Automate Self-Updating RAG System with OpenAI, Google Drive, and Qdrant
This workflow automates a Retrieval-Augmented Generation (RAG) system, integrating Google Drive, Qdrant, and AI models to maintain a self-updating knowledge base for precise, context-aware responses.
This workflow creates a seamless RAG system that automates document processing, vectorization, and retrieval to provide AI-powered answers. It monitors a Google Drive folder for document updates, processes these documents into vector embeddings using OpenAI, and stores them in Qdrant. The system ensures the knowledge base is current by updating vectors upon document changes. User queries trigger retrieval of relevant document chunks, which are then used by Google Gemini to generate contextually
- Platform
- n8n
- Category
- AI
- Price
- $19.99
- Creator
- Jasmine Wong
- set
- wait
- stickyNote
- googleDrive
- httpRequest
- manualTrigger
- splitInBatches
- googleDriveTrigger
- chatTrigger
- chainRetrievalQa
How to import this workflow into n8n
- 1Purchase or download the workflow to get the n8n workflow JSON file.
- 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
- 3Open each node marked with a credential warning and connect your own accounts and API keys.
- 4Run the workflow once manually to verify the data flow, then toggle it to Active.
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