diff --git a/docs/user_guide/13_llm_router.ipynb b/docs/user_guide/13_llm_router.ipynb new file mode 100644 index 00000000..2bc5529b --- /dev/null +++ b/docs/user_guide/13_llm_router.ipynb @@ -0,0 +1,1658 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LLM Routing\n", + "\n", + "RedisVL provides an `LLMRouter` that uses semantic similarity to route queries to the most appropriate LLM model tier. Instead of sending every request to your most expensive model, the router matches incoming queries against reference phrases to determine the right level of model capability.\n", + "\n", + "**The problem**: Applications default to using the most capable (and expensive) LLM for all queries, even when a simpler model would do just fine:\n", + "- *\"Hello, how are you?\"* does **not** need Claude Opus 4.5 ($5/M input tokens)\n", + "- *\"Hello, how are you?\"* is perfectly handled by GPT-4.1 Nano ($0.10/M input tokens)\n", + "\n", + "The `LLMRouter` solves this by classifying queries into **model tiers** (e.g., simple, standard, expert) using Redis vector search over a set of reference phrases that define each tier's \"semantic surface area.\"\n", + "\n", + "This notebook walks through every aspect of the LLM Router:\n", + "1. Quick start with pretrained config\n", + "2. Routing queries across tiers\n", + "3. Defining custom tiers\n", + "4. Cost-optimized routing\n", + "5. Multi-match routing and aggregation methods\n", + "6. Dynamic tier management\n", + "7. Persistence and serialization\n", + "8. Async usage\n", + "9. LiteLLM integration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:47.604936Z", + "iopub.status.busy": "2026-02-16T19:52:47.604702Z", + "iopub.status.idle": "2026-02-16T19:52:47.610313Z", + "shell.execute_reply": "2026-02-16T19:52:47.609441Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "warnings.filterwarnings(\"ignore\", message=\".*IProgress.*\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quick Start with a Pretrained Config\n", + "\n", + "The fastest way to get started is with the built-in `\"default\"` pretrained configuration. It ships with **3 model tiers** and **pre-computed embeddings** (from `sentence-transformers/all-mpnet-base-v2`), so it loads instantly without needing to embed anything.\n", + "\n", + "The three tiers are grounded in [Bloom's Taxonomy](https://en.wikipedia.org/wiki/Bloom%27s_taxonomy) of cognitive complexity:\n", + "\n", + "| Tier | Bloom's Level | Model | Cost (input) | Example Tasks |\n", + "|------|--------------|-------|-------------|---------------|\n", + "| **simple** | Remember / Understand | `openai/gpt-4.1-nano` | $0.10/M | Greetings, factual QA, format conversion |\n", + "| **standard** | Apply / Analyze | `anthropic/claude-sonnet-4-5` | $3.00/M | Code explanation, summarization, analysis |\n", + "| **expert** | Evaluate / Create | `anthropic/claude-opus-4-5` | $5.00/M | Research, system architecture, formal proofs |" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:47.612428Z", + "iopub.status.busy": "2026-02-16T19:52:47.612243Z", + "iopub.status.idle": "2026-02-16T19:52:52.400728Z", + "shell.execute_reply": "2026-02-16T19:52:52.400119Z" + } + }, + "outputs": [], + "source": [ + "from redisvl.extensions.llm_router import LLMRouter\n", + "\n", + "router = LLMRouter.from_pretrained(\n", + " \"default\",\n", + " redis_url=\"redis://localhost:6379\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:52.402530Z", + "iopub.status.busy": "2026-02-16T19:52:52.402216Z", + "iopub.status.idle": "2026-02-16T19:52:52.405414Z", + "shell.execute_reply": "2026-02-16T19:52:52.404739Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Router name: llm-router-default\n", + "Tier count: 3\n", + "\n", + "--- simple ---\n", + " Model: openai/gpt-4.1-nano\n", + " References: 18 phrases\n", + " Threshold: 0.5\n", + " Cost (in): $0.0001/1k tokens\n", + " Bloom's: ['Remember', 'Understand']\n", + "\n", + "--- standard ---\n", + " Model: anthropic/claude-sonnet-4-5\n", + " References: 18 phrases\n", + " Threshold: 0.6\n", + " Cost (in): $0.003/1k tokens\n", + " Bloom's: ['Apply', 'Analyze']\n", + "\n", + "--- expert ---\n", + " Model: anthropic/claude-opus-4-5\n", + " References: 18 phrases\n", + " Threshold: 0.7\n", + " Cost (in): $0.005/1k tokens\n", + " Bloom's: ['Evaluate', 'Create']\n", + "\n" + ] + } + ], + "source": [ + "# Inspect what was loaded\n", + "print(\"Router name:\", router.name)\n", + "print(\"Tier count:\", len(router.tiers))\n", + "print()\n", + "\n", + "for tier in router.tiers:\n", + " print(f\"--- {tier.name} ---\")\n", + " print(f\" Model: {tier.model}\")\n", + " print(f\" References: {len(tier.references)} phrases\")\n", + " print(f\" Threshold: {tier.distance_threshold}\")\n", + " print(f\" Cost (in): ${tier.metadata.get('cost_per_1k_input', 'N/A')}/1k tokens\")\n", + " print(f\" Bloom's: {tier.metadata.get('blooms_taxonomy', [])}\")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:52.425188Z", + "iopub.status.busy": "2026-02-16T19:52:52.425035Z", + "iopub.status.idle": "2026-02-16T19:52:54.916612Z", + "shell.execute_reply": "2026-02-16T19:52:54.916088Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "\r\n", + "Index Information:\r\n", + "╭────────────────────────┬────────────────────────┬────────────────────────┬────────────────────────┬────────────────────────┬\b╮\r\n", + "│ Index Name │ Storage Type │ Prefixes │ Index Options │ Indexing │\r\n", + "├────────────────────────┼────────────────────────┼────────────────────────┼────────────────────────┼────────────────────────┼\b┤\r\n", + "| llm-router-default | HASH | ['llm-router-default'] | [] | 0 |\r\n", + "╰────────────────────────┴────────────────────────┴────────────────────────┴────────────────────────┴────────────────────────┴\b╯\r\n", + "Index Fields:\r\n", + "╭─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬\b╮\r\n", + "│ Name │ Attribute │ Type │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │\r\n", + "├─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┼\b┤\r\n", + "│ reference_id │ reference_id │ TAG │ SEPARATOR │ , │ │ │ │ │ │ │\r\n", + "│ route_name │ route_name │ TAG │ SEPARATOR │ , │ │ │ │ │ │ │\r\n", + "│ reference │ reference │ TEXT │ WEIGHT │ 1 │ │ │ │ │ │ │\r\n", + "│ vector │ vector │ VECTOR │ algorithm │ FLAT │ data_type │ FLOAT32 │ dim │ 768 │ distance_metric │ COSINE │\r\n", + "╰─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┴\b╯\r\n" + ] + } + ], + "source": [ + "# View the underlying Redis index\n", + "!rvl index info -i llm-router-default" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Routing Queries\n", + "\n", + "The `route()` method takes a text query, embeds it, and finds the best matching tier. It returns an `LLMRouteMatch` with the tier name, model string, distance, confidence, and any alternative matches." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:54.918679Z", + "iopub.status.busy": "2026-02-16T19:52:54.918546Z", + "iopub.status.idle": "2026-02-16T19:52:55.016173Z", + "shell.execute_reply": "2026-02-16T19:52:55.015415Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LLMRouteMatch(tier='simple', model='openai/gpt-4.1-nano', distance=0.382999330759, confidence=0.8085003346205, alternatives=[], metadata={'provider': 'openai', 'cost_per_1k_input': 0.0001, 'cost_per_1k_output': 0.0004, 'blooms_taxonomy': ['Remember', 'Understand']})" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# A simple greeting -> routes to the 'simple' tier\n", + "match = router.route(\"hi, how are you doing today?\")\n", + "match" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:55.018045Z", + "iopub.status.busy": "2026-02-16T19:52:55.017912Z", + "iopub.status.idle": "2026-02-16T19:52:55.020345Z", + "shell.execute_reply": "2026-02-16T19:52:55.019809Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tier: simple\n", + "Model: openai/gpt-4.1-nano\n", + "Distance: 0.3830\n", + "Confidence: 0.8085\n", + "Alternatives: []\n" + ] + } + ], + "source": [ + "print(f\"Tier: {match.tier}\")\n", + "print(f\"Model: {match.model}\")\n", + "print(f\"Distance: {match.distance:.4f}\")\n", + "print(f\"Confidence: {match.confidence:.4f}\")\n", + "print(f\"Alternatives: {match.alternatives}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `distance` is the cosine distance (0-2, lower is closer). The `confidence` is derived as `1 - distance/2`, giving a 0-1 score where higher is better.\n", + "\n", + "The `alternatives` field shows other tiers that also matched, along with their distances. This is useful for understanding how close the decision was.\n", + "\n", + "Let's try more queries across the complexity spectrum:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:55.021857Z", + "iopub.status.busy": "2026-02-16T19:52:55.021749Z", + "iopub.status.idle": "2026-02-16T19:52:55.167448Z", + "shell.execute_reply": "2026-02-16T19:52:55.167025Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Simple Tier Queries ===\n", + " 'what is the capital of France?'\n", + " -> simple (openai/gpt-4.1-nano) distance=0.1417\n", + " 'thanks for your help!'\n", + " -> simple (openai/gpt-4.1-nano) distance=0.1270\n", + " 'translate hello to Spanish'\n", + " -> simple (openai/gpt-4.1-nano) distance=0.2537\n" + ] + } + ], + "source": [ + "# Simple tier queries\n", + "simple_queries = [\n", + " \"what is the capital of France?\",\n", + " \"thanks for your help!\",\n", + " \"translate hello to Spanish\",\n", + "]\n", + "\n", + "print(\"=== Simple Tier Queries ===\")\n", + "for q in simple_queries:\n", + " m = router.route(q)\n", + " print(f\" '{q}'\")\n", + " print(f\" -> {m.tier} ({m.model}) distance={m.distance:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:55.168616Z", + "iopub.status.busy": "2026-02-16T19:52:55.168533Z", + "iopub.status.idle": "2026-02-16T19:52:55.254869Z", + "shell.execute_reply": "2026-02-16T19:52:55.254427Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Standard Tier Queries ===\n", + " 'explain how garbage collection works in Java'\n", + " -> standard (anthropic/claude-sonnet-4-5) distance=0.0000\n", + " 'write unit tests for this Python class'\n", + " -> standard (anthropic/claude-sonnet-4-5) distance=0.4141\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 'compare and contrast microservices vs monolith architectures'\n", + " -> standard (anthropic/claude-sonnet-4-5) distance=0.1792\n" + ] + } + ], + "source": [ + "# Standard tier queries\n", + "standard_queries = [\n", + " \"explain how garbage collection works in Java\",\n", + " \"write unit tests for this Python class\",\n", + " \"compare and contrast microservices vs monolith architectures\",\n", + "]\n", + "\n", + "print(\"=== Standard Tier Queries ===\")\n", + "for q in standard_queries:\n", + " m = router.route(q)\n", + " print(f\" '{q}'\")\n", + " print(f\" -> {m.tier} ({m.model}) distance={m.distance:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:55.256043Z", + "iopub.status.busy": "2026-02-16T19:52:55.255949Z", + "iopub.status.idle": "2026-02-16T19:52:55.361293Z", + "shell.execute_reply": "2026-02-16T19:52:55.360892Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Expert Tier Queries ===\n", + " 'architect a fault-tolerant distributed database replication system'\n", + " -> expert (anthropic/claude-opus-4-5) distance=0.2583\n", + " 'prove this mathematical theorem using formal methods'\n", + " -> expert (anthropic/claude-opus-4-5) distance=0.2947\n", + " 'design a novel algorithm for NP-hard graph partitioning'\n", + " -> expert (anthropic/claude-opus-4-5) distance=0.2461\n" + ] + } + ], + "source": [ + "# Expert tier queries\n", + "expert_queries = [\n", + " \"architect a fault-tolerant distributed database replication system\",\n", + " \"prove this mathematical theorem using formal methods\",\n", + " \"design a novel algorithm for NP-hard graph partitioning\",\n", + "]\n", + "\n", + "print(\"=== Expert Tier Queries ===\")\n", + "for q in expert_queries:\n", + " m = router.route(q)\n", + " print(f\" '{q}'\")\n", + " print(f\" -> {m.tier} ({m.model}) distance={m.distance:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:55.362372Z", + "iopub.status.busy": "2026-02-16T19:52:55.362278Z", + "iopub.status.idle": "2026-02-16T19:52:55.378550Z", + "shell.execute_reply": "2026-02-16T19:52:55.378134Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No match: tier=None, model=None\n", + "Bool check: False\n" + ] + } + ], + "source": [ + "# A query that doesn't match any tier returns an empty match\n", + "match = router.route(\"xyzzy plugh random gibberish 12345 asdf\")\n", + "print(f\"No match: tier={match.tier}, model={match.model}\")\n", + "print(f\"Bool check: {bool(match)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:55.379840Z", + "iopub.status.busy": "2026-02-16T19:52:55.379752Z", + "iopub.status.idle": "2026-02-16T19:52:55.382825Z", + "shell.execute_reply": "2026-02-16T19:52:55.382387Z" + } + }, + "outputs": [], + "source": [ + "# Clean up\n", + "router.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining Custom Tiers\n", + "\n", + "For production use, you'll want to define tiers tailored to your application. Each `ModelTier` specifies:\n", + "\n", + "- **`name`**: Unique identifier (e.g., `\"simple\"`, `\"coding\"`, `\"research\"`)\n", + "- **`model`**: A [LiteLLM-compatible](https://docs.litellm.ai/docs/providers) model string (e.g., `\"anthropic/claude-sonnet-4-5\"`)\n", + "- **`references`**: Example phrases that define this tier's semantic surface area. More references = better coverage.\n", + "- **`metadata`**: Arbitrary dict for costs, capabilities, provider info, etc.\n", + "- **`distance_threshold`**: Maximum cosine distance for matching (Redis COSINE: 0-2). Lower values require stricter matching.\n", + "\n", + "The quality of your reference phrases is the most important factor in routing accuracy. They should be representative of the *kinds* of queries you expect for each tier." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:55.384007Z", + "iopub.status.busy": "2026-02-16T19:52:55.383929Z", + "iopub.status.idle": "2026-02-16T19:52:57.463329Z", + "shell.execute_reply": "2026-02-16T19:52:57.462824Z" + } + }, + "outputs": [], + "source": [ + "from redisvl.extensions.llm_router import LLMRouter, ModelTier\n", + "\n", + "tiers = [\n", + " ModelTier(\n", + " name=\"simple\",\n", + " model=\"openai/gpt-4.1-nano\",\n", + " references=[\n", + " \"hello\",\n", + " \"hi there\",\n", + " \"thanks\",\n", + " \"goodbye\",\n", + " \"what time is it?\",\n", + " \"how are you?\",\n", + " \"yes\",\n", + " \"no\",\n", + " \"ok thanks\",\n", + " ],\n", + " metadata={\n", + " \"provider\": \"openai\",\n", + " \"cost_per_1k_input\": 0.0001,\n", + " \"cost_per_1k_output\": 0.0004,\n", + " },\n", + " distance_threshold=0.5,\n", + " ),\n", + " ModelTier(\n", + " name=\"reasoning\",\n", + " model=\"anthropic/claude-sonnet-4-5\",\n", + " references=[\n", + " \"analyze this code for bugs\",\n", + " \"explain how neural networks learn\",\n", + " \"compare and contrast these approaches\",\n", + " \"write a detailed blog post about\",\n", + " \"debug this issue step by step\",\n", + " \"summarize this research paper\",\n", + " \"write unit tests for this class\",\n", + " \"refactor this code for readability\",\n", + " ],\n", + " metadata={\n", + " \"provider\": \"anthropic\",\n", + " \"cost_per_1k_input\": 0.003,\n", + " \"cost_per_1k_output\": 0.015,\n", + " },\n", + " distance_threshold=0.6,\n", + " ),\n", + " ModelTier(\n", + " name=\"expert\",\n", + " model=\"anthropic/claude-opus-4-5\",\n", + " references=[\n", + " \"prove this mathematical theorem\",\n", + " \"architect a distributed system for millions of users\",\n", + " \"write a research paper analyzing\",\n", + " \"review this legal contract for issues\",\n", + " \"design a novel algorithm for\",\n", + " \"create a comprehensive security audit report\",\n", + " ],\n", + " metadata={\n", + " \"provider\": \"anthropic\",\n", + " \"cost_per_1k_input\": 0.005,\n", + " \"cost_per_1k_output\": 0.025,\n", + " },\n", + " distance_threshold=0.7,\n", + " ),\n", + "]\n", + "\n", + "router = LLMRouter(\n", + " name=\"custom-router\",\n", + " tiers=tiers,\n", + " redis_url=\"redis://localhost:6379\",\n", + " overwrite=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:57.464842Z", + "iopub.status.busy": "2026-02-16T19:52:57.464753Z", + "iopub.status.idle": "2026-02-16T19:52:57.466880Z", + "shell.execute_reply": "2026-02-16T19:52:57.466432Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tier names: ['simple', 'reasoning', 'expert']\n", + "Thresholds: {'simple': 0.5, 'reasoning': 0.6, 'expert': 0.7}\n", + "Default tier: None\n" + ] + } + ], + "source": [ + "print(\"Tier names:\", router.tier_names)\n", + "print(\"Thresholds:\", router.tier_thresholds)\n", + "print(\"Default tier:\", router.default_tier)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:57.467928Z", + "iopub.status.busy": "2026-02-16T19:52:57.467857Z", + "iopub.status.idle": "2026-02-16T19:52:57.553009Z", + "shell.execute_reply": "2026-02-16T19:52:57.552544Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'simple' -> openai/gpt-4.1-nano\n", + "'reasoning' -> anthropic/claude-sonnet-4-5\n", + "'expert' -> anthropic/claude-opus-4-5\n" + ] + } + ], + "source": [ + "# Verify routing works\n", + "match = router.route(\"hello, how are you?\")\n", + "print(f\"'{match.tier}' -> {match.model}\")\n", + "\n", + "match = router.route(\"analyze this code for bugs and security issues\")\n", + "print(f\"'{match.tier}' -> {match.model}\")\n", + "\n", + "match = router.route(\"design a fault-tolerant consensus protocol\")\n", + "print(f\"'{match.tier}' -> {match.model}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using Pre-Computed Vectors\n", + "\n", + "If you've already embedded the query (e.g., from an upstream pipeline), you can pass the vector directly to avoid double-embedding:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:57.554432Z", + "iopub.status.busy": "2026-02-16T19:52:57.554334Z", + "iopub.status.idle": "2026-02-16T19:52:57.606013Z", + "shell.execute_reply": "2026-02-16T19:52:57.605576Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-computed vector route: simple (openai/gpt-4.1-nano)\n" + ] + } + ], + "source": [ + "vector = router.vectorizer.embed(\"hello\")\n", + "match = router.route(vector=vector)\n", + "print(f\"Pre-computed vector route: {match.tier} ({match.model})\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cost-Optimized Routing\n", + "\n", + "When multiple tiers have similar semantic distances, cost optimization adds a penalty proportional to the model's cost. This biases the router toward cheaper tiers when the semantic match is close.\n", + "\n", + "The formula is:\n", + "```\n", + "adjusted_distance = distance + (cost_per_1k_input * cost_weight)\n", + "```\n", + "\n", + "The `cost_weight` parameter (0-1) controls how much cost influences the decision. Default is 0.1." