You can integrate MongoDB Vector Search with Haystack to build custom applications with LLMs and implement retrieval-augmented generation (RAG). This tutorial demonstrates how to start using MongoDB Vector Search with Haystack to perform semantic search on your data and build a RAG implementation. Specifically, you perform the following actions:
Set up the environment.
Create a MongoDB Vector Search index.
Store custom data in MongoDB.
Implement RAG by using MongoDB Vector Search to answer questions on your data.
Work with a runnable version of this tutorial as a Python notebook.
Background
Haystack is a framework for building custom applications with LLMs, embedding models, and vector search. By integrating MongoDB Vector Search with Haystack, you can use MongoDB as a vector database and use MongoDB Vector Search to implement RAG by retrieving semantically similar documents from your data. To learn more about RAG, see Retrieval-Augmented Generation (RAG) with MongoDB.