Automate Your Document AI with n8n and Mistral AI
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Effortlessly build a powerful document Q&A system by automatically indexing your local files and leveraging Mistral AI. This workflow transforms your unstructured data into an intelligent, searchable knowledge base.
About This Workflow
This n8n workflow automates the process of creating an AI-powered Q&A system from your local documents. It begins by monitoring a designated folder for new or modified files. As files are added or changed, the workflow reads their content, processes them into manageable chunks, and generates vector embeddings using Mistral AI. These embeddings are then stored, enabling efficient similarity searches. Finally, a chat trigger allows you to ask questions, and the workflow utilizes a retrieval-augmented generation (RAG) chain with Mistral AI to provide accurate answers based on your document content. This solution is ideal for businesses and individuals looking to unlock the knowledge hidden within their file repositories.
Key Features
- Automated File Monitoring: Real-time detection of new or updated files in a specified local directory.
- AI-Powered Embeddings: Utilizes Mistral AI to convert document content into meaningful vector representations.
- Intelligent Q&A System: Enables natural language querying of your documents.
- Flexible Data Processing: Handles various file types and structures for comprehensive analysis.
- Scalable Vector Storage: Designed to work with vector databases like Qdrant for efficient data retrieval.
How To Use
- Configure Local File Trigger: Set the
pathparameter to the directory you want to monitor for files. Ensure n8n has read access to this folder. - Set Variables: Define the
directorywhere your files are located and optionally setfile_added,file_changed, andfile_deletedvariables based on file events. - Read File Content: Use the
Read Filenode, dynamically setting thefileSelectorto the path of the file detected by the trigger. - Prepare Embedding Document: Use the
Set Variablesnode (Prepare Embedding Document) to structure the file content with metadata like file location and creation time. - Document Loading and Splitting: Employ the
Default Data LoaderandRecursive Character Text Splitternodes to process the document text into chunks suitable for embedding. - Generate Embeddings: Connect your Mistral AI credentials to the
Embeddings Mistral Cloudnode to create vector representations of your document chunks. - Chat Trigger: Set up the
Chat Triggernode to listen for incoming chat messages. - Question and Answer Chain: Configure the
Question and Answer Chainnode to process incoming questions, retrieve relevant information using embeddings, and generate an answer with theMistral Cloud Chat Model.
Apps Used
Workflow JSON
{
"id": "17c04d3f-91fb-4618-a7d7-3cf75a7d09f4",
"name": "Automate Your Document AI with n8n and Mistral AI",
"nodes": 11,
"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: 17c04d3f-91fb...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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