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:57.607212Z", + "iopub.status.busy": "2026-02-16T19:52:57.607121Z", + "iopub.status.idle": "2026-02-16T19:52:59.528838Z", + "shell.execute_reply": "2026-02-16T19:52:59.528198Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query: 'help me understand this code'\n", + " Default routing: reasoning (distance=0.5674)\n", + " Cost-optimized routing: reasoning (distance=0.5674)\n" + ] + } + ], + "source": [ + "from redisvl.extensions.llm_router.schema import RoutingConfig\n", + "\n", + "cost_router = LLMRouter(\n", + " name=\"cost-router\",\n", + " tiers=tiers,\n", + " routing_config=RoutingConfig(\n", + " cost_optimization=True,\n", + " cost_weight=0.3, # Higher weight = stronger cost preference\n", + " ),\n", + " redis_url=\"redis://localhost:6379\",\n", + " overwrite=True,\n", + ")\n", + "\n", + "# Compare results with and without cost optimization\n", + "query = \"help me understand this code\"\n", + "\n", + "match_default = router.route(query)\n", + "match_cost = cost_router.route(query)\n", + "\n", + "print(f\"Query: '{query}'\")\n", + "print(f\" Default routing: {match_default.tier} (distance={match_default.distance:.4f})\")\n", + "print(f\" Cost-optimized routing: {match_cost.tier} (distance={match_cost.distance:.4f})\")\n", + "\n", + "cost_router.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multi-Match Routing (`route_many`)\n", + "\n", + "Use `route_many()` to get multiple tier matches, ordered by distance. This is useful when you want to see how a query scores across all tiers, or implement fallback logic." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:59.530151Z", + "iopub.status.busy": "2026-02-16T19:52:59.530030Z", + "iopub.status.idle": "2026-02-16T19:52:59.546988Z", + "shell.execute_reply": "2026-02-16T19:52:59.546439Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[LLMRouteMatch(tier='reasoning', model='anthropic/claude-sonnet-4-5', distance=0.524655163288, confidence=0.737672418356, alternatives=[], metadata={'provider': 'anthropic', 'cost_per_1k_input': 0.003, 'cost_per_1k_output': 0.015})]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matches = router.route_many(\"explain machine learning concepts in detail\", max_k=3)\n", + "matches" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:59.548172Z", + "iopub.status.busy": "2026-02-16T19:52:59.548092Z", + "iopub.status.idle": "2026-02-16T19:52:59.550222Z", + "shell.execute_reply": "2026-02-16T19:52:59.549712Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " #1: reasoning (distance=0.5247, model=anthropic/claude-sonnet-4-5)\n" + ] + } + ], + "source": [ + "for i, m in enumerate(matches):\n", + " print(f\" #{i+1}: {m.tier} (distance={m.distance:.4f}, model={m.model})\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aggregation Methods\n", + "\n", + "Each tier may have multiple reference phrases. The router aggregates distances across all matching references within a tier using one of three methods:\n", + "\n", + "- **`avg`** (default): Average distance across all matching references\n", + "- **`min`**: Minimum distance (closest single reference match)\n", + "- **`sum`**: Sum of all distances\n", + "\n", + "The `min` method is useful when you want a single strong match to be decisive:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:59.551504Z", + "iopub.status.busy": "2026-02-16T19:52:59.551416Z", + "iopub.status.idle": "2026-02-16T19:52:59.577444Z", + "shell.execute_reply": "2026-02-16T19:52:59.577100Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AVG aggregation:\n", + " reasoning: distance=0.4042\n", + " expert: distance=0.6231\n", + "\n", + "MIN aggregation:\n", + " reasoning: distance=0.0758\n", + " expert: distance=0.5718\n" + ] + } + ], + "source": [ + "from redisvl.extensions.llm_router.schema import DistanceAggregationMethod\n", + "\n", + "query = \"analyze this code and find potential bugs\"\n", + "\n", + "# Default: avg aggregation\n", + "matches_avg = router.route_many(query, max_k=3, aggregation_method=DistanceAggregationMethod.avg)\n", + "# Min aggregation\n", + "matches_min = router.route_many(query, max_k=3, aggregation_method=DistanceAggregationMethod.min)\n", + "\n", + "print(\"AVG aggregation:\")\n", + "for m in matches_avg:\n", + " print(f\" {m.tier}: distance={m.distance:.4f}\")\n", + "\n", + "print(\"\\nMIN aggregation:\")\n", + "for m in matches_min:\n", + " print(f\" {m.tier}: distance={m.distance:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note the different distances: with `min`, the distance reflects the single closest reference, while `avg` averages across all matching references in each tier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dynamic Tier Management\n", + "\n", + "Tiers can be added, removed, and updated at runtime without recreating the router." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add a new tier" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:59.578968Z", + "iopub.status.busy": "2026-02-16T19:52:59.578880Z", + "iopub.status.idle": "2026-02-16T19:52:59.644796Z", + "shell.execute_reply": "2026-02-16T19:52:59.644245Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tiers after add: ['simple', 'reasoning', 'expert', 'local']\n" + ] + } + ], + "source": [ + "local_tier = ModelTier(\n", + " name=\"local\",\n", + " model=\"ollama/llama3.2\",\n", + " references=[\"ok\", \"sure\", \"yes\", \"no\", \"got it\"],\n", + " metadata={\"provider\": \"ollama\", \"cost_per_1k_input\": 0},\n", + " distance_threshold=0.3,\n", + ")\n", + "router.add_tier(local_tier)\n", + "print(\"Tiers after add:\", router.tier_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add references to an existing tier\n", + "\n", + "If a tier's semantic coverage is too narrow, you can expand it by adding more reference phrases:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:59.645946Z", + "iopub.status.busy": "2026-02-16T19:52:59.645856Z", + "iopub.status.idle": "2026-02-16T19:52:59.713364Z", + "shell.execute_reply": "2026-02-16T19:52:59.712925Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simple tier now has 12 references:\n", + " - hello\n", + " - hi there\n", + " - thanks\n", + " - goodbye\n", + " - what time is it?\n", + " - how are you?\n", + " - yes\n", + " - no\n", + " - ok thanks\n", + " - howdy partner\n", + " - greetings friend\n", + " - hey what's up\n" + ] + } + ], + "source": [ + "router.add_tier_references(\n", + " tier_name=\"simple\",\n", + " references=[\"howdy partner\", \"greetings friend\", \"hey what's up\"]\n", + ")\n", + "\n", + "tier = router.get_tier(\"simple\")\n", + "print(f\"Simple tier now has {len(tier.references)} references:\")\n", + "for ref in tier.references:\n", + " print(f\" - {ref}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Update a tier's distance threshold\n", + "\n", + "You can tune the strictness of matching per tier. Lower thresholds require closer matches:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:59.714755Z", + "iopub.status.busy": "2026-02-16T19:52:59.714669Z", + "iopub.status.idle": "2026-02-16T19:52:59.717968Z", + "shell.execute_reply": "2026-02-16T19:52:59.717564Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before: {'simple': 0.5, 'reasoning': 0.6, 'expert': 0.7, 'local': 0.3}\n", + "After: {'simple': 0.4, 'reasoning': 0.6, 'expert': 0.7, 'local': 0.3}\n" + ] + } + ], + "source": [ + "print(\"Before:\", router.tier_thresholds)\n", + "\n", + "router.update_tier_threshold(\"simple\", 0.4) # Stricter matching\n", + "\n", + "print(\"After:\", router.tier_thresholds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove a tier" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:59.719169Z", + "iopub.status.busy": "2026-02-16T19:52:59.719096Z", + "iopub.status.idle": "2026-02-16T19:52:59.726151Z", + "shell.execute_reply": "2026-02-16T19:52:59.725841Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tiers after remove: ['simple', 'reasoning', 'expert']\n", + "Thresholds: {'simple': 0.5, 'reasoning': 0.6, 'expert': 0.7}\n" + ] + } + ], + "source": [ + "router.remove_tier(\"local\")\n", + "print(\"Tiers after remove:\", router.tier_names)\n", + "\n", + "# Reset the simple threshold back to 0.5 for the rest of the demo\n", + "router.update_tier_threshold(\"simple\", 0.5)\n", + "print(\"Thresholds:\", router.tier_thresholds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Retrieve a tier by name" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:59.727367Z", + "iopub.status.busy": "2026-02-16T19:52:59.727279Z", + "iopub.status.idle": "2026-02-16T19:52:59.729452Z", + "shell.execute_reply": "2026-02-16T19:52:59.728966Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tier: reasoning\n", + "Model: anthropic/claude-sonnet-4-5\n", + "Threshold: 0.6\n", + "References: ['analyze this code for bugs', 'explain how neural networks learn', 'compare and contrast these approaches', 'write a detailed blog post about', 'debug this issue step by step', 'summarize this research paper', 'write unit tests for this class', 'refactor this code for readability']\n", + "\n", + "Non-existent: None\n" + ] + } + ], + "source": [ + "tier = router.get_tier(\"reasoning\")\n", + "print(f\"Tier: {tier.name}\")\n", + "print(f\"Model: {tier.model}\")\n", + "print(f\"Threshold: {tier.distance_threshold}\")\n", + "print(f\"References: {tier.references}\")\n", + "\n", + "# Non-existent tier returns None\n", + "print(f\"\\nNon-existent: {router.get_tier('nonexistent')}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Persistence and Serialization\n", + "\n", + "Routers can be serialized and restored in several formats." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dictionary round-trip" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:59.731013Z", + "iopub.status.busy": "2026-02-16T19:52:59.730912Z", + "iopub.status.idle": "2026-02-16T19:52:59.733431Z", + "shell.execute_reply": "2026-02-16T19:52:59.733079Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': 'custom-router',\n", + " 'tiers': [{'name': 'simple',\n", + " 'model': 'openai/gpt-4.1-nano',\n", + " 'references': ['hello',\n", + " 'hi there',\n", + " 'thanks',\n", + " 'goodbye',\n", + " 'what time is it?',\n", + " 'how are you?',\n", + " 'yes',\n", + " 'no',\n", + " 'ok thanks',\n", + " 'howdy partner',\n", + " 'greetings friend',\n", + " \"hey what's up\"],\n", + " 'metadata': {'provider': 'openai',\n", + " 'cost_per_1k_input': 0.0001,\n", + " 'cost_per_1k_output': 0.0004},\n", + " 'distance_threshold': 0.5},\n", + " {'name': 'reasoning',\n", + " 'model': 'anthropic/claude-sonnet-4-5',\n", + " 'references': ['analyze this code for bugs',\n", + " 'explain how neural networks learn',\n", + " 'compare and contrast these approaches',\n", + " 'write a detailed blog post about',\n", + " 'debug this issue step by step',\n", + " 'summarize this research paper',\n", + " 'write unit tests for this class',\n", + " 'refactor this code for readability'],\n", + " 'metadata': {'provider': 'anthropic',\n", + " 'cost_per_1k_input': 0.003,\n", + " 'cost_per_1k_output': 0.015},\n", + " 'distance_threshold': 0.6},\n", + " {'name': 'expert',\n", + " 'model': 'anthropic/claude-opus-4-5',\n", + " 'references': ['prove this mathematical theorem',\n", + " 'architect a distributed system for millions of users',\n", + " 'write a research paper analyzing',\n", + " 'review this legal contract for issues',\n", + " 'design a novel algorithm for',\n", + " 'create a comprehensive security audit report'],\n", + " 'metadata': {'provider': 'anthropic',\n", + " 'cost_per_1k_input': 0.005,\n", + " 'cost_per_1k_output': 0.025},\n", + " 'distance_threshold': 0.7}],\n", + " 'vectorizer': {'type': 'hf',\n", + " 'model': 'sentence-transformers/all-mpnet-base-v2'},\n", + " 'routing_config': {'max_k': 1,\n", + " 'aggregation_method': 'avg',\n", + " 'cost_optimization': False,\n", + " 'cost_weight': 0.1}}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "router_dict = router.to_dict()\n", + "router_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:52:59.734601Z", + "iopub.status.busy": "2026-02-16T19:52:59.734513Z", + "iopub.status.idle": "2026-02-16T19:53:01.695457Z", + "shell.execute_reply": "2026-02-16T19:53:01.694956Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dict round-trip OK\n" + ] + } + ], + "source": [ + "# Restore from dict (reconnects to same Redis index since name matches)\n", + "router_from_dict = LLMRouter.from_dict(\n", + " router_dict,\n", + " redis_url=\"redis://localhost:6379\",\n", + ")\n", + "\n", + "assert router_from_dict.to_dict() == router.to_dict()\n", + "print(\"Dict round-trip OK\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### YAML serialization" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:01.696656Z", + "iopub.status.busy": "2026-02-16T19:53:01.696568Z", + "iopub.status.idle": "2026-02-16T19:53:03.900573Z", + "shell.execute_reply": "2026-02-16T19:53:03.900028Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "YAML round-trip OK\n" + ] + } + ], + "source": [ + "router.to_yaml(\"llm_router.yaml\", overwrite=True)\n", + "\n", + "router_from_yaml = LLMRouter.from_yaml(\n", + " \"llm_router.yaml\",\n", + " redis_url=\"redis://localhost:6379\",\n", + ")\n", + "\n", + "assert router_from_yaml.to_dict() == router.to_dict()\n", + "print(\"YAML round-trip OK\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reconnect to an existing router\n", + "\n", + "If the router's Redis index still exists, you can reconnect without needing the original config. The router config is persisted in Redis alongside the index:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:03.902226Z", + "iopub.status.busy": "2026-02-16T19:53:03.902108Z", + "iopub.status.idle": "2026-02-16T19:53:05.844833Z", + "shell.execute_reply": "2026-02-16T19:53:05.844339Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconnected to 'custom-router' with tiers: ['simple', 'reasoning', 'expert']\n", + "Route test: simple (openai/gpt-4.1-nano)\n" + ] + } + ], + "source": [ + "router_reconnected = LLMRouter.from_existing(\n", + " name=\"custom-router\",\n", + " redis_url=\"redis://localhost:6379\",\n", + ")\n", + "\n", + "print(f\"Reconnected to '{router_reconnected.name}' with tiers: {router_reconnected.tier_names}\")\n", + "\n", + "# Routing still works\n", + "match = router_reconnected.route(\"hi there, how are you?\")\n", + "print(f\"Route test: {match.tier} ({match.model})\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Export/Import with pre-computed embeddings\n", + "\n", + "For sharing router configs across environments (or loading without an embedding model), you can export with pre-computed vectors:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:05.846163Z", + "iopub.status.busy": "2026-02-16T19:53:05.846064Z", + "iopub.status.idle": "2026-02-16T19:53:06.066988Z", + "shell.execute_reply": "2026-02-16T19:53:06.066533Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exported with embeddings\n" + ] + } + ], + "source": [ + "# Export: embeds all references and saves vectors alongside text\n", + "router.export_with_embeddings(\"my_router_pretrained.json\")\n", + "print(\"Exported with embeddings\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:06.068165Z", + "iopub.status.busy": "2026-02-16T19:53:06.068028Z", + "iopub.status.idle": "2026-02-16T19:53:06.075194Z", + "shell.execute_reply": "2026-02-16T19:53:06.074736Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Config name: custom-router\n", + "Vectorizer: {'type': 'hf', 'model': 'sentence-transformers/all-mpnet-base-v2'}\n", + "Tiers: 3\n", + "First reference vector length: 768\n" + ] + } + ], + "source": [ + "# Peek at the structure\n", + "import json\n", + "\n", + "with open(\"my_router_pretrained.json\") as f:\n", + " data = json.load(f)\n", + "\n", + "print(f\"Config name: {data['name']}\")\n", + "print(f\"Vectorizer: {data['vectorizer']}\")\n", + "print(f\"Tiers: {len(data['tiers'])}\")\n", + "print(f\"First reference vector length: {len(data['tiers'][0]['references'][0]['vector'])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:06.076443Z", + "iopub.status.busy": "2026-02-16T19:53:06.076358Z", + "iopub.status.idle": "2026-02-16T19:53:08.058648Z", + "shell.execute_reply": "2026-02-16T19:53:08.058112Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Imported router route: simple (openai/gpt-4.1-nano)\n" + ] + } + ], + "source": [ + "# Import: loads pre-computed vectors directly, no embedding needed\n", + "router_imported = LLMRouter.from_pretrained(\n", + " \"my_router_pretrained.json\",\n", + " redis_url=\"redis://localhost:6379\",\n", + ")\n", + "\n", + "match = router_imported.route(\"hi there, how are you?\")\n", + "print(f\"Imported router route: {match.tier} ({match.model})\")\n", + "\n", + "# Note: router_imported shares the same Redis index as `router`\n", + "# (same name in the exported config), so we don't delete it separately." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Async Usage\n", + "\n", + "The `AsyncLLMRouter` provides the same functionality using async I/O. Since Python's `__init__` can't be async, use the `create()` classmethod factory to instantiate, or `from_pretrained()` / `from_existing()` which are also async." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:08.060383Z", + "iopub.status.busy": "2026-02-16T19:53:08.060271Z", + "iopub.status.idle": "2026-02-16T19:53:10.205348Z", + "shell.execute_reply": "2026-02-16T19:53:10.204936Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Async router tiers: ['simple', 'standard', 'expert']\n" + ] + } + ], + "source": [ + "import logging\n", + "logging.getLogger(\"redisvl.utils.vectorize.base\").setLevel(logging.ERROR)\n", + "\n", + "from redisvl.extensions.llm_router import AsyncLLMRouter, ModelTier\n", + "\n", + "# Create from pretrained (async)\n", + "async_router = await AsyncLLMRouter.from_pretrained(\n", + " \"default\",\n", + " redis_url=\"redis://localhost:6379\",\n", + ")\n", + "\n", + "print(f\"Async router tiers: {async_router.tier_names}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:10.206777Z", + "iopub.status.busy": "2026-02-16T19:53:10.206687Z", + "iopub.status.idle": "2026-02-16T19:53:10.268253Z", + "shell.execute_reply": "2026-02-16T19:53:10.267841Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simple: simple (openai/gpt-4.1-nano)\n", + "Standard: standard (anthropic/claude-sonnet-4-5)\n", + "Expert: expert (anthropic/claude-opus-4-5)\n" + ] + } + ], + "source": [ + "# Route queries (async)\n", + "match = await async_router.route(\"hi, how are you?