704 lines
24 KiB
Python
704 lines
24 KiB
Python
#!/usr/bin/env python3
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"""
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FastAPI 服务入口 - Text2SQL NL Chat API
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包装 Text2SQLOrchestrator 为 REST API 服务，供前端调用
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"""
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from __future__ import annotations
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import os
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import sys
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import json
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import html as html_lib
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import asyncio
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import logging
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from pathlib import Path
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from typing import Optional, List, Dict, Any, AsyncIterator
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from contextlib import asynccontextmanager
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from fastapi import FastAPI, HTTPException, Body, Path as FPath
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import StreamingResponse, JSONResponse
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from pydantic import BaseModel, Field
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from dotenv import load_dotenv
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_REPO_DIR = Path(__file__).resolve().parent
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_BACKEND_DIR = _REPO_DIR / "backend"
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if str(_BACKEND_DIR) not in sys.path:
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sys.path.insert(0, str(_BACKEND_DIR))
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from main import setup_environment, load_schema, create_orchestrator, resolve_sql_dialect
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from agents.orchestrator import GenerationResult
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from nl_lite_store import lite_nl_store
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from utils.dialog_classifier import DialogIntent, classify_dialog
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from utils.dialog_context import (
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last_assistant_was_data_query,
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messages_to_text2sql_context,
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)
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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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# 加载 .env 文件（支持 PyInstaller 打包后的目录结构）
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def _find_env_file() -> Path:
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"""查找 .env 文件，支持多种运行环境"""
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import sys
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# 尝试1: 当前工作目录
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cwd_env = Path.cwd() / ".env"
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if cwd_env.exists():
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return cwd_env
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# 尝试2: 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_env = Path(sys._MEIPASS) / ".env"
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if meipass_env.exists():
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return meipass_env
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# 尝试3: 脚本/可执行文件所在目录
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if getattr(sys, 'frozen', False):
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# PyInstaller 打包后
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exe_dir = Path(sys.executable).parent
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env_path = exe_dir / ".env"
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if env_path.exists():
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return env_path
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else:
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# 开发环境
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script_dir = Path(__file__).resolve().parent
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env_path = script_dir / ".env"
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if env_path.exists():
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return env_path
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# 默认返回当前目录
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return cwd_env
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env_file = _find_env_file()
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if env_file.exists():
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load_dotenv(env_file)
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logger.info(f"[OK] 已加载配置文件: {env_file}")
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else:
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logger.warning(f"[WARN] 未找到 .env 文件: {env_file}")
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orchestrator = None
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schema_manager = None
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def get_orchestrator():
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"""获取或初始化 orchestrator"""
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global orchestrator, schema_manager
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if orchestrator is not None:
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return orchestrator
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if not setup_environment():
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raise RuntimeError(
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"环境检查失败（请查看上方 WARNING/INFO，常见原因："
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"USE_LOCAL_EMBEDDING=true 但未配置或找不到 EMBEDDING_MODEL_PATH；"
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"或 USE_LOCAL_EMBEDDING=false 但未配好 OPENAI_* / MODELSCOPE_* / DASHSCOPE_*；"
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"或 Schema 文件路径不对、DEEPSEEK_API_KEY 未设置）"
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)
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schema_path = os.getenv("SCHEMA_PATH", "./data/schemas/G3SB_MCDataDictionary_table_structure.json")
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schema_meta_path = os.getenv("SCHEMA_META_PATH", None)
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schema_manager = load_schema(schema_path, schema_meta_path)
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class Args:
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api_key = os.getenv("DEEPSEEK_API_KEY")
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model = os.getenv("MODEL_PRIMARY", "deepseek-chat")
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temperature = float(os.getenv("TEMPERATURE", "0.3"))
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max_tokens = int(os.getenv("MAX_TOKENS", "4096"))
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max_retry = int(os.getenv("MAX_RETRY", "2"))
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embedding_model = None
