SQL Copilot

SQL Copilot is an agentic AI to SQL interface powered by LangGraph, featuring knowledge base management and self-evolution capabilities. The system not only allows users to query databases using natural language but also continuously learns from historical interactions to optimize knowledge base structure and query performance.

video.mp4

TODO

• Integrate deepanalyze
• Refactor routing judgment using a fine-tuned model
• Build knowledge based on graphrag
• Metric management and proactive data governance
• Metadata query routing
• Optimize page streaming output effect

✨ Core Features

🧠 Knowledge Base Management

• Intelligent Knowledge Storage: Automatically stores successful query patterns, table sql
• Hybrid Search RAG: Conducts multi vector similarity search with a rerank for rearrangement
• Pattern Optimization: Dynamically adjusts knowledge base weights based on query frequency and success rates

💡 Intelligent Query

• Natural Language Understanding: Converts complex user questions into accurate SQL queries
• Graph Workflow: Uses state graphs to manage query analysis, intent recognition, SQL generation, and error recovery
• Schema Awareness: Automatically understands database structure and relationships
• Human-in-the-Loop: Actively incorporates human feedback and guidance throughout the query process
• Self-Correction: Automatically fixes invalid SQL queries and learns correction strategies

⚡ Technical Dependencies

• LangGraph: Workflow orchestration
• LangChain: LLM integration
• Milvus: Vector database (for knowledge base storage)
• SQLAlchemy: Database abstraction
• Pandas: Data manipulation

⚡ Dependencies

• LangGraph: Workflow orchestration
• LangChain: LLM integration
• Milvus: Vector database
• SQLAlchemy: Database abstraction
• Pandas: Data manipulation

🚀Quick Start

1. Environment Configuration

Configure LLM Service

Create configuration file config/llm_config.json:

{
  "chat": {
    "model_name": "gpt-4",
    "api_key": "your-openai-api-key",
    "base_url": "https://api.openai.com/v1",
    "temperature": 0.1,
    "max_tokens": 2000
  }
}

Configure Database Connection

Create config/database_config.json:

{
  "chenjie": {
    "name": "Example Database",
    "type": "mysql",
    "host": "localhost",
    "port": 3306,
    "username": "your_username",
    "password": "your_password",
    "database": "your_database"
  }
}

5. Initialize System

# Initialize database structure
python -c "from app import init; init('chenjie')"

6. Launch Web Application

python web_app.py

# Or use interactive mode
python app.py

7. Start Using

Open your browser and navigate to http://localhost:5123. Enter your natural language queries in the chat interface, for example:

• "Query the product with the highest sales"
• "Analyze sales trends across regions"
• "Find the most active users from last month"