518 lines
18 KiB
Python
518 lines
18 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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验证高置信度未匹配记录
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功能：比对 keyword_matcher 结果与原始 Excel，找出高置信度未匹配行，调用 LLM 二次验证。
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用法：
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python3 verify_high_confidence.py -o original.xlsx -m matched.xlsx
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python3 verify_high_confidence.py -o original.xlsx -m matched.xlsx --mock --limit 5
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"""
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import argparse
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import json
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import os
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import sys
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import time
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import List, Optional
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import pandas as pd
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# 可选依赖
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try:
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import openai
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HAS_OPENAI = True
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except ImportError:
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HAS_OPENAI = False
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try:
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import requests
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import urllib3
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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HAS_REQUESTS = True
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except ImportError:
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HAS_REQUESTS = False
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# ========== 常量与配置 ==========
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CONFIDENCE_LEVELS = ["High", "Medium"]
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REQUEST_DELAY = 0.5
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# 环境变量映射: api_type -> (key_env, url_env, model_env, default_model)
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ENV_MAPPING = {
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"openai": ("OPENAI_API_KEY", "OPENAI_BASE_URL", "OPENAI_MODEL", "gpt-4o-mini"),
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"dmx": ("DMX_API_KEY", "DMX_BASE_URL", "DMX_MODEL", "gpt-4o-mini"),
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"dify": ("DIFY_API_KEY", "DIFY_BASE_URL", "DIFY_MODEL", "dify-chatflow"),
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"ollama": (None, "OLLAMA_BASE_URL", "OLLAMA_MODEL", "qwen2.5:7b"),
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}
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SYSTEM_PROMPT = """你是一位化学品风险识别专家。请分析文本内容，判断是否涉及管制化学品、毒品前体或非法药物交易。
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请以 JSON 格式回答，包含以下字段：
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- is_risky: 布尔值，是否涉及风险
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- substances: 数组，涉及的具体物质名称或CAS号
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- risk_level: 字符串，风险等级（高/中/低）
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- reason: 字符串，判定理由（简要）
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示例输出：
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{"is_risky": true, "substances": ["甲基苯丙胺", "CAS 537-46-2"], "risk_level": "高", "reason": "文本中明确提到毒品名称和交易信息"}
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"""
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USER_PROMPT_TEMPLATE = """请分析以下内容是否涉及管制化学品或毒品：
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【图片分析结果】
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{raw_response}
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【原始文本】
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{original_text}
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请以 JSON 格式输出分析结果。"""
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# ========== 数据类 ==========
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@dataclass
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class VerifyConfig:
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api_type: str = "openai"
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api_key: str = ""
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base_url: Optional[str] = None
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model: str = "gpt-4o-mini"
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user_id: str = "default-user"
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@dataclass
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class VerificationResult:
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is_risky: Optional[bool] = None
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substances: List[str] = field(default_factory=list)
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risk_level: str = ""
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reason: str = ""
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raw_response: str = ""
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def to_columns(self) -> dict:
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return {
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"llm_is_risky": self.is_risky,
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"llm_substances": " | ".join(self.substances) if self.substances else "",
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"llm_risk_level": self.risk_level,
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"llm_reason": self.reason,
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"llm_raw_response": self.raw_response,
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}
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# ========== 工具函数 ==========
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def load_env_file(env_path: str) -> None:
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"""从 .env 文件加载环境变量"""
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env_file = Path(env_path)
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if not env_file.exists():
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return
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print(f"加载环境配置: {env_file}")
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with open(env_file, "r", encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if not line or line.startswith("#"):
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continue
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if line.startswith("export "):
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line = line[7:]
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if "=" in line:
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key, _, value = line.partition("=")
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os.environ[key.strip()] = value.strip().strip('"').strip("'")
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def get_config() -> VerifyConfig:
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"""获取验证配置，优先使用 VERIFY_ 前缀"""
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api_type = (os.getenv("VERIFY_API_TYPE") or os.getenv("LLM_API_TYPE") or "openai").lower()
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mapping = ENV_MAPPING.get(api_type, (None, None, None, "gpt-4o-mini"))
