311 lines
10 KiB
Python
311 lines
10 KiB
Python
"""MCP 服务 (M8) — FastMCP 国际财经 Deep Research 工具。
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暴露 5 个 MCP Tool 给 Claude Code / Cherry Studio 调用。
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"""
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import json
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import logging
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from dataclasses import dataclass
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from datetime import datetime
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from pathlib import Path
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from typing import Any
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from mcp.server.fastmcp import FastMCP
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from crawler.utils import get_news_day
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from embedding.client import (
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EmbeddingConfig,
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embed_batch,
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load_embedding_config,
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make_embedding_client,
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)
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from vectorstore.client import VectorStore, make_qdrant_client
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from vectorstore.models import SearchFilter
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logger = logging.getLogger(__name__)
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# --------------------------------------------------------------------------- #
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# 单例 Backend
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# --------------------------------------------------------------------------- #
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@dataclass
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class _Backend:
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emb_config: EmbeddingConfig
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emb_client: Any
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vector_store: VectorStore
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_backend: _Backend | None = None
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def _get_backend() -> _Backend:
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"""延迟初始化 MCP 后端（embedder + vector_store）。"""
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global _backend
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if _backend is None:
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config = load_embedding_config()
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client = make_embedding_client(config)
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qdrant = make_qdrant_client()
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store = VectorStore(qdrant)
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_backend = _Backend(emb_config=config, emb_client=client, vector_store=store)
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logger.info("MCP 后端就绪: embed=%s, qdrant=%d 条",
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config.model, store.count())
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return _backend
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# --------------------------------------------------------------------------- #
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# 嵌入 + 检索
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# --------------------------------------------------------------------------- #
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def _search(
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query: str,
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top_k: int = 10,
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search_filter: SearchFilter | None = None,
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score_threshold: float = 0.3,
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) -> list[dict[str, Any]]:
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"""嵌入查询 → Qdrant 检索 → 返回 dict 列表。"""
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be = _get_backend()
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vectors = embed_batch(be.emb_client, be.emb_config, [query])
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if not vectors:
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return []
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results = be.vector_store.query(
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query_vector=vectors[0],
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top_k=top_k,
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search_filter=search_filter,
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score_threshold=score_threshold,
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)
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return [
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{
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"title": r.title,
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"title_zh": r.title_zh,
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"url": r.url,
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"source": r.source_id,
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"score": round(r.score, 4),
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"publish_time": r.publish_time,
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"events": r.events,
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"content_zh_preview": r.content_zh_preview,
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}
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for r in results
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]
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def _load_today_events(date_str: str | None = None) -> list[dict]:
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"""加载当日高重要度事件。"""
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day = date_str or get_news_day()
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ev_dir = Path(f"data/events/{day}")
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if not ev_dir.is_dir():
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return []
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events: list[dict] = []
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for fp in sorted(ev_dir.glob("*.json")):
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if fp.name == "index.json":
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continue
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try:
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data = json.loads(fp.read_text(encoding="utf-8"))
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for ev in data.get("events", []):
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if ev.get("importance", 0) >= 4:
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events.append({
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**ev,
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"title": data.get("title", ""),
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"title_zh": data.get("title_zh", ""),
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"url": data.get("url", ""),
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"source_id": data.get("source_id", ""),
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"publish_time": data.get("publish_time", ""),
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})
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except (json.JSONDecodeError, OSError):
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pass
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return sorted(events, key=lambda e: -e.get("importance", 0))
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def _fmt_results(hits: list[dict[str, Any]], query: str) -> str:
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"""检索结果 → Markdown 格式化。"""
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if not hits:
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return f"未找到与「{query}」相关的结果。"
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lines = [f"## 🔍 检索结果: {query}", "", f"共 {len(hits)} 条:", ""]
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for i, h in enumerate(hits, 1):
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title = h.get("title_zh") or h.get("title", "无标题")
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sentiment_map = {"positive": "🟢利好", "neutral": "⚪中性", "negative": "🔴利空"}
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lines.append(f"### {i}. {title}")
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lines.append(f"- 来源: {h['source']} | 相似度: {h['score']}")
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lines.append(f"- 🔗 {h.get('url', '')}")
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for ev in h.get("events", []):
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s = sentiment_map.get(ev.get("sentiment", ""), "")
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codes = ",".join(ev.get("stock_codes", [])[:5])
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code_str = f" [{codes}]" if codes else ""
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lines.append(
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f" - {s} [{ev.get('event_type', '')}] "
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f"重要度{ev.get('importance', '')} {ev.get('summary_zh', '')}{code_str}"
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)
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lines.append("")
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return "\n".join(lines)
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# --------------------------------------------------------------------------- #
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# MCP 服务器 & 工具
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# --------------------------------------------------------------------------- #