\")\n", + "print(f\"Simple: {match.tier} ({match.model})\")\n", + "\n", + "match = await async_router.route(\"explain how neural networks learn\")\n", + "print(f\"Standard: {match.tier} ({match.model})\")\n", + "\n", + "match = await async_router.route(\"architect a fault-tolerant distributed system\")\n", + "print(f\"Expert: {match.tier} ({match.model})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:10.269710Z", + "iopub.status.busy": "2026-02-16T19:53:10.269626Z", + "iopub.status.idle": "2026-02-16T19:53:10.285633Z", + "shell.execute_reply": "2026-02-16T19:53:10.285212Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " standard: distance=0.5297\n", + " expert: distance=0.6355\n" + ] + } + ], + "source": [ + "# Route many (async)\n", + "matches = await async_router.route_many(\"summarize this research paper\", max_k=3)\n", + "for m in matches:\n", + " print(f\" {m.tier}: distance={m.distance:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:10.286702Z", + "iopub.status.busy": "2026-02-16T19:53:10.286630Z", + "iopub.status.idle": "2026-02-16T19:53:12.303496Z", + "shell.execute_reply": "2026-02-16T19:53:12.302961Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Custom async: fast (openai/gpt-4o-mini)\n" + ] + } + ], + "source": [ + "# Or create with custom tiers (async)\n", + "custom_async = await AsyncLLMRouter.create(\n", + " name=\"async-custom\",\n", + " tiers=[\n", + " ModelTier(\n", + " name=\"fast\",\n", + " model=\"openai/gpt-4o-mini\",\n", + " references=[\"hello\", \"thanks\", \"what is\"],\n", + " distance_threshold=0.5,\n", + " ),\n", + " ModelTier(\n", + " name=\"smart\",\n", + " model=\"openai/gpt-4o\",\n", + " references=[\"analyze this\", \"explain how\", \"compare these\"],\n", + " distance_threshold=0.6,\n", + " ),\n", + " ],\n", + " redis_url=\"redis://localhost:6379\",\n", + " overwrite=True,\n", + ")\n", + "\n", + "match = await custom_async.route(\"hi there!\")\n", + "print(f\"Custom async: {match.tier} ({match.model})\")\n", + "\n", + "await custom_async.delete()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:12.304756Z", + "iopub.status.busy": "2026-02-16T19:53:12.304643Z", + "iopub.status.idle": "2026-02-16T19:53:12.307669Z", + "shell.execute_reply": "2026-02-16T19:53:12.307346Z" + } + }, + "outputs": [], + "source": [ + "await async_router.delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LiteLLM Integration\n", + "\n", + "The router returns LiteLLM-compatible model strings, making integration straightforward. Here's a typical pattern:\n", + "\n", + "```python\n", + "from litellm import completion\n", + "from redisvl.extensions.llm_router import LLMRouter\n", + "\n", + "router = LLMRouter.from_pretrained(\"default\", redis_url=\"redis://localhost:6379\")\n", + "\n", + "def smart_completion(query: str, **kwargs):\n", + " \"\"\"Route to the best model, then call it.\"\"\"\n", + " match = router.route(query)\n", + " \n", + " if not match:\n", + " # Fallback to a default model if no tier matched\n", + " model = \"anthropic/claude-sonnet-4-5\"\n", + " else:\n", + " model = match.model\n", + " print(f\"Routed to {match.tier} tier ({model}), confidence={match.confidence:.2f}\")\n", + " \n", + " return completion(\n", + " model=model,\n", + " messages=[{\"role\": \"user\", \"content\": query}],\n", + " **kwargs,\n", + " )\n", + "\n", + "# Simple query -> GPT-4.1 Nano ($0.10/M)\n", + "response = smart_completion(\"What is 2 + 2?\")\n", + "\n", + "# Complex query -> Opus 4.5 ($5/M)\n", + "response = smart_completion(\"Design a distributed consensus algorithm with Byzantine fault tolerance\")\n", + "```\n", + "\n", + "For async applications:\n", + "\n", + "```python\n", + "from litellm import acompletion\n", + "from redisvl.extensions.llm_router import AsyncLLMRouter\n", + "\n", + "router = await AsyncLLMRouter.from_pretrained(\"default\", redis_url=\"redis://localhost:6379\")\n", + "\n", + "async def smart_completion(query: str, **kwargs):\n", + " match = await router.route(query)\n", + " model = match.model if match else \"anthropic/claude-sonnet-4-5\"\n", + " return await acompletion(\n", + " model=model,\n", + " messages=[{\"role\": \"user\", \"content\": query}],\n", + " **kwargs,\n", + " )\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleanup" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:12.308982Z", + "iopub.status.busy": "2026-02-16T19:53:12.308906Z", + "iopub.status.idle": "2026-02-16T19:53:12.312105Z", + "shell.execute_reply": "2026-02-16T19:53:12.311785Z" + } + }, + "outputs": [], + "source": [ + "router.delete()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "execution": { + "iopub.execute_input": "2026-02-16T19:53:12.313298Z", + "iopub.status.busy": "2026-02-16T19:53:12.313229Z", + "iopub.status.idle": "2026-02-16T19:53:12.315299Z", + "shell.execute_reply": "2026-02-16T19:53:12.314991Z" + } + }, + "outputs": [], + "source": [ + "# Remove temp files\n", + "import os\n", + "for f in [\"llm_router.yaml\", \"my_router_pretrained.json\"]:\n", + " if os.path.exists(f):\n", + " os.remove(f)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/redisvl/extensions/llm_router/DESIGN.md b/redisvl/extensions/llm_router/DESIGN.md new file mode 100644 index 00000000..6f638a67 --- /dev/null +++ b/redisvl/extensions/llm_router/DESIGN.md @@ -0,0 +1,385 @@ +# LLM Router Extension - Design Document + +## Overview + +The LLM Router is an extension to RedisVL that provides intelligent, cost-optimized LLM model selection using semantic routing. Instead of routing queries to topics (like SemanticRouter), it routes queries to **model tiers** - selecting the cheapest LLM capable of handling each task. + +## Problem Statement + +### The LLM Cost Problem +Modern applications often default to using the most capable (and expensive) LLM for all queries, even when simpler models would suffice: +- "Hello, how are you?" -> Claude Opus 4.5 ($5/M tokens) +- "Hello, how are you?" -> GPT-4.1 Nano ($0.10/M tokens) + +### Existing Solutions and Their Limitations + +**RouteLLM** (CMU/LMSys): +- Binary classification only (strong vs weak model) +- No support for >2 tiers +- Requires training data or preference matrices + +**NVIDIA LLM Router Blueprint**: +- Complexity classification approach (simple/moderate/complex) +- Provides the taxonomy basis but no open-source Redis-native implementation + +**RouterArena / Bloom's Taxonomy Approach**: +- Maps query complexity to Bloom's cognitive levels +- Informs our tier design but lacks production routing infrastructure + +**OpenRouter Auto-Router**: +- Black box routing decisions +- Data flows through third-party servers +- No transparency into why a model was selected +- Can't self-host or customize + +**NotDiamond**: +- Proprietary ML model for routing +- Requires API calls for every routing decision +- No local/offline capability + +**FrugalGPT**: +- Sequential cascade approach (try cheap first, escalate) +- Higher latency due to serial model calls + +## Solution: Semantic Model Tier Routing + +Repurpose RedisVL's battle-tested SemanticRouter for model selection: + +``` +SemanticRouter -> LLMRouter +----------------------------------------- +Route -> ModelTier +route.name -> tier.name (simple/standard/expert) +route.references -> tier.references (task complexity examples) +route.metadata -> tier.metadata (cost, capabilities) +RouteMatch -> LLMRouteMatch (includes model string) +``` + +### Architecture + +``` ++---------------------------------------------------------------+ +| LLMRouter | ++---------------------------------------------------------------+ +| +-------------+ +-------------+ +-------------+ | +| | Simple | | Standard | | Expert | | +| | Tier | | Tier | | Tier | | +| +-------------+ +-------------+ +-------------+ | +| | gpt-4.1-nano| | sonnet 4.5 | | opus 4.5 | | +| | $0.10/M | | $3/M | | $5/M | | +| | threshold: | | threshold: | | threshold: | | +| | 0.5 | | 0.6 | | 0.7 | | +| +-------------+ +-------------+ +-------------+ | +| | | | | +| +----------------+----------------+ | +| v | +| +------------------------+ | +| | Redis Vector Index | | +| | (reference phrases) | | +| +------------------------+ | ++---------------------------------------------------------------+ + | + v + +-------------+ + | Query | + | "analyze | + | this..." | + +-------------+ + | + v + +-------------+ + | LiteLLM | + | (optional) | + +-------------+ +``` + +## Key Design Decisions + +### 1. Model Tiers, Not Individual Models + +Routes map to **tiers** (simple, standard, expert) rather than specific models. This provides: +- Abstraction from model churn (swap haiku -> gemini-flash without changing routes) +- Clear mental model for users +- Easy cost optimization within tiers + +### 2. Bloom's Taxonomy-Grounded Tiers + +The default pretrained config maps tiers to Bloom's Taxonomy cognitive levels: +- **Simple** (Remember/Understand): Factual recall, greetings, format conversion +- **Standard** (Apply/Analyze): Code explanation, summarization, moderate analysis +- **Expert** (Evaluate/Create): Research, architecture, formal reasoning + +This is informed by RouterArena's finding that cognitive complexity correlates with model capability requirements. + +### 3. LiteLLM-Compatible Model Strings + +Tier model identifiers use LiteLLM format (`provider/model`): +```python +ModelTier( + name="standard", + model="anthropic/claude-sonnet-4-5", # Works directly with LiteLLM + ... +) +``` + +### 4. Per-Tier Distance Thresholds + +Each tier has its own `distance_threshold`, allowing fine-grained control: +```python +simple_tier = ModelTier(..., distance_threshold=0.5) # Strict match +expert_tier = ModelTier(..., distance_threshold=0.7) # Looser match +``` + +### 5. Cost-Aware Routing + +When `cost_optimization=True`, the router adds a cost penalty to distances: +```python +adjusted_distance = distance + (cost_per_1k * cost_weight) +``` +This prefers cheaper tiers when semantic distances are close. + +### 6. Pretrained Configs with Embedded Vectors + +The built-in `default.json` provides a ready-to-use 3-tier configuration: +```python +# Instant setup - no embedding model needed at load time +router = LLMRouter.from_pretrained("default", redis_client=client) +``` + +The pretrained config includes pre-computed embeddings from +`sentence-transformers/all-mpnet-base-v2`, with 18 reference phrases per tier +covering the Bloom's Taxonomy spectrum. + +Custom configs can also be exported and shared: +```python +# Export (one-time, with embedding model) +router.export_with_embeddings("my_router.json") + +# Import (no embedding needed) +router = LLMRouter.from_pretrained("my_router.json", redis_client=client) +``` + +### 7. Async Support + +`AsyncLLMRouter` provides the same functionality using async I/O. Since +`__init__` cannot be async, it uses a `create()` classmethod factory: + +```python +router = await AsyncLLMRouter.create( + name="my-router", + tiers=tiers, + redis_client=async_client, +) +match = await router.route("hello") +``` + +Key async method mapping: + +| Sync (`LLMRouter`) | Async (`AsyncLLMRouter`) | +|---------------------|--------------------------| +| `__init__()` | `await create()` | +| `from_existing()` | `await from_existing()` | +| `route()` | `await route()` | +| `route_many()` | `await route_many()` | +| `add_tier()` | `await add_tier()` | +| `remove_tier()` | `await remove_tier()` | +| `from_dict()` | `await from_dict()` | +| `from_pretrained()` | `await from_pretrained()` | +| `delete()` | `await delete()` | + +## Module Structure + +``` +redisvl/extensions/llm_router/ ++-- __init__.py # Public exports (LLMRouter, AsyncLLMRouter, schemas) ++-- DESIGN.md # This document ++-- schema.py # Pydantic models +| +-- ModelTier # Tier definition +| +-- LLMRouteMatch # Routing result +| +-- RoutingConfig # Router configuration +| +-- Pretrained* # Export/import schemas ++-- router.py # LLMRouter + AsyncLLMRouter implementations ++-- pretrained/ + +-- __init__.py # Pretrained loader (get_pretrained_path) + +-- default.json # Standard 3-tier config (simple/standard/expert) +``` + +## API Examples + +### Basic Usage + +```python +from redisvl.extensions.llm_router import LLMRouter, ModelTier + +tiers = [ + ModelTier( + name="simple", + model="openai/gpt-4.1-nano", + references=[ + "hello", "hi there", "thanks", "goodbye", + "what time is it?", "how are you?", + ], + metadata={"cost_per_1k_input": 0.0001}, + distance_threshold=0.5, + ), + ModelTier( + name="standard", + model="anthropic/claude-sonnet-4-5", + references=[ + "analyze this code for bugs", + "explain how neural networks learn", + "compare and contrast these approaches", + ], + metadata={"cost_per_1k_input": 0.003}, + distance_threshold=0.6, + ), + ModelTier( + name="expert", + model="anthropic/claude-opus-4-5", + references=[ + "prove this mathematical theorem", + "architect a distributed system", + "write a research paper analyzing", + ], + metadata={"cost_per_1k_input": 0.005}, + distance_threshold=0.7, + ), +] + +router = LLMRouter( + name="my-llm-router", + tiers=tiers, + redis_url="redis://localhost:6379", +) + +# Route a query +match = router.route("hello, how's it going?") +print(match.tier) # "simple" +print(match.model) # "openai/gpt-4.1-nano" + +# Use with LiteLLM (optional integration) +from litellm import completion +response = completion(model=match.model, messages=[{"role": "user", "content": query}]) +``` + +### Cost-Optimized Routing + +```python +router = LLMRouter( + name="cost-aware-router", + tiers=tiers, + cost_optimization=True, # Prefer cheaper tiers when distances are close + redis_url="redis://localhost:6379", +) +``` + +### Pretrained Router + +```python +# Load without needing an embedding model for the references +router = LLMRouter.from_pretrained( + "default", # Built-in config, or path to JSON + redis_client=client, +) +``` + +### Async Usage + +```python +from redisvl.extensions.llm_router import AsyncLLMRouter + +router = await AsyncLLMRouter.create( + name="my-async-router", + tiers=tiers, + redis_url="redis://localhost:6379", +) + +match = await router.route("explain how garbage collection works") +print(match.model) # "anthropic/claude-sonnet-4-5" + +# Or load from pretrained +router = await AsyncLLMRouter.from_pretrained("default", redis_client=client) + +await router.delete() +``` + +## Comparison with SemanticRouter + +| Feature | SemanticRouter | LLMRouter | +|---------|---------------|-----------| +| Purpose | Topic classification | Model selection | +| Output | Route name | Model string + metadata | +| Cost awareness | No | Yes | +| Pretrained configs | No | Yes | +| Per-route thresholds | Yes | Yes | +| LiteLLM integration | No | Yes (model strings) | +| Async support | No | Yes (`AsyncLLMRouter`) | + +## Testing + +### Unit Tests (`tests/unit/test_llm_router_schema.py`) +- Schema validation +- Pydantic model behavior +- Threshold bounds +- Empty/invalid inputs + +### Integration Tests (`tests/integration/test_llm_router.py`) +- Router initialization +- Routing accuracy +- Cost optimization behavior +- Serialization (dict, YAML, JSON) +- Pretrained import/export +- Pretrained config loading (`from_pretrained("default")`) +- Tier management (add, remove, update) +- Persistence (from_existing) + +### Async Integration Tests (`tests/integration/test_async_llm_router.py`) +- Mirrors all sync tests with `AsyncLLMRouter` +- Uses `async_client` fixture and async skip helpers +- Tests `create()` factory, async routing, serialization, tier management +- Pretrained config loading + +Run tests: +```bash +uv run pytest tests/unit/test_llm_router_schema.py -v +uv run pytest tests/integration/test_llm_router.py -v +uv run pytest tests/integration/test_async_llm_router.py -v +``` + +## Future Enhancements + +### 1. `complete()` Method +Direct LiteLLM integration for one-liner usage: +```python +response = router.complete("analyze this code", messages=[...]) +``` + +### 2. Capability Filtering +Filter tiers by capability before routing: +```python +match = router.route("generate an image", capabilities=["vision"]) +``` + +### 3. Budget Constraints +Enforce cost limits: +```python +router = LLMRouter(..., max_cost_per_1k=0.01) # Never select opus +``` + +### 4. Fallback Chains +Define fallback order when primary tier unavailable: +```python +tier = ModelTier(..., fallback=["standard", "simple"]) +``` + +## References + +- [RedisVL SemanticRouter](https://docs.redisvl.com/en/latest/user_guide/semantic_router.html) +- [LiteLLM Model List](https://docs.litellm.ai/docs/providers) +- [RouteLLM](https://github.com/lm-sys/RouteLLM) - LMSys binary router framework +- [NVIDIA LLM Router Blueprint](https://build.nvidia.com/blueprints/llm-router) - Complexity-based routing +- [RouterArena / Bloom's Taxonomy](https://arxiv.org/abs/2412.06644) - Cognitive complexity for routing +- [FrugalGPT](https://arxiv.org/abs/2305.05176) - Cost-efficient LLM strategies +- [OpenRouter](https://openrouter.ai/) - Auto-routing concept +- [NotDiamond](https://notdiamond.ai/) - ML-based model routing +- [Unify.ai](https://unify.ai/) - Quality-cost tradeoff routing diff --git a/redisvl/extensions/llm_router/__init__.py b/redisvl/extensions/llm_router/__init__.py new file mode 100644 index 00000000..0e423309 --- /dev/null +++ b/redisvl/extensions/llm_router/__init__.py @@ -0,0 +1,56 @@ +"""LLM Router extension for intelligent model selection. + +This module provides semantic routing for LLM model tier selection. +Routes queries to the most appropriate model based on semantic similarity +to reference phrases. + +Example: + >>> from redisvl.extensions.llm_router import LLMRouter, ModelTier + >>> + >>> tiers = [ + ... ModelTier( + ... name="simple", + ... model="openai/gpt-4.1-nano", + ... references=["hello", "hi", "thanks"], + ... distance_threshold=0.5, + ... ), + ... ModelTier( + ... name="reasoning", + ... model="anthropic/claude-sonnet-4-5", + ... references=["analyze this", "explain how"], + ... distance_threshold=0.6, + ... ), + ... ] + >>> + >>> router = LLMRouter( + ... name="my-router", + ... tiers=tiers, + ... redis_url="redis://localhost:6379", + ... ) + >>> + >>> match = router.route("hello, how are you?") + >>> print(f"Use {match.model} for this query") +""" + +from redisvl.extensions.llm_router.router import AsyncLLMRouter, LLMRouter +from redisvl.extensions.llm_router.schema import ( + DistanceAggregationMethod, + LLMRouteMatch, + ModelTier, + PretrainedReference, + PretrainedRouterConfig, + PretrainedTier, + RoutingConfig, +) + +__all__ = [ + "AsyncLLMRouter", + "LLMRouter", + "ModelTier", + "LLMRouteMatch", + "RoutingConfig", + "DistanceAggregationMethod", + "PretrainedReference", + "PretrainedTier", + "PretrainedRouterConfig", +] diff --git a/redisvl/extensions/llm_router/pretrained/__init__.py b/redisvl/extensions/llm_router/pretrained/__init__.py new file mode 100644 index 00000000..ee6cec7b --- /dev/null +++ b/redisvl/extensions/llm_router/pretrained/__init__.py @@ -0,0 +1,27 @@ +"""Pretrained LLM Router configurations. + +This module provides