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 for installation, setup, and usage examples.