AI-Powered Document Q&A System with Google Drive
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Automate the creation of a powerful AI knowledge base directly from your Google Drive. This workflow ingests new PDF documents, extracts their content, embeds them using Google Gemini, and stores them in Pinecone. Users can then query this knowledge base via a chat interface to get AI-generated answers grounded in your documents.
About This Workflow
This comprehensive n8n workflow empowers you to build and maintain a dynamic Retrieval Augmented Generation (RAG) system. It seamlessly integrates with Google Drive, automatically processing new PDF files added to a designated folder. Each document undergoes text extraction, cleaning, and embedding with Google Gemini's advanced models. These rich embeddings and document chunks are then stored in a Pinecone vector database, forming your custom knowledge base. When a user submits a query, the system intelligently retrieves the most relevant document sections, crafting a context-rich prompt for an AI agent to generate accurate, detailed, and markdown-formatted answers. Eliminate manual information retrieval and provide instant, accurate responses powered by your own data.
Key Features
- Automated Document Ingestion: Automatically monitors a specific Google Drive folder for new PDF files and processes them for your AI knowledge base.
- Intelligent Text Processing: Extracts text from PDFs, cleans and normalizes it, and splits it into optimized chunks for efficient retrieval.
- Advanced Embedding with Google Gemini: Utilizes Google Gemini's embedding models for both document indexing and user query processing, ensuring semantic accuracy.
- Pinecone Vector Database Integration: Leverages Pinecone for high-performance storage and retrieval of document embeddings, enabling fast and relevant search.
- Context-Aware AI Responses: Generates AI answers grounded in your specific documents, reducing hallucinations and improving factual accuracy.
- Interactive Chat Interface: Allows users to interact with your knowledge base through a chat trigger, making information retrieval intuitive.
How To Use
- Set Up Google Drive Trigger: Configure the 'Monitor Google Drive for New Files' node to watch your desired folder containing PDF documents. Connect your Google Drive OAuth2 credential.
- Connect AI & Vector Store Credentials: Provide your Google Gemini (PaLM) API credentials for embedding generation and your Pinecone API credentials for vector database operations. Ensure the
n8n-rag-demoindex exists in Pinecone, or adjust the node to use your preferred index. - Adjust Document Processing (Optional): Modify the 'Clean and Normalize PDF Text' node's JavaScript code or the 'Split Document Text into Chunks' parameters (e.g.,
chunkSize,chunkOverlap) to fine-tune document processing for your specific content. - Integrate Chat Interface: Deploy the 'Chat Message Trigger' webhook URL into your desired chat application (e.g., Slack, Telegram, custom frontend) to enable user interaction.
- Test and Refine: Add a new PDF to your monitored Google Drive folder and then send a query via your chat interface to ensure the RAG system is functioning as expected and providing accurate answers.
Apps Used
Workflow JSON
{
"id": "c6f44fec-0d0c-4a9f-9372-f6f3ed1ecb81",
"name": "AI-Powered Document Q&A System with Google Drive",
"nodes": 24,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: c6f44fec-0d0c...
About the Author
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Connecting CRM, Notion, and Slack to automate your life.
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