RAG Customer Support Chatbot with OpenAI & Pinecone

This workflow implements a sophisticated Retrieval-Augmented Generation (RAG) customer support chatbot. It receives user messages via webhook, retrieves conversation history for context, performs semantic search in a Pinecone vector database for relevant knowledge base documents, assembles context, and uses OpenAI to generate accurate, informed responses. Responses are then stored back in memory for continuity across sessions.

A companion document ingestion flow handles knowledge base population: it pulls files from Google Drive, extracts text, chunks content, generates embeddings with OpenAI, and upserts vectors into Pinecone. This ensures the chatbot always has access to up-to-date, organization-specific information.

Benefits include reduced response times, higher accuracy over generic AI chatbots, scalability for growing support needs, and seamless integration with existing tools. Use cases span e-commerce customer queries, IT helpdesks, SaaS product support, and any scenario requiring domain-specific AI assistance without hallucinations.