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vector_db = os.getenv("VECTOR_DB_PATH", "./data/embeddings/chroma")
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no_vector_search = False # 启用向量搜索（ChromaDB 已修复）
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no_fewshot = not os.getenv("FEWSHOT_ENABLED", "true").lower() in ("true", "1", "yes")
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fewshot_top_k = int(os.getenv("FEWSHOT_TOP_K", "3"))
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fewshot_min_rating = int(os.getenv("FEWSHOT_MIN_RATING", "7"))
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no_translate_en = os.getenv("TRANSLATE_EN_TO_ZH", "true").strip().lower() 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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orchestrator = create_orchestrator(schema_manager, Args())
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logger.info("[OK] Orchestrator 初始化完成")
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return orchestrator
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class NLChatRequest(BaseModel):
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message: str = Field(..., description="用户输入的自然语言问题")
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service_code: Optional[str] = Field(None, description="模型路由: openai | deepseek")
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lang_code: Optional[str] = Field("auto", description="语言: zh | en | tc | auto")
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taskId: Optional[str] = Field(None, description="任务ID")
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session_id: Optional[str] = Field(None, description="会话ID")
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visitor_biz_id: Optional[str] = Field(None, description="访客业务ID")
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user_id: Optional[str] = Field(None, description="用户ID")
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streaming_throttle: Optional[int] = Field(None, description="流式节流参数")
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class IntentPayload(BaseModel):
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intent: str
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confidence: Optional[float] = None
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reason: Optional[str] = None
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class DataQueryResult(BaseModel):
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sql: str = Field(..., description="生成的SQL语句")
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columns: List[str] = Field(default_factory=list, description="查询结果列名")
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rows: List[Dict[str, Any]] = Field(default_factory=list, description="查询结果行数据")
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row_count: int = Field(0, description="结果行数")
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truncated: bool = Field(False, description="是否截断")
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sql_explain: Optional[str] = Field(None, description="SQL自然语言说明")
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can_export: Optional[bool] = Field(True, description="是否允许导出")
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db_execution_status: Optional[int] = Field(
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None,
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description="库探针：1=有数据行，0=无行需追问补充，-1=执行失败(重试)，None=未探针",
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)
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db_empty_feedback: Optional[str] = Field(
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None, description="探针0时的无行说明与追问（含问题分析）"
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)
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follow_up_required: bool = Field(
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False,
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description="True 表示探针为0：需用户补充条件后重新提问以重新生成SQL",
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)
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sql_delivery_message: Optional[str] = Field(
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None, description="探针为1时LLM生成的面向用户SQL交付说明"
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)
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class NLChatSuccessData(BaseModel):
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intent: IntentPayload
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branch_result: DataQueryResult
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stream_narrative: Optional[str] = Field(None, description="流式叙述（SQL块上方说明）")
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stream_narrative_after: Optional[str] = Field(None, description="流式叙述（SQL块下方说明）")
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class ApiEnvelope(BaseModel):
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code: int = Field(200, description="状态码，200表示成功")
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msg: str = Field("", description="消息")
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data: Optional[NLChatSuccessData] = Field(None, description="响应数据")
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class ErrorResponse(BaseModel):
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code: int
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msg: str
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data: Optional[Any] = None
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class SessionCreateBody(BaseModel):
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title: Optional[str] = None
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user_id: Optional[str] = None
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visitor_biz_id: Optional[str] = None
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class SessionTitlePatchBody(BaseModel):
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title: Optional[str] = None
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user_id: Optional[str] = None
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visitor_biz_id: Optional[str] = None
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class SessionMessagePatchBody(BaseModel):
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content: str
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user_id: Optional[str] = None
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visitor_biz_id: Optional[str] = None
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class SqlExecuteBody(BaseModel):
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sql: str
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max_rows: Optional[int] = None
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user_id: Optional[str] = None
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visitor_biz_id: Optional[str] = None
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chat_session_id: Optional[str] = None
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chat_message_id: Optional[int] = None
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class FavoriteCreateBody(BaseModel):
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fav_type: str = Field(..., description="sql | function | report")
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name: str = ""