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key_env, url_env, model_env, default_model = mapping
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return VerifyConfig(
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api_type=api_type,
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api_key=os.getenv("VERIFY_API_KEY") or (os.getenv(key_env) if key_env else "") or "",
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base_url=os.getenv("VERIFY_BASE_URL") or (os.getenv(url_env) if url_env else None),
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model=os.getenv("VERIFY_MODEL") or (os.getenv(model_env) if model_env else default_model) or default_model,
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user_id=os.getenv("VERIFY_USER_ID") or os.getenv("DIFY_USER_ID") or "default-user",
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)
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def parse_json_response(content: str) -> dict:
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"""从 LLM 响应提取 JSON，处理 markdown 代码块"""
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# 移除 markdown 代码块
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if "```json" in content:
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start = content.find("```json") + 7
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end = content.find("```", start)
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content = content[start:end].strip()
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elif "```" in content:
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start = content.find("```") + 3
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end = content.find("```", start)
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content = content[start:end].strip()
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try:
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start = content.find("{")
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end = content.rfind("}") + 1
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if start >= 0 and end > start:
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return json.loads(content[start:end])
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except json.JSONDecodeError:
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pass
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return {"is_risky": None, "substances": [], "risk_level": "未知", "reason": "JSON 解析失败"}
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def build_prompt(row: pd.Series, max_len: int = 3000) -> str:
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"""构建用户提示"""
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raw = str(row.get("raw_response", "") or "")
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text = str(row.get("文本", "") or "")
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if len(raw) > max_len:
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raw = raw[:max_len] + "...(截断)"
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if len(text) > max_len:
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text = text[:max_len] + "...(截断)"
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return USER_PROMPT_TEMPLATE.format(raw_response=raw, original_text=text)
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# ========== 验证器类 ==========
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class LLMVerifier(ABC):
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"""LLM 验证器抽象基类"""
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@abstractmethod
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def verify(self, row: pd.Series) -> VerificationResult:
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pass
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class OpenAIVerifier(LLMVerifier):
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"""OpenAI 兼容 API 验证器 (支持 OpenAI, DMX, Ollama)"""
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def __init__(self, config: VerifyConfig):
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if not HAS_OPENAI:
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raise ImportError("请安装 openai: pip install openai")
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if config.api_type != "ollama" and not config.api_key:
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raise ValueError("未提供 API Key")
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base_url = config.base_url
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if config.api_type == "ollama":
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base_url = (config.base_url or "http://localhost:11434") + "/v1"
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self.client = openai.OpenAI(
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api_key=config.api_key or "ollama",
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base_url=base_url,
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)
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self.model = config.model
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def verify(self, row: pd.Series) -> VerificationResult:
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prompt = build_prompt(row)
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try:
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": prompt},
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],
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temperature=0.1,
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max_tokens=500,
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)
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content = response.choices[0].message.content or ""
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if response.choices[0].finish_reason != "stop":
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return VerificationResult(
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risk_level="错误",
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reason=f"响应不完整 (finish_reason={response.choices[0].finish_reason})",
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raw_response=content,
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)
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parsed = parse_json_response(content)
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return VerificationResult(
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is_risky=parsed.get("is_risky"),
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substances=parsed.get("substances", []),
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risk_level=parsed.get("risk_level", ""),
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reason=parsed.get("reason", ""),
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raw_response=content,
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)
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except Exception as e:
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return VerificationResult(risk_level="错误", reason=f"API 调用失败: {e}", raw_response=str(e))
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class DifyVerifier(LLMVerifier):
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"""Dify API 验证器"""
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def __init__(self, config: VerifyConfig):
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if not HAS_REQUESTS:
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raise ImportError("请安装 requests: pip install requests")
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if not config.api_key:
