Moves LLM connector guides to explore-analyze section (#4224)

## Summary

Moves the LLM connector guides from the security docs section to the new
AI section within explore-analyze. This fixes
elastic/docs-content-internal/issues/487 as part of
elastic/docs-content-internal/issues/298. The purpose is to update the
IA for these docs to be solution-agnostic, since they are helpful to
users of any solution, not just security.

This PR:
- Moves the four connector-specific guides to the recently created
section, in a new sub-section.
- Creates a landing page for the new subsection.
- Moves `explore-analyze/ai-features.md` to
`explore-analyze/ai-features/ai-features.md` (this is just cleanup from
our [previous PR](#3768)).
- Updates links to the LLM connector guides and creates corresponding
redirects.
- Moves the mapped_pages frontmatter from the page within the security
solution docs that previously was the landing page for the connector
guides (the page should still exist as there are some security-specific
guides that were not moved) to the new landing page within
explore-analyze. I would appreciate a check on my thinking here, but my
thinking is that we should generally be sending users to the new landing
page.
- Updates the security LLM connectors landing page introduction.
- Updates Elastic Managed LLM snippet.
- Fixes a few Vale-identified sentence-level issues in the Amazon
Bedrock guide.

## Generative AI disclosure
1. Did you use a generative AI (GenAI) tool to assist in creating this
contribution?
- [x ] Yes  
- [ ] No  

Tool(s) and model(s) used:
CoPilot with GPT 4.1 to help with the redirects.

---------

Co-authored-by: Mike Birnstiehl <114418652+mdbirnstiehl@users.noreply.github.com>
Co-authored-by: florent-leborgne <florent.leborgne@elastic.co>

Author: 3 people

explore-analyze/ai-features.md

@@ -20,8 +20,8 @@ For pricing information, refer to [pricing](https://www.elastic.co/pricing).
 
 ## Requirements
 
-- To use Elastic's AI-powered features, you need an appropriate license and feature tier. These vary by solution and feature. Refer to each feature's documentation to learn more.
-- Most features require at least one working LLM connector. To learn about setting up large language model (LLM) connectors used by AI-powered features, refer to [](/solutions/security/ai/set-up-connectors-for-large-language-models-llm.md). Elastic Managed LLM is available by default if your license supports it.
+- To use Elastic's AI-powered features, you need an appropriate subscription level or serverless feature tier. These vary by solution and feature. Refer to each feature's documentation to learn more.
+- Most features require at least one working LLM connector. To learn about setting up large language model (LLM) connectors used by AI-powered features, refer to [](/explore-analyze/ai-features/llm-guides/llm-connectors.md). Elastic Managed LLM is available by default if your license supports it.
 
 ## AI-powered features on the Elastic platform
 
@@ -87,7 +87,7 @@ The [Model Context Protocol (MCP)](/solutions/search/mcp.md) lets you connect AI
 
 ## AI-powered features in {{observability}}
 
-{{observability}}'s AI-powered features all require an [LLM connector](/solutions/security/ai/set-up-connectors-for-large-language-models-llm.md). When you use one of these features, you can select any LLM connector that's configured in your environment. The connector you select for one feature does not affect which connector any other feature uses. For specific configuration instructions, refer to each feature's documentation.
+{{observability}}'s AI-powered features all require an [LLM connector](/explore-analyze/ai-features/llm-guides/llm-connectors.md). When you use one of these features, you can select any LLM connector that's configured in your environment. The connector you select for one feature does not affect which connector any other feature uses. For specific configuration instructions, refer to each feature's documentation.
 
 ### AI assistant for {{observability}}
 
@@ -104,20 +104,20 @@ The [Model Context Protocol (MCP)](/solutions/search/mcp.md) lets you connect AI
 
 ## AI-powered features in {{elastic-sec}}
 
-{{elastic-sec}}'s AI-powered features all require an [LLM connector](/solutions/security/ai/set-up-connectors-for-large-language-models-llm.md). When you use one of these features, you can select any LLM connector that's configured in your environment. The connector you select for one feature does not affect which connector any other feature uses. For specific configuration instructions, refer to each feature's documentation.
+{{elastic-sec}}'s AI-powered features all require an [LLM connector](/explore-analyze/ai-features/llm-guides/llm-connectors.md). When you use one of these features, you can select any LLM connector that's configured in your environment. The connector you select for one feature does not affect which connector any other feature uses. For specific configuration instructions, refer to each feature's documentation.
 
 ### AI Assistant for Security
 
 [Elastic AI Assistant for Security](/solutions/security/ai/ai-assistant.md) helps you with tasks such as alert investigation, incident response, and query generation throughout {{elastic-sec}}. It provides a chat interface where you can ask questions about the {{stack}} and your data, and provides contextual insights that explain errors and messages and suggest remediation steps.
 
