Redis for AI documentation

An overview of Redis for AI documentation

Redis stores and indexes vector embeddings that semantically represent unstructured data including text passages, images, videos, or audio. Store vectors and the associated metadata within hashes or JSON documents for indexing and querying.

Vector	RAG	RedisVL
Redis vector database quick start guide	Retrieval-Augmented Generation quick start guide	Redis vector Python client library documentation

Overview

1. Create a vector index: Redis maintains a secondary index over your data with a defined schema (including vector fields and metadata). Redis supports FLAT and HNSW vector index types.
2. Store and update vectors: Redis stores vectors and metadata in hashes or JSON objects.
3. Search with vectors: Redis supports several advanced querying strategies with vector fields including k-nearest neighbor (KNN), vector range queries, and metadata filters.
4. Configure vector queries at runtime. Select the best filter mode to optimize query execution.

Concepts

Learn to perform vector search and use gateways and semantic caching in your AI/ML projects.

Search	AI Gateways	Semantic Caching
Vector search guide	Deploy an enhanced gateway with Redis	Semantic caching for faster, smarter LLM apps

Ecosystem integrations

• Amazon Bedrock setup guide
• LangChain Redis Package: Smarter AI apps with advanced vector storage and faster caching)
• Redis Cloud available on Vercel
• Create a Redis Cloud database with the Vercel integration
• Building a RAG application with Redis and Spring AI
• Deploy GenAI apps faster with Redis and NVIDIA NIM
• Building LLM Applications with Kernel Memory and Redis

Examples

Get started with the following Redis Python notebooks.

• The place to start if you are brand new to Redis

Hybrid and vector search

• Implementing hybrid search with Redis
• Vector search with Redis Python client
• Vector search with Redis Vector Library

RAG

• RAG from scratch with the Redis Vector Library
• RAG using Redis and LangChain
• RAG using Redis and LlamaIndex
• Advanced RAG with redisvl
• RAG using Redis and Nvidia
• Utilize RAGAS framework to evaluate RAG performance
• Notebook for additional tips and techniques to improve RAG quality

LLM session management

• LLM session manager with semantic similarity
• Handle multiple simultaneous chats with one instance

Semantic caching

• Build a semantic cache using the Doc2Cache framework and Llama3.1
• Build a semantic cache with Redis and Google Gemini

Agents

• Notebook to get started with lang-graph and agents
• Notebook to get started with lang-graph and agents

Recommendation systems

• Intro content filtering example with redisvl
• Intro collaborative filtering example with redisvl