Caching LLMs Response With MongoDB Atlas and Vector Search

Caching LLMs Response With MongoDB Atlas and Vector Search

Discover how to reduce API costs and improve response times for Large Language Models (LLMs) by implementing semantic caching using MongoDB Atlas and Vector Search. Learn to efficiently handle LLM queries by storing and retrieving embeddings—numerical vectors representing the semantic meaning of text—reducing the need for repeated API calls. This guide covers setting up a FastAPI server, integrating OpenAI, embedding LLM responses, and utilizing MongoDB Atlas’s advanced vector search capabilities. Perfect for developers looking to optimize AI-driven applications, lower operational costs, and enhance scalability.

Read more on Developer Center

Author: Kanin Kearpimy