-This feature requires an [LLM connector](/solutions/security/ai/set-up-connectors-for-large-language-models-llm.md).
+This feature requires an [LLM connector](/explore-analyze/ai-features/llm-guides/llm-connectors.md).
 
 
 ### Attack Discovery
 
 [Attack Discovery](/solutions/security/ai/attack-discovery.md) uses AI to triage your alerts and identify potential threats. Each "discovery" represents a potential attack and describes relationships among alerts to identify related users and hosts, map alerts to the MITRE ATT&CK matrix, and help identify threat actors.
 
-This feature requires an [LLM connector](/solutions/security/ai/set-up-connectors-for-large-language-models-llm.md).
+This feature requires an [LLM connector](/explore-analyze/ai-features/llm-guides/llm-connectors.md).
 
 
 ### Automatic Migration
@@ -127,14 +127,14 @@ This feature requires an [LLM connector](/solutions/security/ai/set-up-connector
 * Splunk rules
 * Splunk dashboards
 
-This feature requires an [LLM connector](/solutions/security/ai/set-up-connectors-for-large-language-models-llm.md).
+This feature requires an [LLM connector](/explore-analyze/ai-features/llm-guides/llm-connectors.md).
 
 
 ### Automatic Import
 
 [Automatic Import](/solutions/security/get-started/automatic-import.md) helps you ingest data from sources that do not have prebuilt Elastic integrations. It uses AI to parse a sample of the data you want to ingest, and creates a new integration specifically for that type of data.
 
-This feature requires an [LLM connector](/solutions/security/ai/set-up-connectors-for-large-language-models-llm.md).
+This feature requires an [LLM connector](/explore-analyze/ai-features/llm-guides/llm-connectors.md).
 
 
 ### Automatic Troubleshooting
@@ -144,4 +144,4 @@ This feature requires an [LLM connector](/solutions/security/ai/set-up-connector
 * **Policy responses**: Detect warnings or failures in {{elastic-defend}}’s integration policies.
 * **Third-party antivirus (AV) software**: Identify installed third-party antivirus (AV) products that might conflict with {{elastic-defend}}.
 
-This feature requires an [LLM connector](/solutions/security/ai/set-up-connectors-for-large-language-models-llm.md).
+This feature requires an [LLM connector](/explore-analyze/ai-features/llm-guides/llm-connectors.md).

explore-analyze/ai-features/ai-assistant.md

@@ -38,7 +38,7 @@ AI Assistant requires specific privileges and a large language model (LLM) conne
 
 To learn more about configuring LLM connectors, refer to:
 
-- [Enable LLM access](../../solutions/security/ai/set-up-connectors-for-large-language-models-llm.md)
+- [Enable LLM access](/explore-analyze/ai-features/llm-guides/llm-connectors.md)
 
 ## Prompt best practices [rag-for-esql]
 Elastic AI Assistant allows you to take full advantage of the Elastic platform to improve your operations. It can help you write an ES|QL query for a particular use case, or answer general questions about how to use the platform. Its ability to assist you depends on the specificity and detail of your questions. The more context and detail you provide, the more tailored and useful its responses will be.

…security/ai/connect-to-amazon-bedrock.md …/llm-guides/connect-to-amazon-bedrock.mdsolutions/security/ai/connect-to-amazon-bedrock.md renamed to explore-analyze/ai-features/llm-guides/connect-to-amazon-bedrock.md solutions/security/ai/connect-to-amazon-bedrock.md renamed to explore-analyze/ai-features/llm-guides/connect-to-amazon-bedrock.md

@@ -63,7 +63,7 @@ The following video demonstrates these steps (click to watch).
 
 ### Configure an IAM User [_configure_an_iam_user]
 
-Next, assign the policy you just created to a new user:
+Next, assign the policy you created to a new user:
 
 1. Return to the **IAM** menu. Select **Users** from the navigation menu, then click **Create User**.
 2. Name the user, then click **Next**.
@@ -82,7 +82,7 @@ The following video demonstrates these steps (click to watch).
 Create the access keys that will authenticate your Elastic connector:
 