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raise ValueError("未提供 Dify API Key")
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self.base_url = (config.base_url or "").rstrip("/")
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self.api_key = config.api_key
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self.user_id = config.user_id
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def verify(self, row: pd.Series) -> VerificationResult:
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prompt = f"{SYSTEM_PROMPT}\n\n{build_prompt(row)}"
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try:
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resp = requests.post(
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f"{self.base_url}/v1/chat-messages",
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headers={"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"},
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json={"inputs": {}, "query": prompt, "response_mode": "blocking", "user": self.user_id},
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timeout=120,
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verify=False,
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)
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resp.raise_for_status()
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content = resp.json().get("answer", "")
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parsed = parse_json_response(content)
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return VerificationResult(
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is_risky=parsed.get("is_risky"),
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substances=parsed.get("substances", []),
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risk_level=parsed.get("risk_level", ""),
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reason=parsed.get("reason", ""),
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raw_response=content,
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)
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except Exception as e:
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return VerificationResult(risk_level="错误", reason=f"Dify 调用失败: {e}", raw_response=str(e))
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class MockVerifier(LLMVerifier):
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"""Mock 验证器（测试用）"""
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RISK_KEYWORDS = [
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"毒品", "非法", "管制", "药物", "化学品", "CAS", "阿片", "芬太尼",
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"冰毒", "大麻", "可卡因", "海洛因", "摇头丸", "麻黄碱",
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"fentanyl", "methamphetamine", "cocaine", "heroin", "mdma",
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"ketamine", "lsd", "precursor", "controlled",
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]
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def verify(self, row: pd.Series) -> VerificationResult:
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all_text = f"{row.get('raw_response', '')} {row.get('文本', '')}".lower()
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found = [kw for kw in self.RISK_KEYWORDS if kw.lower() in all_text]
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is_risky = len(found) > 0
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return VerificationResult(
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is_risky=is_risky,
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substances=found[:5],
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risk_level="中" if is_risky else "低",
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reason=f"Mock模式 - 发现关键词: {found[:3]}" if is_risky else "Mock模式 - 未发现风险关键词",
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raw_response="(mock)",
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)
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def create_verifier(config: VerifyConfig) -> LLMVerifier:
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"""根据配置创建验证器"""
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if config.api_type == "mock":
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return MockVerifier()
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elif config.api_type == "dify":
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return DifyVerifier(config)
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elif config.api_type in ("openai", "dmx", "ollama"):
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return OpenAIVerifier(config)
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else:
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raise ValueError(f"不支持的 API 类型: {config.api_type}")
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# ========== 数据处理 ==========
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def load_excel(file_path: Path) -> pd.DataFrame:
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"""加载 Excel 文件"""
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if not file_path.exists():
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raise FileNotFoundError(f"文件不存在: {file_path}")
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return pd.read_excel(file_path)
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def find_unmatched(
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original_df: pd.DataFrame,
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matched_df: pd.DataFrame,
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confidence_col: str = "confidence",
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confidence_levels: List[str] = None,
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) -> pd.DataFrame:
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"""找出高置信度但未被关键词匹配的行"""
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levels = confidence_levels or CONFIDENCE_LEVELS
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if confidence_col not in original_df.columns:
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print(f"警告: 原始文件中不存在 '{confidence_col}' 列")
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print(f"可用列: {original_df.columns.tolist()}")
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return pd.DataFrame()
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# 高置信度行索引
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conf_lower = original_df[confidence_col].astype(str).str.lower()
|
|||
|
|
levels_lower = [l.lower() for l in levels]
|
|||
|
|
high_conf_idx = set(original_df[conf_lower.isin(levels_lower)].index)
|
|||
|
|
matched_idx = set(matched_df.index)
|
|||
|
|
unmatched_idx = high_conf_idx - matched_idx
|
|||
|
|
|
|||
|
|
# 统计信息
|
|||
|
|
print(f"\n{'='*50}")
|
|||
|
|
print("数据比对统计")
|
|||
|
|
print(f"{'='*50}")
|
|||
|
|
print(f"原始数据总行数: {len(original_df)}")
|
|||
|
|
print(f"高置信度 ({'/'.join(levels)}) 行数: {len(high_conf_idx)}")
|
|||
|
|
print(f"关键词匹配到的行数: {len(matched_idx)}")
|
|||
|
|
print(f"高置信度中已匹配: {len(high_conf_idx & matched_idx)}")
|
|||
|
|
print(f"高置信度中未匹配 (需验证): {len(unmatched_idx)}")
|
|||
|
|
print(f"{'='*50}\n")
|
|||
|
|
|
|||
|
|
if not unmatched_idx:
|
|||
|
|
return pd.DataFrame()
|
|||
|
|
return original_df.loc[list(unmatched_idx)].copy()
|
|||
|
|
|
|||
|
|
|
|||
|
|
def verify_batch(df: pd.DataFrame, verifier: LLMVerifier, delay: float = REQUEST_DELAY, limit: int = 0) -> pd.DataFrame:
|
|||
|
|
"""批量验证记录"""
|
|||
|
|
if limit > 0:
|
|||
|
|
df = df.head(limit)
|
|||
|
|
|
|||
|
|
total = len(df)
|
|||
|
|
print(f"开始 LLM 验证，共 {total} 条记录...")
