简体中文 | English

• Natural Language to SQL: Convert everyday language descriptions into SQL queries automatically
• Multi-Database Support: Compatible with SQL Server, MySQL, PostgreSQL, and SQLite
• Intelligent Context Understanding: Based on chat history to understand user query intent
• Vector Search Integration: Support semantic similarity search
• Syntax Validation: Automatically check generated SQL syntax correctness
• MCP Protocol Support: Seamless integration with IDE tools (Cursor, Trae, etc.)
• Intelligent Q&A Example System: Improve SQL generation accuracy through example learning

This project supports SQL Server, MySQL, PostgreSQL and SQLite. Configuration example:

"Text2SqlOpenAI": {
  "Key": "your-api-key",
  "EndPoint": "https://api.antsk.cn/",
  "ChatModel": "gpt-4o",
  "EmbeddingModel": "text-embedding-ada-002"
},
"Text2SqlConnection": {
  "DbType": "Sqlite", //PostgreSQL
  "DBConnection": "Data Source=text2sql.db",
  "VectorConnection": "text2sqlmem.db",
  "VectorSize": 1536 //Required for PostgreSQL, optional for SQLite
}

1. Database Abstraction Layer

   • Multi-database support via strategy pattern
   • Standardized operations through IDatabaseProvider interface
   • Dynamic loading of database drivers (SQLite/Postgres/MySql/SqlServer)
   • Auto-generated database-specific SQL dialects

2. Vector Database Integration

   • SQLite in-memory vector search
   • PostgreSQL pgvector extension support
   • Unified IVectorRepository interface
   • Cosine similarity/Euclidean distance calculations

3. Q&A Example System

   • Manual and correction-based example creation
   • Semantic search for relevant examples
   • Category organization and usage statistics
   • Batch operations and example management

4. MCP Protocol Server

   • Full Text2SQL functionality via MCP tools
   • IDE integration support (Cursor, Trae, etc.)
   • Database schema and query execution tools
   • Context-aware connection management

flowchart TD
    A[User Natural Language Query] --> B{Database Connection Selected}
    B -->|Not Selected| C[Prompt to Select Database]
    B -->|Selected| D[Save User Message to Chat History]
    
    D --> E[Semantic Search for Relevant Schema]
    E --> F[Vector Database Query]
    F --> G[Relevance Scoring & Table Relationship Inference]
    G --> H[Build Schema Context]
    
    H --> I[Call LLM to Generate SQL]
    I --> J[Use Semantic Kernel Plugins]
    J --> K[SQL Security Check]
    
    K -->|Query Statement| L[Auto Execute SQL]
    K -->|Operation Statement| M[Generate SQL Only<br/>No Auto Execution]
    
    L --> N{Execution Successful}
    N -->|Success| O[Return Query Results]
    N -->|Failed| P[SQL Optimization]
    
    P --> Q[Optimize SQL Using Error Info]
    Q --> R[Re-execute Optimized SQL]
    R --> S[Return Final Results]
    
    M --> T[Prompt Manual Execution]
    O --> U[Save Response to Chat History]
    S --> U
    T --> U
    U --> V[Display Results to User]
    
    style A fill:#e1f5fe
    style V fill:#e8f5e8
    style K fill:#fff3e0
    style P fill:#fce4ec

flowchart TD
    A[Database Connection Config] --> B[Schema Training Service]
    B --> C[Extract Database Table Structure]
    C --> D[Get Table/Column/Foreign Key Info]
    D --> E[Generate Table Description Text]
    
    E --> F[Text Vectorization]
    F --> G[Store to Vector Database]
    
    G --> H{Vector Storage Type}
    H -->|SQLite| I[SQLiteMemoryStore]
    H -->|PostgreSQL| J[PostgresMemoryStore with pgvector]
    
