How-To Guides#
How-to guides are task-oriented recipes that help you accomplish specific goals. Each guide focuses on solving a particular problem and can be completed independently.
Cache LLM Responses – semantic caching to reduce costs and latency
Use LangCache as the LLM cache – managed cache service with LangCache
Manage LLM Message History – persistent chat history with relevancy retrieval
Route Queries with SemanticRouter – classify intents and route queries
Query and Filter Data – combine tag, numeric, geo, and text filters
Use Advanced Query Types – hybrid, multi-vector, range, and text queries
Write SQL Queries for Redis – translate SQL to Redis query syntax
Create Embeddings with Vectorizers – OpenAI, Cohere, HuggingFace, and more
Cache Embeddings – reduce costs by caching embedding vectors
Rerank Search Results – improve relevance with cross-encoders and rerankers
Optimize Indexes with SVS-VAMANA – graph-based vector search with compression
Choose a Storage Type – Hash vs JSON formats and nested data
Manage Indices with the CLI – create, inspect, and delete indices from your terminal
Run RedisVL MCP – expose an existing Redis index to MCP clients
Quick Reference#
I want to… |
Guide |
|---|---|
Cache LLM responses |
|
Use LangCache (managed) for LLM caching |
|
Store chat history |
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Route queries by intent |
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Filter results by multiple criteria |
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Use hybrid or multi-vector queries |
|
Translate SQL to Redis |
|
Choose an embedding model |
|
Speed up embedding generation |
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Improve search accuracy |
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Optimize index performance |
|
Decide on storage format |
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Manage indices from terminal |
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Expose an index through MCP |