{
  "schema_version": 2,
  "id": "integrate/redis-ai-libraries",
  "title": "Redis for AI",
  "url": "https://redis.io/docs/latest/integrate/redis-ai-libraries/",
  "summary": "Build AI applications with Redis vector database and semantic caching",
  "tags": [
    "docs",
    "integrate",
    "oss",
    "rs",
    "rc"
  ],
  "last_updated": "2026-09-17T14:55:03+01:00",
  "children": [],
  "page_type": "content",
  "content_hash": "8af0c9b7302a6d3e8d5cbfda7b3208dc333ff3151a17cc6141c43a4423da2841",
  "sections": [
    {
      "id": "overview-1",
      "title": "Overview",
      "role": "overview",
      "text": "Build powerful AI applications using Redis as your vector database with specialized libraries for Python, JavaScript, and Java."
    },
    {
      "id": "overview",
      "title": "Overview",
      "role": "overview",
      "text": "Redis provides comprehensive AI libraries and tools to help you build intelligent applications with vector search, retrieval-augmented generation (RAG), semantic caching, and more. Whether you're working with LangChain, LlamaIndex, or building custom AI solutions, Redis has the tools you need.\n\n[Explore the complete Redis for AI documentation](https://redis.io/docs/latest/develop/ai)"
    },
    {
      "id": "key-features",
      "title": "Key Features",
      "role": "content",
      "text": "- **Vector Search**: Store and query vector embeddings with HNSW and FLAT index types\n- **Semantic Caching**: Cache LLM responses to reduce costs and improve performance\n- **RAG Support**: Build retrieval-augmented generation applications with popular frameworks\n- **Multi-language Support**: Libraries available for Python, JavaScript, and Java\n- **Real-time Performance**: Sub-millisecond query latency for production AI applications"
    },
    {
      "id": "ai-libraries",
      "title": "AI Libraries",
      "role": "content",
      "text": ""
    },
    {
      "id": "redisvl-python",
      "title": "RedisVL (Python)",
      "role": "content",
      "text": "The Redis Vector Library (RedisVL) is a Python client library for building AI applications with Redis.\n\n- **Vector Search**: Create and query vector indexes with ease\n- **Semantic Caching**: Built-in LLM cache for faster responses\n- **RAG Utilities**: Tools for building retrieval-augmented generation apps\n- **Framework Integration**: Works with LangChain, LlamaIndex, and more\n\n[Learn more about RedisVL](https://redis.io/docs/latest/develop/ai/redisvl)"
    },
    {
      "id": "langchain-integration",
      "title": "LangChain Integration",
      "role": "content",
      "text": "Use Redis with LangChain for vector stores, semantic caching, and chat message history.\n\n- **Vector Store**: Store and retrieve embeddings for RAG applications\n- **Semantic Cache**: Cache LLM responses based on semantic similarity\n- **Chat History**: Persist conversation history for AI agents\n\n[Learn more about LangChain integration](https://redis.io/docs/latest/integrate/langchain-redis)"
    },
    {
      "id": "client-libraries-with-vector-search",
      "title": "Client Libraries with Vector Search",
      "role": "content",
      "text": "All major Redis client libraries support vector search operations:\n\n- **redis-py (Python)**: [Vector search guide](https://redis.io/docs/latest/develop/clients/redis-py/vecsearch)\n- **node-redis (JavaScript)**: [Vector search guide](https://redis.io/docs/latest/develop/clients/nodejs/vecsearch)\n- **Jedis (Java)**: [Vector search guide](https://redis.io/docs/latest/develop/clients/jedis/vecsearch)\n- **NRedisStack (C#/.NET)**: [Vector search guide](https://redis.io/docs/latest/develop/clients/dotnet/nredisstack/vecsearch)\n- **go-redis (Go)**: [Vector search guide](https://redis.io/docs/latest/develop/clients/go/vecsearch)"
    },
    {
      "id": "getting-started",
      "title": "Getting Started",
      "role": "setup",
      "text": ""
    },
    {
      "id": "quick-start-guides",
      "title": "Quick Start Guides",
      "role": "content",
      "text": "- [Redis vector database quick start](https://redis.io/docs/latest/develop/get-started/search-tutorial/vector-search)\n- [RAG quick start guide](https://redis.io/docs/latest/develop/get-started/rag)"
    },
    {
      "id": "tutorials-and-examples",
      "title": "Tutorials and Examples",
      "role": "content",
      "text": "Explore our [AI notebooks collection](https://redis.io/docs/latest/develop/ai/notebook-collection) with examples for:\n\n- RAG implementations with RedisVL, LangChain, and LlamaIndex\n- Advanced RAG techniques and optimizations\n- Integration with cloud platforms like Azure and Vertex AI"
    },
    {
      "id": "video-tutorials",
      "title": "Video Tutorials",
      "role": "content",
      "text": "Watch our [AI video collection](https://redis.io/docs/latest/develop/ai/ai-videos) for practical demonstrations."
    },
    {
      "id": "use-cases",
      "title": "Use Cases",
      "role": "content",
      "text": "- **Retrieval-Augmented Generation (RAG)**: Enhance LLM responses with relevant context\n- **Semantic Search**: Find similar documents, images, or products\n- **Recommendation Systems**: Build real-time personalized recommendations\n- **AI Agents**: Create autonomous agents with memory and tool use\n- **Chatbots**: Build conversational AI with context and history"
    },
    {
      "id": "additional-resources",
      "title": "Additional Resources",
      "role": "content",
      "text": "- [Complete AI documentation](https://redis.io/docs/latest/develop/ai)\n- [Ecosystem integrations](https://redis.io/docs/latest/develop/ai/ecosystem-integrations)\n- [Vector search benchmarks](https://redis.io/blog/benchmarking-results-for-vector-databases/)\n- [RAG best practices](https://redis.io/blog/get-better-rag-responses-with-ragas/)"
    }
  ],
  "examples": []
}