    I --> K[Schema Training Complete]
    J --> K
    
    K --> L[Wait for User Query]
    L --> M[Semantic Search]
    M --> N[Relevance Matching]
    N --> O[Return Relevant Table Structure]
    
    style A fill:#e3f2fd
    style F fill:#f3e5f5
    style G fill:#e8f5e8
    style M fill:#fff3e0

flowchart LR
    subgraph "UI Layer"
        A[Blazor Frontend Pages]
        B[Database Connection Selection]
        C[Chat Input Box]
        D[SQL Result Display]
    end
    
    subgraph "Service Layer"
        E[ChatService<br/>Chat Service]
        F[SchemaTrainingService<br/>Schema Training Service]
        G[SemanticService<br/>Semantic Service]
        H[SqlExecutionService<br/>SQL Execution Service]
        I[QAExampleService<br/>Q&A Example Service]
        J[McpServer<br/>MCP Protocol Server]
    end
    
    subgraph "Data Access Layer"
        K[DatabaseConnectionRepository<br/>Database Connection Repository]
        L[ChatMessageRepository<br/>Chat Message Repository]
        M[DatabaseSchemaRepository<br/>Schema Repository]
        N[SchemaEmbeddingRepository<br/>Vector Embedding Repository]
        O[QAExampleRepository<br/>Q&A Example Repository]
    end
    
    subgraph "External Services"
        P[OpenAI API<br/>LLM Service]
        Q[Vector Database<br/>SQLite/PostgreSQL]
        R[Business Database<br/>Multi-Database Support]
        S[MCP Clients<br/>IDE Tool Integration]
    end
    
    A --> E
    B --> K
    C --> E
    D --> H
    
    E --> F
    E --> G
    E --> H
    E --> L
    E --> I
    
    F --> M
    F --> N
    G --> Q
    H --> K
    H --> R
    I --> O
    J --> S
    
    E --> P
    G --> P
    
    style A fill:#e1f5fe
    style E fill:#f3e5f5
    style P fill:#fff3e0
    style Q fill:#e8f5e8
    style J fill:#fce4ec
    style I fill:#e3f2fd

Text2Sql.Net integrates Model Context Protocol, serving as an MCP server to provide Text2SQL functionality for various AI development tools.

• get_database_connections: Get all database connection configurations
• get_database_schema: Get database table structure information
• generate_sql: Generate SQL queries from natural language
• execute_sql: Execute SQL query statements
• get_chat_history: Get chat history records
• get_table_structure: Get detailed structure of specified tables
• get_all_tables: Get all table information

In MCP-supported IDEs (such as Cursor, Trae, etc.), you can connect to Text2Sql.Net with the following configuration:

{
  "mcpServers": {
    "text2sql": {
      "name": "Text2Sql.Net - sqlserver",
      "type": "sse",
      "description": "智能Text2SQL服务 - 。支持自然语言转SQL查询。兼容Cursor、Trae等IDE。",
      "isActive": true,
      "url": "http://localhost:5000/mcp/sse?connectionId=xxxxxx"
    }
  }
}

After configuration, you can directly interact with databases using natural language in your IDE:

• "Show the structure of all user tables"
• "Query order data from the last week"
• "Count the number of products in each category"

1. Code Development: Quickly generate database query code in IDE
2. Data Analysis: Rapidly explore data through natural language
3. Report Generation: Quickly build complex statistical queries
4. System Integration: Integrate Text2SQL capabilities into other tool chains

Text2Sql.Net provides an intelligent Q&A example management system that improves SQL generation accuracy through learning and accumulating examples.

• Example Management: Support manual creation and correction-generated Q&A examples
• Semantic Search: Match relevant examples based on vector similarity
• Category Organization: Support basic queries, complex queries, aggregate queries, etc.
• Usage Statistics: Track example usage frequency and effectiveness
• Batch Operations: Support batch enable, disable, and delete examples

• Basic Queries: Simple SELECT statements and basic filtering
• Complex Queries: Multi-table joins, subqueries, and complex scenarios
• Aggregate Queries: Include GROUP BY, SUM, COUNT and other aggregate functions
• Join Queries: Multi-table JOIN operations
• Correction Examples: Examples generated from incorrect SQL corrections

When users input queries, the system will:

1. Vectorize the user question
2. Perform semantic search in the example library
3. Return the most relevant examples (default relevance threshold 0.7)
4. Provide relevant examples as context to LLM
5. Update example usage statistics

{
  "question": "Query the number of active users in the last month",
  "sqlQuery": "SELECT COUNT(DISTINCT user_id) FROM user_activities WHERE activity_date >= DATE_SUB(NOW(), INTERVAL 1 MONTH)",
  "category": "aggregate",
  "description": "Count distinct users with activity records in the last 30 days"
}

1. Manual Creation: Directly add Q&A pairs in the management interface
2. Correction Generation: Automatically create examples when users correct wrong SQL
3. Batch Import: Support batch generation of examples from existing query history

Join our developer community through WeChat (ID: xuzeyu91) or visit AntSK for more RAG solutions.