Use Cases
RedisVL powers a wide range of AI applications. Here's how to apply its features to common use cases.
🧠 Agent Context
Provide agents with the right information at the right time.
- RAG — Retrieval-Augmented Generation with vector search and hybrid queries
- Memory — Persistent message history across sessions
- Context Engineering — Combine filtering, reranking, and embeddings to curate the optimal context window
⚡ Agent Optimization
Reduce latency and cost for AI workloads.
- Semantic Caching — Cache LLM responses by meaning with SemanticCache
- Embeddings Caching — Avoid redundant embedding calls with EmbeddingsCache
- Semantic Routing — Route queries to the right handler with SemanticRouter
🔍 General Search
Build search experiences that understand meaning, not just keywords.
- Semantic Search — Vector queries with complex filtering
- Hybrid Search — Combine keyword and vector search with advanced query types
- SQL Translation — Use familiar SQL syntax with SQLQuery
🎯 Personalization & RecSys
Drive engagement with personalized recommendations.
- User Similarity — Find similar users or items using vector search
- Real-Time Ranking — Combine vector similarity with metadata filtering and reranking
- Multi-Signal Matching — Search across multiple embedding fields with MultiVectorQuery