101 lines
4.0 KiB
Python
101 lines
4.0 KiB
Python
# -*- coding: utf-8 -*-
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"""
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从新闻文本中 AI 提取结构化数据，映射到面板 schema
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输出符合 panel_schema 的字段，供 db_merge 写入
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"""
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import json
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import os
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import re
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from datetime import datetime, timezone
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from typing import Any, Dict, List, Optional
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from panel_schema import validate_category, validate_severity, validate_summary
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CLEANER_AI_DISABLED = os.environ.get("CLEANER_AI_DISABLED", "0") == "1"
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OLLAMA_MODEL = os.environ.get("OLLAMA_MODEL", "llama3.1")
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def _call_ollama_extract(text: str, timeout: int = 10) -> Optional[Dict[str, Any]]:
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"""调用 Ollama 提取结构化数据。输出 JSON，仅包含新闻中可明确推断的字段"""
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if CLEANER_AI_DISABLED or not text or len(str(text).strip()) < 10:
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return None
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try:
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import requests
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prompt = f"""从以下美伊/中东新闻中提取可推断的数值，输出 JSON，仅包含有明确依据的字段。无依据则省略该字段。
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要求：
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- summary: 1-2句中文事实，≤80字
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- category: deployment|alert|intel|diplomatic|other
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- severity: low|medium|high|critical
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- us_personnel_killed, iran_personnel_killed 等：仅当新闻明确提及具体数字时填写
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- retaliation_sentiment: 0-100，仅当新闻涉及伊朗报复情绪时
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- wall_street_value: 0-100，仅当新闻涉及美股/市场反应时
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原文：{str(text)[:500]}
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直接输出 JSON，不要解释："""
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r = requests.post(
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"http://localhost:11434/api/chat",
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json={
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"model": OLLAMA_MODEL,
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"messages": [{"role": "user", "content": prompt}],
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"stream": False,
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"options": {"num_predict": 256},
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},
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timeout=timeout,
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)
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if r.status_code != 200:
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return None
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raw = (r.json().get("message", {}).get("content", "") or "").strip()
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raw = re.sub(r"^```\w*\s*|\s*```$", "", raw)
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return json.loads(raw)
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except Exception:
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return None
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def extract_from_news(text: str, timestamp: Optional[str] = None) -> Dict[str, Any]:
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"""
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从新闻文本提取结构化数据，严格符合面板 schema
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返回: { situation_update?, combat_losses_delta?, retaliation?, wall_street?, ... }
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"""
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ts = timestamp or datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%S.000Z")
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out: Dict[str, Any] = {}
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parsed = _call_ollama_extract(text)
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if not parsed:
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return out
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# situation_update
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if parsed.get("summary"):
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out["situation_update"] = {
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"summary": validate_summary(str(parsed["summary"])[:120], 120),
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"category": validate_category(str(parsed.get("category", "other")).lower()),
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"severity": validate_severity(str(parsed.get("severity", "medium")).lower()),
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"timestamp": ts,
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}
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# combat_losses 增量（仅数字字段）
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loss_us = {}
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loss_ir = {}
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for k in ["personnel_killed", "personnel_wounded", "civilian_killed", "civilian_wounded", "bases_destroyed", "bases_damaged", "aircraft", "warships", "armor", "vehicles"]:
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uk = f"us_{k}"
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ik = f"iran_{k}"
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if uk in parsed and isinstance(parsed[uk], (int, float)):
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loss_us[k] = max(0, int(parsed[uk]))
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if ik in parsed and isinstance(parsed[ik], (int, float)):
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loss_ir[k] = max(0, int(parsed[ik]))
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if loss_us or loss_ir:
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out["combat_losses_delta"] = {}
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if loss_us:
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out["combat_losses_delta"]["us"] = loss_us
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if loss_ir:
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out["combat_losses_delta"]["iran"] = loss_ir
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# retaliation
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if "retaliation_sentiment" in parsed:
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v = parsed["retaliation_sentiment"]
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if isinstance(v, (int, float)) and 0 <= v <= 100:
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out["retaliation"] = {"value": int(v), "time": ts}
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# wall_street
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if "wall_street_value" in parsed:
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v = parsed["wall_street_value"]
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if isinstance(v, (int, float)) and 0 <= v <= 100:
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out["wall_street"] = {"time": ts, "value": int(v)}
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return out
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