Build a document-upload RAG chatbot with OpenAI, Pinecone and daily analytics

Overview

This workflow implements a complete Retrieval-Augmented Generation (RAG) knowledge assistant with built-in document ingestion, conversational AI, and automated analytics using n8n, OpenAI, and Pinecone.

The system allows users to upload documents, automatically convert them into embeddings, query the knowledge base through a chat interface, and receive daily reports about chatbot performance and document usage.

Instead of manually searching through documentation, users can ask questions in natural language and receive answers grounded in the uploaded files. The workflow retrieves the most relevant document chunks from a vector database and provides them to the language model as context, ensuring accurate and source-based responses.

In addition to answering questions, the workflow records all chat interactions and generates daily usage analytics. These reports summarize chatbot activity, highlight the most referenced documents, and identify failed lookups where information could not be found.

This architecture is useful for teams building internal knowledge assistants, documentation chatbots, AI support tools, or searchable company knowledge bases powered by Retrieval-Augmented Generation.

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How It Works