Build an AI-Powered Q&A Agent for Local Documents with Mistral and Qdrant
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This workflow creates an intelligent Q&A agent that monitors a local folder for file changes. Leveraging Mistral AI for embeddings and a Qdrant vector database for storage, it automatically indexes document content. Users can then ask natural language questions about their local files, receiving instant, AI-generated answers.
About This Workflow
This robust n8n workflow revolutionizes how you interact with your local documents. It automatically detects new or modified files within a specified folder, extracts their content, and transforms it into searchable data using Mistral AI embeddings. This vectorized knowledge is stored in Qdrant, creating a dynamic, self-updating knowledge base. Subsequently, it powers an AI assistant, utilizing Mistral's advanced chat models and LangChain's Q&A capabilities, to provide accurate, context-aware answers to user queries about the indexed documents. Say goodbye to manual searching and hello to instant, intelligent insights from your local files.
Key Features
- Real-time File Monitoring: Automatically triggers the workflow upon file additions, changes, or deletions in a local directory.
- AI-Powered Document Indexing: Utilizes Mistral AI to generate semantic embeddings for document content, enhancing searchability.
- Dynamic Vector Knowledge Base: Integrates with Qdrant to store and manage document vectors, ensuring your knowledge base is always up-to-date.
- Natural Language Q&A: Enables users to ask questions in natural language and receive intelligent answers powered by Mistral's chat models.
- Scalable & Extensible: Built with n8n, offering flexibility to integrate with various services and customize logic.
How To Use
- Configure Local File Trigger: Specify the
pathto the local folder you wish to monitor (e.g.,/home/node/host_mount/local_file_search). Ensure n8n (especially in Docker) has appropriate volume mounts for access. Select theeventsyou want to trigger the workflow (add, change, unlink). - Set Up Mistral AI Credentials: Provide your Mistral Cloud API key in the
Mistral Cloud accountcredentials for both theEmbeddings Mistral CloudandMistral Cloud Chat Modelnodes. - Define Qdrant Collection: In the
Set Variablesnode, ensure theqdrant_collectionvariable matches your desired Qdrant collection name where document embeddings will be stored. - Review Data Processing: Customize the
Read File,Prepare Embedding Document,Default Data Loader, andRecursive Character Text Splitternodes if your document types or metadata requirements differ. - Test the Chat Trigger: Activate the workflow and interact with the
Chat Triggerwebhook to test the Q&A agent functionality. Add or modify files in your monitored folder to see the indexing in action.
Apps Used
Workflow JSON
{
"id": "c01f00e2-566f-418c-b024-1441dd4e01cf",
"name": "Build an AI-Powered Q&A Agent for Local Documents with Mistral and Qdrant",
"nodes": 25,
"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: c01f00e2-566f...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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