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#!/usr/bin/env python3
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"""LLM API使用示例"""
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import os
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from pydantic import BaseModel
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from typing import List
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# 导入LLM API
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try:
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from client import LLMClient
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from utils import chat, call_llm
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from exceptions import LLMAPIError, APIKeyError
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except ImportError:
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from llm_api import LLMClient
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from llm_api.utils import chat, call_llm
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from llm_api.exceptions import LLMAPIError, APIKeyError
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class SentimentAnalysis(BaseModel):
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"""情感分析结果模型"""
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sentiment: str # positive, negative, neutral
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confidence: float # 0.0 to 1.0
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summary: str
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keywords: List[str]
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class StockAnalysis(BaseModel):
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"""股票分析结果模型"""
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symbol: str
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recommendation: str # buy, sell, hold
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target_price: float
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risk_level: str # low, medium, high
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reasoning: str
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def basic_chat_example():
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"""基本聊天示例"""
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print("=== 基本聊天示例 ===")
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client = LLMClient()
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try:
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# 简单聊天
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response = client.chat("你好，请介绍一下你自己")
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print(f"AI回复: {response}")
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# 带系统消息的聊天
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response = client.chat(
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"分析一下苹果公司的投资价值",
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system_message="你是一位专业的金融分析师",
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model="gpt-4o",
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provider="OpenAI"
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)
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print(f"\n金融分析: {response}")
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except APIKeyError as e:
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print(f"API密钥错误: {e}")
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except LLMAPIError as e:
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print(f"LLM API错误: {e}")
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def structured_output_example():
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"""结构化输出示例"""
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print("\n=== 结构化输出示例 ===")
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client = LLMClient()
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try:
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# 情感分析
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text = "今天股市大涨，我的投资组合收益率达到了15%，心情非常好！"
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result = client.chat_with_structured_output(
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f"请分析以下文本的情感：'{text}'",
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pydantic_model=SentimentAnalysis,
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model="gpt-4o",
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provider="OpenAI"
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)
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print(f"情感: {result.sentiment}")
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print(f"置信度: {result.confidence}")
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print(f"摘要: {result.summary}")
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print(f"关键词: {', '.join(result.keywords)}")
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# 股票分析
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stock_result = client.chat_with_structured_output(
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"分析特斯拉(TSLA)股票的投资价值，当前价格约为250美元",
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pydantic_model=StockAnalysis,
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system_message="你是一位资深的股票分析师，请提供专业的投资建议",
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model="gpt-4o"
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)
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print(f"\n股票代码: {stock_result.symbol}")
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print(f"投资建议: {stock_result.recommendation}")
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print(f"目标价格: ${stock_result.target_price}")
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print(f"风险等级: {stock_result.risk_level}")
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print(f"分析理由: {stock_result.reasoning}")
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except Exception as e:
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print(f"结构化输出错误: {e}")
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def ollama_example():
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"""Ollama本地模型示例"""
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print("\n=== Ollama本地模型示例 ===")
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client = LLMClient()
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try:
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# 使用Ollama模型
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response = client.chat(
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"写一首关于人工智能的短诗",
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model="llama3.1:latest",
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provider="Ollama"
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)
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print(f"AI诗歌: {response}")
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# Ollama结构化输出
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analysis = client.chat_with_structured_output(
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"分析'人工智能将改变世界'这句话的情感",
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pydantic_model=SentimentAnalysis,
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model="llama3.1:latest",
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provider="Ollama"
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)
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print(f"\nOllama情感分析:")
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print(f"情感: {analysis.sentiment}")
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print(f"置信度: {analysis.confidence}")
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except Exception as e:
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print(f"Ollama示例错误: {e}")
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print("提示: 请确保Ollama已安装并且模型已下载")
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def compatibility_example():
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"""兼容性示例（与原有代码兼容）"""
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print("\n=== 兼容性示例 ===")
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try:
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# 使用兼容的chat函数
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response = chat(
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"什么是量化交易？",
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model="gpt-4o",
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provider="OpenAI"
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)
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print(f"量化交易解释: {response}")
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# 使用兼容的call_llm函数
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result = call_llm(
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prompt="分析比特币的投资风险",
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pydantic_model=SentimentAnalysis,
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model_name="gpt-4o",
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provider="OpenAI"
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)
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print(f"\n比特币风险分析:")
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print(f"情感: {result.sentiment}")
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print(f"摘要: {result.summary}")
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except Exception as e:
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print(f"兼容性示例错误: {e}")
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def model_info_example():
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"""模型信息示例"""
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print("\n=== 模型信息示例 ===")
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client = LLMClient()
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# 列出所有可用模型
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models = client.list_available_models()
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print(f"可用模型数量: {len(models)}")
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# 显示前5个模型
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print("\n前5个模型:")
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for model in models[:5]:
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print(f"- {model.display_name} ({model.provider})")
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# 获取特定模型信息
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model_info = client.get_model_info("gpt-4o", "OpenAI")
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if model_info:
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print(f"\nGPT-4o信息:")
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print(f"显示名称: {model_info.display_name}")
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print(f"支持JSON模式: {model_info.has_json_mode()}")
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print(f"支持流式输出: {model_info.supports_streaming}")
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def main():
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"""主函数"""
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print("LLM API 使用示例")
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print("=" * 50)
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# 检查环境变量
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if not os.getenv("OPENAI_API_KEY"):
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print("警告: 未设置OPENAI_API_KEY环境变量")
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print("请在.env文件中设置API密钥")
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return
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try:
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# 运行各种示例
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basic_chat_example()
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structured_output_example()
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compatibility_example()
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model_info_example()
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# 如果Ollama可用，运行Ollama示例
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try:
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from ollama_utils import is_ollama_installed
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except ImportError:
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from llm_api.ollama_utils import is_ollama_installed
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if is_ollama_installed():
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ollama_example()
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else:
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print("\n提示: 安装Ollama以体验本地模型功能")
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except KeyboardInterrupt:
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print("\n用户中断")
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except Exception as e:
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print(f"\n运行示例时出错: {e}")
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if __name__ == "__main__":
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main() |
