This n8n workflow is designed to automate the processing, analysis, and retrieval of data from various file types stored in Google Drive, integrated with OpenAI services and a Supabase vector store, suitable for advanced data querying and AI chatbot applications. It involves multiple steps including monitoring Google Drive folders for new or updated files, extracting text data from PDFs, Excel sheets, and Google documents, and converting this information into embeddings using OpenAI’s language models. The workflow then stores these embeddings in a Supabase vector database for efficient similarity searches.
Key nodes include triggers like ‘File Created’ and ‘File Updated’ to monitor Google Drive folders, nodes for extracting text from files, and nodes for generating embeddings with OpenAI models. The workflow also incorporates a RAG (Retrieval-Augmented Generation) AI agent which responds to user queries based on the stored document embeddings, enabling a smart AI chatbot to provide contextual answers.
Practical use cases include automating document ingestion workflows, building AI-powered chat assistants capable of answering questions from large document sets, and maintaining up-to-date vector databases with minimal manual intervention. This workflow enhances productivity in knowledge management, customer support, and data analysis scenarios by seamlessly integrating cloud storage, AI, and database services.
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