feat: v1.2.0 - 图片去重与管理、微信机器人优化、搜索设置可配置
主要功能: - 图片上传时 OCR 内容去重（3个上传端点统一使用公共函数 _check_ocr_duplicate） - 图片管理 Tab：展示所有图片、手动删除、一键去重 - 搜索结果详情弹窗增加删除按钮（带确认弹窗） - 图片管理卡片点击查看详情（复用 showOcrDetailModal） - 搜索限制和 LLM 批量判断数量可通过网站设置 - MiniMax API 调用添加 reasoning_split=True - 企业微信机器人：WebSocket 长连接、图片搜索、配置化搜索数量 - 版本号升级至 1.2.0
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EduBrain Dev
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app/mcp/server.py
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297
app/mcp/server.py
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"""
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MCP Server 实现
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基于 MCP SDK 提供知识库查询工具，供 AI Agent 调用
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工具列表:
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1. search_knowledge - 语义搜索知识库
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2. get_page - 获取知识页面详情
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3. list_courses - 列出所有课程
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4. get_page_chunks - 获取页面的分块内容
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"""
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from __future__ import annotations
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import json
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import logging
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from mcp.server.fastmcp import FastMCP
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logger = logging.getLogger(__name__)
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# ──────────────────────────── 创建 MCP Server ────────────────────────────
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mcp = FastMCP(
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name="EduBrain-Knowledge",
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instructions=(
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"中文直播教育知识库助手。你可以使用以下工具来搜索和查询知识库中的内容：\n"
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"- search_knowledge: 语义搜索知识库，支持按课程、讲师、日期过滤\n"
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"- get_page: 获取指定知识页面的完整内容\n"
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"- list_courses: 列出知识库中所有课程\n"
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"- get_page_chunks: 获取页面的分块内容（用于精确定位）\n"
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"- search_images: 根据关键字搜索已 OCR 识别的聊天截图，返回图片路径"
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),
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)
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# ──────────────────────────── 工具定义 ────────────────────────────
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@mcp.tool()
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async def search_knowledge(
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query: str,
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top_k: int = 5,
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course_name: str = "",
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teacher_name: str = "",
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threshold: float = 0.5,
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) -> str:
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"""
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语义搜索知识库。
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Args:
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query: 搜索查询文本（自然语言描述你要查找的知识点）
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top_k: 返回结果数量，默认 5
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course_name: 按课程名称过滤（可选）
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teacher_name: 按讲师名称过滤（可选）
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threshold: 相似度阈值（0-1），默认 0.5
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Returns:
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搜索结果列表，包含标题、内容摘要和相似度分数
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"""
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from app.database import async_session_factory
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from app.schemas.search import SearchRequest
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from app.services.search_service import SearchService
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async with async_session_factory() as session:
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try:
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request = SearchRequest(
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query=query,
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top_k=top_k,
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course_name=course_name or None,
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teacher_name=teacher_name or None,
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threshold=threshold,
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use_fulltext=True,
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)
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service = SearchService(session)
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response = await service.search(request)
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if not response.results:
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return f"未找到与「{query}」相关的知识内容。"
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# 格式化搜索结果
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output_parts = [
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f"找到 {response.total} 条相关结果（耗时 {response.elapsed_ms:.0f}ms）：\n"
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]
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for i, result in enumerate(response.results, 1):
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output_parts.append(
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f"【{i}】{result.page_title} "
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f"(相似度: {result.score:.2%})"
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)
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if result.course_name:
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output_parts.append(f" 课程: {result.course_name}")
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if result.teacher_name:
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output_parts.append(f" 讲师: {result.teacher_name}")
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# 截断内容避免过长
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content = result.content[:200]
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if len(result.content) > 200:
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content += "..."
