⚒️ Document Q&A system with Voyage-Context-3 embeddings and MongoDB Atlas
⚡ 1,267 views · ⚒️ Engineering
Description
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!
🔗 Nodes Used
HTTP Request, MongoDB, Execute Sub-workflow, Execute Workflow Trigger, OpenAI Chat Model, Extract from File
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File