216 lines
7.5 KiB
Python
216 lines
7.5 KiB
Python
"""
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Text2SQL 共享启动逻辑：环境检查、Schema 加载、Orchestrator 构造。
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供 `main` CLI 与 `api_server` 复用，避免 API 层依赖 CLI 入口模块。
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"""
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from __future__ import annotations
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import logging
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import os
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import sys
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from pathlib import Path
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from typing import Any, Optional
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from dotenv import load_dotenv
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logger = logging.getLogger(__name__)
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def _repo_root() -> Path:
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"""仓库根目录（含 data/、.env、api_server.py 的目录）。"""
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return Path(__file__).resolve().parent.parent
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def load_project_env() -> None:
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"""加载项目根目录 .env，供后续 os.getenv 使用。"""
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load_dotenv(_repo_root() / ".env")
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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() -> bool:
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"""环境检查"""
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load_project_env()
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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("未设置 OPENAI_EMBEDDING_MODEL（远程 Embedding 模型名）")
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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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"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（支持相对路径和绝对路径）
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schema_path_str = os.getenv(
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"SCHEMA_PATH", "./data/schemas/G3SB_MCDataDictionary_table_structure.json"
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)
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schema_path = Path(schema_path_str)
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# 如果是相对路径，尝试从多个位置查找
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if not schema_path.is_absolute():
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# 尝试1: PyInstaller 临时目录（单文件模式）
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if getattr(sys, "frozen", False) and hasattr(sys, "_MEIPASS"):
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meipass_schema = Path(sys._MEIPASS) / schema_path_str
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if meipass_schema.exists():
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schema_path = meipass_schema
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# 尝试2: 当前工作目录
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if not schema_path.exists():
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schema_path = Path.cwd() / schema_path_str
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# 尝试3: 仓库根目录（本文件位于 backend/）
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if not schema_path.exists():
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script_dir = Path(__file__).resolve().parent.parent
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schema_path = script_dir / schema_path_str
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# 尝试4: 可执行文件所在目录
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if not schema_path.exists() and getattr(sys, "frozen", False):
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exe_dir = Path(sys.executable).parent
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schema_path = exe_dir / schema_path_str
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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 LLM Key（DeepSeek / OpenAI 可切换）
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llm_sc = (os.getenv("LLM_SERVICE_CODE") or "").strip().lower()
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if llm_sc and llm_sc not in ("deepseek", "openai"):
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logger.warning("未知 LLM_SERVICE_CODE=%r（仅支持 deepseek/openai）", llm_sc)
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return False
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if llm_sc == "openai":
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if not (os.getenv("OPENAI_API_KEY") or "").strip():
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logger.warning("LLM_SERVICE_CODE=openai 但 OPENAI_API_KEY 未设置")
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return False
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else:
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if not (os.getenv("DEEPSEEK_API_KEY") or "").strip():
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logger.warning("环境变量 DEEPSEEK_API_KEY 未设置（默认 LLM_SERVICE_CODE=deepseek）")
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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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logger.info(f"[OK] 环境检查通过")
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logger.info(f" - Schema: {schema_path}")
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logger.info(
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" - LLM: %s",
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(llm_sc or "deepseek(auto)"),
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)
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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: Any, args: Any):
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"""创建编排器"""
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from agents.orchestrator import Text2SQLOrchestrator
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from llm.router import create_llm_client, resolve_llm_service_code
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from llm.deepseek_client import DeepSeekConfig
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translate_en = os.getenv("TRANSLATE_EN_TO_ZH", "true").strip().lower() not in (
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"0",
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"false",
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"no",
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"off",
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)
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if getattr(args, "no_translate_en", False):
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translate_en = False
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sc = resolve_llm_service_code()
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if sc == "deepseek":
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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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cfg = 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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llm_client = create_llm_client("deepseek", **cfg.__dict__)
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else:
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# openai：完全由 OPENAI_* 决定；同时沿用 temperature/max_tokens 作为默认值覆盖
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llm_client = create_llm_client(
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"openai",
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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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llm_client=llm_client,
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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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translate_english_to_zh=translate_en,
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)
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return orchestrator
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