|
|||
|
|
print("-" * 50)
|
|||
|
|
|
|||
|
|
results = []
|
|||
|
|
start_time = time.time()
|
|||
|
|
|
|||
|
|
for i, (idx, row) in enumerate(df.iterrows()):
|
|||
|
|
if (i + 1) % 10 == 0 or i == 0 or i == total - 1:
|
|||
|
|
elapsed = time.time() - start_time
|
|||
|
|
speed = (i + 1) / elapsed if elapsed > 0 else 0
|
|||
|
|
print(f"进度: {i + 1}/{total} ({(i+1)/total*100:.1f}%) - 速度: {speed:.1f} 条/秒")
|
|||
|
|
|
|||
|
|
result = verifier.verify(row)
|
|||
|
|
results.append({"original_index": idx, **result.to_columns()})
|
|||
|
|
|
|||
|
|
if delay > 0 and i < total - 1:
|
|||
|
|
time.sleep(delay)
|
|||
|
|
|
|||
|
|
results_df = pd.DataFrame(results).set_index("original_index")
|
|||
|
|
verified_df = df.copy()
|
|||
|
|
for col in results_df.columns:
|
|||
|
|
verified_df[col] = results_df[col]
|
|||
|
|
return verified_df
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ========== 结果输出 ==========
|
|||
|
|
def save_results(df: pd.DataFrame, output_file: Path, risky_only: bool = False) -> None:
|
|||
|
|
"""保存结果"""
|
|||
|
|
if risky_only and "llm_is_risky" in df.columns:
|
|||
|
|
df = df[df["llm_is_risky"] == True]
|
|||
|
|
df.to_excel(output_file, index=False, engine="openpyxl")
|
|||
|
|
print(f"\n已保存 {len(df)} 条记录到: {output_file}")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def print_summary(df: pd.DataFrame) -> None:
|
|||
|
|
"""打印验证摘要"""
|
|||
|
|
print(f"\n{'='*50}")
|
|||
|
|
print("验证结果摘要")
|
|||
|
|
print(f"{'='*50}")
|
|||
|
|
|
|||
|
|
total = len(df)
|
|||
|
|
if "llm_is_risky" not in df.columns:
|
|||
|
|
print(f"总记录数: {total}")
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
risky = (df["llm_is_risky"] == True).sum()
|
|||
|
|
not_risky = (df["llm_is_risky"] == False).sum()
|
|||
|
|
unknown = total - risky - not_risky
|
|||
|
|
|
|||
|
|
print(f"总验证数: {total}")
|
|||
|
|
print(f" ├─ LLM 判定有风险: {risky} ({risky/total*100:.1f}%)")
|
|||
|
|
print(f" ├─ LLM 判定无风险: {not_risky} ({not_risky/total*100:.1f}%)")
|
|||
|
|
if unknown > 0:
|
|||
|
|
print(f" └─ 判定失败/未知: {unknown}")
|
|||
|
|
|
|||
|
|
if "llm_risk_level" in df.columns:
|
|||
|
|
print(f"\n风险等级分布:")
|
|||
|
|
for level, count in df["llm_risk_level"].value_counts().items():
|
|||
|
|
print(f" - {level}: {count}")
|
|||
|
|
print(f"{'='*50}")
|
|||
|
|
|
|||
|
|
|
|||
|
|
# ========== CLI ==========
|
|||
|
|
def parse_args():
|
|||
|
|
parser = argparse.ArgumentParser(
|
|||
|
|
description="验证高置信度未匹配记录",
|
|||
|
|
formatter_class=argparse.RawDescriptionHelpFormatter,
|
|||
|
|
epilog="""
|
|||
|
|
示例:
|
|||
|
|
python3 verify_high_confidence.py -o original.xlsx -m matched.xlsx
|
|||
|
|
python3 verify_high_confidence.py -o original.xlsx -m matched.xlsx --mock --limit 5
|
|||
|
|
python3 verify_high_confidence.py -o original.xlsx -m matched.xlsx --api dmx --model gpt-4o-mini
|
|||
|
|
""",
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
parser.add_argument("-o", "--original", required=True, help="原始 Excel 文件路径")
|
|||
|
|
parser.add_argument("-m", "--matched", required=True, help="keyword_matcher 匹配结果文件路径")
|
|||
|
|
parser.add_argument("-r", "--result", help="输出结果文件路径 (默认: 原始文件名_llm_verified.xlsx)")
|
|||
|
|
|
|||
|
|
parser.add_argument("--env-file", help="环境变量文件路径 (默认: ../.env)")
