions: == Next steps -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Analyze detailed request logs and transform operations -* xref:ai-agents:ai-gateway/observability-metrics.adoc[]: Set up metrics dashboards * xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Implement advanced routing rules for model prefix routing * xref:ai-agents:mcp/remote/overview.adoc[]: Deploy Remote MCP servers for custom tools diff --git a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc index 45041ea5d..cd7dda2b1 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/cursor-user.adoc @@ -869,7 +869,6 @@ This sends only search + orchestrator tools initially, reducing token usage sign == Next steps * xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Configure deferred tool loading to work within Cursor's 40-tool limit -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor Cursor requests in the gateway dashboard * xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Use CEL expressions to route Cursor requests based on context == Related pages diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc index fb2cf320f..730d95498 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-admin.adoc @@ -321,7 +321,7 @@ Implement token rotation for security: . Create a new token before the existing token expires . Update organization-level GitHub Copilot configuration with the new token -. Monitor usage of the old token in xref:ai-agents:ai-gateway/observability-logs.adoc[request logs] +. Monitor usage of the old token in (observability dashboard) . Revoke the old token after the configuration update propagates == Multi-tenant deployment strategies @@ -647,7 +647,6 @@ GitHub Copilot generates distinct request patterns: |Track usage by team (if using multi-tenant strategies) |=== -For detailed metrics configuration, see xref:ai-agents:ai-gateway/observability-metrics.adoc[]. === Query logs via API @@ -824,7 +823,5 @@ Causes and solutions: == Next steps -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Analyze detailed request logs and transform operations -* xref:ai-agents:ai-gateway/observability-metrics.adoc[]: Set up metrics dashboards * xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Implement advanced routing rules for model aliasing -* xref:ai-agents:ai-gateway/migration-guide.adoc[]: Migrate from direct provider access to AI Gateway + diff --git a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc index b52f79d3b..c8b23edb2 100644 --- a/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc +++ b/modules/ai-agents/pages/ai-gateway/integrations/github-copilot-user.adoc @@ -953,7 +953,6 @@ Generate project-specific cost reports from the gateway dashboard. == Next steps -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor GitHub Copilot requests in the gateway dashboard * xref:ai-agents:ai-gateway/cel-routing-cookbook.adoc[]: Use CEL expressions to route Copilot requests based on context * xref:ai-agents:ai-gateway/mcp-aggregation-guide.adoc[]: Learn about MCP tool integration (if using Copilot Workspace) diff --git a/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc index a5367ad38..68ba38cb0 100644 --- a/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc +++ b/modules/ai-agents/pages/ai-gateway/mcp-aggregation-guide.adoc @@ -1024,5 +1024,3 @@ response = agent.run("Find all premium users in the database") == Next steps -* xref:ai-agents:ai-gateway/observability-logs.adoc[]: Monitor MCP usage in request logs. -* xref:ai-agents:ai-gateway/observability-metrics.adoc[]: Track MCP metrics and costs. \ No newline at end of file diff --git a/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc b/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc index 62e60a4d3..6af235d8a 100644 --- a/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc +++ b/modules/ai-agents/pages/ai-gateway/what-is-ai-gateway.adoc @@ -26,7 +26,7 @@ Finally, observability is fragmented across provider dashboards. You cannot reco Redpanda AI Gateway addresses these challenges through four core capabilities: -=== 1. Unified LLM access (single endpoint for all providers) +=== Unified LLM access (single endpoint for all providers) AI Gateway provides a single OpenAI-compatible endpoint that routes requests to multiple LLM providers. Instead of integrating with each provider's SDK separately, you configure your application once and switch providers by changing only the model parameter. @@ -80,7 +80,7 @@ response = client.chat.completions.create( To switch providers, you change only the `model` parameter from `openai/gpt-4o` to `anthropic/claude-sonnet-3.5`. No code changes or redeployment needed. -=== 2. Policy-based routing and cost control +=== Policy-based routing and cost control AI Gateway lets you define routing rules, rate limits, and budgets once, then enforces them automatically for all requests. @@ -98,7 +98,7 @@ You can also set different rate limits and spend limits per environment to preve For reliability, you can configure provider pools with automatic failover. If you configure OpenAI GPT-4 as your primary model and Anthropic Claude Opus as the fallback, the gateway automatically routes requests to the fallback when it detects rate limits or timeouts from the primary provider. This configuration can significantly improve uptime (potentially up to 99.9% in some configurations) even during provider outages. -=== 3. MCP aggregation and orchestration +=== MCP aggregation and orchestration AI Gateway aggregates multiple MCP (Model Context Protocol) servers and provides deferred tool loading, which dramatically reduces token costs for AI agents. @@ -108,7 +108,7 @@ With AI Gateway, you configure approved MCP servers once, and the gateway loads For complex workflows, AI Gateway provides a JavaScript-based orchestrator tool that reduces multi-step workflows from multiple round trips to a single call. For example, you can create a workflow that searches a vector database and, if the results are insufficient, falls back to web search—all in one orchestration step. -=== 4. Unified observability and cost tracking +=== Unified observability and cost tracking AI Gateway provides a single dashboard that tracks all LLM traffic across providers, eliminating the need to switch between multiple provider dashboards. @@ -116,6 +116,7 @@ The dashboard tracks request volume per gateway, model, and provider, along with This unified view helps you answer critical questions such as which model is the most cost-effective for your use case, why a specific user request failed, how much your staging environment costs per week, and what the latency difference is between providers for your workload. +ifdef::show-gateway-patterns[] == Common gateway patterns === Team isolation @@ -145,6 +146,8 @@ request.headers["x-customer-tier"] == "pro" ? "anthropic/claude-sonnet-3.5" : "anthropic/claude-haiku" ---- +endif::[] + == When to use AI Gateway AI Gateway is ideal for organizations that: diff --git a/modules/ai-agents/pages/ai-gateway/migration-guide.adoc b/modules/ai-agents/partials/migration-guide.adoc similarity index 100% rename from modules/ai-agents/pages/ai-gateway/migration-guide.adoc rename to modules/ai-agents/partials/migration-guide.adoc diff --git a/modules/ai-agents/pages/ai-gateway/observability-logs.adoc b/modules/ai-agents/partials/observability-logs.adoc similarity index 100% rename from modules/ai-agents/pages/ai-gateway/observability-logs.adoc rename to modules/ai-agents/partials/observability-logs.adoc diff --git a/modules/ai-agents/pages/ai-gateway/observability-metrics.adoc b/modules/ai-agents/partials/observability-metrics.adoc similarity index 100% rename from modules/ai-agents/pages/ai-gateway/observability-metrics.adoc rename to modules/ai-agents/partials/observability-metrics.adoc