# RedisVL

```json metadata
{
  "title": "RedisVL",
  "description": "This is the Redis vector library (RedisVL).",
  "categories": ["docs","integrate","stack","oss","rs","rc","oss","clients"],
  "group": "library",
  "tableOfContents": {"sections":[{"id":"overview","title":"Overview"},{"id":"key-features","title":"Key Features"},{"id":"getting-started","title":"Getting Started"}]}

,
  "codeExamples": []
}
```
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.

## Overview

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.

## Key Features

- **Vector Search**: High-performance similarity search with multiple distance metrics
- **Semantic Caching**: Intelligent caching for AI model responses and embeddings
- **Schema Management**: Declarative schema definition for vector indexes
- **Multiple Vectorizers**: Built-in support for OpenAI, Hugging Face, and custom embeddings
- **Query Filtering**: Advanced filtering capabilities for precise search results
- **Real-time Updates**: Live vector index updates and real-time search
- **Python Integration**: Native Python API with pandas and NumPy compatibility
- **Production Ready**: Enterprise-grade performance and reliability with Redis

## Getting Started

Refer to the complete [RedisVL documentation](https://redis.io/docs/latest/develop/ai/redisvl/) for installation, setup, and usage examples.