AI Knowledge Assistant: Sheets, OpenAI, Supabase Vector Search

This workflow creates an intelligent AI-powered knowledge assistant that processes user messages from Google Sheets, retrieves relevant context from a Supabase vector database using OpenAI embeddings, and generates contextual responses via a LangChain agent powered by GPT-4. It incorporates chat history for continuity, web search capabilities via SerpAPI for up-to-date information, and stores responses back in Sheets with unique IDs.

Key components include a Google Sheets trigger for new messages, context preparation with system prompts and history, vector store queries for knowledge base access, and the core AI agent that orchestrates tools like memory buffer, vector search, and web tools. Setup involves configuring Sheets columns (user_message, ai_response, id), Supabase with embeddings, OpenAI credentials, and SerpAPI.

Benefits include automated, scalable customer support, internal knowledge querying, and enhanced decision-making without manual intervention. Use cases span business ops like shipment queries, ERP assistance, content generation, or any scenario needing RAG (Retrieval-Augmented Generation) with conversational memory, saving hours on research and response drafting while ensuring accuracy through vector similarity search and real-time web data.