407 lines
13 KiB
Python
407 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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Text2SQL 多智能体系统 - CLI演示入口
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用法：
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python main.py "查询2024年1月销售额最高的前5个产品"
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python main.py --question "查询所有状态为Active的账户数量"
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python main.py --interactive # 交互式模式
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"""
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import os
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import sys
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import argparse
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import logging
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from pathlib import Path
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from typing import Optional
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from dotenv import load_dotenv
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S'
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)
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logger = logging.getLogger(__name__)
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_DEFAULT_EMBEDDING_PATH = "./data/models/Qwen3-Embedding-0.6B"
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def _load_project_env():
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"""加载项目根目录 .env（与 main.py 同目录），供后续 os.getenv 使用。"""
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load_dotenv(Path(__file__).resolve().parent / ".env")
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def _embedding_model_path() -> str:
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return os.getenv("EMBEDDING_MODEL_PATH", _DEFAULT_EMBEDDING_PATH).strip()
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def resolve_sql_dialect(name: str) -> str:
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"""CLI / 配置中的方言别名统一为 sqlglot 方言名（SQL Server -> tsql）。"""
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n = (name or "sqlserver").lower().strip()
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if n in ("sqlserver", "mssql"):
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return "tsql"
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return n
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def setup_environment():
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"""环境检查"""
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_load_project_env()
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use_local_emb = os.getenv("USE_LOCAL_EMBEDDING", "true").strip().lower() in (
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"1", "true", "yes", "on",
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)
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if use_local_emb:
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# 与 .env 中 EMBEDDING_MODEL_PATH 及 utils.embedding 一致
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model_path = Path(_embedding_model_path())
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if not model_path.exists():
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logger.warning(f"Embedding模型不存在: {model_path}")
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logger.info("请先下载模型：")
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logger.info(" modelscope download --model 'Qwen/Qwen3-Embedding-0.6B' "
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f"--local_dir '{model_path}'")
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logger.info("或使用远程 Embedding API：USE_LOCAL_EMBEDDING=false，并配置 "
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"MODELSCOPE_API_KEY、或 OPENAI_API_KEY+OPENAI_EMBEDDING_MODEL"
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"（及可选 OPENAI_BASE_URL）、或 DASHSCOPE_*（百炼）")
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return False
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else:
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ms_key = os.getenv("MODELSCOPE_API_KEY", "").strip()
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oa_key = os.getenv("OPENAI_API_KEY", "").strip()
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oa_key_ok = oa_key and not (
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oa_key.startswith("http://") or oa_key.startswith("https://")
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)
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if ms_key:
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pass # ModelScope：BASE_URL / MODEL 有默认值，仅需 KEY
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elif oa_key_ok:
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if not os.getenv("OPENAI_EMBEDDING_MODEL", "").strip():
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logger.warning("USE_LOCAL_EMBEDDING=false 但未设置 OPENAI_EMBEDDING_MODEL")
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return False
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else:
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if not os.getenv("DASHSCOPE_API_KEY", "").strip():
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logger.warning(
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"USE_LOCAL_EMBEDDING=false 但未设置 MODELSCOPE_API_KEY、"
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"OPENAI_API_KEY+OPENAI_EMBEDDING_MODEL 或 DASHSCOPE_API_KEY"
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)
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return False
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base = (
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os.getenv("DASHSCOPE_BASE_URL") or os.getenv("DASHSCOPE_base_url", "")
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).strip()
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if not base:
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logger.warning("未设置 DASHSCOPE_BASE_URL（或 DASHSCOPE_base_url）")
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return False
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if not os.getenv("DASHSCOPE_MODEL", "").strip():
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logger.warning("未设置 DASHSCOPE_MODEL")
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return False
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# 检查Schema文件
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schema_path = Path("./data/schemas/G3SB_MCDataDictionary_table_structure.json")
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if not schema_path.exists():
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logger.warning(f"Schema文件不存在: {schema_path}")
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logger.info("请将G3SB Schema文件放置在 ./data/schemas/ 目录")
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return False
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# 检查API Key
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if not os.getenv("DEEPSEEK_API_KEY"):
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logger.warning("环境变量 DEEPSEEK_API_KEY 未设置")
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logger.info("请在 .env 文件中配置，或 export DEEPSEEK_API_KEY=your_key")
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return False
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return True
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def _default_g3sb_meta_path(structure_path: str) -> Optional[str]:
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"""若存在与 table_structure 同名的 table_meta 文件则返回其路径。"""
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p = Path(structure_path)
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if "table_structure" not in p.name:
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return None
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cand = p.parent / p.name.replace("table_structure", "table_meta")
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return str(cand) if cand.is_file() else None
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def load_schema(schema_path: str, schema_meta_path: Optional[str] = None):
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"""加载 Schema；G3SB structure JSON 会自动尝试配对 table_meta（可用 --schema-meta 指定）。"""
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from schema.manager import SchemaManager
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meta = (
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schema_meta_path
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if schema_meta_path is not None
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else _default_g3sb_meta_path(schema_path)
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)
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logger.info(f"加载Schema: {schema_path}")
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if meta:
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logger.info(f" 表注释(meta): {meta}")
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schema_mgr = SchemaManager.load_from_json(
