Semantic Caching for LLMs: FastAPI, Redis, and Embeddings

The architecture and request flow described in the lesson outline a multi-layer caching strategy for an LLM-backed service, aiming to minimize latency and unnecessary model calls. Here's a detailed breakdown of how this system operates:

High-Level System Components

1. API Layer (FastAPI): Acts as the entry point for user requests, orchestrating the caching pipeline.
2. Exact-Match Cache: Uses Redis to perform fast hash-based lookups for identical queries.
3. Embedding Model (Ollama): Converts text queries into semantic vectors when needed.
4. Semantic Cache: Also uses Redis but stores embeddings and responses, performing similarity matching.
5. LLM (Ollama): Serves as the final fallback to generate a response if no suitable cache entry exists.

End-to-End Request Flow

Step 1: Request enters the API

• The FastAPI-based API receives a text query along with optional flags like bypass_cache.
• Input validation ensures that only meaningful queries proceed, preventing invalid entries from polluting the cache.

Step 2: Exact-Match Cache Lookup

• The incoming query is normalized and hashed.
• Redis checks if an identical query has been answered before

Read the full article at Blog - PyImageSearch

---

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.