Semantic RAG Cache Layer

A drop-in caching middleware for RAG pipelines that reuses retrieval plans for semantically similar queries.

Difficulty: weekend | Stack: Python, FastAPI, LangChain or LlamaIndex, Redis, sentence-transformers, numpy

Who this is for

Backend developers running high-traffic RAG APIs who want to cut retrieval latency and LLM costs without changing their core pipeline.

Build steps

1. Define a retrieval plan schema (query embedding, retrieved chunk IDs, metadata) and a Redis store with TTL for caching plans.
2. Build a semantic similarity lookup: on each incoming query, embed it and do a cosine similarity search against cached query embeddings (store embeddings in a Redis hash or small in-memory FAISS index).
3. If similarity exceeds a configurable threshold (e.g. 0.92), return the cached plan’s chunks directly, bypassing the vector store retrieval.
4. Wrap the cache layer as FastAPI middleware or a LangChain/LlamaIndex callback so it slots into existing pipelines with minimal code changes.
5. Add a metrics endpoint that reports cache hit rate, average latency saved, and threshold distribution — so users can tune the similarity cutoff.

Risks

• Threshold tuning is brittle: too low causes semantic collisions returning wrong chunks; too high negates cache value — there is no universal right answer.
• Cache invalidation is unsolved: if the underlying document store is updated, stale retrieval plans will silently return outdated chunks.
• Embedding drift if the similarity model used at cache-write time differs from the one used at cache-read time after a dependency upgrade.