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