feat: 累积功能变更 — 聊天集成、租户管理、小程序更新、ETL 增强、迁移脚本
包含多个会话的累积代码变更： - backend: AI 聊天服务、触发器调度、认证增强、WebSocket、调度器最小间隔 - admin-web: ETL 状态页、任务管理、调度配置、登录优化 - miniprogram: 看板页面、聊天集成、UI 组件、导航更新 - etl: DWS 新任务（finance_area_daily/board_cache）、连接器增强 - tenant-admin: 项目初始化 - db: 19 个迁移脚本（etl_feiqiu 11 + zqyy_app 8） - packages/shared: 枚举和工具函数更新 - tools: 数据库工具、报表生成、健康检查 - docs: PRD/架构/部署/合约文档更新 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@@ -18,12 +18,12 @@ from __future__ import annotations
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import json
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import logging
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import os
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from fastapi import APIRouter, Depends, HTTPException, Query, status
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from fastapi.responses import StreamingResponse
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from app.ai.bailian_client import BailianClient
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from app.ai.config import AIConfig
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from app.ai.dashscope_client import DashScopeClient
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from app.auth.dependencies import CurrentUser
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from app.database import get_connection
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from app.middleware.permission import require_approved
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@@ -39,6 +39,14 @@ from app.schemas.xcx_chat import (
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SendMessageResponse,
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)
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from app.services.chat_service import ChatService
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from app.trace.decorators import trace_service
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from app.trace.sse_wrapper import (
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record_ai_call,
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record_ai_error,
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record_sse_end,
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record_sse_start,
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record_sse_token,
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)
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logger = logging.getLogger(__name__)
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@@ -49,6 +57,7 @@ router = APIRouter(prefix="/api/xcx/chat", tags=["小程序 CHAT"])
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@router.get("/history", response_model=ChatHistoryResponse)
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@trace_service("查询对话历史", "List chat history")
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async def list_chat_history(
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page: int = Query(1, ge=1),
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page_size: int = Query(20, ge=1, le=100),
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@@ -75,6 +84,7 @@ async def list_chat_history(
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@router.get("/messages", response_model=ChatMessagesResponse)
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@trace_service("通过上下文查询消息", "Get messages by context")
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async def get_chat_messages_by_context(
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context_type: str = Query(..., alias="contextType"),
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context_id: str = Query(..., alias="contextId"),
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@@ -111,6 +121,7 @@ async def get_chat_messages_by_context(
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@router.get("/{chat_id}/messages", response_model=ChatMessagesResponse)
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@trace_service("查询对话消息", "Get chat messages")
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async def get_chat_messages(
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chat_id: int,
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page: int = Query(1, ge=1),
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@@ -140,6 +151,7 @@ async def get_chat_messages(
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@router.post("/stream")
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@trace_service("SSE 流式对话", "Chat stream SSE")
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async def chat_stream(
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body: ChatStreamRequest,
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user: CurrentUser = Depends(require_approved()),
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@@ -174,16 +186,74 @@ async def chat_stream(
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- event: done\\ndata: {"messageId": ..., "createdAt": "..."}\\n\\n
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- event: error\\ndata: {"message": "..."}\\n\\n
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"""
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import time as _time
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full_reply_parts: list[str] = []
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tokens_total = 0
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_sse_start_ts = _time.perf_counter()
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# SSE trace: 流开始
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record_sse_start(
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endpoint="/api/xcx/chat/stream",
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user_id=user.user_id,
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chat_id=str(body.chat_id),
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)
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try:
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bailian = _get_bailian_client()
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client = _get_dashscope_client()
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config = AIConfig.from_env()
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# 获取历史消息作为上下文
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messages = _build_ai_messages(body.chat_id)
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# 构建 prompt（最近 20 条历史 + 当前消息已在历史中）
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prompt = _build_prompt(body.chat_id)
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# 流式调用百炼 API
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async for chunk in bailian.chat_stream(messages):
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# 构建 biz_params（用户身份信息）
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biz_params = {
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"User_ID": str(user.user_id),
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"Role": getattr(user, "role", "coach"),
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"Nickname": getattr(user, "nickname", ""),
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}
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# 看板入口：注入页面上下文到 prompt
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if body.source_page:
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try:
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from app.ai.data_fetchers import build_page_text
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filters = {}
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if body.page_context:
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filters = body.page_context
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context_id = filters.pop("contextId", None)
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page_text = await build_page_text(
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source_page=body.source_page,
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context_id=context_id,
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site_id=user.site_id,
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filters=filters if filters else None,
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)
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if page_text:
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prompt = f"[页面上下文: {body.source_page}]\n{page_text}\n\n{prompt}"
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except Exception:
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logger.warning("页面上下文注入失败: source_page=%s", body.source_page, exc_info=True)
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# 获取 session_id（对话复用）
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session_id = svc.get_session_id(body.chat_id) if hasattr(svc, "get_session_id") else None
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# SSE trace: AI 调用
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record_ai_call(
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app_id=config.app_id_1_chat,
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prompt_length=len(prompt),
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session_id=session_id or "",
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)
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# 流式调用 DashScope Application API
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async for chunk in client.call_app_stream(
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app_id=config.app_id_1_chat,
