Document Q&A with Voyage-Context-3 Embeddings & MongoDB

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