pre-built router configurations that can be loaded +without needing to re-embed references. +""" + +from pathlib import Path + +PRETRAINED_DIR = Path(__file__).parent + + +def get_pretrained_path(name: str) -> Path: + """Get path to a pretrained configuration. + + Args: + name: Pretrained config name (e.g., "default", "cost_optimized") + + Returns: + Path to the pretrained JSON file. + """ + path = PRETRAINED_DIR / f"{name}.json" + if not path.exists(): + available = [f.stem for f in PRETRAINED_DIR.glob("*.json")] + raise ValueError( + f"Pretrained config '{name}' not found. " f"Available: {available}" + ) + return path diff --git a/redisvl/extensions/llm_router/pretrained/default.json b/redisvl/extensions/llm_router/pretrained/default.json new file mode 100644 index 00000000..69f797b3 --- /dev/null +++ b/redisvl/extensions/llm_router/pretrained/default.json @@ -0,0 +1,41805 @@ +{ + "name": "llm-router-default", + "version": "1.0.0", + "vectorizer": { + "type": "hf", + "model": "sentence-transformers/all-mpnet-base-v2" + }, + "tiers": [ + { + "name": "simple", + "model": "openai/gpt-4.1-nano", + "references": [ + { + "text": "hello how are you", + "vector": [ + 0.033491481095552444, + 0.0031940052285790443, + -0.0217621847987175, + -0.014097214676439762, + 0.0039068725891411304, + 0.00932876393198967, + -0.016192153096199036, + 0.04105057939887047, + 0.0014517620438709855, + -0.01747949607670307, + -0.014033138751983643, + -0.021695364266633987, + 0.0037698661908507347, + 0.04326736554503441, + 0.012991008348762989, + -0.054483942687511444, + 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"Create" + ] + }, + "distance_threshold": 0.7 + } + ], + "routing_config": { + "max_k": 1, + "aggregation_method": "avg", + "cost_optimization": false + } +} \ No newline at end of file diff --git a/redisvl/extensions/llm_router/router.py b/redisvl/extensions/llm_router/router.py new file mode 100644 index 00000000..f4fdddcb --- /dev/null +++ b/redisvl/extensions/llm_router/router.py @@ -0,0 +1,1508 @@ +"""LLM Router implementation. + +Intelligent LLM model selection using semantic routing. Routes queries to the +most appropriate model tier based on semantic similarity to reference phrases. +""" + +import json +from pathlib import Path +from typing import Any, Dict, List, Optional, Union + +import redis.commands.search.reducers as reducers +import yaml +from pydantic import BaseModel, ConfigDict, Field, PrivateAttr +from redis.commands.search.aggregation import AggregateRequest, AggregateResult, Reducer +from redis.exceptions import ResponseError + +from redisvl.extensions.constants import ROUTE_VECTOR_FIELD_NAME +from redisvl.extensions.llm_router.schema import ( + DistanceAggregationMethod, + LLMRouteMatch, + ModelTier, + PretrainedReference, + PretrainedRouterConfig, + PretrainedTier, + RoutingConfig, +) +from redisvl.extensions.router.schema import Route, SemanticRouterIndexSchema +from redisvl.index import AsyncSearchIndex, SearchIndex +from redisvl.query import VectorRangeQuery +from redisvl.redis.connection import RedisConnectionFactory +from redisvl.redis.utils import convert_bytes, hashify, make_dict +from redisvl.types import AsyncRedisClient, SyncRedisClient +from redisvl.utils.log import get_logger +from redisvl.utils.utils import model_to_dict, scan_by_pattern +from redisvl.utils.vectorize.base import BaseVectorizer +from redisvl.utils.vectorize.text.huggingface import HFTextVectorizer + +logger = get_logger(__name__) + + +class LLMRouter(BaseModel): + """Intelligent LLM Router for model tier selection. + + Routes queries to the most appropriate LLM model based on semantic similarity + to reference phrases. Uses Redis vector search for fast, scalable routing. + + Example: + >>> from redisvl.extensions.llm_router import LLMRouter, ModelTier + >>> + >>> tiers = [ + ... ModelTier( + ... name="simple", + ... model="openai/gpt-4.1-nano", + ... references=["hello", "hi", "thanks"], + ... distance_threshold=0.5, + ... ), + ... ModelTier( + ... name="reasoning", + ... model="anthropic/claude-sonnet-4-5", + ... references=["analyze this", "explain how"], + ... distance_threshold=0.6, + ... ), + ... ] + >>> + >>> router = LLMRouter( + ... name="my-router", + ... tiers=tiers, + ... redis_url="redis://localhost:6379", + ... ) + >>> + >>> match = router.route("hello, how are you?") + >>> print(match.tier, match.model) + simple openai/gpt-4.1-nano + """ + + name: str + """Router name (also used as Redis index prefix).""" + + tiers: List[ModelTier] + """List of model tiers for routing.""" + + vectorizer: BaseVectorizer = Field(default_factory=HFTextVectorizer) + """Vectorizer for embedding queries and references.""" + + routing_config: RoutingConfig = Field(default_factory=RoutingConfig) + """Configuration for routing behavior.""" + + _index: SearchIndex = PrivateAttr() + + model_config = ConfigDict(arbitrary_types_allowed=True) + + def __init__( + self, + name: str, + tiers: List[ModelTier], + vectorizer: Optional[BaseVectorizer] = None, + routing_config: Optional[RoutingConfig] = None, + redis_client: Optional[SyncRedisClient] = None, + redis_url: str = "redis://localhost:6379", + overwrite: bool = False, + cost_optimization: bool = False, + connection_kwargs: Dict[str, Any] = {}, + **kwargs, + ): + """Initialize the LLMRouter. + + Args: + name: Router name (used as Redis index prefix). + tiers: List of ModelTier objects defining routing targets. + vectorizer: Vectorizer for embeddings. Defaults to HFTextVectorizer. + routing_config: Configuration for routing behavior. + redis_client: Existing Redis client. Defaults to None. + redis_url: Redis connection URL. Defaults to "redis://localhost:6379". + overwrite: Whether to overwrite existing index. Defaults to False. + cost_optimization: Enable cost-aware routing. Defaults to False. + connection_kwargs: Additional Redis connection arguments. + """ + # Set up vectorizer + if vectorizer is None: + vectorizer = HFTextVectorizer( + model="sentence-transformers/all-mpnet-base-v2" + ) + elif not isinstance(vectorizer, BaseVectorizer): + raise TypeError("Must provide a valid redisvl.vectorizer class.") + + # Set up routing config + if routing_config is None: + routing_config = RoutingConfig(cost_optimization=cost_optimization) + elif cost_optimization: + routing_config.cost_optimization = True + + super().__init__( + name=name, + tiers=tiers, + vectorizer=vectorizer, + routing_config=routing_config, + ) + + self._initialize_index(redis_client, redis_url, overwrite, **connection_kwargs) + + # Store router config in Redis + self._index.client.json().set(f"{self.name}:router_config", ".", self.to_dict()) # type: ignore + + @classmethod + def from_existing( + cls, + name: str, + redis_client: Optional[SyncRedisClient] = None, + redis_url: str = "redis://localhost:6379", + **kwargs, + ) -> "LLMRouter": + """Reconnect to an existing LLMRouter. + + Args: + name: Router name. + redis_client: Existing Redis client. + redis_url: Redis connection URL. + + Returns: + LLMRouter instance connected to existing index. + """ + if redis_client: + RedisConnectionFactory.validate_sync_redis(redis_client) + elif redis_url: + redis_client = RedisConnectionFactory.get_redis_connection( + redis_url=redis_url, **kwargs + ) + + if redis_client is None: + raise ValueError("Could not establish Redis connection.") + + router_dict = redis_client.json().get(f"{name}:router_config") + if not isinstance(router_dict, dict): + raise ValueError(f"No router config found for {name}") + + return cls.from_dict( + router_dict, redis_url=redis_url, redis_client=redis_client + ) + + def _initialize_index( + self, + redis_client: Optional[SyncRedisClient] = None, + redis_url: str = "redis://localhost:6379", + overwrite: bool = False, + **connection_kwargs, + ): + """Initialize the Redis search index.""" + schema = SemanticRouterIndexSchema.from_params( + self.name, self.vectorizer.dims, self.vectorizer.dtype # type: ignore + ) + + self._index = SearchIndex( + schema=schema, + redis_client=redis_client, + redis_url=redis_url, + **connection_kwargs, + ) + + existed = self._index.exists() + if not overwrite and existed: + existing_index = SearchIndex.from_existing( + self.name, redis_client=self._index.client + ) + if existing_index.schema.to_dict() != self._index.schema.to_dict(): + raise ValueError( + f"Existing index {self.name} schema does not match. " + "Set overwrite=True to recreate." + ) + + self._index.create(overwrite=overwrite, drop=False) + + if not existed or overwrite: + self._add_tiers(self.tiers) + + def _add_tiers(self, tiers: List[ModelTier]): + """Add tiers to the router index.""" + tier_references: List[Dict[str, Any]] = [] + keys: List[str] = [] + + for tier in tiers: + # Embed all references for this tier + reference_vectors = self.vectorizer.embed_many( + tier.references, as_buffer=True + ) + + for i, reference in enumerate(tier.references): + reference_hash = hashify(reference) + tier_references.append( + { + "reference_id": reference_hash, + "route_name": tier.name, # Use route_name for compatibility + "reference": reference, + "vector": reference_vectors[i], + } + ) + keys.append(self._tier_ref_key(tier.name, reference_hash)) + + # Add tier to local list if not present + if not self.get_tier(tier.name): + self.tiers.append(tier) + + self._index.load(tier_references, keys=keys) + + def _tier_ref_key(self, tier_name: str, reference_hash: str) -> str: + """Generate key for a tier reference.""" + sep = self._index.key_separator + prefix = ( + self._index.prefix.rstrip(sep) + if sep and self._index.prefix + else self._index.prefix + ) + if prefix: + return f"{prefix}{sep}{tier_name}{sep}{reference_hash}" + return f"{tier_name}{sep}{reference_hash}" + + def _tier_pattern(self, tier_name: str) -> str: + """Generate search pattern for tier references.""" + sep = self._index.key_separator + prefix = ( + self._index.prefix.rstrip(sep) + if sep and self._index.prefix + else self._index.prefix + ) + if prefix: + return f"{prefix}{sep}{tier_name}{sep}*" + return f"{tier_name}{sep}*" + + @property + def tier_names(self) -> List[str]: + """Get list of tier names.""" + return [tier.name for tier in self.tiers] + + @property + def tier_thresholds(self) -> Dict[str, float]: + """Get distance thresholds for each tier.""" + return {tier.name: tier.distance_threshold for tier in self.tiers} + + @property + def default_tier(self) -> Optional[str]: + """Get default tier name.""" + return self.routing_config.default_tier + + def get_tier(self, tier_name: str) -> Optional[ModelTier]: + """Get a tier by name.""" + return next((t for t in self.tiers if t.name == tier_name), None) + + def add_tier(self, tier: ModelTier): + """Add a new tier to the router.""" + if self.get_tier(tier.name): + raise ValueError(f"Tier {tier.name} already exists") + self._add_tiers([tier]) + self._update_router_config() + + def remove_tier(self, tier_name: str): + """Remove a tier from the router.""" + tier = self.get_tier(tier_name) + if tier is None: + logger.warning(f"Tier {tier_name} not found") + return + + # Delete references from index + pattern = self._tier_pattern(tier_name) + keys = scan_by_pattern(self._index.client, pattern) # type: ignore + if keys: + self._index.drop_keys(list(keys)) + + # Remove from local list + self.tiers = [t for t in self.tiers if t.name != tier_name] + self._update_router_config() + + def add_tier_references( + self, + tier_name: str, + references: Union[str, List[str]], + ): + """Add references to an existing tier.""" + tier = self.get_tier(tier_name) + if tier is None: + raise ValueError(f"Tier {tier_name} not found") + + if isinstance(references, str): + references = [references] + + # Embed and add references + reference_vectors = self.vectorizer.embed_many(references, as_buffer=True) + tier_references = [] + keys = [] + + for i, reference in enumerate(references): + reference_hash = hashify(reference) + tier_references.append( + { + "reference_id": reference_hash, + "route_name": tier_name, + "reference": reference, + "vector": reference_vectors[i], + } + ) + keys.append(self._tier_ref_key(tier_name, reference_hash)) + + self._index.load(tier_references, keys=keys) + tier.references.extend(references) + self._update_router_config() + + def update_tier_threshold(self, tier_name: str, threshold: float): + """Update a tier's distance threshold.""" + tier = self.get_tier(tier_name) + if tier is None: + raise ValueError(f"Tier {tier_name} not found") + if not (0 < threshold <= 2): + raise ValueError("Threshold must be in range (0, 2]") + tier.distance_threshold = threshold + self._update_router_config() + + def _distance_threshold_filter(self) -> str: + """Build filter expression for per-tier thresholds.""" + filters = [] + for tier in self.tiers: + filters.append( + f"(@route_name == '{tier.name}' && @distance < {tier.distance_threshold})" + ) + return " || ".join(filters) + + def _build_aggregate_request( + self, + vector_range_query: VectorRangeQuery, + aggregation_method: DistanceAggregationMethod, + max_k: int, + ) -> AggregateRequest: + """Build Redis aggregation request.""" + if aggregation_method == DistanceAggregationMethod.min: + agg_func = reducers.min + elif aggregation_method == DistanceAggregationMethod.sum: + agg_func = reducers.sum # type: ignore + else: + agg_func = reducers.avg # type: ignore + + query_str = str(vector_range_query).split(" RETURN")[0] + request = ( + AggregateRequest(query_str) + .group_by("@route_name", agg_func("vector_distance").alias("distance")) + .sort_by("@distance", max=max_k) + .dialect(2) + ) + request.filter(self._distance_threshold_filter()) + return request + + def _get_tier_matches( + self, + vector: List[float], + aggregation_method: DistanceAggregationMethod, + max_k: int = 1, + ) -> List[LLMRouteMatch]: + """Get matching tiers for a vector.""" + if not self.tiers: + return [] + distance_threshold = max(t.distance_threshold for t in self.tiers) + + query = VectorRangeQuery( + vector=vector, + vector_field_name=ROUTE_VECTOR_FIELD_NAME, + distance_threshold=float(distance_threshold), + return_fields=["route_name"], + ) + + request = self._build_aggregate_request(query, aggregation_method, max_k) + + try: + result = self._index.aggregate(request, query.params) + except ResponseError as e: + if "VSS is not yet supported on FT.AGGREGATE" in str(e): + raise RuntimeError("LLM routing requires Redis 7.x or greater") + raise + + matches = [] + for row in result.rows: + row_dict = make_dict(convert_bytes(row)) + tier_name = row_dict["route_name"] + tier = self.get_tier(tier_name) + distance = float(row_dict["distance"]) + + matches.append( + LLMRouteMatch( + tier=tier_name, + model=tier.model if tier else None, + distance=distance, + confidence=1 - (distance / 2), # Convert to 0-1 range + metadata=tier.metadata if tier else {}, + ) + ) + + return matches + + def _apply_cost_optimization( + self, matches: List[LLMRouteMatch] + ) -> List[LLMRouteMatch]: + """Re-rank matches considering cost.""" + if not matches or not self.routing_config.cost_optimization: + return matches + + cost_weight = self.routing_config.cost_weight + ranked = [] + + for match in matches: + cost = match.metadata.get("cost_per_1k_input", 0) + # Add cost penalty to distance (normalized) + adjusted_distance = match.distance + (cost * cost_weight) + ranked.append((match, adjusted_distance)) + + ranked.sort(key=lambda x: x[1]) + return [m for m, _ in ranked] + + def route( + self, + query: Optional[str] = None, + vector: Optional[List[float]] = None, + aggregation_method: Optional[DistanceAggregationMethod] = None, + ) -> LLMRouteMatch: + """Route a query to the best matching tier. + + Args: + query: Text query to route. + vector: Pre-computed embedding vector. + aggregation_method: Override default aggregation method. + + Returns: + LLMRouteMatch with tier, model, and routing metadata. + """ + if vector is None: + if query is None: + raise ValueError("Must provide query or vector") + vector = self.vectorizer.embed(query) + + aggregation_method = ( + aggregation_method or self.routing_config.aggregation_method + ) + + matches = self._get_tier_matches( + vector, aggregation_method, max_k=len(self.tiers) + ) + matches = self._apply_cost_optimization(matches) + + if not matches: + # Return default tier or empty match + if self.default_tier: + tier = self.get_tier(self.default_tier) + if tier: + return LLMRouteMatch( + tier=tier.name, + model=tier.model, + metadata=tier.metadata, + ) + return LLMRouteMatch() + + top_match = matches[0] + top_match.alternatives = [(m.tier, m.distance) for m in matches[1:]] + return top_match + + def route_many( + self, + query: Optional[str] = None, + vector: Optional[List[float]] = None, + max_k: Optional[int] = None, + aggregation_method: Optional[DistanceAggregationMethod] = None, + ) -> List[LLMRouteMatch]: + """Route a query and return multiple tier matches. + + Args: + query: Text query to route. + vector: Pre-computed embedding vector. + max_k: Maximum number of matches to return. + aggregation_method: Override default aggregation method. + + Returns: + List of LLMRouteMatch objects ordered by distance. + """ + if vector is None: + if query is None: + raise ValueError("Must provide query or vector") + vector = self.vectorizer.embed(query) + + max_k = max_k or self.routing_config.max_k + aggregation_method = ( + aggregation_method or self.routing_config.aggregation_method + ) + + matches = self._get_tier_matches(vector, aggregation_method, max_k) + return self._apply_cost_optimization(matches) + + def __call__( + self, + query: Optional[str] = None, + vector: Optional[List[float]] = None, + ) -> LLMRouteMatch: + """Shorthand for route().""" + return self.route(query=query, vector=vector) + + # Serialization methods + + def to_dict(self) -> Dict[str, Any]: + """Serialize router to dictionary.""" + return { + "name": self.name, + "tiers": [model_to_dict(tier) for tier in self.tiers], + "vectorizer": { + "type": self.vectorizer.type, + "model": self.vectorizer.model, + }, + "routing_config": model_to_dict(self.routing_config), + } + + @classmethod + def from_dict( + cls, + data: Dict[str, Any], + **kwargs, + ) -> "LLMRouter": + """Create router from dictionary.""" + from redisvl.utils.vectorize import vectorizer_from_dict + + try: + name = data["name"] + tiers_data = data["tiers"] + vectorizer_data = data["vectorizer"] + routing_config_data = data.get("routing_config", {}) + except KeyError as e: + raise ValueError(f"Missing required field: {e}") + + vectorizer = vectorizer_from_dict(vectorizer_data) + if not vectorizer: + raise ValueError(f"Could not load vectorizer: {vectorizer_data}") + + tiers = [ModelTier(**t) for t in tiers_data] + routing_config = RoutingConfig(**routing_config_data) + + return cls( + name=name, + tiers=tiers, + vectorizer=vectorizer, + routing_config=routing_config, + **kwargs, + ) + + def