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desc: Optional[str] = None
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user_id: Optional[str] = None
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visitor_biz_id: Optional[str] = None
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sql: Optional[str] = None
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sql_explain: Optional[str] = None
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path: Optional[str] = None
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reportPath: Optional[str] = None
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params: Optional[str] = None
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def _sse_data(obj: Dict[str, Any]) -> bytes:
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return f"data: {json.dumps(obj, ensure_ascii=False)}\n\n".encode("utf-8")
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def _conversation_nl_dict(reply: str) -> Dict[str, Any]:
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"""寒暄/元问题等：与前端 BUSINESS_MANUAL + branch_result.answer 一致。"""
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r = (reply or "").strip() or "您好。"
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return {
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"intent": {"intent": "BUSINESS_MANUAL", "confidence": 1.0, "reason": "conversation"},
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"branch_result": {"answer": r},
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}
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async def _load_session_text2sql_context(
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request: NLChatRequest,
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) -> tuple[str, bool]:
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"""
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在写入本轮之前读取会话历史，构造 Text2SQL 上文，并判断上一轮助手是否为数据查询。
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"""
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sid = (request.session_id or "").strip()
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if not sid:
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return "", False
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data = await lite_nl_store.get_messages(
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request.user_id,
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request.visitor_biz_id,
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sid,
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limit=200,
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offset=0,
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)
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if not data:
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return "", False
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items = data.get("items") or []
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if not items:
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return "", False
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last_data = last_assistant_was_data_query(items)
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block, _n = messages_to_text2sql_context(items)
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return block, last_data
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async def _append_session_if_needed(request: NLChatRequest, user_text: str, data: Dict[str, Any]) -> None:
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if not (request.session_id and request.session_id.strip()):
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return
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try:
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assistant_json = json.dumps(data, ensure_ascii=False)
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await lite_nl_store.append_exchange(
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request.user_id,
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request.visitor_biz_id,
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request.session_id.strip(),
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user_text,
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assistant_json,
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)
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except Exception as e:
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logger.warning(f"[API] 会话落库跳过: {e}")
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def _nl_dict_from_generation(result: GenerationResult) -> Dict[str, Any]:
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sql = (result.sql or "").strip()
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explain_parts: List[str] = []
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if result.metadata.get("db_empty_feedback"):
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explain_parts.append(str(result.metadata["db_empty_feedback"]))
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if result.warnings:
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explain_parts.extend(str(w) for w in result.warnings)
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if result.errors:
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explain_parts.extend(str(e) for e in result.errors)
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explain = "; ".join(explain_parts) if explain_parts else None
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branch_result: Dict[str, Any] = {
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"sql": sql,
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"columns": [],
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"rows": [],
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"row_count": 0,
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"truncated": False,
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}
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if explain:
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branch_result["sql_explain"] = explain
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dbs = result.metadata.get("db_execution_status")
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if dbs is not None:
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branch_result["db_execution_status"] = dbs
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dbe = result.metadata.get("db_empty_feedback")
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if dbe:
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branch_result["db_empty_feedback"] = dbe
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branch_result["follow_up_required"] = bool(result.valid and dbs == 0)
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sdm = result.metadata.get("sql_delivery_message")
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if sdm:
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branch_result["sql_delivery_message"] = str(sdm)
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conf = 1.0 if result.valid else 0.0
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if result.valid:
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reason = f"使用了 {len(result.tables_used)} 张表"
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else:
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reason = result.errors[0] if result.errors else "SQL生成未通过验证"
