176 lines
4.8 KiB
Python
176 lines
4.8 KiB
Python
"""
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LLM服务 - 智谱AI GLM-4集成
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"""
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from typing import Optional, List, Dict, Any
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from app.config import get_settings
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from app.utils.logger import logger
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try:
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from zhipuai import ZhipuAI
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ZHIPUAI_AVAILABLE = True
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except ImportError:
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ZHIPUAI_AVAILABLE = False
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logger.warning("zhipuai包未安装，LLM功能将不可用")
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class LLMService:
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"""LLM服务类"""
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def __init__(self):
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"""初始化LLM服务"""
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settings = get_settings()
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if not ZHIPUAI_AVAILABLE:
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logger.warning("智谱AI SDK未安装")
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self.client = None
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return
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if not settings.zhipuai_api_key:
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logger.warning("智谱AI API Key未配置")
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self.client = None
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return
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try:
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self.client = ZhipuAI(api_key=settings.zhipuai_api_key)
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logger.info("智谱AI LLM服务初始化成功")
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except Exception as e:
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logger.error(f"智谱AI初始化失败: {e}")
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self.client = None
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def chat(
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self,
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messages: List[Dict[str, str]],
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model: str = "glm-4",
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temperature: float = 0.7,
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max_tokens: int = 2000
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) -> Optional[str]:
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"""
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调用LLM进行对话
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Args:
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messages: 消息列表 [{"role": "user", "content": "..."}]
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model: 模型名称
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temperature: 温度参数
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max_tokens: 最大token数
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Returns:
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LLM响应文本
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"""
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if not self.client:
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logger.error("LLM客户端未初始化")
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return None
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try:
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logger.info(f"调用LLM: model={model}, messages={len(messages)}条")
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response = self.client.chat.completions.create(
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model=model,
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens
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)
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if response.choices:
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content = response.choices[0].message.content
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logger.info(f"LLM响应成功，长度: {len(content) if content else 0}")
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return content
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else:
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logger.warning("LLM响应中没有choices")
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return None
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except Exception as e:
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logger.error(f"LLM调用失败: {type(e).__name__}: {e}")
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import traceback
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logger.error(f"详细错误: {traceback.format_exc()}")
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return None
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def analyze_intent(self, user_message: str) -> Dict[str, Any]:
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"""
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使用LLM分析用户意图
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Args:
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user_message: 用户消息
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Returns:
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意图分析结果
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"""
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if not self.client:
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return {"type": "unknown", "confidence": 0}
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prompt = f"""你是一个股票分析助手的意图识别模块。请分析用户的查询意图。
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用户消息：{user_message}
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请识别以下意图类型之一：
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1. market_data - 查询实时行情、价格
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2. technical_analysis - 技术分析、技术指标
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3. fundamental - 基本面信息、公司信息
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4. visualization - K线图、图表
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5. unknown - 无法识别
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请以JSON格式返回：
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{{
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"type": "意图类型",
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"confidence": 0.0-1.0,
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"stock_name": "提取的股票名称（如果有）"
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}}
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"""
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try:
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response = self.chat([{"role": "user", "content": prompt}], temperature=0.3)
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if response:
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import json
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# 尝试解析JSON
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result = json.loads(response)
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return result
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except Exception as e:
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logger.error(f"意图分析失败: {e}")
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return {"type": "unknown", "confidence": 0}
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def generate_analysis_summary(
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self,
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stock_code: str,
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stock_name: str,
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data: Dict[str, Any]
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) -> str:
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"""
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使用LLM生成分析总结
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Args:
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stock_code: 股票代码
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stock_name: 股票名称
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data: 分析数据
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Returns:
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分析总结文本
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"""
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if not self.client:
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return "LLM服务不可用，无法生成智能分析"
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prompt = f"""你是一个专业的股票分析师。请根据以下数据对{stock_name}({stock_code})进行分析总结。
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数据：
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{data}
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请提供：
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1. 当前状态评估
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2. 技术指标解读
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3. 投资建议（仅供参考）
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注意：
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- 使用专业但易懂的语言
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- 控制在200字以内
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- 必须声明"仅供参考，不构成投资建议"
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"""
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try:
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response = self.chat([{"role": "user", "content": prompt}], temperature=0.7)
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return response or "分析生成失败"
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except Exception as e:
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logger.error(f"分析总结生成失败: {e}")
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return "分析生成失败"
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# 创建全局实例
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llm_service = LLMService()
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