to_yaml(self, file_path: str, overwrite: bool = True): + """Save router to YAML file.""" + fp = Path(file_path).resolve() + if fp.exists() and not overwrite: + raise FileExistsError(f"File {file_path} already exists") + + with open(fp, "w") as f: + yaml.dump(self.to_dict(), f, sort_keys=False) + + @classmethod + def from_yaml(cls, file_path: str, **kwargs) -> "LLMRouter": + """Load router from YAML file.""" + fp = Path(file_path).resolve() + if not fp.exists(): + raise FileNotFoundError(f"File {file_path} not found") + + with open(fp) as f: + data = yaml.safe_load(f) + + return cls.from_dict(data, **kwargs) + + def export_with_embeddings(self, file_path: str): + """Export router with pre-computed embeddings. + + This allows loading the router without re-embedding references. + + Args: + file_path: Path to JSON file. + """ + fp = Path(file_path).resolve() + + pretrained_tiers = [] + for tier in self.tiers: + # Get embeddings for all references + vectors = self.vectorizer.embed_many(tier.references) + + references = [ + PretrainedReference(text=ref, vector=vec) + for ref, vec in zip(tier.references, vectors) + ] + + pretrained_tiers.append( + PretrainedTier( + name=tier.name, + model=tier.model, + references=references, + metadata=tier.metadata, + distance_threshold=tier.distance_threshold, + ) + ) + + config = PretrainedRouterConfig( + name=self.name, + vectorizer={ + "type": self.vectorizer.type, + "model": self.vectorizer.model, + }, + tiers=pretrained_tiers, + routing_config=model_to_dict(self.routing_config), + ) + + with open(fp, "w") as f: + json.dump(config.model_dump(), f, indent=2) + + @classmethod + def from_pretrained( + cls, + config_name_or_path: str, + redis_client: Optional[SyncRedisClient] = None, + redis_url: str = "redis://localhost:6379", + **kwargs, + ) -> "LLMRouter": + """Load router from pretrained config with embeddings. + + This skips the embedding step by using pre-computed vectors. + Accepts either a file path or a built-in config name (e.g., "default"). + + Args: + config_name_or_path: Path to pretrained JSON file, or name of a + built-in config (e.g., "default"). + redis_client: Redis client. + redis_url: Redis URL. + + Returns: + LLMRouter loaded without re-embedding. + """ + import numpy as np + + from redisvl.utils.vectorize import vectorizer_from_dict + + fp = Path(config_name_or_path) + if not fp.exists(): + from redisvl.extensions.llm_router.pretrained import get_pretrained_path + + fp = get_pretrained_path(config_name_or_path) + + with open(fp) as f: + data = json.load(f) + + config = PretrainedRouterConfig(**data) + + # Create vectorizer (for future queries, not for loading) + vectorizer = vectorizer_from_dict(config.vectorizer) + if not vectorizer: + raise ValueError(f"Could not load vectorizer: {config.vectorizer}") + + # Set up connection — prefer provided client over URL + if redis_client: + RedisConnectionFactory.validate_sync_redis(redis_client) + elif redis_url: + redis_client = RedisConnectionFactory.get_redis_connection( + redis_url=redis_url, **kwargs + ) + + if redis_client is None: + raise ValueError("Could not establish Redis connection") + + # Create index schema + schema = SemanticRouterIndexSchema.from_params( + config.name, vectorizer.dims, vectorizer.dtype # type: ignore + ) + + index = SearchIndex( + schema=schema, + redis_client=redis_client, + ) + index.create(overwrite=True, drop=False) + + # Load pre-computed embeddings directly + tiers = [] + all_references = [] + all_keys = [] + + for pt in config.tiers: + tier = ModelTier( + name=pt.name, + model=pt.model, + references=[r.text for r in pt.references], + metadata=pt.metadata, + distance_threshold=pt.distance_threshold, + ) + tiers.append(tier) + + # Use pre-computed vectors (convert to buffer) + for ref in pt.references: + vector_buffer = np.array(ref.vector, dtype=np.float32).tobytes() + reference_hash = hashify(ref.text) + + sep = index.key_separator + prefix = ( + index.prefix.rstrip(sep) if sep and index.prefix else index.prefix + ) + if prefix: + key = f"{prefix}{sep}{pt.name}{sep}{reference_hash}" + else: + key = f"{pt.name}{sep}{reference_hash}" + + all_references.append( + { + "reference_id": reference_hash, + "route_name": pt.name, + "reference": ref.text, + "vector": vector_buffer, + } + ) + all_keys.append(key) + + index.load(all_references, keys=all_keys) + + # Create router instance using Pydantic's model_construct to bypass __init__ + routing_config = RoutingConfig(**config.routing_config) + + router = cls.model_construct( + name=config.name, + tiers=tiers, + vectorizer=vectorizer, + routing_config=routing_config, + ) + # Set private attribute directly + object.__setattr__(router, "_index", index) + + # Store config + redis_client.json().set(f"{config.name}:router_config", ".", router.to_dict()) + + return router + + def _update_router_config(self): + """Update router config in Redis.""" + self._index.client.json().set(f"{self.name}:router_config", ".", self.to_dict()) + + def delete(self): + """Delete the router index.""" + self._index.client.delete(f"{self.name}:router_config") + self._index.delete(drop=True) + + def clear(self): + """Clear all tier references.""" + self._index.clear() + self.tiers = [] + + +class AsyncLLMRouter(BaseModel): + """Async LLM Router for model tier selection. + + Provides the same functionality as :class:`LLMRouter` but uses async I/O. + Since ``__init__`` cannot be async, use the :meth:`create` classmethod + factory to instantiate. + + Example: + >>> from redisvl.extensions.llm_router import AsyncLLMRouter, ModelTier + >>> + >>> tiers = [ + ... ModelTier( + ... name="simple", + ... model="openai/gpt-4.1-nano", + ... references=["hello", "hi", "thanks"], + ... distance_threshold=0.5, + ... ), + ... ] + >>> + >>> router = await AsyncLLMRouter.create( + ... name="my-router", + ... tiers=tiers, + ... redis_url="redis://localhost:6379", + ... ) + >>> + >>> match = await router.route("hello, how are you?") + >>> print(match.tier, match.model) + """ + + name: str + """Router name (also used as Redis index prefix).""" + + tiers: List[ModelTier] + """List of model tiers for routing.""" + + vectorizer: BaseVectorizer = Field(default_factory=HFTextVectorizer) + """Vectorizer for embedding queries and references.""" + + routing_config: RoutingConfig = Field(default_factory=RoutingConfig) + """Configuration for routing behavior.""" + + _index: AsyncSearchIndex = PrivateAttr() + + model_config = ConfigDict(arbitrary_types_allowed=True) + + @classmethod + async def create( + cls, + name: str, + tiers: List[ModelTier], + vectorizer: Optional[BaseVectorizer] = None, + routing_config: Optional[RoutingConfig] = None, + redis_client: Optional[AsyncRedisClient] = None, + redis_url: str = "redis://localhost:6379", + overwrite: bool = False, + cost_optimization: bool = False, + connection_kwargs: Dict[str, Any] = {}, + **kwargs, + ) -> "AsyncLLMRouter": + """Create an AsyncLLMRouter instance (async factory). + + Args: + name: Router name (used as Redis index prefix). + tiers: List of ModelTier objects defining routing targets. + vectorizer: Vectorizer for embeddings. Defaults to HFTextVectorizer. + routing_config: Configuration for routing behavior. + redis_client: Existing async Redis client. + redis_url: Redis connection URL. + overwrite: Whether to overwrite existing index. + cost_optimization: Enable cost-aware routing. + connection_kwargs: Additional Redis connection arguments. + + Returns: + AsyncLLMRouter instance. + """ + if vectorizer is None: + vectorizer = HFTextVectorizer( + model="sentence-transformers/all-mpnet-base-v2" + ) + elif not isinstance(vectorizer, BaseVectorizer): + raise TypeError("Must provide a valid redisvl.vectorizer class.") + + if routing_config is None: + routing_config = RoutingConfig(cost_optimization=cost_optimization) + elif cost_optimization: + routing_config.cost_optimization = True + + router = cls.model_construct( + name=name, + tiers=tiers, + vectorizer=vectorizer, + routing_config=routing_config, + ) + + await router._initialize_index( + redis_client, redis_url, overwrite, **connection_kwargs + ) + + client = router._index.client + await client.json().set(f"{name}:router_config", ".", router.to_dict()) # type: ignore + return router + + @classmethod + async def from_existing( + cls, + name: str, + redis_client: Optional[AsyncRedisClient] = None, + redis_url: str = "redis://localhost:6379", + **kwargs, + ) -> "AsyncLLMRouter": + """Reconnect to an existing AsyncLLMRouter. + + Args: + name: Router name. + redis_client: Existing async Redis client. + redis_url: Redis connection URL. + + Returns: + AsyncLLMRouter instance connected to existing index. + """ + if redis_client: + await RedisConnectionFactory.validate_async_redis(redis_client) + elif redis_url: + redis_client = await RedisConnectionFactory._get_aredis_connection( + redis_url=redis_url, **kwargs + ) + + if redis_client is None: + raise ValueError("Could not establish Redis connection.") + + router_dict = await redis_client.json().get(f"{name}:router_config") # type: ignore + if not isinstance(router_dict, dict): + raise ValueError(f"No router config found for {name}") + + return await cls.from_dict( + router_dict, redis_url=redis_url, redis_client=redis_client + ) + + async def _initialize_index( + self, + redis_client: Optional[AsyncRedisClient] = None, + redis_url: str = "redis://localhost:6379", + overwrite: bool = False, + **connection_kwargs, + ): + """Initialize the Redis search index (async).""" + schema = SemanticRouterIndexSchema.from_params( + self.name, self.vectorizer.dims, self.vectorizer.dtype # type: ignore + ) + + self._index = AsyncSearchIndex( + schema=schema, + redis_client=redis_client, + redis_url=redis_url, + **connection_kwargs, + ) + + existed = await self._index.exists() + if not overwrite and existed: + existing_index = await AsyncSearchIndex.from_existing( + self.name, redis_client=self._index.client + ) + if existing_index.schema.to_dict() != self._index.schema.to_dict(): + raise ValueError( + f"Existing index {self.name} schema does not match. " + "Set overwrite=True to recreate." + ) + + await self._index.create(overwrite=overwrite, drop=False) + + if not existed or overwrite: + await self._add_tiers(self.tiers) + + async def _add_tiers(self, tiers: List[ModelTier]): + """Add tiers to the router index (async).""" + tier_references: List[Dict[str, Any]] = [] + keys: List[str] = [] + + for tier in tiers: + reference_vectors = await self.vectorizer.aembed_many( + tier.references, as_buffer=True + ) + + for i, reference in enumerate(tier.references): + reference_hash = hashify(reference) + tier_references.append( + { + "reference_id": reference_hash, + "route_name": tier.name, + "reference": reference, + "vector": reference_vectors[i], + } + ) + keys.append(self._tier_ref_key(tier.name, reference_hash)) + + if not self.get_tier(tier.name): + self.tiers.append(tier) + + await self._index.load(tier_references, keys=keys) + + def _tier_ref_key(self, tier_name: str, reference_hash: str) -> str: + """Generate key for a tier reference.""" + sep = self._index.key_separator + prefix = ( + self._index.prefix.rstrip(sep) + if sep and self._index.prefix + else self._index.prefix + ) + if prefix: + return f"{prefix}{sep}{tier_name}{sep}{reference_hash}" + return f"{tier_name}{sep}{reference_hash}" + + def _tier_pattern(self, tier_name: str) -> str: + """Generate search pattern for tier references.""" + sep = self._index.key_separator + prefix = ( + self._index.prefix.rstrip(sep) + if sep and self._index.prefix + else self._index.prefix + ) + if prefix: + return f"{prefix}{sep}{tier_name}{sep}*" + return f"{tier_name}{sep}*" + + @property + def tier_names(self) -> List[str]: + """Get list of tier names.""" + return [tier.name for tier in self.tiers] + + @property + def tier_thresholds(self) -> Dict[str, float]: + """Get distance thresholds for each tier.""" + return {tier.name: tier.distance_threshold for tier in self.tiers} + + @property + def default_tier(self) -> Optional[str]: + """Get default tier name.""" + return self.routing_config.default_tier + + def get_tier(self, tier_name: str) -> Optional[ModelTier]: + """Get a tier by name.""" + return next((t for t in self.tiers if t.name == tier_name), None) + + async def add_tier(self, tier: ModelTier): + """Add a new tier to the router.""" + if self.get_tier(tier.name): + raise ValueError(f"Tier {tier.name} already exists") + await self._add_tiers([tier]) + await self._update_router_config() + + async def remove_tier(self, tier_name: str): + """Remove a tier from the router.""" + tier = self.get_tier(tier_name) + if tier is None: + logger.warning(f"Tier {tier_name} not found") + return + + pattern = self._tier_pattern(tier_name) + client = self._index.client + keys = [] + async for key in client.scan_iter(match=pattern): # type: ignore + keys.append(key) + if keys: + await self._index.drop_keys(keys) + + self.tiers = [t for t in self.tiers if t.name != tier_name] + await self._update_router_config() + + async def add_tier_references( + self, + tier_name: str, + references: Union[str, List[str]], + ): + """Add references to an existing tier.""" + tier = self.get_tier(tier_name) + if tier is None: + raise ValueError(f"Tier {tier_name} not found") + + if isinstance(references, str): + references = [references] + + reference_vectors = await self.vectorizer.aembed_many( + references, as_buffer=True + ) + tier_references = [] + keys = [] + + for i, reference in enumerate(references): + reference_hash = hashify(reference) + tier_references.append( + { + "reference_id": reference_hash, + "route_name": tier_name, + "reference": reference, + "vector": reference_vectors[i], + } + ) + keys.append(self._tier_ref_key(tier_name, reference_hash)) + + await self._index.load(tier_references, keys=keys) + tier.references.extend(references) + await self._update_router_config() + + async def update_tier_threshold(self, tier_name: str, threshold: float): + """Update a tier's distance threshold.""" + tier = self.get_tier(tier_name) + if tier is None: + raise ValueError(f"Tier {tier_name} not found") + if not (0 < threshold <= 2): + raise ValueError("Threshold must be in range (0, 2]") + tier.distance_threshold = threshold + await self._update_router_config() + + def _distance_threshold_filter(self) -> str: + """Build filter expression for per-tier thresholds.""" + filters = [] + for tier in self.tiers: + filters.append( + f"(@route_name == '{tier.name}' && @distance < {tier.distance_threshold})" + ) + return " || ".join(filters) + + def _build_aggregate_request( + self, + vector_range_query: VectorRangeQuery, + aggregation_method: DistanceAggregationMethod, + max_k: int, + ) -> AggregateRequest: + """Build Redis aggregation request.""" + if aggregation_method == DistanceAggregationMethod.min: + agg_func = reducers.min + elif aggregation_method == DistanceAggregationMethod.sum: + agg_func = reducers.sum # type: ignore + else: + agg_func = reducers.avg # type: ignore + + query_str = str(vector_range_query).split(" RETURN")[0] + request = ( + AggregateRequest(query_str) + .group_by("@route_name", agg_func("vector_distance").alias("distance")) + .sort_by("@distance", max=max_k) + .dialect(2) + ) + request.filter(self._distance_threshold_filter()) + return request + + async def _get_tier_matches( + self, + vector: List[float], + aggregation_method: DistanceAggregationMethod, + max_k: int = 1, + ) -> List[LLMRouteMatch]: + """Get matching tiers for a vector (async).""" + if not self.tiers: + return [] + distance_threshold = max(t.distance_threshold for t in self.tiers) + + query = VectorRangeQuery( + vector=vector, + vector_field_name=ROUTE_VECTOR_FIELD_NAME, + distance_threshold=float(distance_threshold), + return_fields=["route_name"], + ) + + request = self._build_aggregate_request(query, aggregation_method, max_k) + + try: + result = await self._index.aggregate(request, query.params) + except ResponseError as e: + if "VSS is not yet supported on FT.AGGREGATE" in str(e): + raise RuntimeError("LLM routing requires Redis 7.x or greater") + raise + + matches = [] + for row in result.rows: + row_dict = make_dict(convert_bytes(row)) + tier_name = row_dict["route_name"] + tier = self.get_tier(tier_name) + distance = float(row_dict["distance"]) + + matches.append( + LLMRouteMatch( + tier=tier_name, + model=tier.model if tier else None, + distance=distance, + confidence=1 - (distance / 2), + metadata=tier.metadata if tier else {}, + ) + ) + + return matches + + def _apply_cost_optimization( + self, matches: List[LLMRouteMatch] + ) -> List[LLMRouteMatch]: + """Re-rank matches considering cost.""" + if not matches or not self.routing_config.cost_optimization: + return matches + + cost_weight = self.routing_config.cost_weight + ranked = [] + + for match in matches: + cost = match.metadata.get("cost_per_1k_input", 0) + adjusted_distance = match.distance + (cost * cost_weight) + ranked.append((match, adjusted_distance)) + + ranked.sort(key=lambda x: x[1]) + return [m for m, _ in ranked] + + async def route( + self, + query: Optional[str] = None, + vector: Optional[List[float]] = None, + aggregation_method: Optional[DistanceAggregationMethod] = None, + ) -> LLMRouteMatch: + """Route a query to the best matching tier (async). + + Args: + query: Text query to route. + vector: Pre-computed embedding vector. + aggregation_method: Override default aggregation method. + + Returns: + LLMRouteMatch with tier, model, and routing metadata. + """ + if vector is None: + if query is None: + raise ValueError("Must provide query or vector") + vector = await self.vectorizer.aembed(query) + + aggregation_method = ( + aggregation_method or self.routing_config.aggregation_method + ) + + matches = await self._get_tier_matches( + vector, aggregation_method, max_k=len(self.tiers) + ) + matches = self._apply_cost_optimization(matches) + + if not matches: + if self.default_tier: + tier = self.get_tier(self.default_tier) + if tier: + return LLMRouteMatch( + tier=tier.name, + model=tier.model, + metadata=tier.metadata, + ) + return LLMRouteMatch() + + top_match = matches[0] + top_match.alternatives = [(m.tier, m.distance) for m in matches[1:]] + return top_match + + async def route_many( + self, + query: Optional[str] = None, + vector: Optional[List[float]] = None, + max_k: Optional[int] = None, + aggregation_method: Optional[DistanceAggregationMethod] = None, + ) -> List[LLMRouteMatch]: + """Route a query and return multiple tier matches (async). + + Args: + query: Text query to route. + vector: Pre-computed embedding vector. + max_k: Maximum number of matches to return. + aggregation_method: Override default aggregation method. + + Returns: + List of LLMRouteMatch objects ordered by distance. + """ + if vector is