Intelligent Document Q&A with Langchain and Qdrant
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Automate intelligent question answering over your documents. This workflow leverages Langchain and Qdrant to ingest, embed, and query your data, enabling sophisticated AI-driven insights.
About This Workflow
This n8n workflow orchestrates a powerful AI-driven question answering system. It begins by triggering on a new Google Drive file, then processes the document using a recursive text splitter and the Cohere embeddings model to create vector representations. These embeddings are stored and indexed in Qdrant for efficient retrieval. When a query is made, the system retrieves relevant document chunks from Qdrant, utilizes a Question and Answer Chain with a DeepSeek Chat Model, and leverages AI agent capabilities for complex reasoning and structured output. The workflow also includes options for memory management and integrates with Google Drive for output storage, offering a comprehensive solution for building intelligent Q&A bots.
Key Features
- Automated Document Ingestion: Trigger workflows automatically on new files in Google Drive.
- Advanced Text Processing: Utilize recursive character splitting for optimal document chunking.
- Vector Embeddings: Employ Cohere for powerful text embeddings, enabling semantic search.
- Efficient Vector Storage: Leverage Qdrant for high-performance vector database operations.
- AI-Powered Q&A: Utilize Langchain's Question and Answer Chain and AI Agents with DeepSeek chat models for intelligent responses.
- Structured Output: Extract information in a defined format using structured output parsers.
How To Use
- Set up Google Drive Trigger: Configure the 'File Created' node to monitor your desired Google Drive folder for new files.
- Configure Document Loading and Splitting: Adjust the 'Default Data Loader' and 'Recursive Character Text Splitter' to match your document types and desired chunk sizes.
- Set up Embeddings and Vector Store: Choose your preferred embedding model (e.g., 'Embeddings Cohere') and configure the 'Qdrant Vector Store' nodes to connect to your Qdrant instance and define collection names.
- Integrate Q&A Chain and Agent: Connect the 'Question and Answer Chain' and 'AI Agent' nodes, ensuring they point to your configured vector store and chat models (e.g., 'DeepSeek Chat Model').
- Configure Memory and Output: Set up the 'Simple Memory' node for conversational context and the 'Structured Output Parser' for desired output format. Configure the 'Google Drive (create)' node to save results.
- Optional: Cleanup: Utilize the 'Delete Collection' node for managing Qdrant collections if needed.
Apps Used
Workflow JSON
{
"id": "3dca1aec-53f9-4f29-a09d-0b89af502086",
"name": "Intelligent Document Q&A with Langchain and Qdrant",
"nodes": 21,
"category": "Marketing",
"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: 3dca1aec-53f9...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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