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REDISVL
AI-native vector search for Redis — embeddings, hybrid queries, and RAG
MIT
ABOUT
Teams already using Redis for caching or session storage often need vector search for RAG pipelines and semantic search, but implementing vector indexing on top of raw Redis requires low-level knowledge of the Redisearch module and manual embedding management. RedisVL wraps all of that into a clean Python client with automatic index creation, built-in embedding providers (OpenAI, HuggingFace, Cohere), and hybrid vector + full-text queries.
INSTALL
pip install redisvlINTEGRATION GUIDE
1. Add semantic search to existing Redis-backed applications without migrating to a separate vector database
2. Build RAG pipelines that combine vector similarity search with full-text metadata filtering in a single query
3. Index and search code embeddings for natural language code retrieval across large codebases
4. Power real-time recommendation systems that combine collaborative filtering with semantic embeddings
5. Store and query session-based embeddings for conversational AI with automatic index management
TAGS
vector-searchredisembeddingssemantic-searchraghybrid-searchpython