|
|||
|
|
parser.add_argument("--api", choices=["openai", "dmx", "dify", "ollama"], help="LLM API 类型")
|
|||
|
|
parser.add_argument("--model", help="LLM 模型名称")
|
|||
|
|
parser.add_argument("--base-url", help="API base URL")
|
|||
|
|
parser.add_argument("--api-key", help="API Key")
|
|||
|
|
parser.add_argument("--mock", action="store_true", help="使用 mock 模式（不调用 API）")
|
|||
|
|
|
|||
|
|
parser.add_argument("--confidence", nargs="+", default=["High", "Medium"], help="需要验证的置信度级别")
|
|||
|
|
parser.add_argument("--confidence-col", default="confidence", help="置信度列名")
|
|||
|
|
|
|||
|
|
parser.add_argument("--delay", type=float, default=REQUEST_DELAY, help="API 请求间隔秒数")
|
|||
|
|
parser.add_argument("--limit", type=int, default=0, help="限制验证条数 (0=全部)")
|
|||
|
|
parser.add_argument("--risky-only", action="store_true", help="只保存有风险的记录")
|
|||
|
|
|
|||
|
|
return parser.parse_args()
|
|||
|
|
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
args = parse_args()
|
|||
|
|
|
|||
|
|
# 加载 .env
|
|||
|
|
base_dir = Path(__file__).resolve().parent
|
|||
|
|
env_file = args.env_file or str(base_dir.parent / ".env")
|
|||
|
|
load_env_file(env_file)
|
|||
|
|
|
|||
|
|
# 获取配置
|
|||
|
|
config = get_config()
|
|||
|
|
|
|||
|
|
# 命令行参数覆盖
|
|||
|
|
if args.mock:
|
|||
|
|
config.api_type = "mock"
|
|||
|
|
elif args.api:
|
|||
|
|
config.api_type = args.api
|
|||
|
|
if args.model:
|
|||
|
|
config.model = args.model
|
|||
|
|
if args.base_url:
|
|||
|
|
config.base_url = args.base_url
|
|||
|
|
if args.api_key:
|
|||
|
|
config.api_key = args.api_key
|
|||
|
|
|
|||
|
|
# 文件路径
|
|||
|
|
original_file = Path(args.original)
|
|||
|
|
matched_file = Path(args.matched)
|
|||
|
|
result_file = Path(args.result) if args.result else original_file.parent / f"{original_file.stem}_llm_verified.xlsx"
|
|||
|
|
|
|||
|
|
print("=" * 60)
|
|||
|
|
print("高置信度未匹配记录验证")
|
|||
|
|
print("=" * 60)
|
|||
|
|
print(f"原始文件: {original_file}")
|
|||
|
|
print(f"匹配结果: {matched_file}")
|
|||
|
|
print(f"输出文件: {result_file}")
|
|||
|
|
print(f"置信度级别: {args.confidence}")
|
|||
|
|
print(f"API 类型: {config.api_type}")
|
|||
|
|
print(f"模型: {config.model}")
|
|||
|
|
if config.base_url:
|
|||
|
|
print(f"Base URL: {config.base_url}")
|
|||
|
|
|
|||
|
|
# 加载数据
|
|||
|
|
print("\n正在加载数据...")
|
|||
|
|
original_df = load_excel(original_file)
|
|||
|
|
matched_df = load_excel(matched_file)
|
|||
|
|
|
|||
|
|
# 找出未匹配的高置信度行
|
|||
|
|
unmatched_df = find_unmatched(original_df, matched_df, args.confidence_col, args.confidence)
|
|||
|
|
|
|||
|
|
if unmatched_df.empty:
|
|||
|
|
print("\n所有高置信度行都已被关键词匹配，无需验证。")
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
# 创建验证器
|
|||
|
|
try:
|
|||
|
|
verifier = create_verifier(config)
|
|||
|
|
except (ImportError, ValueError) as e:
|
|||
|
|
print(f"\n错误: {e}")
|
|||
|
|
sys.exit(1)
|
|||
|
|
|
|||
|
|
# 执行验证
|
|||
|
|
verified_df = verify_batch(unmatched_df, verifier, delay=args.delay, limit=args.limit)
|
|||
|
|
|
|||
|
|
# 打印摘要并保存
|
|||
|
|
print_summary(verified_df)
|
|||
|
|
save_results(verified_df, result_file, args.risky_only)
|
|||
|
|
print("\n✓ 验证完成！")
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main()
|