Postgres Contextual RAG Chatbot

This workflow is for any team or individual who wants to create a smart chatbot. This chatbot uses your own documents to answer questions. It is great for customer support, internal knowledge bases, or answering frequently asked questions. This workflow builds a smart chatbot with memory. It uses Google Drive documents for answers. The chatbot answers questions based on these documents. It also remembers past conversations. You must connect your Google Drive account. Set up a PostgreSQL database. Then, link your OpenAI account. Finally, set the Google Drive folder the workflow monitors. You need access to a Google Drive account, an OpenAI account, and a PostgreSQL database. You can change the AI model. You can also adjust how documents are split and used. You can also change the chatbot persona and documents in Google Drive. Key Technologies: n8n,Google Drive,PostgreSQL,OpenAI (GPT-4.1-mini, text-embedding-3-small),Langchain libraries Automation Value: Automatically updates the knowledge base when Google Drive files change. Provides quick, accurate answers to user questions using AI. Maintains chat history for better continuity. Reduces manual effort in answering repeated queries. Automates the process of extracting, embedding, and storing document information. Best Practices: Ensure proper indexing on the documents_pg table for efficient vector search and metadata filtering. Regularly monitor the Google Drive folder for unauthorized or irrelevant document uploads. Optimize chunk sizes and overlap for the text splitters to balance context and search performance. Refine the "RAG AI Agent" system message for desired conversational tone, accuracy, and error handling. Implement a reliable backup strategy for the PostgreSQL database. Consider implementing access controls for the chat webhook if it's not meant for public access.