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payload: Dict[str, Any] = {
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"intent": {"intent": "DATA_QUERY", "confidence": conf, "reason": reason},
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"branch_result": branch_result,
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}
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qo = result.metadata.get("question_original")
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qz = result.metadata.get("question_zh_normalized")
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if qo and qz:
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payload["query_normalization"] = {"original": qo, "zh": qz}
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return payload
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def _sql_gen_stream_html(result: GenerationResult) -> str:
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sql = (result.sql or "").strip()
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inner = json.dumps({"sql": sql}, ensure_ascii=False)
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parts = [f"<data>{inner}</data>"]
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explain = ""
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if result.metadata.get("sql_delivery_message"):
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parts.append(
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f'<div class="sql-delivery-body">{html_lib.escape(str(result.metadata["sql_delivery_message"]))}</div>'
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)
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if result.metadata.get("db_empty_feedback"):
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explain = str(result.metadata["db_empty_feedback"])
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elif result.warnings:
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explain = str(result.warnings[0])
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elif result.errors:
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explain = "; ".join(str(e) for e in result.errors[:5])
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if explain.strip():
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parts.append(f'<div class="sql-explain-body">{html_lib.escape(explain)}</div>')
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return "".join(parts)
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async def _run_generate(
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question: str,
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top_k: int = 20,
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dialog_context: Optional[str] = None,
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) -> GenerationResult:
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orch = get_orchestrator()
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dialect = resolve_sql_dialect(os.getenv("TEXT2SQL_DIALECT", "sqlserver"))
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dc = (dialog_context or "").strip() or None
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def _call() -> GenerationResult:
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return orch.generate(
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question=question.strip(),
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dialect=dialect,
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top_k_candidates=top_k,
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dialog_context=dc,
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)
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return await asyncio.to_thread(_call)
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async def _chat_stream_events(request: NLChatRequest) -> AsyncIterator[bytes]:
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if not request.message or not request.message.strip():
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yield _sse_data({"code": 400, "msg": "消息内容不能为空", "data": None})
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return
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text = request.message.strip()
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orch = get_orchestrator()
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dialog_block, last_data = await _load_session_text2sql_context(request)
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classified = await asyncio.to_thread(
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classify_dialog,
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text,
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last_turn_was_data_query=last_data,
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dialog_context=dialog_block or None,
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llm_client=orch.deepseek,
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)
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if classified.intent == DialogIntent.CONVERSATION:
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reply = classified.reply_suggestion or ""
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logger.info("[API/stream] 对话意图: conversation（跳过 Text2SQL，与 CLI single_query 一致）")
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yield _sse_data({"stage": "orchestrator", "stream_kind": "content", "content": "CHAT"})
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yield _sse_data({"stage": "chat", "stream_kind": "content", "content": reply})
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data_dict = _conversation_nl_dict(reply)
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yield _sse_data({"code": 200, "msg": "success", "data": data_dict})
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await _append_session_if_needed(request, text, data_dict)
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return
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yield _sse_data({"stage": "orchestrator", "stream_kind": "content", "content": "DATA_QUERY"})
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try:
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result = await _run_generate(text, dialog_context=dialog_block or None)
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except Exception as e:
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logger.error(f"[API/stream] 生成异常: {e}")
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yield _sse_data({"code": 500, "msg": str(e), "data": None})
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return
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yield _sse_data({"stage": "sql_gen", "stream_kind": "content", "content": _sql_gen_stream_html(result)})
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data_dict = _nl_dict_from_generation(result)
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msg = "success" if result.valid else "partial"
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yield _sse_data({"code": 200, "msg": msg, "data": data_dict})
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await _append_session_if_needed(request, text, data_dict)
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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logger.info("=" * 60)
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logger.info("Text2SQL API Server 启动中...")
