Table of Contents

Semantic Caching for LLMs: FastAPI, Redis, and Embeddings

Introduction: Why Semantic Caching Matters for LLM Systems

How Semantic Caching Works for LLMs: Embeddings and Similarity Search Explained

Semantic Caching Architecture and Request Flow

Configuring Your Environment for Semantic Caching: FastAPI, Redis, and Ollama Setup

Project Structure

FastAPI Entry Point for Semantic Caching: Wiring the API Service

FastAPI Ask Endpoint: End-to-End Semantic Caching Request Flow

Embeddings: Turning Text into Semantic Vectors

The Semantic Cache: Cosine Similarity, Redis Storage, and Reusing Meaning

Cache Entries: What Exactly Gets Stored?

End-to-End Demo: Verifying Core Cache Behavior

Summary

In this lesson, you will learn how to build a semantic cache for LLM applications using FastAPI, Redis, and embedding-based similarity search, and how requests flow from exact matches to semantic matches before falling back to the LLM.

This lesson is the 1st in a 2-part series on Semantic Caching…