None: + if query is None: + raise ValueError("Must provide query or vector") + vector = await self.vectorizer.aembed(query) + + max_k = max_k or self.routing_config.max_k + aggregation_method = ( + aggregation_method or self.routing_config.aggregation_method + ) + + matches = await self._get_tier_matches(vector, aggregation_method, max_k) + return self._apply_cost_optimization(matches) + + # Serialization methods + + def to_dict(self) -> Dict[str, Any]: + """Serialize router to dictionary.""" + return { + "name": self.name, + "tiers": [model_to_dict(tier) for tier in self.tiers], + "vectorizer": { + "type": self.vectorizer.type, + "model": self.vectorizer.model, + }, + "routing_config": model_to_dict(self.routing_config), + } + + @classmethod + async def from_dict( + cls, + data: Dict[str, Any], + **kwargs, + ) -> "AsyncLLMRouter": + """Create router from dictionary (async).""" + from redisvl.utils.vectorize import vectorizer_from_dict + + try: + name = data["name"] + tiers_data = data["tiers"] + vectorizer_data = data["vectorizer"] + routing_config_data = data.get("routing_config", {}) + except KeyError as e: + raise ValueError(f"Missing required field: {e}") + + vectorizer = vectorizer_from_dict(vectorizer_data) + if not vectorizer: + raise ValueError(f"Could not load vectorizer: {vectorizer_data}") + + tiers = [ModelTier(**t) for t in tiers_data] + routing_config = RoutingConfig(**routing_config_data) + + return await cls.create( + name=name, + tiers=tiers, + vectorizer=vectorizer, + routing_config=routing_config, + **kwargs, + ) + + def to_yaml(self, file_path: str, overwrite: bool = True): + """Save router to YAML file.""" + fp = Path(file_path).resolve() + if fp.exists() and not overwrite: + raise FileExistsError(f"File {file_path} already exists") + + with open(fp, "w") as f: + yaml.dump(self.to_dict(), f, sort_keys=False) + + @classmethod + async def from_yaml(cls, file_path: str, **kwargs) -> "AsyncLLMRouter": + """Load router from YAML file (async).""" + fp = Path(file_path).resolve() + if not fp.exists(): + raise FileNotFoundError(f"File {file_path} not found") + + with open(fp) as f: + data = yaml.safe_load(f) + + return await cls.from_dict(data, **kwargs) + + async def export_with_embeddings(self, file_path: str): + """Export router with pre-computed embeddings (async). + + Args: + file_path: Path to JSON file. + """ + fp = Path(file_path).resolve() + + pretrained_tiers = [] + for tier in self.tiers: + vectors = await self.vectorizer.aembed_many(tier.references) + + references = [ + PretrainedReference(text=ref, vector=vec) + for ref, vec in zip(tier.references, vectors) + ] + + pretrained_tiers.append( + PretrainedTier( + name=tier.name, + model=tier.model, + references=references, + metadata=tier.metadata, + distance_threshold=tier.distance_threshold, + ) + ) + + config = PretrainedRouterConfig( + name=self.name, + vectorizer={ + "type": self.vectorizer.type, + "model": self.vectorizer.model, + }, + tiers=pretrained_tiers, + routing_config=model_to_dict(self.routing_config), + ) + + with open(fp, "w") as f: + json.dump(config.model_dump(), f, indent=2) + + @classmethod + async def from_pretrained( + cls, + config_name_or_path: str, + redis_client: Optional[AsyncRedisClient] = None, + redis_url: str = "redis://localhost:6379", + **kwargs, + ) -> "AsyncLLMRouter": + """Load router from pretrained config with embeddings (async). + + Accepts either a file path or a built-in config name (e.g., "default"). + + Args: + config_name_or_path: Path to pretrained JSON file, or name of a + built-in config (e.g., "default"). + redis_client: Async Redis client. + redis_url: Redis URL. + + Returns: + AsyncLLMRouter loaded without re-embedding. + """ + import numpy as np + + from redisvl.utils.vectorize import vectorizer_from_dict + + fp = Path(config_name_or_path) + if not fp.exists(): + from redisvl.extensions.llm_router.pretrained import get_pretrained_path + + fp = get_pretrained_path(config_name_or_path) + + with open(fp) as f: + data = json.load(f) + + config = PretrainedRouterConfig(**data) + + vectorizer = vectorizer_from_dict(config.vectorizer) + if not vectorizer: + raise ValueError(f"Could not load vectorizer: {config.vectorizer}") + + # Prefer provided client over URL + if redis_client: + await RedisConnectionFactory.validate_async_redis(redis_client) + elif redis_url: + redis_client = await RedisConnectionFactory._get_aredis_connection( + redis_url=redis_url, **kwargs + ) + + if redis_client is None: + raise ValueError("Could not establish Redis connection") + + schema = SemanticRouterIndexSchema.from_params( + config.name, vectorizer.dims, vectorizer.dtype # type: ignore + ) + + index = AsyncSearchIndex( + schema=schema, + redis_client=redis_client, + ) + await index.create(overwrite=True, drop=False) + + tiers = [] + all_references = [] + all_keys = [] + + for pt in config.tiers: + tier = ModelTier( + name=pt.name, + model=pt.model, + references=[r.text for r in pt.references], + metadata=pt.metadata, + distance_threshold=pt.distance_threshold, + ) + tiers.append(tier) + + for ref in pt.references: + vector_buffer = np.array(ref.vector, dtype=np.float32).tobytes() + reference_hash = hashify(ref.text) + + sep = index.key_separator + prefix = ( + index.prefix.rstrip(sep) if sep and index.prefix else index.prefix + ) + if prefix: + key = f"{prefix}{sep}{pt.name}{sep}{reference_hash}" + else: + key = f"{pt.name}{sep}{reference_hash}" + + all_references.append( + { + "reference_id": reference_hash, + "route_name": pt.name, + "reference": ref.text, + "vector": vector_buffer, + } + ) + all_keys.append(key) + + await index.load(all_references, keys=all_keys) + + routing_config = RoutingConfig(**config.routing_config) + + router = cls.model_construct( + name=config.name, + tiers=tiers, + vectorizer=vectorizer, + routing_config=routing_config, + ) + object.__setattr__(router, "_index", index) + + await redis_client.json().set( # type: ignore + f"{config.name}:router_config", ".", router.to_dict() + ) + + return router + + async def _update_router_config(self): + """Update router config in Redis.""" + client = self._index.client + await client.json().set(f"{self.name}:router_config", ".", self.to_dict()) # type: ignore + + async def delete(self): + """Delete the router index.""" + client = self._index.client + await client.delete(f"{self.name}:router_config") # type: ignore + await self._index.delete(drop=True) + + async def clear(self): + """Clear all tier references.""" + await self._index.clear() + self.tiers = [] diff --git a/redisvl/extensions/llm_router/schema.py b/redisvl/extensions/llm_router/schema.py new file mode 100644 index 00000000..68e08275 --- /dev/null +++ b/redisvl/extensions/llm_router/schema.py @@ -0,0 +1,218 @@ +"""Schema definitions for LLM Router. + +This module defines the Pydantic models for model tiers, routing configuration, +and route match results. +""" + +from enum import Enum +from typing import Any, Dict, List, Optional + +from pydantic import BaseModel, ConfigDict, Field, field_validator +from typing_extensions import Annotated + + +class ModelTier(BaseModel): + """Model representing a routing tier with associated LLM model and metadata. + + A ModelTier defines a category of queries that should be routed to a specific + LLM model. Each tier has reference phrases that define its "semantic surface area" + and metadata about the model's capabilities and costs. + + Attributes: + name: Unique identifier for the tier (e.g., "simple", "reasoning", "expert") + model: LiteLLM-compatible model identifier (e.g., "openai/gpt-4.1-nano") + references: Example phrases that should route to this tier + metadata: Model configuration including costs and capabilities + distance_threshold: Maximum cosine distance for matching (0-2, lower is stricter) + + Example: + >>> tier = ModelTier( + ... name="simple", + ... model="openai/gpt-4.1-nano", + ... references=["hello", "hi there", "thanks"], + ... metadata={"cost_per_1k_input": 0.0001}, + ... distance_threshold=0.5, + ... ) + """ + + model_config = ConfigDict(protected_namespaces=()) + + name: str + """Unique identifier for the tier.""" + + model: str + """LiteLLM-compatible model identifier (e.g., 'anthropic/claude-sonnet-4-5').""" + + references: List[str] + """Example phrases that define this tier's semantic space.""" + + metadata: Dict[str, Any] = Field(default_factory=dict) + """Model metadata including costs, capabilities, provider info.""" + + distance_threshold: Annotated[float, Field(strict=True, gt=0, le=2)] = 0.5 + """Maximum cosine distance for matching this tier (Redis COSINE: 0-2).""" + + @field_validator("name") + @classmethod + def name_must_not_be_empty(cls, v): + if not v or not v.strip(): + raise ValueError("Tier name must not be empty") + return v + + @field_validator("references") + @classmethod + def references_must_not_be_empty(cls, v): + if not v: + raise ValueError("References must not be empty") + if any(not ref.strip() for ref in v): + raise ValueError("All references must be non-empty strings") + return v + + @field_validator("model") + @classmethod + def model_must_not_be_empty(cls, v): + if not v or not v.strip(): + raise ValueError("Model must not be empty") + return v + + +class LLMRouteMatch(BaseModel): + """Result of routing a query to an LLM model tier. + + Contains information about which tier was selected, the model to use, + and routing metadata like distance and confidence scores. + + Attributes: + tier: Name of the matched tier (None if no match) + model: LiteLLM model identifier to use + distance: Cosine distance to the matched tier (lower is better) + confidence: Routing confidence score (0-1, higher is better) + alternatives: Other possible tier matches with their distances + """ + + tier: Optional[str] = None + """Name of the matched tier.""" + + model: Optional[str] = None + """LiteLLM model identifier.""" + + distance: Optional[float] = None + """Cosine distance to the matched tier references.""" + + confidence: Optional[float] = None + """Routing confidence (1 - distance/2), range 0-1.""" + + alternatives: List[tuple] = Field(default_factory=list) + """Alternative tier matches as (tier_name, distance) tuples.""" + + metadata: Dict[str, Any] = Field(default_factory=dict) + """Tier metadata (costs, capabilities, etc.).""" + + def __bool__(self) -> bool: + """Return True if a tier was matched.""" + return self.tier is not None + + +class DistanceAggregationMethod(Enum): + """Method for aggregating distances across multiple references.""" + + avg = "avg" + """Average of distances to all matching references.""" + + min = "min" + """Minimum distance (closest reference).""" + + sum = "sum" + """Sum of distances.""" + + +class RoutingConfig(BaseModel): + """Configuration for LLM routing behavior. + + Attributes: + max_k: Maximum number of tier matches to return + aggregation_method: How to aggregate distances across references + cost_optimization: Whether to prefer cheaper tiers when distances are close + cost_weight: Weight for cost in routing decisions (0-1) + default_tier: Tier to use when no match is found + """ + + model_config = ConfigDict(extra="ignore") + + max_k: Annotated[int, Field(strict=True, gt=0)] = 1 + """Maximum number of tier matches to return.""" + + aggregation_method: DistanceAggregationMethod = Field( + default=DistanceAggregationMethod.avg + ) + """Method for aggregating distances.""" + + cost_optimization: bool = False + """Whether to prefer cheaper tiers when distances are close.""" + + cost_weight: Annotated[float, Field(ge=0, le=1)] = 0.1 + """Weight for cost penalty in routing (0 = ignore cost, 1 = cost dominates).""" + + default_tier: Optional[str] = None + """Tier to use when no match found (None = return no match).""" + + +class PretrainedReference(BaseModel): + """A reference with pre-computed embedding vector. + + Used for exporting/importing routers with embeddings to avoid + re-computing embeddings on load. + """ + + text: str + """The reference text.""" + + vector: List[float] + """Pre-computed embedding vector.""" + + +class PretrainedTier(BaseModel): + """A tier with pre-computed embeddings for all references. + + Used in pretrained router configurations. + """ + + model_config = ConfigDict(protected_namespaces=()) + + name: str + """Tier name.""" + + model: str + """LiteLLM model identifier.""" + + references: List[PretrainedReference] + """References with pre-computed vectors.""" + + metadata: Dict[str, Any] = Field(default_factory=dict) + """Tier metadata.""" + + distance_threshold: float = 0.5 + """Distance threshold.""" + + +class PretrainedRouterConfig(BaseModel): + """Complete router configuration with pre-computed embeddings. + + This format is used for distributing pretrained routers that can be + loaded without needing to re-embed all references. + """ + + name: str + """Router name.""" + + version: str = "1.0.0" + """Configuration version.""" + + vectorizer: Dict[str, Any] + """Vectorizer configuration (type, model name).""" + + tiers: List[PretrainedTier] + """Tiers with pre-computed embeddings.""" + + routing_config: Dict[str, Any] = Field(default_factory=dict) + """Routing configuration.""" diff --git a/scripts/generate_pretrained_config.py b/scripts/generate_pretrained_config.py new file mode 100644 index 00000000..6b50a92f --- /dev/null +++ b/scripts/generate_pretrained_config.py @@ -0,0 +1,184 @@ +#!/usr/bin/env python3 +"""Generate pretrained LLM Router configurations with pre-computed embeddings. + +This script uses HFTextVectorizer to embed reference phrases and serialize +a PretrainedRouterConfig to JSON. Run once to regenerate default.json. + +Usage: + python scripts/generate_pretrained_config.py +""" + +import json +import sys +from pathlib import Path + +# Add project root to path +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from redisvl.extensions.llm_router.schema import ( + PretrainedReference, + PretrainedRouterConfig, + PretrainedTier, +) +from redisvl.utils.vectorize.text.huggingface import HFTextVectorizer + +# Three tiers grounded in Bloom's Taxonomy + NVIDIA LLM Router Blueprint categories +TIERS = [ + { + "name": "simple", + "model": "openai/gpt-4.1-nano", + "distance_threshold": 0.5, + "metadata": { + "provider": "openai", + "cost_per_1k_input": 0.0001, + "cost_per_1k_output": 0.0004, + "blooms_taxonomy": ["Remember", "Understand"], + }, + "references": [ + "hello how are you", + "what is the capital of France", + "convert this to JSON", + "is this email spam or not spam", + "what time is it", + "thanks for your help", + "translate hello to Spanish", + "what does FAQ stand for", + "list the days of the week", + "goodbye see you later", + "what is two plus two", + "define the word algorithm", + "how do you spell necessary", + "what color is the sky", + "who wrote Romeo and Juliet", + "what year did World War 2 end", + "how many continents are there", + "what is the boiling point of water", + ], + }, + { + "name": "standard", + "model": "anthropic/claude-sonnet-4-5", + "distance_threshold": 0.6, + "metadata": { + "provider": "anthropic", + "cost_per_1k_input": 0.003, + "cost_per_1k_output": 0.015, + "blooms_taxonomy": ["Apply", "Analyze"], + }, + "references": [ + "explain how neural networks learn", + "summarize this research paper in detail", + "write a blog post about climate change", + "debug this Python function and explain the issue", + "compare and contrast these two approaches", + "analyze the sentiment of this customer feedback", + "write unit tests for this class", + "explain the tradeoffs between SQL and NoSQL databases", + "create a marketing email for our new product", + "review this pull request for potential issues", + "explain how garbage collection works in Java", + "write a detailed product description for", + "help me refactor this code for better readability", + "outline the pros and cons of microservices architecture", + "draft a project proposal for a new feature", + "explain the difference between TCP and UDP protocols", + "write a recursive function to traverse a binary tree", + "analyze this dataset and identify key trends", + ], + }, + { + "name": "expert", + "model": "anthropic/claude-opus-4-5", + "distance_threshold": 0.7, + "metadata": { + "provider": "anthropic", + "cost_per_1k_input": 0.005, + "cost_per_1k_output": 0.025, + "blooms_taxonomy": ["Evaluate", "Create"], + }, + "references": [ + "prove this mathematical theorem step by step", + "architect a distributed system for millions of concurrent users", + "write a research paper analyzing the impact of", + "design a novel algorithm for graph partitioning", + "review this legal contract and identify potential issues", + "develop a comprehensive business strategy for market entry", + "create a detailed technical specification for a new platform", + "write a compiler optimization pass for loop unrolling", + "formulate a hypothesis and design an experiment to test it", + "analyze the philosophical implications of artificial general intelligence", + "design a machine learning pipeline for production deployment", + "write a formal verification proof for this consensus protocol", + "create a comprehensive security audit report for this system", + "develop a novel approach to solving this NP-hard problem", + "write a peer-reviewed analysis of quantum computing advances", + "architect a fault-tolerant database replication system", + "design an operating system scheduler with real-time guarantees", + "create a mathematical model for predicting market volatility", + ], + }, +] + +OUTPUT_PATH = ( + Path(__file__).parent.parent + / "redisvl" + / "extensions" + / "llm_router" + / "pretrained" + / "default.json" +) + + +def main(): + print("Initializing HFTextVectorizer (sentence-transformers/all-mpnet-base-v2)...") + vectorizer = HFTextVectorizer( + model="sentence-transformers/all-mpnet-base-v2" + ) + + pretrained_tiers = [] + for tier_def in TIERS: + print(f"Embedding {len(tier_def['references'])} references for tier '{tier_def['name']}'...") + vectors = vectorizer.embed_many(tier_def["references"]) + + references = [ + PretrainedReference(text=text, vector=vec) + for text, vec in zip(tier_def["references"], vectors) + ] + + pretrained_tiers.append( + PretrainedTier( + name=tier_def["name"], + model=tier_def["model"], + references=references, + metadata=tier_def["metadata"], + distance_threshold=tier_def["distance_threshold"], + ) + ) + + config = PretrainedRouterConfig( + name="llm-router-default", + version="1.0.0", + vectorizer={ + "type": "hf", + "model": "sentence-transformers/all-mpnet-base-v2", + }, + tiers=pretrained_tiers, + routing_config={ + "max_k": 1, + "aggregation_method": "avg", + "cost_optimization": False, + }, + ) + + OUTPUT_PATH.parent.mkdir(parents=True, exist_ok=True) + with open(OUTPUT_PATH, "w") as f: + json.dump(config.model_dump(), f, indent=2) + + print(f"Wrote pretrained config to {OUTPUT_PATH}") + print(f"Total tiers: {len(pretrained_tiers)}") + for tier in pretrained_tiers: + print(f" {tier.name}: {len(tier.references)} references, model={tier.model}") + + +if __name__ == "__main__": + main() diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py new file mode 100644 index 00000000..a333df76 --- /dev/null +++ b/tests/integration/conftest.py @@ -0,0 +1,37 @@ +""" +Conftest for integration tests - uses local Redis Stack instead of Docker. + +This allows running integration tests in environments where Docker is not +available but Redis Stack is (e.g., dev containers with redis-stack). +""" + +import os + +import pytest + + +# Only override Docker fixtures if Docker is not available +def _docker_available(): + """Check if Docker is available.""" + import shutil + + return shutil.which("docker") is not None + + +if not _docker_available(): + + @pytest.fixture(scope="session", autouse=True) + def redis_container(): + """ + No-op fixture that overrides the session-scoped redis_container fixture + when Docker is not available. Uses local Redis Stack instead. + """ + yield None + + @pytest.fixture(scope="session") + def redis_url(): + """ + Use local Redis instance (assumes Redis Stack is running on localhost:6379). + Can be overridden with REDIS_URL environment variable. + """ + return os.environ.get("REDIS_URL", "redis://localhost:6379") diff --git a/tests/integration/test_async_llm_router.py b/tests/integration/test_async_llm_router.py new file mode 100644 index 00000000..08ec3601 --- /dev/null +++ b/tests/integration/test_async_llm_router.py @@ -0,0 +1,462 @@ +"""Async integration tests for AsyncLLMRouter. + +Mirrors sync LLMRouter tests with async fixtures and await calls. +""" + +import pathlib + +import pytest +from ulid import ULID + +from redisvl.extensions.llm_router import AsyncLLMRouter, LLMRouteMatch, ModelTier +from redisvl.extensions.llm_router.schema import RoutingConfig +from tests.conftest import ( + SKIP_HF, + skip_if_no_redisearch_async, + skip_if_redis_version_below_async, +) + +pytestmark = [ + pytest.mark.asyncio, + pytest.mark.skipif( + SKIP_HF, reason="sentence-transformers not supported on Python 3.14+" + ), +] + + +def get_base_path(): + return pathlib.Path(__file__).parent.resolve() + + +@pytest.fixture +def model_tiers(): + """Define model tiers for testing.""" + return [ + ModelTier( + name="simple", + model="openai/gpt-4.1-nano", + references=[ + "hello", + "hi there", + "what time is it?", + "thanks", + "goodbye", + ], + metadata={ + "provider": "openai", + "cost_per_1k_input": 0.0001, + "cost_per_1k_output": 0.0004, + }, + distance_threshold=0.5, + ), + ModelTier( + name="reasoning", + model="anthropic/claude-sonnet-4-5", + references=[ + "analyze this code for bugs", + "explain how neural networks learn", + "write a detailed blog post about", + "compare and contrast these approaches", + "debug this issue step by step", + ], + metadata={ + "provider": "anthropic", + "cost_per_1k_input": 0.003, + "cost_per_1k_output": 0.015, + }, + distance_threshold=0.6, + ), + ModelTier( + name="expert", + model="anthropic/claude-opus-4-5", + references=[ + "prove this mathematical theorem", + "architect a distributed system for millions of users", + "write a research paper analyzing", + "review this legal contract for issues", + "design a novel algorithm for", + ], + metadata={ + "provider": "anthropic", + "cost_per_1k_input": 0.005, + "cost_per_1k_output": 0.025, + }, + distance_threshold=0.7, + ), + ] + + +@pytest.fixture +async def async_llm_router(async_client, model_tiers, hf_vectorizer): + """Create an AsyncLLMRouter for testing.""" + await skip_if_no_redisearch_async(async_client) + router = await AsyncLLMRouter.create( + name=f"test-async-llm-router-{str(ULID())}", + tiers=model_tiers, + vectorizer=hf_vectorizer, + redis_client=async_client, + overwrite=True, + ) + yield router + await router.delete() + + +class TestAsyncLLMRouterInitialization: + """Test AsyncLLMRouter initialization and configuration.""" + + async def test_initialize_router(self, async_llm_router): + """Router should initialize with tiers.""" + assert async_llm_router.name.startswith("test-async-llm-router-") + assert len(async_llm_router.tiers) == 3 + assert async_llm_router.tier_names == ["simple", "reasoning", "expert"] + + async def test_router_properties(self, async_llm_router): + """Router should expose tier properties.""" + tier_names = async_llm_router.tier_names + assert "simple" in tier_names + assert "reasoning" in tier_names + assert "expert" in tier_names + + thresholds = async_llm_router.tier_thresholds + assert thresholds["simple"] == 0.5 + assert thresholds["reasoning"] == 0.6 + assert thresholds["expert"] == 0.7 + + async def test_get_tier(self, async_llm_router): + """Should retrieve tier by name.""" + tier = async_llm_router.get_tier("simple") + assert tier is not None + assert tier.name == "simple" + assert tier.model == "openai/gpt-4.1-nano" + + async def test_get_nonexistent_tier(self, async_llm_router): + """Should return None for nonexistent tier.""" + tier = async_llm_router.get_tier("nonexistent") + assert tier is None + + +class TestAsyncLLMRouterRouting: + """Test AsyncLLMRouter routing behavior.""" + + async def test_route_simple_query(self, async_llm_router, async_client): + """Simple greetings should route to simple tier.""" + await skip_if_redis_version_below_async(async_client, "7.0.0") + + match = await async_llm_router.route("hello, how are you?") + assert isinstance(match, LLMRouteMatch) + assert match.tier == "simple" + assert match.model == "openai/gpt-4.1-nano" + assert match.distance is not None + assert match.distance <= 0.5 + + async def test_route_reasoning_query(self, async_llm_router, async_client): + """Complex analysis should route to reasoning tier.""" + await skip_if_redis_version_below_async(async_client, "7.0.0") + + match = await async_llm_router.route( + "analyze this code and find potential bugs" + ) + assert match.tier == "reasoning" + assert match.model == "anthropic/claude-sonnet-4-5" + + async def test_route_expert_query(self, async_llm_router, async_client): + """Research-level queries should route to expert tier.""" + await skip_if_redis_version_below_async(async_client, "7.0.0") + + match = await async_llm_router.route( + "design a novel distributed consensus algorithm" + ) + assert match.tier == "expert" + assert match.model == "anthropic/claude-opus-4-5" + + async def test_route_no_match(self, async_llm_router, async_client): + """Unrelated queries should return default tier or no match.""" + await skip_if_redis_version_below_async(async_client, "7.0.0") + + match = await async_llm_router.route("xyzzy plugh random gibberish 12345") + assert match.tier is None or match.tier == async_llm_router.default_tier + + async def test_route_with_vector(self, async_llm_router, async_client): + """Should accept pre-computed vector.""" + await skip_if_redis_version_below_async(async_client, "7.0.0") + + vector = async_llm_router.vectorizer.embed("hello") + match = await async_llm_router.route(vector=vector) + assert match.tier == "simple" + + async def test_route_many(self, async_llm_router, async_client): + """Should return multiple tier matches.""" + await skip_if_redis_version_below_async(async_client, "7.0.0") + + matches = await async_llm_router.route_many( + "explain machine learning concepts", max_k=3 + ) + assert len(matches) > 0 + assert all(isinstance(m, LLMRouteMatch) for m in matches) + + +class TestAsyncLLMRouterCostOptimization: + """Test cost-aware routing behavior.""" + + async def test_cost_optimization_prefers_cheaper( + self, async_client, model_tiers, hf_vectorizer + ): + """With cost optimization, should prefer cheaper tiers when close.""" + await skip_if_no_redisearch_async(async_client) + await skip_if_redis_version_below_async(async_client, "7.0.0") + + router = await AsyncLLMRouter.create( + name=f"test-async-cost-router-{str(ULID())}", + tiers=model_tiers, + vectorizer=hf_vectorizer, + redis_client=async_client, + cost_optimization=True, + overwrite=True, + ) + + try: + match = await router.route("hello there, how are you?") + assert match.tier is not None or router.default_tier is not None + finally: + await router.delete() + + +class TestAsyncLLMRouterSerialization: + """Test AsyncLLMRouter serialization and persistence.""" + + async def test_to_dict(self, async_llm_router): + """Should serialize to dictionary.""" + router_dict = async_llm_router.to_dict() + assert router_dict["name"] == async_llm_router.name + assert len(router_dict["tiers"]) == 3 + assert router_dict["vectorizer"]["type"] == async_llm_router.vectorizer.type + + async def test_from_dict(self, async_llm_router): + """Should deserialize from dictionary.""" + router_dict = async_llm_router.to_dict() + new_router = await AsyncLLMRouter.from_dict( + router_dict, + redis_client=async_llm_router._index.client, + overwrite=True, + ) + assert new_router.name == async_llm_router.name + assert len(new_router.tiers) == len(async_llm_router.tiers) + + async def test_to_yaml(self, async_llm_router, tmp_path): + """Should serialize to YAML file.""" + yaml_file = tmp_path / "async_llm_router.yaml" + async_llm_router.to_yaml(str(yaml_file)) + assert yaml_file.exists() + + async def test_from_yaml(self, async_llm_router, tmp_path): + """Should deserialize from YAML file.""" + yaml_file = tmp_path / "async_llm_router.yaml" + async_llm_router.to_yaml(str(yaml_file)) + + new_router = await AsyncLLMRouter.from_yaml( + str(yaml_file), + redis_client=async_llm_router._index.client, + overwrite=True, + ) + assert new_router.name == async_llm_router.name + + +class TestAsyncLLMRouterWithEmbeddings: + """Test AsyncLLMRouter with pre-computed embeddings.""" + + async def test_export_with_embeddings(self, async_llm_router, tmp_path): + """Should export router with pre-computed embeddings.""" + json_file = tmp_path / "async_router_with_embeddings.json" + await async_llm_router.export_with_embeddings(str(json_file)) + + assert json_file.exists() + + import json + + with open(json_file) as f: + data = json.load(f) + + assert "tiers" in data + for tier in data["tiers"]: + assert "references" in tier + for ref in tier["references"]: + assert "text" in ref + assert "vector" in ref + assert isinstance(ref["vector"], list) + assert len(ref["vector"]) > 0 + + async def test_import_with_embeddings(self, async_client, hf_vectorizer, tmp_path): + """Should import router without re-embedding.""" + await skip_if_no_redisearch_async(async_client) + await skip_if_redis_version_below_async(async_client, "7.0.0") + + tier = ModelTier( + name="test", + model="test/model", + references=["hello", "world"], + distance_threshold=0.5, + ) + router1 = await AsyncLLMRouter.create( + name=f"async-export-test-{str(ULID())}", + tiers=[tier], + vectorizer=hf_vectorizer, + redis_client=async_client, + overwrite=True, + ) + + json_file = tmp_path / "async_export_test.json" + await router1.export_with_embeddings(str(json_file)) + await router1.delete() + + router2 = await AsyncLLMRouter.from_pretrained( + str(json_file), + redis_client=async_client, + ) + + try: + assert len(router2.tiers) == 1 + assert router2.tiers[0].name == "test" + + match = await router2.route("hello there") + assert match.tier == "test" + finally: + await router2.delete() + + +class TestAsyncLLMRouterTierManagement: + """Test adding/removing tiers.""" + + async def test_add_tier(self, async_llm_router, async_client): + """Should add new tier.""" + await skip_if_redis_version_below_async(async_client, "7.0.0") + + new_tier = ModelTier( + name="local", + model="ollama/llama3.2", + references=["ok", "yes", "no"], + metadata={"cost_per_1k_input": 0}, + distance_threshold=0.3, + ) + await async_llm_router.add_tier(new_tier) + + assert async_llm_router.get_tier("local") is not None + assert "local" in async_llm_router.tier_names + + async def test_remove_tier(self, async_llm_router): + """Should remove tier.""" + await async_llm_router.remove_tier("simple") + + assert async_llm_router.get_tier("simple") is None + assert "simple" not in async_llm_router.tier_names + + async def test_add_tier_references(self, async_llm_router, async_client): + """Should add references to existing tier.""" + await skip_if_redis_version_below_async(async_client, "7.0.0") + + match_before = await async_llm_router.route("hi there") + assert match_before.tier == "simple" + + await async_llm_router.add_tier_references( + tier_name="simple", + references=["howdy", "greetings friend"], + ) + + tier = async_llm_router.get_tier("simple") + assert "howdy" in tier.references + assert "greetings friend" in tier.references + + async def test_update_tier_threshold(self, async_llm_router): + """Should update tier threshold.""" + await async_llm_router.update_tier_threshold("simple", 0.3) + + assert async_llm_router.tier_thresholds["simple"] == 0.3 + + +class TestAsyncLLMRouterFromExisting: + """Test reconnecting to existing router.""" + + async def test_from_existing( + self, async_client, model_tiers, hf_vectorizer, redis_url + ): + """Should reconnect to existing router.""" + await skip_if_no_redisearch_async(async_client) + await skip_if_redis_version_below_async(async_client, "7.0.0") + + router_name = f"async-persist-test-{str(ULID())}" + + router1 = await AsyncLLMRouter.create( + name=router_name, + tiers=model_tiers, + vectorizer=hf_vectorizer, + redis_url=redis_url, + overwrite=True, + ) + + router2 = await AsyncLLMRouter.from_existing( + name=router_name, + redis_url=redis_url, + ) + + try: + assert router2.name == router1.name + assert len(router2.tiers) == len(router1.tiers) + assert router2.tier_names == router1.tier_names + + match = await router2.route("hello") + assert match.tier is not None + finally: + await router1.delete() + + +class TestAsyncLLMRouterPretrained: + """Test AsyncLLMRouter with pretrained configurations.""" + + async def test_from_pretrained_default(self, async_client): + """Should load the built-in default pretrained config.""" + await skip_if_no_redisearch_async(async_client) + + router = await AsyncLLMRouter.from_pretrained( + "default", redis_client=async_client + ) + try: + assert len(router.tiers) == 3 + assert set(router.tier_names) == {"simple", "standard", "expert"} + finally: + await router.delete() + + async def test_pretrained_routes_simple(self, async_client): + """Simple greetings should route to simple tier.""" + await skip_if_no_redisearch_async(async_client) + await skip_if_redis_version_below_async(async_client, "7.0.0") + + router = await AsyncLLMRouter.from_pretrained( + "default", redis_client=async_client + ) + try: + match = await router.route("hi, how are you doing?") + assert match.tier == "simple" + assert match.model == "openai/gpt-4.1-nano" + finally: + await router.delete() + + async def test_pretrained_routes_expert(self, async_client): + """Complex research queries should route to expert tier.""" + await skip_if_no_redisearch_async(async_client) + await skip_if_redis_version_below_async(async_client, "7.0.0") + + router = await AsyncLLMRouter.from_pretrained( + "default", redis_client=async_client + ) + try: + match = await router.route("architect a fault-tolerant distributed system") + assert match.tier == "expert" + assert match.model == "anthropic/claude-opus-4-5" + finally: + await router.delete() + + async def test_pretrained_invalid_name(self, async_client): + """Should raise error for unknown pretrained config name.""" + with pytest.raises(ValueError, match="not found"): + await AsyncLLMRouter.from_pretrained( + "nonexistent_config", redis_client=async_client + ) diff --git a/tests/integration/test_llm_router.py b/tests/integration/test_llm_router.py new file mode 100644 index 00000000..9b28ed2f --- /dev/null +++ b/tests/integration/test_llm_router.py @@ -0,0 +1,484 @@ +"""Integration tests for LLMRouter. + +These tests define the expected behavior of the LLM Router extension. +Tests are written first, then implementation follows. +""" + +import pathlib + +import pytest +from ulid import ULID + +from redisvl.extensions.llm_router import LLMRouteMatch, LLMRouter, ModelTier +from redisvl.extensions.llm_router.schema import RoutingConfig +from tests.conftest import SKIP_HF, skip_if_no_redisearch, skip_if_redis_version_below + +pytestmark = pytest.mark.skipif( + SKIP_HF, reason="sentence-transformers not supported on Python 3.14+" +) + + +def get_base_path(): + return pathlib.Path(__file__).parent.resolve() + + +@pytest.fixture +def model_tiers(): + """Define model tiers for testing.""" + return [ + ModelTier( + name="simple", + model="openai/gpt-4.1-nano", + references=[ + "hello", + "hi there", + "what time is it?", + "thanks", + "goodbye", + ], + metadata={ + "provider": "openai", + "cost_per_1k_input": 0.0001, + "cost_per_1k_output": 0.0004, + }, + distance_threshold=0.5, + ), + ModelTier( + name="reasoning", + model="anthropic/claude-sonnet-4-5", + references=[ + "analyze this code for bugs", + "explain how neural networks learn", + "write a detailed blog post about", + "compare and contrast these approaches", + "debug this issue step by step", + ], + metadata={ + "provider": "anthropic", + "cost_per_1k_input": 0.003, + "cost_per_1k_output": 0.015, + }, + distance_threshold=0.6, + ), + ModelTier( + name="expert", + model="anthropic/claude-opus-4-5", + references=[ + "prove this mathematical theorem", + "architect a distributed system for millions of users", + "write a research paper analyzing", + "review this legal contract for issues", + "design a novel algorithm for", + ], + metadata={ + "provider": "anthropic", + "cost_per_1k_input": 0.005, + "cost_per_1k_output": 0.025, + }, + distance_threshold=0.7, + ), + ] + + +@pytest.fixture +def llm_router(client, model_tiers, hf_vectorizer): + """Create an LLMRouter for testing.""" + skip_if_no_redisearch(client) + router = LLMRouter( + name=f"test-llm-router-{str(ULID())}", + tiers=model_tiers, + vectorizer=hf_vectorizer, + redis_client=client, + overwrite=True, + ) + yield router + router.delete() + + +class TestLLMRouterInitialization: + """Test LLMRouter initialization and configuration.""" + + def test_initialize_router(self, llm_router): + """Router should initialize with tiers.""" + assert llm_router.name.startswith("test-llm-router-") + assert len(llm_router.tiers) == 3 + assert llm_router.tier_names == ["simple", "reasoning", "expert"] + + def test_router_properties(self, llm_router): + """Router should expose tier properties.""" + tier_names = llm_router.tier_names + assert "simple" in tier_names + assert "reasoning" in tier_names + assert "expert" in tier_names + + # Check tier thresholds + thresholds = llm_router.tier_thresholds + assert thresholds["simple"] == 0.5 + assert