AI-Powered Document Q&A Chatbot with Qdrant and OpenAI
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Transform your static documents into interactive knowledge bases with this n8n workflow. It enables you to ingest PDFs from Google Drive, embed their content using OpenAI, store them in a Qdrant vector database, and then answer complex questions through a simple webhook API using Retrieval-Augmented Generation (RAG).
About This Workflow
This comprehensive n8n workflow empowers you to build a sophisticated AI-powered Q&A system from your documents. It operates in two main phases: first, it ingests a PDF document from Google Drive, splits it into manageable chunks, generates embeddings using OpenAI, and efficiently stores this contextual information in a Qdrant vector database. The second phase establishes an API endpoint via a webhook, allowing users to submit natural language queries. The workflow dynamically retrieves relevant information from your vector store, augmented by OpenAI's language model, to provide precise and contextually accurate answers. This makes creating intelligent document assistants effortless.
Key Features
- PDF to Knowledge Base: Automatically ingest PDF documents from Google Drive, split them, and convert them into a searchable vector database.
- Retrieval-Augmented Generation (RAG): Combine vector search with advanced LLMs (OpenAI) for highly accurate and contextual answers.
- Dynamic Q&A Endpoint: Expose a webhook endpoint to receive user queries and provide real-time, AI-driven responses, optionally with dynamic collection selection.
- Scalable Vector Storage: Utilizes Qdrant for efficient storage and retrieval of document embeddings.
- Customizable AI Models: Leverage OpenAI's powerful embedding and chat models for intelligent processing.
How To Use
- Set up Credentials: Configure your OpenAI API key and Qdrant API key credentials within n8n.
- Ingest Documents (Step 1):
- Replace the placeholder
Google Drivenode's file ID (1LZezppYrWpMStr4qJXtoIX-Dwzvgehll) with your desired PDF document ID. - Update the
Qdrant Vector Storenode (fb91e2da-0eeb-47a5-aa49-65bf56986857) with your target collection name (e.g.,crowd). - Execute the workflow manually (via "When clicking 'Execute Workflow'" node) to upsert your document into Qdrant.
- Replace the placeholder
- Configure Q&A Endpoint (Step 2):
- The
Webhook1node (feee6d68-2e0d-4d40-897e-c1d833a13bf2) serves as your Q&A API endpoint. Copy its URL. - Review the
Qdrant Vector Store1node (89e14837-d1fc-4b1e-9ebc-7cf3e7fd9a70). It's configured to dynamically use={{ $json.body.company }}for the collection name. Adjust this if you prefer a static collection or a different dynamic parameter.
- The
- Query the System: Send a POST request to your
Webhook1URL with a JSON body containing{"input": "Your question here", "company": "your_collection_name"}. (Ensureyour_collection_namematches what you set in Step 2). - Receive Answers: The workflow will process your query using RAG and respond with the AI-generated answer. For a visual guide, check out the embedded YouTube tutorial in the workflow's sticky note!
Apps Used
Workflow JSON
{
"id": "f774899a-7e2c-4c55-a9af-03f6c2b9d09f",
"name": "AI-Powered Document Q&A Chatbot with Qdrant and OpenAI",
"nodes": 22,
"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: f774899a-7e2c...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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