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prompt=prompt,
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session_id=session_id,
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biz_params=biz_params,
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):
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full_reply_parts.append(chunk)
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tokens_total += 1
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# SSE trace: 每 10 个 token 记录一次
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record_sse_token(token_count=1, total_tokens=tokens_total)
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yield f"event: message\ndata: {json.dumps({'token': chunk}, ensure_ascii=False)}\n\n"
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# 流结束：拼接完整回复并持久化
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@@ -202,9 +272,23 @@ async def chat_stream(
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)
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yield f"event: done\ndata: {done_data}\n\n"
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# SSE trace: 流正常结束
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_sse_elapsed = (_time.perf_counter() - _sse_start_ts) * 1000
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record_sse_end(
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total_tokens=tokens_total,
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total_duration_ms=_sse_elapsed,
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completed=True,
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)
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except Exception as e:
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logger.error("SSE 流式对话异常: %s", e, exc_info=True)
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# SSE trace: AI 错误
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record_ai_error(
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error_type=type(e).__name__,
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message=str(e),
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)
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# 如果已有部分回复，仍然持久化
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if full_reply_parts:
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partial = "".join(full_reply_parts)
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@@ -220,6 +304,14 @@ async def chat_stream(
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)
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yield f"event: error\ndata: {error_data}\n\n"
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# SSE trace: 流异常结束
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_sse_elapsed = (_time.perf_counter() - _sse_start_ts) * 1000
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record_sse_end(
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total_tokens=tokens_total,
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total_duration_ms=_sse_elapsed,
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completed=False,
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)
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return StreamingResponse(
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event_generator(),
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media_type="text/event-stream",
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@@ -235,6 +327,7 @@ async def chat_stream(
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@router.post("/{chat_id}/messages", response_model=SendMessageResponse)
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@trace_service("发送消息", "Send message")
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async def send_message(
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chat_id: int,
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body: SendMessageRequest,
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@@ -280,27 +373,25 @@ def _to_message_item(msg: dict) -> ChatMessageItem:
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)
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def _get_bailian_client() -> BailianClient:
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"""从环境变量构建 BailianClient，缺失时报错。"""
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api_key = os.environ.get("BAILIAN_API_KEY")
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base_url = os.environ.get("BAILIAN_BASE_URL")
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model = os.environ.get("BAILIAN_MODEL")
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if not api_key or not base_url or not model:
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raise RuntimeError(
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"百炼 API 环境变量缺失，需要 BAILIAN_API_KEY、BAILIAN_BASE_URL、BAILIAN_MODEL"
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)
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return BailianClient(api_key=api_key, base_url=base_url, model=model)
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def _get_dashscope_client() -> DashScopeClient:
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"""从环境变量构建 DashScopeClient，缺失时报错。"""
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config = AIConfig.from_env()
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return DashScopeClient(api_key=config.api_key, workspace_id=config.workspace_id)
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def _build_ai_messages(chat_id: int) -> list[dict]:
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"""构建发送给 AI 的消息列表（含历史上下文）。"""
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def _build_prompt(chat_id: int) -> str:
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"""构建发送给 DashScope Application 的 prompt。
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从 ai_messages 取最近 20 条历史，拼接为文本 prompt。
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百炼 Application API 的 System Prompt 在控制台配置，此处只传用户对话内容。
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"""
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conn = get_connection()
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try:
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with conn.cursor() as cur:
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cur.execute(
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"""
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SELECT role, content FROM biz.ai_messages
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WHERE conversation_id = %s
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WHERE conversation_id = %s AND role != 'system'
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ORDER BY created_at ASC
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""",
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(chat_id,),
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@@ -309,21 +400,22 @@ def _build_ai_messages(chat_id: int) -> list[dict]:
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finally:
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conn.close()
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messages: list[dict] = []
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# 取最近 20 条
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recent = history[-20:] if len(history) > 20 else history
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for role, msg_content in recent:
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messages.append({"role": role, "content": msg_content})
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# 如果没有 system 消息，添加默认 system prompt
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if not messages or messages[0]["role"] != "system":
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system_prompt = {
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"role": "system",
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"content": json.dumps(
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{"task": "你是台球门店的 AI 助手，根据用户的问题和当前页面上下文提供帮助。"},
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ensure_ascii=False,
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),
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}
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messages.insert(0, system_prompt)
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# 如果只有一条（刚发送的用户消息），直接返回内容
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if len(recent) == 1:
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return recent[0][1]
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return messages
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# 多条历史：拼接为对话格式，最后一条为当前用户消息
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parts: list[str] = []
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for role, msg_content in recent[:-1]:
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label = "用户" if role == "user" else "AI"
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parts.append(f"{label}: {msg_content}")
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# 最后一条是当前用户消息，作为主 prompt
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current_msg = recent[-1][1] if recent else ""
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if parts:
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context = "\n".join(parts)
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return f"[历史对话]\n{context}\n\n[当前问题]\n{current_msg}"
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return current_msg
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