thresholds["reasoning"] == 0.6 + assert thresholds["expert"] == 0.7 + + def test_get_tier(self, llm_router): + """Should retrieve tier by name.""" + tier = llm_router.get_tier("simple") + assert tier is not None + assert tier.name == "simple" + assert tier.model == "openai/gpt-4.1-nano" + + def test_get_nonexistent_tier(self, llm_router): + """Should return None for nonexistent tier.""" + tier = llm_router.get_tier("nonexistent") + assert tier is None + + +class TestLLMRouterRouting: + """Test LLMRouter routing behavior.""" + + def test_route_simple_query(self, llm_router): + """Simple greetings should route to simple tier.""" + skip_if_redis_version_below(llm_router._index.client, "7.0.0") + + match = llm_router.route("hello, how are you?") + assert isinstance(match, LLMRouteMatch) + assert match.tier == "simple" + assert match.model == "openai/gpt-4.1-nano" + assert match.distance is not None + assert match.distance <= 0.5 + + def test_route_reasoning_query(self, llm_router): + """Complex analysis should route to reasoning tier.""" + skip_if_redis_version_below(llm_router._index.client, "7.0.0") + + match = llm_router.route("analyze this code and find potential bugs") + assert match.tier == "reasoning" + assert match.model == "anthropic/claude-sonnet-4-5" + + def test_route_expert_query(self, llm_router): + """Research-level queries should route to expert tier.""" + skip_if_redis_version_below(llm_router._index.client, "7.0.0") + + match = llm_router.route("design a novel distributed consensus algorithm") + assert match.tier == "expert" + assert match.model == "anthropic/claude-opus-4-5" + + def test_route_no_match(self, llm_router): + """Unrelated queries should return default tier or no match.""" + skip_if_redis_version_below(llm_router._index.client, "7.0.0") + + match = llm_router.route("xyzzy plugh random gibberish 12345") + # Should return None or default tier depending on config + assert match.tier is None or match.tier == llm_router.default_tier + + def test_route_with_vector(self, llm_router): + """Should accept pre-computed vector.""" + skip_if_redis_version_below(llm_router._index.client, "7.0.0") + + vector = llm_router.vectorizer.embed("hello") + match = llm_router.route(vector=vector) + assert match.tier == "simple" + + def test_route_many(self, llm_router): + """Should return multiple tier matches.""" + skip_if_redis_version_below(llm_router._index.client, "7.0.0") + + matches = llm_router.route_many("explain machine learning concepts", max_k=3) + assert len(matches) > 0 + assert all(isinstance(m, LLMRouteMatch) for m in matches) + + +class TestLLMRouterCostOptimization: + """Test cost-aware routing behavior.""" + + def test_cost_optimization_prefers_cheaper( + self, client, model_tiers, hf_vectorizer + ): + """With cost optimization, should prefer cheaper tiers when close.""" + skip_if_no_redisearch(client) + skip_if_redis_version_below(client, "7.0.0") + + router = LLMRouter( + name=f"test-cost-router-{str(ULID())}", + tiers=model_tiers, + vectorizer=hf_vectorizer, + redis_client=client, + cost_optimization=True, + overwrite=True, + ) + + try: + # Query that closely matches simple tier references + match = router.route("hello there, how are you?") + # With cost optimization enabled, should match a tier + # The exact tier depends on semantic similarity + assert match.tier is not None or router.default_tier is not None + finally: + router.delete() + + +class TestLLMRouterSerialization: + """Test LLMRouter serialization and persistence.""" + + def test_to_dict(self, llm_router): + """Should serialize to dictionary.""" + router_dict = llm_router.to_dict() + assert router_dict["name"] == llm_router.name + assert len(router_dict["tiers"]) == 3 + assert router_dict["vectorizer"]["type"] == llm_router.vectorizer.type + + def test_from_dict(self, llm_router): + """Should deserialize from dictionary.""" + router_dict = llm_router.to_dict() + new_router = LLMRouter.from_dict( + router_dict, + redis_client=llm_router._index.client, + overwrite=True, + ) + try: + assert new_router.name == llm_router.name + assert len(new_router.tiers) == len(llm_router.tiers) + finally: + # Don't delete - same index as original + pass + + def test_to_yaml(self, llm_router, tmp_path): + """Should serialize to YAML file.""" + yaml_file = tmp_path / "llm_router.yaml" + llm_router.to_yaml(str(yaml_file)) + assert yaml_file.exists() + + def test_from_yaml(self, llm_router, tmp_path): + """Should deserialize from YAML file.""" + yaml_file = tmp_path / "llm_router.yaml" + llm_router.to_yaml(str(yaml_file)) + + new_router = LLMRouter.from_yaml( + str(yaml_file), + redis_client=llm_router._index.client, + overwrite=True, + ) + assert new_router.name == llm_router.name + + +class TestLLMRouterWithEmbeddings: + """Test LLMRouter with pre-computed embeddings.""" + + def test_export_with_embeddings(self, llm_router, tmp_path): + """Should export router with pre-computed embeddings.""" + json_file = tmp_path / "router_with_embeddings.json" + llm_router.export_with_embeddings(str(json_file)) + + assert json_file.exists() + + import json + + with open(json_file) as f: + data = json.load(f) + + # Verify embeddings are included + assert "tiers" in data + for tier in data["tiers"]: + assert "references" in tier + for ref in tier["references"]: + assert "text" in ref + assert "vector" in ref + assert isinstance(ref["vector"], list) + assert len(ref["vector"]) > 0 + + def test_import_with_embeddings(self, client, hf_vectorizer, tmp_path): + """Should import router without re-embedding.""" + skip_if_no_redisearch(client) + skip_if_redis_version_below(client, "7.0.0") + + # Create a simple router and export + tier = ModelTier( + name="test", + model="test/model", + references=["hello", "world"], + distance_threshold=0.5, + ) + router1 = LLMRouter( + name=f"export-test-{str(ULID())}", + tiers=[tier], + vectorizer=hf_vectorizer, + redis_client=client, + overwrite=True, + ) + + json_file = tmp_path / "export_test.json" + router1.export_with_embeddings(str(json_file)) + router1.delete() + + # Import - should not call vectorizer.embed() + router2 = LLMRouter.from_pretrained( + str(json_file), + redis_client=client, + ) + + try: + assert len(router2.tiers) == 1 + assert router2.tiers[0].name == "test" + + # Verify routing works + match = router2.route("hello there") + assert match.tier == "test" + finally: + router2.delete() + + +class TestLLMRouterTierManagement: + """Test adding/removing tiers.""" + + def test_add_tier(self, llm_router): + """Should add new tier.""" + skip_if_redis_version_below(llm_router._index.client, "7.0.0") + + new_tier = ModelTier( + name="local", + model="ollama/llama3.2", + references=["ok", "yes", "no"], + metadata={"cost_per_1k_input": 0}, + distance_threshold=0.3, + ) + llm_router.add_tier(new_tier) + + assert llm_router.get_tier("local") is not None + assert "local" in llm_router.tier_names + + def test_remove_tier(self, llm_router): + """Should remove tier.""" + llm_router.remove_tier("simple") + + assert llm_router.get_tier("simple") is None + assert "simple" not in llm_router.tier_names + + def test_add_tier_references(self, llm_router): + """Should add references to existing tier.""" + skip_if_redis_version_below(llm_router._index.client, "7.0.0") + + # First verify existing references still route correctly + match_before = llm_router.route("hi there") + assert match_before.tier == "simple" + + # Add new references + llm_router.add_tier_references( + tier_name="simple", references=["howdy", "greetings friend"] + ) + + # Verify references were added to the tier object + tier = llm_router.get_tier("simple") + assert "howdy" in tier.references + assert "greetings friend" in tier.references + + def test_update_tier_threshold(self, llm_router): + """Should update tier threshold.""" + llm_router.update_tier_threshold("simple", 0.3) + + assert llm_router.tier_thresholds["simple"] == 0.3 + + +class TestLLMRouterFromExisting: + """Test reconnecting to existing router.""" + + def test_from_existing(self, client, model_tiers, hf_vectorizer, redis_url): + """Should reconnect to existing router.""" + skip_if_no_redisearch(client) + skip_if_redis_version_below(client, "7.0.0") + + router_name = f"persist-test-{str(ULID())}" + + # Create router + router1 = LLMRouter( + name=router_name, + tiers=model_tiers, + vectorizer=hf_vectorizer, + redis_url=redis_url, + overwrite=True, + ) + + # Reconnect + router2 = LLMRouter.from_existing( + name=router_name, + redis_url=redis_url, + ) + + try: + assert router2.name == router1.name + assert len(router2.tiers) == len(router1.tiers) + assert router2.tier_names == router1.tier_names + + # Verify routing works + match = router2.route("hello") + assert match.tier is not None + finally: + router1.delete() + + +class TestLLMRouterPretrained: + """Test LLMRouter with pretrained configurations.""" + + def test_from_pretrained_default(self, client): + """Should load the built-in default pretrained config.""" + skip_if_no_redisearch(client) + + router = LLMRouter.from_pretrained("default", redis_client=client) + try: + assert len(router.tiers) == 3 + assert set(router.tier_names) == {"simple", "standard", "expert"} + finally: + router.delete() + + def test_pretrained_routes_simple(self, client): + """Simple greetings should route to simple tier.""" + skip_if_no_redisearch(client) + skip_if_redis_version_below(client, "7.0.0") + + router = LLMRouter.from_pretrained("default", redis_client=client) + try: + match = router.route("hi, how are you doing?") + assert match.tier == "simple" + assert match.model == "openai/gpt-4.1-nano" + finally: + router.delete() + + def test_pretrained_routes_standard(self, client): + """Moderate analysis should route to standard tier.""" + skip_if_no_redisearch(client) + skip_if_redis_version_below(client, "7.0.0") + + router = LLMRouter.from_pretrained("default", redis_client=client) + try: + match = router.route("explain how garbage collection works") + assert match.tier == "standard" + assert match.model == "anthropic/claude-sonnet-4-5" + finally: + router.delete() + + def test_pretrained_routes_expert(self, client): + """Complex research queries should route to expert tier.""" + skip_if_no_redisearch(client) + skip_if_redis_version_below(client, "7.0.0") + + router = LLMRouter.from_pretrained("default", redis_client=client) + try: + match = router.route("architect a fault-tolerant distributed system") + assert match.tier == "expert" + assert match.model == "anthropic/claude-opus-4-5" + finally: + router.delete() + + def test_pretrained_route_many(self, client): + """Should return multiple matches from pretrained config.""" + skip_if_no_redisearch(client) + skip_if_redis_version_below(client, "7.0.0") + + router = LLMRouter.from_pretrained("default", redis_client=client) + try: + matches = router.route_many("explain machine learning concepts", max_k=3) + assert len(matches) > 0 + assert all(isinstance(m, LLMRouteMatch) for m in matches) + finally: + router.delete() + + def test_pretrained_invalid_name(self, client): + """Should raise error for unknown pretrained config name.""" + with pytest.raises(ValueError, match="not found"): + LLMRouter.from_pretrained("nonexistent_config", redis_client=client) diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py new file mode 100644 index 00000000..e64f51f7 --- /dev/null +++ b/tests/unit/conftest.py @@ -0,0 +1,27 @@ +""" +Conftest for unit tests - overrides Docker fixtures to allow tests to run +without Docker/Redis infrastructure. + +Unit tests should test pure logic without external dependencies. +""" + +import pytest + + +@pytest.fixture(scope="session", autouse=True) +def redis_container(): + """ + No-op fixture that overrides the session-scoped redis_container fixture + from the root conftest.py. This allows unit tests to run without Docker. + """ + yield None + + +@pytest.fixture(scope="session") +def redis_url(): + """ + Dummy redis_url fixture for unit tests. + Unit tests should not depend on this - if they do, they should be + integration tests instead. + """ + return "redis://localhost:6379" # Not actually used diff --git a/tests/unit/test_llm_router_schema.py b/tests/unit/test_llm_router_schema.py new file mode 100644 index 00000000..4e24f9dd --- /dev/null +++ b/tests/unit/test_llm_router_schema.py @@ -0,0 +1,250 @@ +"""Unit tests for LLM Router schema.""" + +import pytest +from pydantic import ValidationError + +from redisvl.extensions.llm_router.schema import ( + DistanceAggregationMethod, + LLMRouteMatch, + ModelTier, + PretrainedReference, + PretrainedRouterConfig, + PretrainedTier, + RoutingConfig, +) + + +class TestModelTier: + """Tests for ModelTier schema.""" + + def test_valid_tier(self): + """Should create valid tier.""" + tier = ModelTier( + name="simple", + model="openai/gpt-4.1-nano", + references=["hello", "hi"], + distance_threshold=0.5, + ) + assert tier.name == "simple" + assert tier.model == "openai/gpt-4.1-nano" + assert tier.references == ["hello", "hi"] + assert tier.distance_threshold == 0.5 + + def test_tier_with_metadata(self): + """Should accept metadata.""" + tier = ModelTier( + name="simple", + model="test/model", + references=["hello"], + metadata={ + "cost_per_1k_input": 0.0001, + "capabilities": ["chat"], + }, + ) + assert tier.metadata["cost_per_1k_input"] == 0.0001 + assert "chat" in tier.metadata["capabilities"] + + def test_empty_name_fails(self): + """Should reject empty name.""" + with pytest.raises(ValidationError): + ModelTier( + name="", + model="test/model", + references=["hello"], + ) + + def test_empty_model_fails(self): + """Should reject empty model.""" + with pytest.raises(ValidationError): + ModelTier( + name="test", + model="", + references=["hello"], + ) + + def test_empty_references_fails(self): + """Should reject empty references.""" + with pytest.raises(ValidationError): + ModelTier( + name="test", + model="test/model", + references=[], + ) + + def test_whitespace_reference_fails(self): + """Should reject whitespace-only references.""" + with pytest.raises(ValidationError): + ModelTier( + name="test", + model="test/model", + references=["hello", " "], + ) + + def test_threshold_bounds(self): + """Should validate threshold bounds (0, 2].""" + # Valid thresholds + ModelTier(name="t", model="m", references=["r"], distance_threshold=0.1) + ModelTier(name="t", model="m", references=["r"], distance_threshold=2.0) + + # Invalid: <= 0 + with pytest.raises(ValidationError): + ModelTier(name="t", model="m", references=["r"], distance_threshold=0) + + with pytest.raises(ValidationError): + ModelTier(name="t", model="m", references=["r"], distance_threshold=-0.1) + + # Invalid: > 2 + with pytest.raises(ValidationError): + ModelTier(name="t", model="m", references=["r"], distance_threshold=2.1) + + +class TestLLMRouteMatch: + """Tests for LLMRouteMatch schema.""" + + def test_empty_match(self): + """Empty match should be falsy.""" + match = LLMRouteMatch() + assert not match + assert match.tier is None + assert match.model is None + + def test_valid_match(self): + """Valid match should be truthy.""" + match = LLMRouteMatch( + tier="simple", + model="test/model", + distance=0.3, + confidence=0.85, + ) + assert match + assert match.tier == "simple" + assert match.confidence == 0.85 + + def test_match_with_alternatives(self): + """Should store alternative matches.""" + match = LLMRouteMatch( + tier="simple", + model="test/model", + distance=0.3, + alternatives=[("reasoning", 0.5), ("expert", 0.7)], + ) + assert len(match.alternatives) == 2 + assert match.alternatives[0] == ("reasoning", 0.5) + + def test_match_with_metadata(self): + """Should store tier metadata.""" + match = LLMRouteMatch( + tier="simple", + model="test/model", + metadata={"cost_per_1k_input": 0.0001}, + ) + assert match.metadata["cost_per_1k_input"] == 0.0001 + + +class TestRoutingConfig: + """Tests for RoutingConfig schema.""" + + def test_defaults(self): + """Should have sensible defaults.""" + config = RoutingConfig() + assert config.max_k == 1 + assert config.aggregation_method == DistanceAggregationMethod.avg + assert config.cost_optimization is False + assert config.cost_weight == 0.1 + assert config.default_tier is None + + def test_custom_config(self): + """Should accept custom values.""" + config = RoutingConfig( + max_k=3, + aggregation_method=DistanceAggregationMethod.min, + cost_optimization=True, + cost_weight=0.5, + default_tier="simple", + ) + assert config.max_k == 3 + assert config.aggregation_method == DistanceAggregationMethod.min + assert config.cost_optimization is True + assert config.default_tier == "simple" + + def test_cost_weight_bounds(self): + """Cost weight should be 0-1.""" + RoutingConfig(cost_weight=0) + RoutingConfig(cost_weight=1) + + with pytest.raises(ValidationError): + RoutingConfig(cost_weight=-0.1) + + with pytest.raises(ValidationError): + RoutingConfig(cost_weight=1.1) + + def test_max_k_positive(self): + """max_k should be positive.""" + with pytest.raises(ValidationError): + RoutingConfig(max_k=0) + + with pytest.raises(ValidationError): + RoutingConfig(max_k=-1) + + +class TestPretrainedSchemas: + """Tests for pretrained configuration schemas.""" + + def test_pretrained_reference(self): + """Should store text and vector.""" + ref = PretrainedReference( + text="hello", + vector=[0.1, 0.2, 0.3], + ) + assert ref.text == "hello" + assert ref.vector == [0.1, 0.2, 0.3] + + def test_pretrained_tier(self): + """Should store tier with embedded references.""" + tier = PretrainedTier( + name="simple", + model="test/model", + references=[ + PretrainedReference(text="hello", vector=[0.1, 0.2]), + PretrainedReference(text="hi", vector=[0.3, 0.4]), + ], + distance_threshold=0.5, + ) + assert tier.name == "simple" + assert len(tier.references) == 2 + assert tier.references[0].text == "hello" + + def test_pretrained_router_config(self): + """Should store complete pretrained config.""" + config = PretrainedRouterConfig( + name="test-router", + version="1.0.0", + vectorizer={"type": "hf", "model": "test-model"}, + tiers=[ + PretrainedTier( + name="simple", + model="test/model", + references=[ + PretrainedReference(text="hello", vector=[0.1]), + ], + ) + ], + ) + assert config.name == "test-router" + assert config.version == "1.0.0" + assert len(config.tiers) == 1 + + +class TestDistanceAggregationMethod: + """Tests for aggregation method enum.""" + + def test_values(self): + """Should have expected values.""" + assert DistanceAggregationMethod.avg.value == "avg" + assert DistanceAggregationMethod.min.value == "min" + assert DistanceAggregationMethod.sum.value == "sum" + + def test_from_string(self): + """Should parse from string.""" + assert DistanceAggregationMethod("avg") == DistanceAggregationMethod.avg + assert DistanceAggregationMethod("min") == DistanceAggregationMethod.min