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schema_path, g3sb_meta_path=meta
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)
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stats = schema_mgr.get_statistics()
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logger.info(
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f"[OK] Schema加载完成: {stats['database']}, "
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f"共{stats['total_tables']}张表, {stats['total_columns']}个字段"
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)
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return schema_mgr
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def create_orchestrator(schema_mgr, args):
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"""创建编排器"""
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from agents.orchestrator import Text2SQLOrchestrator
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from llm.deepseek_client import DeepSeekConfig
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api_key = (args.api_key or os.getenv("DEEPSEEK_API_KEY") or "").strip()
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if not api_key:
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raise ValueError(
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"未配置 DeepSeek API Key：请在 .env 中设置 DEEPSEEK_API_KEY，"
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"或使用命令行参数 --api-key"
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)
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base_url = (os.getenv("DEEPSEEK_BASE_URL") or "https://api.deepseek.com").strip()
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config = DeepSeekConfig(
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api_key=api_key,
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base_url=base_url,
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model_name=args.model or "deepseek-chat",
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temperature=args.temperature,
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max_tokens=args.max_tokens,
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)
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orchestrator = Text2SQLOrchestrator(
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schema_manager=schema_mgr,
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deepseek_config=config,
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embedding_model_path=args.embedding_model,
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vector_db_path=args.vector_db,
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max_retry=args.max_retry,
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use_vector_search=not args.no_vector_search,
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# Few-shot配置
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fewshot_enabled=not args.no_fewshot,
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fewshot_top_k=args.fewshot_top_k,
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fewshot_min_rating=args.fewshot_min_rating,
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)
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return orchestrator
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def single_query(orchestrator, question: str, dialect: str = "tsql"):
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"""单次查询"""
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import time
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logger.info(f"[Q] 问题: {question}")
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start = time.time()
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result = orchestrator.generate(
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question=question,
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dialect=dialect,
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top_k_candidates=20
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)
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elapsed = time.time() - start
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print("\n" + "=" * 60)
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print("生成结果:")
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print("=" * 60)
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if result.valid:
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print(f"[OK] SQL (耗时 {elapsed:.2f}s, 尝试 {result.attempts} 次):\n")
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print(result.sql)
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else:
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print(f"[FAIL] 生成失败 (尝试 {result.attempts} 次)")
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for err in result.errors:
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print(f" - {err}")
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if result.warnings:
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print("\n[WARN] 警告:")
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for w in result.warnings:
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print(f" - {w}")
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print(f"\n使用表: {result.tables_used}")
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return result
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def interactive_mode(orchestrator, dialect: str = "tsql"):
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"""交互式模式"""
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print("\n" + "=" * 60)
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print("Text2SQL 交互模式（输入 'quit' 或 'exit' 退出）")
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print("=" * 60 + "\n")
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while True:
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try:
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question = input("❓ 请输入问题: ").strip()
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if question.lower() in ('quit', 'exit', 'q'):
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print("再见！")
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break
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if not question:
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continue
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result = single_query(orchestrator, question, dialect)
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print()
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except KeyboardInterrupt:
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print("\n再见！")
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break
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except Exception as e:
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logger.error(f"查询失败: {e}")
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def batch_mode(orchestrator, questions: list, dialect: str = "tsql"):
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"""批量查询模式"""
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print(f"\n批量模式：共 {len(questions)} 个问题\n")
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results = []
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for i, question in enumerate(questions, 1):
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print(f"[{i}/{len(questions)}] {question}")
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result = single_query(orchestrator, question, dialect)
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results.append(result)
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print()
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# 统计
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success_count = sum(1 for r in results if r.valid)
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print("=" * 60)
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print(f"统计: {success_count}/{len(questions)} 成功 "
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f"({success_count/len(questions)*100:.1f}%)")
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return results
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def main():
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parser = argparse.ArgumentParser(
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description="Text2SQL 多智能体系统 - 自然语言生成SQL"
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)
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parser.add_argument(
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"question",
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nargs="?",
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help="自然语言问题（如不提供则进入交互模式）"
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)
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parser.add_argument(
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"--schema", "-s",
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default="./data/schemas/G3SB_MCDataDictionary_table_structure.json",
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help="Schema文件路径（默认: ./data/schemas/G3SB_MCDataDictionary_table_structure.json）"