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logger.info("=" * 60)
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try:
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get_orchestrator()
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logger.info("[OK] 服务已就绪")
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except Exception as e:
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logger.error(f"服务初始化失败: {e}")
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raise
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yield
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logger.info("Text2SQL API Server 已关闭")
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app = FastAPI(
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title="Text2SQL NL Chat API",
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description="自然语言转SQL的对话接口",
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version="1.0.0",
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lifespan=lifespan
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)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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@app.get("/health")
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async def health_check():
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"""健康检查接口"""
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return {"status": "ok", "message": "Text2SQL API is running"}
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@app.post("/g3sb/api/nl/chat")
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async def nl_chat(request: NLChatRequest):
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"""
|
||
自然语言对话接口（非流式），响应结构与流式结束包一致。
|
||
寒暄/致谢等先经 classify_dialog，与 CLI 一致。
|
||
"""
|
||
try:
|
||
if not request.message or not request.message.strip():
|
||
raise HTTPException(status_code=400, detail="消息内容不能为空")
|
||
text = request.message.strip()
|
||
logger.info(f"[API] 问题: {text[:100]}...")
|
||
orch = get_orchestrator()
|
||
dialog_block, last_data = await _load_session_text2sql_context(request)
|
||
classified = await asyncio.to_thread(
|
||
classify_dialog,
|
||
text,
|
||
last_turn_was_data_query=last_data,
|
||
dialog_context=dialog_block or None,
|
||
llm_client=orch.deepseek,
|
||
)
|
||
if classified.intent == DialogIntent.CONVERSATION:
|
||
reply = classified.reply_suggestion or ""
|
||
logger.info("[API/chat] 对话意图: conversation（跳过 Text2SQL）")
|
||
data_dict = _conversation_nl_dict(reply)
|
||
await _append_session_if_needed(request, text, data_dict)
|
||
return {"code": 200, "msg": "success", "data": data_dict}
|
||
|
||
result = await _run_generate(text, dialog_context=dialog_block or None)
|
||
data_dict = _nl_dict_from_generation(result)
|
||
response_data = NLChatSuccessData.model_validate(data_dict)
|
||
msg = "success" if result.valid else "partial"
|
||
if result.valid:
|
||
logger.info(f"[API] 生成成功: {(result.sql or '')[:80]}...")
|
||
else:
|
||
logger.warning(f"[API] 生成失败: {result.errors}")
|
||
await _append_session_if_needed(request, text, data_dict)
|
||
return ApiEnvelope(code=200, msg=msg, data=response_data)
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"[API] 异常: {e}")
|
||
raise HTTPException(status_code=500, detail=str(e))
|
||
|
||
|
||
@app.post("/g3sb/api/nl/chat/stream")
|
||
async def nl_chat_stream(request: NLChatRequest):
|
||
"""
|
||
自然语言对话流式接口（SSE）。
|
||
