{
  "schema_version": 2,
  "id": "integrate/redisvl",
  "title": "RedisVL",
  "url": "https://redis.io/docs/latest/integrate/redisvl/",
  "summary": "This is the Redis vector library (RedisVL).",
  "aliases": [
    "/integrate/redisvl/api",
    "/integrate/redisvl/api/cache",
    "/integrate/redisvl/api/filter",
    "/integrate/redisvl/api/query",
    "/integrate/redisvl/api/schema",
    "/integrate/redisvl/api/searchindex",
    "/integrate/redisvl/api/vectorizer",
    "/integrate/redisvl/overview",
    "/integrate/redisvl/overview/cli",
    "/integrate/redisvl/user-guide",
    "/integrate/redisvl/user-guide/get-started",
    "/integrate/redisvl/user-guide/json-v-hashes",
    "/integrate/redisvl/user-guide/query-filter",
    "/integrate/redisvl/user-guide/semantic-caching",
    "/integrate/redisvl/user-guide/vectorizers"
  ],
  "tags": [
    "docs",
    "integrate",
    "stack",
    "oss",
    "rs",
    "rc",
    "oss",
    "clients"
  ],
  "last_updated": "2026-09-17T14:55:03+01:00",
  "children": [],
  "page_type": "content",
  "content_hash": "9ea80602a605280c11294de435e57e9a375c83b99804aa3fea5e3a2107e8e56a",
  "sections": [
    {
      "id": "overview-1",
      "title": "Overview",
      "role": "overview",
      "text": "RedisVL provides a powerful, dedicated Python client library for using Redis as a vector database. Leverage Redis's speed, reliability, and vector-based semantic search capabilities to supercharge your application."
    },
    {
      "id": "overview",
      "title": "Overview",
      "role": "overview",
      "text": "RedisVL (Redis Vector Library) is a Python client library specifically designed for building AI applications with Redis as a vector database. It provides high-level abstractions for vector search, semantic caching, and AI-powered applications while leveraging Redis's performance and reliability."
    },
    {
      "id": "key-features",
      "title": "Key Features",
      "role": "content",
      "text": "- **Vector Search**: High-performance similarity search with multiple distance metrics\n- **Semantic Caching**: Intelligent caching for AI model responses and embeddings\n- **Schema Management**: Declarative schema definition for vector indexes\n- **Multiple Vectorizers**: Built-in support for OpenAI, Hugging Face, and custom embeddings\n- **Query Filtering**: Advanced filtering capabilities for precise search results\n- **Real-time Updates**: Live vector index updates and real-time search\n- **Python Integration**: Native Python API with pandas and NumPy compatibility\n- **Production Ready**: Enterprise-grade performance and reliability with Redis"
    },
    {
      "id": "getting-started",
      "title": "Getting Started",
      "role": "setup",
      "text": "Refer to the complete [RedisVL documentation](https://redis.io/docs/latest/develop/ai/redisvl) for installation, setup, and usage examples."
    }
  ],
  "examples": []
}