A step-by-step guide to building a semantic cache for LLM applications using FastAPI, Redis, and embedding-based similarity search. The system uses a layered caching strategy: exact-match hash lookup first, then cosine similarity comparison of embeddings, and finally LLM fallback on cache miss. Key components include an Ollama-powered embedder, a Redis-backed SemanticCache class with linear scan, a Pydantic cache entry schema, TTL and poisoning checks, and a FastAPI /ask endpoint that orchestrates the full pipeline. The tutorial includes a working demo with curl examples showing cold requests, exact-match hits, semantic hits on paraphrased queries, and cache bypass for debugging.
Table of contents
Semantic Caching for LLMs: FastAPI, Redis, and EmbeddingsIntroduction: Why Semantic Caching Matters for LLM SystemsHow Semantic Caching Works for LLMs: Embeddings and Similarity Search ExplainedSemantic Caching Architecture and Request FlowConfiguring Your Environment for Semantic Caching: FastAPI, Redis, and Ollama SetupProject StructureFastAPI Entry Point for Semantic Caching: Wiring the API ServiceFastAPI Ask Endpoint: End-to-End Semantic Caching Request FlowEmbeddings: Turning Text into Semantic VectorsThe Semantic Cache: Cosine Similarity, Redis Storage, and Reusing MeaningCache Entries: What Exactly Gets Stored?End-to-End Demo: Verifying Core Cache BehaviorSummarySort: