Local File Monitoring and Intelligent Q&A for Bank Statements Workflow

Workflow Name

Local File Monitoring and Intelligent Q&A for Bank Statements Workflow

Key Features and Highlights

This workflow enables real-time monitoring of bank statement files within a specified local folder. It automatically synchronizes file additions, deletions, and modifications to the Qdrant vector database. Utilizing the Mistral AI model, it generates vector embeddings of the files and builds an intelligent Q&A system capable of efficient and accurate interactive queries based on historical bank statement content.

Core Problems Addressed

Traditional bank statement management faces challenges such as untimely file updates, difficulty in content retrieval, and lack of intelligent interpretation of historical data. This workflow resolves these pain points by automating file monitoring and vectorized storage, significantly enhancing the efficiency of bank statement management and the user experience in querying.

Application Scenarios

• Automated management and retrieval of historical bank statements by finance departments
• Rapid response to customer inquiries about statements in bank customer service centers
• Intelligent analysis and Q&A of personal or corporate financial data
• Other scenarios requiring intelligent retrieval and interaction with local document content

Main Process Steps

1. Monitor Target Folder
   Use a local file trigger to listen in real time for file creation, modification, and deletion events within the folder.

2. Handle File Events
   Differentiate file events (addition, modification, deletion) via conditional branching to trigger corresponding synchronization operations.

3. Synchronize to Qdrant Vector Database

   • On deletion, remove the corresponding vector points to keep the database synchronized with local files.
   • On modification, delete the old vector points first, then generate and insert new vectors.
   • On addition, read the file content, generate vectors, and insert them into Qdrant.

4. Text Preprocessing and Vector Generation
   Use a recursive character splitter to chunk the file content, then call the Mistral Cloud embedding model to generate text vectors.

5. Build Intelligent Q&A AI Agent
   Combine the Qdrant vector retriever with the Mistral Cloud chat model to establish a Q&A chain tailored for bank statements, supporting real-time user queries triggered via chat.

Involved Systems or Services

• n8n Local File Trigger: Monitors local folder events
• Qdrant Vector Database: Stores and manages vector representations of file content for efficient retrieval
• Mistral Cloud AI Services: Generates text embeddings and supports chat-based Q&A functionality
• n8n Built-in Nodes: Such as file reading, conditional logic, HTTP requests, etc., to implement workflow logic

Target Users and Value

• Enterprises and finance teams aiming for automated management and intelligent querying of bank statements
• Banking institutions and related service providers seeking to improve customer inquiry efficiency
• Developers and automation enthusiasts interested in building intelligent Q&A systems based on local data
• Any scenario requiring real-time synchronization and intelligent analysis of local document content

By seamlessly integrating the local file system with a powerful vector database and AI models, this workflow delivers an efficient and intelligent solution for bank statement management, enabling users to effortlessly access and leverage historical financial data.