Automate Your Knowledge Base with AI-Powered File Ingestion and Q&A
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This n8n workflow automates the ingestion of local files into a vector database (Qdrant) and creates an AI-powered Q&A agent using Mistral AI. It monitors a specified folder for file changes, processes the content, and makes it searchable and queryable.
About This Workflow
Transform your local documents into an intelligent, queryable knowledge base with this powerful n8n workflow. It leverages the Local File Trigger to detect new or modified files in a designated directory. Each file's content is then processed, embedded using Mistral AI, and stored in Qdrant for efficient vector search. A Chat Trigger then enables users to ask questions, which are answered by a Q&A chain powered by Mistral AI and the ingested document embeddings. This provides a seamless way to build custom AI assistants that can understand and respond based on your specific file repository.
Key Features
- Automated File Monitoring: Detects new, changed, or deleted files in a specified local directory in real-time.
- AI-Powered Embeddings: Utilizes Mistral AI to generate vector embeddings for file content, enabling semantic search.
- Vector Database Integration: Stores embeddings in Qdrant for efficient and scalable data retrieval.
- Interactive Q&A Agent: Creates a conversational AI that can answer questions based on the ingested files.
- Customizable Metadata: Allows for rich metadata to be added to ingested documents for advanced filtering and organization.
How To Use
- Configure the Local File Trigger: Set the
pathto the directory you want to monitor (e.g.,/home/node/BankStatements). Ensure n8n has access to this directory. - Set Variables: The
Set Variablesnode defines the target directory and how file events (add, change, unlink) are handled. It also sets the Qdrant collection name. - Read and Prepare File Data: The
Read Filenode reads the content of changed files. ThePrepare Embedding Documentnode structures the data, including the file location and content, preparing it for embedding. - Generate Embeddings: The
Embeddings Mistral Cloudnode uses Mistral AI to create vector representations of the document content. - Load Documents into Vector Database: The
Default Data LoaderandRecursive Character Text Splitternodes prepare the data for ingestion into Qdrant (implicitly handled by subsequent nodes if this were a full workflow). - Set up Chat Trigger: The
Chat Triggernode is configured to receive incoming chat messages, initiating the Q&A process. - Build the Q&A Chain: The
Question and Answer Chainnode, coupled with theMistral Cloud Chat Model, processes user queries, retrieves relevant information from the vector database, and generates a response.
Apps Used
Workflow JSON
{
"id": "27358021-7aab-4792-87c3-2b0af6c29e80",
"name": "Automate Your Knowledge Base with AI-Powered File Ingestion and Q&A",
"nodes": 29,
"category": "DevOps",
"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: 27358021-7aab...
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