Local File Monitoring and Intelligent Q&A for Bank Statements Workflow
This workflow achieves intelligent management and querying of bank statements by monitoring the addition, deletion, and modification events within a local folder. It utilizes a vector database to synchronize file content, generating efficient semantic vector embeddings that support natural language interaction, thereby enhancing query accuracy and response speed. Users can quickly locate and understand a large number of financial documents, significantly improving the utilization efficiency of financial data and the querying experience.
Workflow Name
Local File Monitoring and Intelligent Q&A for Bank Statements Workflow
Key Features and Highlights
- Real-time monitoring of file creation, modification, and deletion events within specified local folders
- Synchronization of file vector representations with the Qdrant vector database to ensure high consistency between the vector store and local files
- Generation of semantic embeddings for file content using Mistral AI models, enabling efficient semantic search
- Construction of an intelligent AI agent based on a question-answering chain to support natural language queries and interactions for bank statements
- Automated file content chunking, loading, and indexing to enhance query accuracy and response speed
Core Problems Addressed
Traditional file management systems struggle to provide intelligent content retrieval and rapid responses, especially when handling large volumes of bank statements. This workflow solves the synchronization challenges between the file system and vector database, and leverages advanced AI models to enable intelligent Q&A over historical bank statements, significantly improving the utilization efficiency and query experience of financial data.
Application Scenarios
- Automated management and querying of local bank statements for corporate finance departments
- Rapid location and comprehension of extensive financial document content for individuals or institutions
- Transformation of local file content into semantically searchable and Q&A-enabled data assets
- Building internal knowledge bases that support natural language interaction for querying historical file information
Main Process Steps
- The Local File Trigger monitors a specified folder (e.g.,
/home/node/BankStatements), capturing file creation, modification, and deletion events. - Based on the type of file event, the workflow branches via a Conditional Node to handle each case separately:
- For deletion events, the corresponding vector points are removed from Qdrant via its API to maintain synchronization.
- For modification events, the old vector points are first deleted, then the updated file content is read to generate new vector representations, which are updated in Qdrant.
- For new files, the content is read, vectorized, and inserted into the Qdrant vector database.
- A Recursive Text Splitter chunks the file content to optimize vector generation quality.
- Semantic vectors are generated through the Mistral Cloud Embeddings Node and stored and retrieved using Qdrant.
- A Chat Trigger and Q&A Chain Node, combined with the Mistral Chat model, are configured to build an intelligent Q&A agent tailored for bank statements.
- Users initiate queries via a Webhook interface; the system returns precise answers based on vector retrieval and the Q&A chain.
Involved Systems or Services
- n8n Local File Trigger: Listens to file system events
- Qdrant Vector Database: Manages and stores file vector data
- Mistral Cloud AI Services: Provides text embedding and conversational language model capabilities
- n8n Q&A Chain Node: Implements intelligent question-answering functionality
- Webhook: Supports external invocation and interaction
Target Users and Value Proposition
- Finance professionals and accountants seeking to improve management and query efficiency of large volumes of bank statements
- IT and automation developers aiming to rapidly build intelligent file management and Q&A systems
- Corporate knowledge management teams constructing intelligent knowledge bases based on file content
- Users who need to convert local documents into intelligent Q&A resources to enhance information utilization and response speed
This workflow combines a highly automated synchronization mechanism between local files and the vector database with powerful AI-driven Q&A capabilities, achieving intelligent management and natural language querying of local bank statements, thereby significantly enhancing the intelligence level and user experience of financial data.