Intelligent Document Q&A with AI and Vector Stores
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Empower your workflows with an AI agent that can answer questions by intelligently retrieving information from your documents. This solution leverages Langchain, Ollama, Qdrant, and Google Drive to create a powerful, context-aware question-answering system.
About This Workflow
This n8n workflow enables sophisticated question answering by integrating Large Language Models (LLMs) with a retrieval-augmented generation (RAG) approach. It listens for chat messages and uses an AI Agent to understand queries. For factual answers, it queries a Qdrant vector store, populated with embeddings generated by Ollama from your Google Drive documents. This ensures the AI has access to your specific knowledge base, providing accurate and relevant responses. The workflow also supports maintaining conversation history via a Postgres chat memory, allowing for more natural and contextually aware interactions. Documents added or updated in specified Google Drive folders are automatically processed to update the knowledge base.
Key Features
- Contextual Q&A: Ask questions and get answers directly from your document knowledge base.
- Automated Knowledge Ingestion: Seamlessly update your AI's knowledge by adding/updating files in Google Drive.
- Persistent Chat Memory: Maintains conversation context for more natural AI interactions.
- Flexible AI Model Integration: Supports Ollama for both chat models and embedding generation.
- Scalable Vector Storage: Utilizes Qdrant for efficient storage and retrieval of document embeddings.
How To Use
- Configure Triggers: Set up the
When chat message receivednode for your chat interface and theFile CreatedandFile Updatednodes to monitor your Google Drive folders. - Set up AI Components: Connect the
Ollama Chat ModelandPostgres Chat Memorynodes for conversational AI capabilities. Configure theEmbeddings OllamaandQdrant Vector Storenodes to process and store document embeddings. - Define the Agent: Use the
AI Agentnode to define the AI's persona and prompt, ensuring it knows how to use available tools. - Integrate Vector Store Tool: Connect the
Answer questions with a vector storenode to your Qdrant vector store and embedding model. This node acts as a tool for the AI Agent. - Process Google Drive Files: Configure the
Set File IDnode to extract file and folder IDs from Google Drive triggers. Use theDownload FileandExtract Document Textnodes to retrieve and prepare document content. - Update Vector Store: Connect the extracted document text to a node that adds these embeddings to your Qdrant vector store (this part might require an additional node not explicitly shown, or extension of existing nodes).
- Connect and Test: Ensure all nodes are correctly connected and authenticated with your respective services (Ollama, Postgres, Qdrant, Google Drive).
Apps Used
Workflow JSON
{
"id": "5e106c62-3512-4551-bc52-8ca3d6ba4628",
"name": "Intelligent Document Q&A with AI and Vector Stores",
"nodes": 17,
"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: 5e106c62-3512...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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