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mcp = FastMCP(
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name="国际财经DeepResearch",
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instructions="国际财经新闻 Deep Research 知识库。语义检索英文财经新闻的翻译与投资事件分析。",
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)
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# ── Tool 1: search_news ──
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@mcp.tool()
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def search_news(query: str, top_k: int = 10) -> str:
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"""语义检索国际财经新闻知识库。
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参数:
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query: 自然语言查询（中文或英文，如 "美联储利率决议" "Apple earnings"）
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top_k: 返回条数（默认 10）
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返回: Markdown 格式检索结果，含中英文标题、来源、相似度、事件摘要、URL。
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"""
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logger.info("search_news query=%r top_k=%d", query, top_k)
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hits = _search(query, top_k=top_k)
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return _fmt_results(hits, query)
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# ── Tool 2: search_by_stock ──
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@mcp.tool()
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def search_by_stock(stock_code: str, top_k: int = 10) -> str:
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"""检索指定美股代码相关的投资事件。
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参数:
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stock_code: 美股代码（如 AAPL、TSLA、MSFT，1-5 个大写字母）
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top_k: 返回条数（默认 10）
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返回: Markdown 格式检索结果，含与该股票相关的事件、情绪和重要度。
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"""
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code = stock_code.strip().upper()
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logger.info("search_by_stock code=%r top_k=%d", code, top_k)
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# 带股票过滤的搜索
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hits = _search(code, top_k=top_k, search_filter=SearchFilter(stock_codes=[code]))
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if not hits:
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# 降级：纯语义搜索
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logger.info("stock 精确命中 0 条，降级为纯语义搜索")
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hits = _search(code, top_k=top_k)
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return _fmt_results(hits, f"{code} 相关事件")
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# ── Tool 3: search_by_sentiment ──
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@mcp.tool()
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def search_by_sentiment(query: str, sentiment: str = "all", top_k: int = 20) -> str:
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"""按情绪倾向检索新闻。
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参数:
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query: 自然语言查询
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sentiment: positive(利好) / negative(利空) / neutral(中性) / all(全部，默认)
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top_k: 返回条数（默认 20）
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返回: Markdown 检索结果 + 情绪分布统计。
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"""
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logger.info("search_by_sentiment query=%r sentiment=%r", query, sentiment)
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filt = None
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if sentiment in ("positive", "negative", "neutral"):
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filt = SearchFilter(sentiment=sentiment)
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hits = _search(query, top_k=top_k, search_filter=filt)
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# 情绪统计
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pos = sum(1 for h in hits for ev in h.get("events", [])
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if ev.get("sentiment") == "positive")
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neg = sum(1 for h in hits for ev in h.get("events", [])
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if ev.get("sentiment") == "negative")
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neu = sum(1 for h in hits for ev in h.get("events", [])
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if ev.get("sentiment") == "neutral")
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header = (
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f"## 📊 情绪趋势: {query}\n\n"
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f"共 {len(hits)} 条 | 🟢利好 {pos} | 🔴利空 {neg} | ⚪中性 {neu}\n"
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)
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return header + "\n" + _fmt_results(hits, query)
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# ── Tool 4: get_today_events ──
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@mcp.tool()
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def get_today_events(importance_min: int = 4, limit: int = 15) -> str:
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"""获取当日重要投资事件。
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参数:
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importance_min: 最低重要度（默认 4）
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limit: 最多返回条数（默认 15）
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返回: Markdown 格式的当日重要事件列表。
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"""
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logger.info("get_today_events importance_min=%d limit=%d", importance_min, limit)
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events = _load_today_events()
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events = [e for e in events if e.get("importance", 0) >= importance_min]
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events = events[:limit]
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if not events:
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return "当日暂无符合条件的重要事件。"
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sentiment_map = {"positive": "🟢利好", "neutral": "⚪中性", "negative": "🔴利空"}
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lines = [
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f"## 📅 今日重要事件（重要度 ≥ {importance_min}）",
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"",
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f"共 {len(events)} 条:",
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"",
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]
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for i, ev in enumerate(events, 1):
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s = sentiment_map.get(ev.get("sentiment", ""), "")
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codes = ",".join(ev.get("stock_codes", [])[:5])
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code_str = f" [{codes}]" if codes else ""
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lines.append(f"### {i}. [{ev.get('event_type', '')}]{s} 重要度{ev.get('importance', '')}")
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lines.append(f"- **{ev.get('title_zh', ev.get('title', ''))}**")
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lines.append(f"- {ev.get('summary_zh', '')}{code_str}")
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lines.append(f"- 来源: {ev.get('source_id', '')} | {ev.get('publish_time', '')[:10]}")
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lines.append(f"- 🔗 {ev.get('url', '')}")
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lines.append("")
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return "\n".join(lines)
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# ── Tool 5: get_stats ──
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@mcp.tool()
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def get_stats() -> str:
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"""获取系统统计信息（M1-M6 管道数据、Qdrant 库规模等）。
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返回: Markdown 格式的系统统计概览。
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"""
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logger.info("get_stats")
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be = _get_backend()
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count = be.vector_store.count()
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info = be.vector_store.info()
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lines = [
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"## 📊 国际财经 Deep Research 系统统计",
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"",
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f"**Collection**: {info.name}",
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f"**向量总数**: {count}",
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f"**嵌入模型**: {be.emb_config.model} ({be.emb_config.dimension} 维)",
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f"**Qdrant 模式**: {'文件模式' if not info.exists else '运行中'}",
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f"**生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
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"",
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"### 可用工具",
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"",
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"- `search_news` — 语义检索新闻",
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"- `search_by_stock` — 按美股代码检索",
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"- `search_by_sentiment` — 按情绪过滤检索",
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"- `get_today_events` — 获取当日重要事件",
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"- `get_stats` — 系统统计概览",
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]
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return "\n".join(lines)
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