事件体为 JSON：分片 delta `{stage, stream_kind, content}` 或结束包 `{code, msg, data}`。
|
||
"""
|
||
return StreamingResponse(
|
||
_chat_stream_events(request),
|
||
media_type="text/event-stream",
|
||
headers={
|
||
"Cache-Control": "no-cache",
|
||
"Connection": "keep-alive",
|
||
"X-Accel-Buffering": "no",
|
||
},
|
||
)
|
||
|
||
|
||
@app.post("/g3sb/api/nl/sessions")
|
||
async def nl_create_session(body: SessionCreateBody):
|
||
row = await lite_nl_store.create_session(body.user_id, body.visitor_biz_id, body.title)
|
||
return {"code": 200, "msg": "success", "data": row}
|
||
|
||
|
||
@app.get("/g3sb/api/nl/sessions")
|
||
async def nl_list_sessions(
|
||
user_id: Optional[str] = None,
|
||
visitor_biz_id: Optional[str] = None,
|
||
limit: int = 100,
|
||
offset: int = 0,
|
||
):
|
||
data = await lite_nl_store.list_sessions(user_id, visitor_biz_id, limit, offset)
|
||
return {"code": 200, "msg": "success", "data": data}
|
||
|
||
|
||
@app.get("/g3sb/api/nl/sessions/{session_id}/messages")
|
||
async def nl_get_messages(
|
||
session_id: str = FPath(..., description="会话ID"),
|
||
user_id: Optional[str] = None,
|
||
visitor_biz_id: Optional[str] = None,
|
||
limit: int = 500,
|
||
offset: int = 0,
|
||
):
|
||
data = await lite_nl_store.get_messages(user_id, visitor_biz_id, session_id, limit, offset)
|
||
if data is None:
|
||
return JSONResponse(status_code=200, content={"code": 404, "msg": "session not found", "data": None})
|
||
return {"code": 200, "msg": "success", "data": data}
|
||
|
||
|
||
@app.patch("/g3sb/api/nl/sessions/{session_id}/title")
|
||
async def nl_patch_session_title(session_id: str, body: SessionTitlePatchBody):
|
||
row = await lite_nl_store.update_session_title(
|
||
body.user_id, body.visitor_biz_id, session_id, body.title
|
||
)
|
||
if not row:
|
||
return JSONResponse(status_code=200, content={"code": 404, "msg": "session not found", "data": None})
|
||
return {"code": 200, "msg": "success", "data": row}
|
||
|
||
|
||
@app.patch("/g3sb/api/nl/sessions/{session_id}/messages/{message_id}")
|
||
async def nl_patch_session_message(
|
||
session_id: str,
|
||
message_id: int,
|
||
body: SessionMessagePatchBody,
|
||
):
|
||
row = await lite_nl_store.patch_message(
|
||
body.user_id, body.visitor_biz_id, session_id, message_id, body.content
|
||
)
|
||
if not row:
|
||
return JSONResponse(status_code=200, content={"code": 404, "msg": "message not found", "data": None})
|
||
return {"code": 200, "msg": "success", "data": row}
|
||
|
||
|
||
@app.delete("/g3sb/api/nl/sessions/{session_id}")
|
||
async def nl_delete_session(
|
||
session_id: str,
|
||
user_id: Optional[str] = None,
|
||
visitor_biz_id: Optional[str] = None,
|
||
):
|
||
row = await lite_nl_store.delete_session(user_id, visitor_biz_id, session_id)
|
||
if not row:
|
||
return JSONResponse(status_code=200, content={"code": 404, "msg": "session not found", "data": None})
|
||
return {"code": 200, "msg": "success", "data": row}
|
||
|
||
|
||
@app.post("/g3sb/api/nl/sql/execute")
|
||
async def nl_sql_execute(_body: SqlExecuteBody):
|
||
return {
|
||
"code": 501,
|
||
"msg": "本机 Text2SQL 演示服务未接数据库，无法执行 SQL。请配置独立 NL 网关或移除「运行 SQL」依赖。",
|
||
"data": None,
|
||
}
|
||
|
||
|
||
@app.post("/g3sb/api/nl/operation-logs")
|
||
async def nl_post_operation_log(_body: Dict[str, Any] = Body(...)):
|
||
return {"code": 200, "msg": "success", "data": None}
|
||
|
||
|
||
@app.get("/g3sb/api/nl/operation-logs")
|
||
async def nl_list_operation_logs(
|
||
user_id: Optional[str] = None,
