戒酒的李白
143 lines
5.7 KiB
Python
143 lines
5.7 KiB
Python
import openai
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import json
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from typing import List, Dict
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import os
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from datetime import datetime
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from utils.logger import app_logger as logging
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class AIAnalyzer:
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def __init__(self):
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# 从环境变量获取API密钥
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self.api_key = os.getenv('OPENAI_API_KEY')
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if not self.api_key:
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raise ValueError("请设置OPENAI_API_KEY环境变量")
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openai.api_key = self.api_key
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# 不同深度的分析提示词
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self.prompt_templates = {
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'basic': """你是一个专业的舆情分析助手。请对每条消息进行基础的情感分析。
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请按以下JSON格式返回：
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{
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"analysis_results": [
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{
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"message_id": "消息ID",
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"sentiment": "情感倾向 (积极/消极/中性)",
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"sentiment_score": "情感分数 (0-1)",
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"keywords": ["关键词1", "关键词2"],
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"key_points": "简要概述",
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"influence_analysis": "基础影响分析",
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"risk_level": "风险等级 (低/中/高)",
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"timestamp": "分析时间戳"
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}
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]
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}""",
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'standard': """你是一个专业的舆情分析助手。请对每条消息进行标准深度的分析。
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请按以下JSON格式返回：
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{
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"analysis_results": [
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{
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"message_id": "消息ID",
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"sentiment": "情感倾向 (积极/消极/中性)",
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"sentiment_score": "情感分数 (0-1)",
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"keywords": ["关键词1", "关键词2", "关键词3"],
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"key_points": "核心观点概述",
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"influence_analysis": "潜在影响分析",
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"risk_level": "风险等级 (低/中/高)",
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"timestamp": "分析时间戳"
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}
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]
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}""",
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'deep': """你是一个专业的舆情分析助手。请对每条消息进行深度分析。
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请按以下JSON格式返回：
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{
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"analysis_results": [
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{
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"message_id": "消息ID",
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"sentiment": "情感倾向 (积极/消极/中性)",
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"sentiment_score": "情感分数 (0-1)",
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"keywords": ["关键词1", "关键词2", "关键词3", "关键词4", "关键词5"],
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"key_points": "详细的核心观点分析",
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"influence_analysis": "深度影响分析，包括短期和长期影响",
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"risk_factors": ["风险因素1", "风险因素2", "风险因素3"],
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"risk_level": "风险等级 (低/中/高)",
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"suggestions": ["建议1", "建议2", "建议3"],
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"timestamp": "分析时间戳"
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}
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]
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}"""
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}
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async def analyze_messages(self, messages: List[Dict], batch_size: int = 50,
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model_type: str = "gpt-3.5-turbo",
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analysis_depth: str = "standard") -> List[Dict]:
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"""分析一批消息并返回分析结果"""
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try:
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all_results = []
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# 分批处理消息
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for i in range(0, len(messages), batch_size):
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batch = messages[i:i + batch_size]
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formatted_messages = []
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for msg in batch:
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formatted_messages.append(f"消息ID: {msg['id']}\n内容: {msg['content']}")
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messages_text = "\n---\n".join(formatted_messages)
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# 获取对应深度的提示词
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system_prompt = self.prompt_templates.get(analysis_depth, self.prompt_templates['standard'])
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# 调用OpenAI API
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response = await openai.ChatCompletion.acreate(
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model=model_type,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": f"请分析以下消息:\n{messages_text}"}
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],
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temperature=0.3, # 降低随机性
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max_tokens=2000 if analysis_depth != 'deep' else 3000,
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n=1
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)
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try:
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result = json.loads(response.choices[0].message.content)
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if isinstance(result, dict) and 'analysis_results' in result:
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all_results.extend(result['analysis_results'])
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else:
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logging.error(f"API返回格式不正确: {response.choices[0].message.content}")
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except json.JSONDecodeError as e:
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logging.error(f"JSON解析失败: {e}")
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continue
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return all_results
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except Exception as e:
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logging.error(f"AI分析过程出错: {e}")
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return []
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def format_analysis_for_display(self, analysis: Dict) -> Dict:
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"""将分析结果格式化为前端显示格式"""
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base_result = {
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'id': analysis['message_id'],
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'sentiment': analysis['sentiment'],
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'sentiment_score': f"{float(analysis['sentiment_score']):.2%}",
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'keywords': ', '.join(analysis['keywords']),
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'key_points': analysis['key_points'],
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'influence': analysis['influence_analysis'],
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'risk_level': analysis['risk_level'],
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'analysis_time': datetime.fromtimestamp(
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float(analysis['timestamp'])
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).strftime('%Y-%m-%d %H:%M:%S')
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}
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# 如果是深度分析，添加额外信息
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if 'risk_factors' in analysis:
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base_result.update({
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'risk_factors': analysis['risk_factors'],
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'suggestions': analysis['suggestions']
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})
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return base_result
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# 创建全局AI分析器实例
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ai_analyzer = AIAnalyzer() |