RAG Agent: PDF Q&A with Supabase and OpenAI
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This workflow creates a RAG (Retrieval-Augmented Generation) agent that answers questions based on a PDF document stored in Google Drive, utilizing Supabase for vector storage and OpenAI for embeddings and language generation.
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About This Workflow
Overview
This n8n workflow implements a Retrieval-Augmented Generation (RAG) system, enabling an AI agent to answer questions based on specific document content. It starts by downloading a PDF from Google Drive using the 'Download file' node. The document content is then processed and embedded using the 'Embeddings OpenAI' node, and this information is stored in a Supabase vector database via the 'Supabase Vector Store' node. The 'AI Agent' node orchestrates the RAG process, using the 'OpenAI' node for language generation, 'Postgres' for chat memory, and the 'Supabase' vector store to retrieve relevant information. This setup allows for a knowledge base to be queried using natural language.
Key Features
- Downloads PDF documents from Google Drive.
- Leverages Supabase for efficient vector storage and retrieval.
- Uses OpenAI for generating embeddings and natural language responses.
- Implements chat memory using a Postgres database.
- Creates a conversational AI agent capable of answering questions based on provided documents.
How To Use
- Connect Google Drive: Authorize the 'Download file' node with your Google Drive credentials and specify the file ID of the PDF you want to use.
- Connect Supabase: Configure the 'Supabase Vector Store' and 'Supabase' nodes with your Supabase project details, including the API key and URL.
- Connect OpenAI: Provide your OpenAI API key to the 'Embeddings OpenAI' and 'OpenAI' nodes.
- Configure Postgres: Set up the 'Postgres' node with your PostgreSQL connection details for chat memory.
- Set up AI Agent: Connect the 'AI Agent' node to the 'OpenAI' language model, 'Postgres' memory, and the 'Supabase' tool.
- Execute Workflow: Trigger the workflow manually or via the 'Chat' webhook to start interacting with the AI agent.
Apps Used
Workflow JSON
{
"id": "f30c0128-12c9-4978-a1c8-3adc598e6f67",
"name": "RAG Agent: PDF Q&A with Supabase and OpenAI",
"nodes": 0,
"category": "AI Automation",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: f30c0128-12c9...
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AI_Workflow_Bot
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Building complex chains with OpenAI, Claude, and LangChain.
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