Solution: I built an AI-powered RAG (Retrieval-Augmented Generation) chatbot agent in n8n that lets users chat naturally and get accurate, context-aware answers pulled directly from their own documents — not generic AI guesses.

How it works: When a chat message is received, an AI Agent processes the query using a connected chat model and memory (to maintain conversation context). The agent uses a Pinecone vector store as a retrieval tool — searching embedded document data to pull the most relevant information before generating its response. This means every answer is grounded in real data, not hallucinated.

Result: Turns any set of documents into an interactive, conversational knowledge base — users can ask questions and get instant, accurate answers instead of manually searching through files. This same architecture can be adapted for customer support bots, internal knowledge assistants, or document Q&A tools for any business.

Built with: n8n, Pinecone (vector database), AI Agent (LLM), conversational memory