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output_parts.append(f" 内容: {content}")
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output_parts.append("")
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return "\n".join(output_parts)
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except Exception as e:
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logger.error("MCP 搜索失败: %s", e, exc_info=True)
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return f"搜索出错: {str(e)}"
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@mcp.tool()
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async def get_page(page_id: int) -> str:
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"""
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获取指定知识页面的完整内容。
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Args:
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page_id: 知识页面 ID
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Returns:
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页面的完整内容，包括标题、正文、元数据
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"""
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from app.database import async_session_factory
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from app.services.page_service import PageService
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async with async_session_factory() as session:
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try:
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service = PageService(session)
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page = await service.get_page(page_id)
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if page is None:
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return f"未找到 ID 为 {page_id} 的知识页面。"
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parts = [
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f"标题: {page.title}",
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]
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if page.course_name:
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parts.append(f"课程: {page.course_name}")
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if page.teacher_name:
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parts.append(f"讲师: {page.teacher_name}")
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if page.live_date:
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parts.append(f"直播日期: {page.live_date}")
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parts.append(f"\n{page.content}")
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return "\n".join(parts)
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except Exception as e:
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logger.error("MCP 获取页面失败: %s", e, exc_info=True)
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return f"获取页面出错: {str(e)}"
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@mcp.tool()
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async def list_courses() -> str:
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"""
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列出知识库中所有课程。
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Returns:
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课程列表，包含课程名称、讲师、页面数量等信息
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"""
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from sqlalchemy import text
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from app.database import async_session_factory
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async with async_session_factory() as session:
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try:
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result = await session.execute(text("""
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SELECT
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course_name,
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teacher_name,
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COUNT(*) as page_count,
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MIN(live_date) as earliest_date,
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MAX(live_date) as latest_date
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FROM knowledge_pages
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WHERE course_name IS NOT NULL
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GROUP BY course_name, teacher_name
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ORDER BY page_count DESC
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"""))
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rows = result.fetchall()
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if not rows:
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return "知识库中暂无课程数据。"
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parts = [f"共 {len(rows)} 个课程：\n"]
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for i, row in enumerate(rows, 1):
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parts.append(
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f"{i}. {row.course_name}"
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f"（讲师: {row.teacher_name or '未知'}，"
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f"页面: {row.page_count}，"
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f"日期: {row.earliest_date or '未知'} ~ {row.latest_date or '未知'}）"
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)
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return "\n".join(parts)
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except Exception as e:
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logger.error("MCP 列出课程失败: %s", e, exc_info=True)
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return f"列出课程出错: {str(e)}"
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@mcp.tool()
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async def search_images(
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keyword: str,
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top_k: int = 10,
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) -> str:
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"""
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根据关键字搜索已 OCR 识别的聊天截图。
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Args:
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keyword: 搜索关键字（在 OCR 识别文本中查找）
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top_k: 返回结果数量，默认 10
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Returns:
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匹配的图片列表，包含图片路径和 OCR 文本预览
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"""
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from sqlalchemy import select
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from app.database import async_session_factory
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from app.models.base import OCRImage
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async with async_session_factory() as session:
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try:
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like_pattern = f"%{keyword}%"
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result = await session.execute(
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select(OCRImage)
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.where(
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OCRImage.status == "completed",
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OCRImage.ocr_text.ilike(like_pattern),
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)
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.order_by(OCRImage.created_at.desc())
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.limit(top_k)
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)
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images = result.scalars().all()
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if not images:
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return f"未找到包含「{keyword}」的图片。"
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parts = [f"找到 {len(images)} 张包含「{keyword}」的图片：\n"]
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for i, img in enumerate(images, 1):
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preview = (img.ocr_text[:100] + "...") if img.ocr_text and len(img.ocr_text) > 100 else (img.ocr_text or "")
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parts.append(
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f"【{i}】图片路径: {img.file_path}\n"
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f" OCR 预览: {preview}\n"
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f" 置信度: {img.confidence:.0%} | 识别方式: {img.provider}\n"
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)
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return "\n".join(parts)
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except Exception as e:
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logger.error("MCP 图片搜索失败: %s", e, exc_info=True)
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return f"图片搜索出错: {str(e)}"
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@mcp.tool()
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async def get_page_chunks(page_id: int) -> str:
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"""
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获取知识页面的分块内容（用于精确定位知识点）。
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Args:
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page_id: 知识页面 ID
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Returns:
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该页面的所有分块内容列表
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"""
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from app.database import async_session_factory
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from app.services.page_service import PageService
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async with async_session_factory() as session:
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try:
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service = PageService(session)
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chunks = await service.get_page_chunks(page_id)
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if not chunks:
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return f"页面 {page_id} 没有分块数据。"
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parts = [f"页面 {page_id} 共 {len(chunks)} 个分块：\n"]
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for i, chunk in enumerate(chunks, 1):
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content = chunk.content[:300]
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if len(chunk.content) > 300:
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content += "..."
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parts.append(f"【分块 {i}】(ID: {chunk.id})\n{content}\n")
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return "\n".join(parts)
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except Exception as e:
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logger.error("MCP 获取分块失败: %s", e, exc_info=True)
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return f"获取分块出错: {str(e)}"
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# ──────────────────────────── 启动入口 ────────────────────────────
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def run_mcp_server(transport: str = "stdio", port: int = 8001):
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"""
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启动 MCP Server。
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Args:
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transport: 传输协议，支持 "stdio"（默认）或 "sse"
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port: SSE 模式下的端口号
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"""
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if transport == "sse":
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mcp.run(transport="sse", port=port)
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else:
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mcp.run(transport="stdio")
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if __name__ == "__main__":
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run_mcp_server()
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