Files
assist/CLAUDE.md
zhaojie c3560b43fd docs: update README and CLAUDE.md to v2.2.0
- Added documentation for audit tracking (IP address, invocation method).
- Updated database model descriptions for enhanced WorkOrder and Conversation fields.
- Documented the new UnifiedConfig system.
- Reflected enhanced logging transparency for knowledge base parsing.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-11 00:08:09 +08:00

3.4 KiB

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

High-Level Architecture

This project is a Python Flask-based web application called "TSP Assistant". It's an intelligent customer service system designed for Telematics Service Providers (TSP).

The backend is built with Flask and utilizes a modular structure with Blueprints. The core application logic resides in the src/ directory.

Key components of the architecture include:

  • Web Framework: The web interface and APIs are built using Flask. The main Flask app is likely configured in src/web/app.py.
  • Modular Routing: The application uses Flask Blueprints for organizing routes. These are located in src/web/blueprints/. Each file in this directory corresponds to a feature area (e.g., agent.py, workorders.py, analytics.py).
  • Intelligent Agent: A core feature is the AI agent. Its logic is contained within the src/agent/ directory, which includes components for planning (planner.py), tool management (tool_manager.py), and execution (executor.py).
  • Database: The application uses a relational database (likely MySQL) with SQLAlchemy as the ORM. Models are defined in src/core/models.py.
  • Configuration: A unified configuration center (src/config/unified_config.py) manages all settings via environment variables and .env files.
  • Real-time Communication: WebSockets are used for real-time features like the intelligent chat. The server logic is in src/web/websocket_server.py.
  • Data Analytics: The system has a dedicated data analysis module located in src/analytics/.
  • Frontend: The frontend is built with Bootstrap 5, Chart.js, and vanilla JavaScript (ES6+). Frontend assets are in src/web/static/ and templates are in src/web/templates/.

Common Commands

Environment Setup

The project can be run using Docker (recommended) or locally.

1. Install Dependencies:

pip install -r requirements.txt

2. Initialize the Database: This script sets up the necessary database tables.

python init_database.py

Running the Application

Local Development: To start the Flask development server:

python start_dashboard.py

The application will be available at http://localhost:5000.

Docker Deployment: The project includes a docker-compose.yml for easy setup of all services (application, database, cache, monitoring).

To start all services:

docker-compose up -d

Or use the provided script:

chmod +x scripts/docker_deploy.sh
./scripts/docker_deploy.sh start

To stop services:

./scripts/docker_deploy.sh stop

Running Tests

The project uses pytest for testing.

pytest

To run tests with coverage:

pytest --cov

Key File Locations

  • Main Application Entry Point: start_dashboard.py (local) or src/web/app.py (via WSGI in production).
  • Flask Blueprints (Routes): src/web/blueprints/
  • Agent Core Logic: src/agent/
  • Database Models: src/core/models.py
  • Frontend Static Assets: src/web/static/ (JS, CSS, images)
  • Frontend HTML Templates: src/web/templates/
  • WebSocket Server: src/web/websocket_server.py
  • Configuration Files: config/
  • Deployment Scripts: scripts/
  • Database Initialization: init_database.py