This n8n workflow automates the process of extracting, processing, and utilizing document data through a series of integrations with Google Drive, OpenAI, Supabase, and chat platforms. It is designed to analyze documents, generate embeddings, and enable intelligent conversational interactions.
The workflow begins with a Google Drive trigger that detects the creation of new files. These files are downloaded, and OCR (Optical Character Recognition) is performed to extract text content. The extracted text is split into smaller chunks, facilitating efficient processing. These chunks are then loaded into a default data loader and embedded using OpenAI’s API to generate vector representations.
These embeddings are stored in a Supabase vector store, creating a searchable database of document data. Meanwhile, a chat trigger listens for incoming messages from platforms like Slack, Telegram, or Gmail. When a message is received, it can invoke a RAG (Retrieval-Augmented Generation) agent powered by Langchain, which retrieves relevant document information from the vector store using embeddings.
The RAG agent interacts with users by providing contextually aware responses, leveraging chat memory for continuity. There’s also a conversational agent (GPT 5) involved in generating sophisticated responses. For communication, the workflow includes nodes to send replies via Slack, Telegram, Gmail, or WhatsApp, enabling automated, real-time engagement.
This workflow is highly practical for scenarios requiring automated document analysis, searchable knowledge bases, and intelligent customer support or information retrieval in chat-based environments.
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