Announcing LangChain Templates for MongoDB Atlas

Since announcing the public preview of MongoDB Atlas Vector Search back in June, we’ve seen tremendous adoption by developers working to build AI-powered applications. The ability to store, index, and query vector embeddings right alongside their operational data in a single, unified platform dramatically boosts engineering velocity while keeping their technology footprint streamlined and efficient.

Atlas Vector Search is used by developers as a key part of the Retrieval-Augmented Generation (RAG) pattern. RAG is used to feed LLMs with the additional data they need to ground their responses, providing outputs that are reliable, relevant, and accurate for the business. One of the key enabling technologies being used to bring external data into LLMs is LangChain. Just one example is healthcare innovator Inovaare who is building AI with MongoDB and LangChain for document classification, information extraction and enrichment, and chatbots over medical data.

Now making it even easier for developers to build AI-powered apps, we are excited to announce our partnership with LangChain in the launch of LangChain Templates!

We have worked with LangChain to create a RAG template using MongoDB Atlas Vector Search and OpenAI. This easy-to-use template can help developers build and deploy a Chatbot application over their own proprietary data. LangChain Templates offer a reference architecture that’s easily deployable as a REST API using LangServe.

We have also been working with LangChain to release the latest features of Atlas Vector Search, like the recently announced dedicated vector search aggregation stage $vectorSearch, to both the MongoDB LangChain python integration as well as the MongoDB LangChain Javascript integration. Similarly, we will continue working with LangChain to create more templates, that will allow developers to bring their ideas to production faster.