241 lines
9.3 KiB
Python
241 lines
9.3 KiB
Python
"""DeepSeek API 驱动的智能情感分析 Agent
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使用 DeepSeek API 实现实时、智能的推文情感分析、解释和处置方案生成。
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"""
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import os
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from typing import Optional, Dict, Any
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import httpx
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from pydantic import BaseModel, Field
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class DeepSeekClient:
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"""DeepSeek API 客户端"""
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def __init__(self, api_key: Optional[str] = None, base_url: Optional[str] = None):
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self.api_key = api_key or os.getenv("DEEPSEEK_API_KEY")
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self.base_url = base_url or os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com")
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if not self.api_key:
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raise ValueError("DeepSeek API Key 未配置，请设置 DEEPSEEK_API_KEY 环境变量")
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async def chat_completion(self, messages: list, model: str = "deepseek-chat", temperature: float = 0.7) -> str:
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"""调用 DeepSeek API 进行对话补全"""
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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data = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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"stream": False
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}
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async with httpx.AsyncClient(timeout=30.0) as client:
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response = await client.post(
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f"{self.base_url}/chat/completions",
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headers=headers,
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json=data
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)
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if response.status_code == 200:
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result = response.json()
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return result["choices"][0]["message"]["content"]
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else:
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raise Exception(f"DeepSeek API 调用失败: {response.status_code} - {response.text}")
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class SentimentAnalysisResult(BaseModel):
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"""情感分析结果"""
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sentiment: str = Field(description="情感类别: negative/neutral/positive")
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confidence: float = Field(description="置信度 (0-1)")
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reasoning: str = Field(description="情感判断的推理过程")
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key_factors: list[str] = Field(description="影响情感判断的关键因素")
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class DisposalPlan(BaseModel):
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"""处置方案"""
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priority: str = Field(description="处理优先级: high/medium/low")
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action_type: str = Field(description="行动类型: response/investigate/monitor/ignore")
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suggested_response: Optional[str] = Field(description="建议回复内容", default=None)
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follow_up_actions: list[str] = Field(description="后续行动建议")
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reasoning: str = Field(description="处置方案制定的理由")
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class TweetAnalysisResult(BaseModel):
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"""推文分析完整结果"""
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tweet_text: str = Field(description="原始推文文本")
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airline: str = Field(description="航空公司")
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sentiment_analysis: SentimentAnalysisResult = Field(description="情感分析结果")
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disposal_plan: DisposalPlan = Field(description="处置方案")
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class DeepSeekTweetAgent:
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"""基于 DeepSeek API 的智能推文分析 Agent"""
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def __init__(self, api_key: Optional[str] = None, base_url: Optional[str] = None):
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self.client = DeepSeekClient(api_key, base_url)
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async def analyze_sentiment(self, text: str, airline: str) -> SentimentAnalysisResult:
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"""使用 DeepSeek API 进行情感分析"""
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prompt = f"""
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请分析以下航空推文的情感倾向，并给出详细的分析过程：
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推文内容："{text}"
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航空公司：{airline}
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请按照以下格式输出分析结果：
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1. 情感类别：negative/neutral/positive
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2. 置信度：0-1之间的数值
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3. 关键因素：列出影响情感判断的关键因素（最多5个）
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4. 推理过程：详细说明情感判断的推理过程
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请确保分析准确、客观，并考虑航空行业的特殊性。
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"""
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messages = [
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{"role": "system", "content": "你是一位专业的航空行业情感分析专家，擅长分析推文中的情感倾向和潜在问题。"},
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{"role": "user", "content": prompt}
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]
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response = await self.client.chat_completion(messages, temperature=0.3)
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# 解析响应并构建结果
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return self._parse_sentiment_response(response, text, airline)
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async def generate_disposal_plan(self, text: str, airline: str, sentiment_result: SentimentAnalysisResult) -> DisposalPlan:
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"""生成处置方案"""
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prompt = f"""
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基于以下推文分析和情感判断结果，为航空公司制定一个合理的处置方案：
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推文内容："{text}"
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航空公司：{airline}
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情感分析结果：
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- 情感类别：{sentiment_result.sentiment}
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- 置信度：{sentiment_result.confidence}
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- 关键因素：{', '.join(sentiment_result.key_factors)}
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- 推理过程：{sentiment_result.reasoning}
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请按照以下格式输出处置方案：
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1. 优先级：high/medium/low
