Local File to RAG QA Chatbot with Mistral AI
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Automate the creation of a Retrieval-Augmented Generation (RAG) QA chatbot from local files. This workflow monitors a folder, processes files with Mistral AI, and stores them in Qdrant for intelligent querying.
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About This Workflow
Overview
This n8n workflow transforms a local folder of documents into a queryable AI knowledge base. It leverages the Local File Trigger to detect new or modified files. The Set Variables node extracts file information and sets up parameters for Qdrant. Files are then read, prepared for embedding with their content, location, and timestamp using Prepare Embedding Document. Embeddings Mistral Cloud generates vector embeddings, and Default Data Loader along with Recursive Character Text Splitter prepares these for a vector store. The workflow integrates with Qdrant (though the provided snippet is incomplete for the Qdrant interaction itself, it implies storage). Crucially, it sets up a Chat Trigger and Question and Answer Chain powered by Mistral AI's Mistral Cloud Chat Model and a Vector Store Retriever, enabling users to ask questions about the processed documents.
Key Features
- Real-time monitoring of local directories for file changes.
- Automated document processing for RAG pipelines.
- Utilizes Mistral AI for embeddings and question-answering.
- Integrates with vector databases (implied Qdrant) for efficient retrieval.
- Creates an interactive QA chatbot experience.
How To Use
- Configure the
Local File Triggerto point to the directory containing your documents. - Ensure your Mistral AI API credentials are set up in n8n.
- Configure the
Set Variablesnode, especially theqdrant_collectionand the path for the local file trigger. - Adjust the embedding and text splitting parameters as needed for your data.
- Set up your Qdrant instance and ensure n8n can connect to it (the provided snippet is missing explicit Qdrant output nodes, but implies its use).
- Configure the
Chat Triggerto receive incoming questions. - The
Question and Answer Chainwill use the Mistral Cloud Chat Model and Vector Store Retriever to find answers from your indexed documents.
Apps Used
Workflow JSON
{
"id": "86609e73-1b13-4354-80f6-be0c8853d6e8",
"name": "Local File to RAG QA Chatbot with Mistral AI",
"nodes": 0,
"category": "AI Research, RAG, and Data Analysis",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 86609e73-1b13...
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AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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