 1. Return to the **IAM** menu. Select **Users** from the navigation menu.
-2. Search for the user you just created, and click its name.
+2. Search for the user you created, and click its name.
 3. Go to the **Security credentials** tab.
 4. Under **Access keys**, click **Create access key**.
 5. Select **Third-party service**, check the box under **Confirmation**, click **Next**, then click **Create access key**.
@@ -102,7 +102,7 @@ Finally, configure the connector in {{kib}}:
 2. Find the **Connectors** page in the navigation menu or use the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md). Then click **Create Connector**, and select **Amazon Bedrock**.
 3. Name your connector.
 4. (Optional) Configure the Amazon Bedrock connector to use a different AWS region where Anthropic models are supported by editing the **URL** field, for example by changing `us-east-1` to `eu-central-1`.
-5. (Optional) Add one of the following strings if you want to use a model other than the default. Note that these model IDs should have a prefix of `us.` or `eu.`, depending on your region, for example `us.anthropic.claude-3-5-sonnet-20240620-v1:0` or `eu.anthropic.claude-3-5-sonnet-20240620-v1:0`.
+5. (Optional) Add one of the following strings if you want to use a model other than the default. These model IDs should have a prefix of `us.` or `eu.`, depending on your region, for example `us.anthropic.claude-3-5-sonnet-20240620-v1:0` or `eu.anthropic.claude-3-5-sonnet-20240620-v1:0`.
 
     * Sonnet 3.5: `us.anthropic.claude-3-5-sonnet-20240620-v1:0` or `eu.anthropic.claude-3-5-sonnet-20240620-v1:0`
     * Sonnet 3.5 v2: `us.anthropic.claude-3-5-sonnet-20241022-v2:0` or `eu.anthropic.claude-3-5-sonnet-20241022-v2:0`

…s/security/ai/connect-to-azure-openai.md …es/llm-guides/connect-to-azure-openai.mdsolutions/security/ai/connect-to-azure-openai.md renamed to explore-analyze/ai-features/llm-guides/connect-to-azure-openai.md solutions/security/ai/connect-to-azure-openai.md renamed to explore-analyze/ai-features/llm-guides/connect-to-azure-openai.md

@@ -1,19 +1,20 @@
 ---
-navigation_title: Connect to a local LLM
+navigation_title: Connect to LM Studio for {{observability}}
 mapped_pages:
   - https://www.elastic.co/guide/en/observability/current/connect-to-local-llm.html
 applies_to:
   stack: ga 9.2
-  serverless: ga
+  serverless:
+    observability: ga
 products:
   - id: observability
 ---
 
-# Connect to your own local LLM
+# Connect to a local LLM for {{observability}} using LM Studio
 
 :::{important}
 Elastic doesn’t support the setup and configuration of local LLMs. The example provided is for reference only.
-Before using a local LLM, evaluate its performance according to the [LLM performance matrix](./llm-performance-matrix.md#evaluate-your-own-model).
+Before using a local LLM, evaluate its performance according to the [LLM performance matrix](/solutions/observability/llm-performance-matrix.md#evaluate-your-own-model).
 :::
 
 This page provides instructions for setting up a connector to a large language model (LLM) of your choice using LM Studio. This allows you to use your chosen model within the {{obs-ai-assistant}}. You’ll first need to set up LM Studio, then download and deploy a model via LM studio and finally configure the connector in your Elastic deployment.
@@ -152,5 +153,5 @@ While local (open-weight) LLMs offer greater privacy and control, they generally
 
 Local LLMs in air-gapped environments have specific installation and configuration instructions for deploying ELSER and configuring product documentation. Refer to the following links for more information:
 
-- [Deploy ELSER in an air-gapped environment](../../explore-analyze/machine-learning/nlp/ml-nlp-elser.md#air-gapped-install)
+- [Deploy ELSER in an air-gapped environment](/explore-analyze/machine-learning/nlp/ml-nlp-elser.md#air-gapped-install)
 - [Configure product documentation for air-gapped-environments](kibana://reference/configuration-reference/ai-assistant-settings.md#configuring-product-doc-for-airgap)

…/security/ai/connect-to-google-vertex.md …s/llm-guides/connect-to-google-vertex.mdsolutions/security/ai/connect-to-google-vertex.md renamed to explore-analyze/ai-features/llm-guides/connect-to-google-vertex.md solutions/security/ai/connect-to-google-vertex.md renamed to explore-analyze/ai-features/llm-guides/connect-to-google-vertex.md

@@ -1,4 +1,5 @@
 ---
+navigation_title: Connect to LM Studio for {{elastic-sec}}
 mapped_pages:
   - https://www.elastic.co/guide/en/security/current/connect-to-byo-llm.html
   - https://www.elastic.co/guide/en/serverless/current/connect-to-byo-llm.html
@@ -11,7 +12,7 @@ products:
   - id: cloud-serverless
 ---
 
-# Connect to your own local LLM using LM Studio
+# Connect to a local LLM for {{elastic-sec}} using LM Studio
 
 This page provides instructions for setting up a connector to a large language model (LLM) of your choice using LM Studio. This allows you to use your chosen model within {{elastic-sec}}. You’ll first need to set up a reverse proxy to communicate with {{elastic-sec}}, then set up LM Studio on a server, and finally configure the connector in your Elastic deployment. [Learn more about the benefits of using a local LLM](https://www.elastic.co/blog/ai-assistant-locally-hosted-models).
 