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)
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parser.add_argument(
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"--schema-meta",
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default=None,
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help="G3SB table_meta.json；默认自动使用同目录下文件名含 table_meta 的配对文件",
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)
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parser.add_argument(
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"--dialect", "-d",
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default=os.getenv("TEXT2SQL_DIALECT", "sqlserver").strip(),
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choices=["mysql", "postgresql", "sqlite", "tsql", "sqlserver", "mssql"],
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help="SQL方言（默认: sqlserver / T-SQL；可用环境变量 TEXT2SQL_DIALECT 覆盖）",
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)
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parser.add_argument(
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"--api-key",
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help="DeepSeek API Key（默认从DEEPSEEK_API_KEY环境变量读取）"
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)
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parser.add_argument(
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"--model", "-m",
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default="deepseek-chat",
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help="DeepSeek模型名称（默认: deepseek-chat）"
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)
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parser.add_argument(
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"--temperature", "-t",
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type=float,
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default=0.3,
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help="生成温度（默认: 0.3）"
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)
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parser.add_argument(
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"--max-tokens",
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type=int,
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default=4096,
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help="最大token数（默认: 4096）"
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)
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parser.add_argument(
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"--max-retry",
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type=int,
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default=2,
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help="最大重试次数（默认: 2）"
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)
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_load_project_env()
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parser.add_argument(
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"--embedding-model",
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default=_embedding_model_path(),
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help="Embedding模型路径（默认来自环境变量 EMBEDDING_MODEL_PATH）"
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)
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parser.add_argument(
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"--vector-db",
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default=os.getenv("VECTOR_DB_PATH", "./data/embeddings/chroma").strip(),
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help="向量数据库路径（默认来自环境变量 VECTOR_DB_PATH）"
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)
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parser.add_argument(
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"--no-vector-search",
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action="store_true",
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help="禁用向量检索（使用所有表）"
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)
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# Few-shot 配置
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parser.add_argument(
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"--no-fewshot",
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action="store_true",
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help="禁用few-shot示例增强"
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)
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parser.add_argument(
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"--fewshot-top-k",
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type=int,
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default=int(os.getenv("FEWSHOT_TOP_K", "3")),
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help="每次使用的few-shot示例数量（默认: 3）"
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)
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parser.add_argument(
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"--fewshot-min-rating",
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type=int,
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default=int(os.getenv("FEWSHOT_MIN_RATING", "7")),
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help="few-shot示例最低评分（默认: 7）"
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)
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parser.add_argument(
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"--interactive", "-i",
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action="store_true",
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help="交互模式"
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)
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parser.add_argument(
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"--batch", "-b",
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help="批量文件路径（每行一个问题）"
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)
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parser.add_argument(
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"--verbose", "-v",
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action="store_true",
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help="详细日志"
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)
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args = parser.parse_args()
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args.dialect = resolve_sql_dialect(args.dialect)
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# 日志级别
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if args.verbose:
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logging.getLogger().setLevel(logging.DEBUG)
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# 环境检查
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if not setup_environment():
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sys.exit(1)
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# 加载Schema
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try:
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schema_mgr = load_schema(args.schema, args.schema_meta)
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except Exception as e:
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logger.error(f"Schema加载失败: {e}")
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sys.exit(1)
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# 创建Orchestrator
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try:
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orchestrator = create_orchestrator(schema_mgr, args)
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except Exception as e:
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logger.error(f"Orchestrator创建失败: {e}")
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sys.exit(1)
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# 根据参数选择模式
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if args.interactive or (not args.question and not args.batch):
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interactive_mode(orchestrator, args.dialect)
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elif args.batch:
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with open(args.batch, 'r', encoding='utf-8') as f:
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questions = [line.strip() for line in f if line.strip()]
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batch_mode(orchestrator, questions, args.dialect)
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elif args.question:
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single_query(orchestrator, args.question, args.dialect)
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else:
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parser.print_help()
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if __name__ == "__main__":
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main()
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