Basic Semantic Cache
Introducing the basic construction of a semantic cache for LLM systems
As AI applications increasingly rely on large language models (LLMs), the need for performance, cost control, and output consistency has never been greater. That’s where semantic caching enters the picture — and I’m excited to share the basic foundation of a semantic cache, now available as an open-source project on GitHub.
This project is a minimal, functional prototype of a semantic caching layer designed for LLM-based systems. It provides a clean, extensible baseline for understanding and experimenting with how semantic similarity can be used to bypass repeated calls to expensive language models, improving both efficiency and responsiveness.
Core Features
- Embedding-Based Caching: Uses vector similarity to detect semantically similar prompts.
- Pluggable Embedder Support: Bring your own embedding model – supports FastEmbed or can be extended.
- In-Memory or Persistent Cache: Swap between development convenience and production readiness.
- Minimal Dependencies: Lightweight, fast, and easy to integrate into any Python project.
- Full Transparency: Simple logging to understand cache hits vs. model calls.
This isn’t a full-scale production cache yet — it’s the basic construction, deliberately lightweight for learning, rapid prototyping, or serving as a stepping stone for more advanced implementations. Think of it as a semantic caching starter kit.
Whether you’re building agent frameworks, chat systems, or custom API orchestration with LLMs, semantic caching is a powerful design pattern — and this repo gives you a clear, testable starting point.