…bservability/connect-to-own-local-llm.md …des/connect-to-lmstudio-observability.mdsolutions/observability/connect-to-own-local-llm.md renamed to explore-analyze/ai-features/llm-guides/connect-to-lmstudio-observability.md solutions/observability/connect-to-own-local-llm.md renamed to explore-analyze/ai-features/llm-guides/connect-to-lmstudio-observability.md

@@ -1,4 +1,5 @@
 ---
+navigation_title: Connect to vLLM for {{elastic-sec}}
 applies_to:
   stack: all
   serverless:
@@ -8,7 +9,7 @@ products:
   - id: cloud-serverless
 ---
 
-# Connect to your own LLM using vLLM (air gapped environments)
+# Connect to your own LLM using vLLM (air-gapped environments)
 This guide shows you how to run an OpenAI-compatible large language model with [vLLM](https://docs.vllm.ai/en/latest/) and connect it to Elastic. The setup runs inside Docker or Podman, is served through an Nginx reverse proxy, and does not require any outbound network access. This makes it a safe option for air-gapped environments or deployments with strict network controls.
 
 The steps below show one example configuration, but you can use any model supported by vLLM, including private and gated models on Hugging Face.
@@ -189,4 +190,4 @@ With your vLLM connector set up, you can use it to power features including:
 * [Automatic import](/solutions/security/get-started/automatic-import.md): Use AI to create custom integrations for third-party data sources.
 * [AI Assistant for Observability and Search](/solutions/observability/observability-ai-assistant.md): Interact with an agent designed to assist with {{observability}} and Search tasks.
 
-You can also learn how to [set up other types of LLM connectors](/solutions/security/ai/set-up-connectors-for-large-language-models-llm.md).
+You can also learn how to [set up other types of LLM connectors](/explore-analyze/ai-features/llm-guides/llm-connectors.md).

…/security/ai/connect-to-own-local-llm.md …m-guides/connect-to-lmstudio-security.mdsolutions/security/ai/connect-to-own-local-llm.md renamed to explore-analyze/ai-features/llm-guides/connect-to-lmstudio-security.md solutions/security/ai/connect-to-own-local-llm.md renamed to explore-analyze/ai-features/llm-guides/connect-to-lmstudio-security.md

@@ -0,0 +1,49 @@
+---
+mapped_pages:
+  - https://www.elastic.co/guide/en/security/current/llm-connector-guides.html
+  - https://www.elastic.co/guide/en/serverless/current/security-llm-connector-guides.html
+applies_to:
+  stack: ga
+  serverless: ga
+products:
+  - id: observability
+  - id: elasticsearch
+  - id: security
+  - id: cloud-serverless
+---
+
+# Configure access to LLMs
+
+Elastic's [AI features](/explore-analyze/ai-features.md) work with the out-of-the-box Elastic Managed LLMs or with third-party LLMs configured using one of the available connectors.
+
+## Elastic Managed LLMs
+
+:::{include} ../../../solutions/_snippets/elastic-managed-llm.md
+:::
+
+## Connect to a third-party or self-managed LLM
+
+Follow these guides to connect to one or more third-party LLM providers:
+
+* [Azure OpenAI](/explore-analyze/ai-features/llm-guides/connect-to-azure-openai.md)
+* [Amazon Bedrock](/explore-analyze/ai-features/llm-guides/connect-to-amazon-bedrock.md)
+* [OpenAI](/explore-analyze/ai-features/llm-guides/connect-to-openai.md)
+* [Google Vertex](/explore-analyze/ai-features/llm-guides/connect-to-google-vertex.md)
+* [Self-managed LLMs](/explore-analyze/ai-features/llm-guides/local-llms-overview.md)
+
+
+## Preconfigured connectors
+
+```{applies_to}
+stack: ga
+serverless: unavailable
+```
+
+You can also use [preconfigured connectors](kibana://reference/connectors-kibana/pre-configured-connectors.md) to set up third-party LLM connectors by editing the `kibana.yml` file. This allows you enable a connector for multiple spaces at once, without performing set up in the {{kib}} UI for each space. 
+
+If you use a preconfigured connector for your LLM connector, we recommend adding the `exposeConfig: true` parameter to the `xpack.actions.preconfigured` section of the `kibana.yml` config file. This parameter makes debugging easier by adding configuration information to the debug logs, including which LLM the connector uses.
+
+
+
+
+