|
||
visitor_biz_id: Optional[str] = None,
|
||
limit: int = 50,
|
||
offset: int = 0,
|
||
q: Optional[str] = None,
|
||
op_type: Optional[str] = None,
|
||
result: Optional[str] = None,
|
||
date_from: Optional[str] = None,
|
||
date_to: Optional[str] = None,
|
||
):
|
||
_ = (user_id, visitor_biz_id, q, op_type, result, date_from, date_to)
|
||
return {"code": 200, "msg": "success", "data": {"items": [], "limit": limit, "offset": offset}}
|
||
|
||
|
||
@app.get("/g3sb/api/nl/favorites")
|
||
async def nl_list_favorites(
|
||
user_id: Optional[str] = None,
|
||
visitor_biz_id: Optional[str] = None,
|
||
):
|
||
grouped = await lite_nl_store.get_favorites_grouped(user_id, visitor_biz_id)
|
||
return {"code": 200, "msg": "success", "data": grouped}
|
||
|
||
|
||
@app.post("/g3sb/api/nl/favorites")
|
||
async def nl_create_favorite(body: FavoriteCreateBody):
|
||
row = await lite_nl_store.add_favorite(body.user_id, body.visitor_biz_id, body.model_dump(exclude_none=True))
|
||
return {"code": 200, "msg": "success", "data": row}
|
||
|
||
|
||
@app.patch("/g3sb/api/nl/favorites/{fav_id}")
|
||
async def nl_patch_favorite(
|
||
fav_id: str,
|
||
body: Dict[str, Any] = Body(default_factory=dict),
|
||
user_id: Optional[str] = None,
|
||
visitor_biz_id: Optional[str] = None,
|
||
):
|
||
patch = {k: v for k, v in body.items() if k not in ("user_id", "visitor_biz_id")}
|
||
ok = await lite_nl_store.patch_favorite(user_id, visitor_biz_id, fav_id, patch)
|
||
if not ok:
|
||
return JSONResponse(status_code=200, content={"code": 404, "msg": "favorite not found", "data": None})
|
||
return {"code": 200, "msg": "success", "data": {}}
|
||
|
||
|
||
@app.delete("/g3sb/api/nl/favorites/{fav_id}")
|
||
async def nl_delete_favorite(
|
||
fav_id: str,
|
||
user_id: Optional[str] = None,
|
||
visitor_biz_id: Optional[str] = None,
|
||
):
|
||
ok = await lite_nl_store.delete_favorite(user_id, visitor_biz_id, fav_id)
|
||
if not ok:
|
||
return JSONResponse(status_code=200, content={"code": 404, "msg": "favorite not found", "data": None})
|
||
return {"code": 200, "msg": "success", "data": {"ok": True}}
|
||
|
||
|
||
@app.get("/g3sb/api/nl/knowledge-docs/uploads")
|
||
async def nl_list_knowledge_uploads(
|
||
doc_type: Optional[str] = None,
|
||
limit: int = 50,
|
||
offset: int = 0,
|
||
):
|
||
_ = doc_type
|
||
return {"code": 200, "msg": "success", "data": {"items": [], "limit": limit, "offset": offset}}
|
||
|
||
|
||
@app.post("/g3sb/api/nl/knowledge-docs/upload")
|
||
async def nl_upload_knowledge_doc():
|
||
return JSONResponse(
|
||
status_code=200,
|
||
content={"code": 501, "msg": "knowledge upload not supported on lite API", "data": None},
|
||
)
|
||
|
||
|
||
@app.get("/g3sb/api/nl/admin/visibility")
|
||
async def admin_visibility():
|
||
"""管理端可见性接口"""
|
||
return {
|
||
"code": 200,
|
||
"msg": "success",
|
||
"data": {
|
||
"admin_visible": True
|
||
}
|
||
}
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import uvicorn
|
||
import multiprocessing
|
||
|
||
port = int(os.getenv("API_PORT", "8041"))
|
||
host = os.getenv("API_HOST", "0.0.0.0")
|
||
|
||
logger.info(f"启动服务: http://{host}:{port}")
|
||
logger.info(f"API文档: http://{host}:{port}/docs")
|
||
|
||
# PyInstaller + multiprocessing（spawn）兼容
|
||
multiprocessing.freeze_support()
|
||
|
||
# PyInstaller 打包后必须使用 app 对象，不能使用字符串模块名
|
||
uvicorn.run(
|
||
app,
|
||
host=host,
|
||
port=port,
|
||
reload=False,
|
||
log_level="info"
|
||
)
|