AI-Powered Research Paper Analysis and Retrieval Workflow

This n8n workflow is designed to automate the process of extracting, embedding, and searching research papers for intelligent Q&A interaction. Starting from manual trigger, it downloads a PDF paper, extracts its pages, and processes the content into manageable chunks. It then uses Voyage-Context-3 to generate contextual embeddings for each chunk, enabling advanced vector similarity searches within a MongoDB database. The workflow supports multiple advanced steps including splitting large documents, storing full text and embeddings, and executing a Q&A loop where user inquiries are refined through clarifying questions and answered with precise, context-aware responses. Practical use cases include academic research assistance, knowledge base building, and automated document analysis, offering a powerful tool for researchers and data analysts to quickly find relevant information within large documents and engage interactively with AI. Ready for deployment, this workflow exemplifies the integration of language models, vector search, and document management to facilitate cutting-edge research workflows.