Document Q&A System with Voyage-Context-3 Embeddings and MongoDB Atlas

On my never-ending quest to find the best embeddings model, I was intrigued to come across Voyage-Context-3 by MongoDB and was excited to give it a try.

This template implements the embedding model on a Arxiv research paper and stores the results in a Vector store. It was only fitting to use Mongo Atlas from the same parent company. This template also includes a RAG-based Q&A agent which taps into the vector store as a test to helps qualify if the embeddings are any good and if this is even noticeable.

How it works This template is split into 2 parts. The first part being the import of a research document which is then chunked and embedded into our vector store. The second part builds a RAG-based Q&A agent to test the vector store retrieval on the research paper.

Read the steps for more details.

How to use First ensure you create a Voyage account voyageai.com and a MongoDB database ready. Start with Step 1 and fill in the "Set Variables" node and Click on the Manual Execute Trigger. This will take care of populating the vector store with the research paper. To use the Q&A agent, it is required to publish the workflow to access the public chat interface. This is because "Respond to Chat" works best in this mode and not in editor mode. To use for your own document, edit the "Set Variables" node to define the URL to your own document. This embeddings approach should work best on larger documents.

Requirements Voyageai.com account for embeddings. You may need to add credit to get a reasonable RPM for this workflow. MongoDB database either self-hosted or online at https://www.mongodb.com. OpenAI account for RAG Q&A agent.

Customising this workflow The Voyage embeddings work with any vector store so feel free to swap out to other such as Qdrant or Pinecone if you're not a fan of MongoDB Atlas. If you're feeling brave, instead of the 3 sequential pages setup I have, why not try the whole document! Fair warning that you may hit memory problems if your instance isn't sufficiently sized - but if it is, go head and share the results!