( Case study )
RAG-based Document Q&A Assistant
Local RAG PDF chatbot using n8n, Ollama, Qdrant, and Llama 3.1 for private document-based question answering.
Problem
Traditional language models do not inherently know the contents of a user's private files. This system solves that by transforming uploaded PDFs into searchable embeddings and retrieving relevant context before generating a response.
Approach
This project combines n8n, Qdrant, Ollama, and a local LLM to build a complete document QA pipeline without relying on paid cloud APIs.
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