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2. 行动类型：response/investigate/monitor/ignore
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3. 建议回复：如果行动类型是response，提供具体的回复建议
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4. 后续行动：列出2-4个具体的后续行动建议
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5. 制定理由：说明为什么制定这样的处置方案
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请确保处置方案符合航空行业的服务标准和客户关系管理最佳实践。
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"""
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messages = [
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{"role": "system", "content": "你是一位航空公司的客户服务专家，擅长制定合理的客户反馈处置方案。"},
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{"role": "user", "content": prompt}
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]
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response = await self.client.chat_completion(messages, temperature=0.5)
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return self._parse_disposal_response(response)
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async def analyze_tweet(self, text: str, airline: str) -> TweetAnalysisResult:
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"""完整的推文分析流程"""
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# 1. 情感分析
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sentiment_result = await self.analyze_sentiment(text, airline)
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# 2. 生成处置方案
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disposal_plan = await self.generate_disposal_plan(text, airline, sentiment_result)
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# 3. 返回完整结果
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return TweetAnalysisResult(
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tweet_text=text,
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airline=airline,
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sentiment_analysis=sentiment_result,
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disposal_plan=disposal_plan
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)
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def _parse_sentiment_response(self, response: str, text: str, airline: str) -> SentimentAnalysisResult:
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"""解析情感分析响应"""
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# 简化解析逻辑，实际应用中可以使用更复杂的解析
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lines = response.strip().split('\n')
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sentiment = "neutral"
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confidence = 0.5
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key_factors = []
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reasoning = ""
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for line in lines:
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line = line.strip()
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if line.startswith("情感类别："):
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sentiment = line.replace("情感类别：", "").strip().lower()
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elif line.startswith("置信度："):
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try:
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confidence = float(line.replace("置信度：", "").strip())
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except:
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confidence = 0.5
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elif line.startswith("关键因素："):
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factors = line.replace("关键因素：", "").strip()
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key_factors = [f.strip() for f in factors.split(',') if f.strip()]
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elif line.startswith("推理过程："):
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reasoning = line.replace("推理过程：", "").strip()
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# 如果解析失败，使用默认值
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if not reasoning:
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reasoning = f"基于推文内容和航空行业特点进行综合分析，判断情感倾向为{sentiment}。"
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return SentimentAnalysisResult(
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sentiment=sentiment,
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confidence=confidence,
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reasoning=reasoning,
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key_factors=key_factors
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)
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def _parse_disposal_response(self, response: str) -> DisposalPlan:
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"""解析处置方案响应"""
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lines = response.strip().split('\n')
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priority = "medium"
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action_type = "monitor"
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suggested_response = None
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follow_up_actions = []
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reasoning = ""
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for line in lines:
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line = line.strip()
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if line.startswith("优先级："):
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priority = line.replace("优先级：", "").strip().lower()
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elif line.startswith("行动类型："):
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action_type = line.replace("行动类型：", "").strip().lower()
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elif line.startswith("建议回复："):
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suggested_response = line.replace("建议回复：", "").strip()
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elif line.startswith("后续行动："):
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actions = line.replace("后续行动：", "").strip()
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follow_up_actions = [a.strip() for a in actions.split(',') if a.strip()]
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elif line.startswith("制定理由："):
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reasoning = line.replace("制定理由：", "").strip()
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if not reasoning:
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reasoning = "基于情感分析结果和航空行业最佳实践制定此处置方案。"
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return DisposalPlan(
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priority=priority,
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action_type=action_type,
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suggested_response=suggested_response,
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follow_up_actions=follow_up_actions,
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reasoning=reasoning
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)
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# 同步版本的包装函数（为了兼容现有接口）
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import asyncio
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def analyze_tweet_deepseek(text: str, airline: str) -> TweetAnalysisResult:
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"""同步版本的推文分析函数"""
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agent = DeepSeekTweetAgent()
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return asyncio.run(agent.analyze_tweet(text, airline)) |