Document Q&A with Voyage-Context-3 Embeddings & MongoDB
RAG-based Q&A system that embeds documents using Voyage-Context-3 and stores them in MongoDB Atlas for intelligent chat retrieval.
This n8n workflow implements a sophisticated Document Q&A system leveraging Voyage-Context-3 embeddings from VoyageAI and MongoDB Atlas as the vector store. It processes research papers (like Arxiv documents) by fetching, chunking, embedding, and storing them for efficient retrieval. The workflow is divided into two main parts: the ingestion pipeline that populates the vector database, and a RAG-based Q&A agent that retrieves relevant chunks to answer queries accurately.
Key benefits include su
- Platform
- n8n
- Category
- AI & Machine Learning
- Price
- $24.99
- Creator
- Jordi Faber
- RAG
- Embeddings
- VoyageAI
- MongoDB
- Atlas
- Vector Store
- Chatbot
- Document QA
- AI Agent
How to import this workflow into n8n
- 1Purchase or download the workflow to get the n8n workflow JSON file.
- 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
- 3Open each node marked with a credential warning and connect your own accounts and API keys.
- 4Run the workflow once manually to verify the data flow, then toggle it to Active.
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