Automated RAG Pipeline with Chatbot Integration

This workflow creates an intelligent retrieval-augmented generation (RAG) pipeline integrated with a chatbot, enabling automated document processing, embedding generation, vector storage, and interactive AI responses. It begins with a Google Drive trigger that detects new or updated files in Google Drive. The files are then fetched and processed through an Ollama-based embedding model to generate vector representations, which are stored in a Qdrant vector database for efficient similarity search. Additionally, a recursive text splitter segments document content into manageable chunks, facilitating effective retrieval.

A separate webhook captures incoming chat messages. When a message is received, the workflow leverages Ollama’s chat model to generate AI responses based on retrieved document embeddings stored in Qdrant, enabling context-aware conversations. The AI agent interacts with users, providing relevant information fetched from the document repository. Practical use cases include building AI-powered customer support chatbots, automated document querying systems, or dynamic knowledge bases that update seamlessly with new Google Drive content.