71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from conf import settings
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# 创建模型
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llm = ChatOpenAI(
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base_url=settings.base_url,
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api_key=settings.api_key,
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model=settings.model_name,
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temperature=0.1
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)
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# 3.初始响应 Prompt: 用于生成第一次的回答
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initial_response_prompt = ChatPromptTemplate.from_template(
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"请根据以下问题给出你的初步回答: {question}"
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)
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initial_response_chain = initial_response_prompt | llm | StrOutputParser()
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# 4.反思 Prompt: 用于接收用户反馈并优化回答
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reflection_prompt = ChatPromptTemplate.from_template(
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"""你是一个专业的、善于反思的AI助手。你之前给出了以下回答：
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---
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{previous_response}
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---
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现在，你收到了用户对你的回答给出的反馈：
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---
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{user_feedback}
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---
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请根据用户的反馈，认真反思你之前的回答，并生成一个更准确、更完善的新回答。
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新回答:"""
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)
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reflection_chain = reflection_prompt | llm | StrOutputParser()
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# 5.模拟反射过程
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def reflect_and_refine(query: str, feedback: str):
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"""模拟一个完整的反射过程，从初始响应到优化后的响应。"""
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print("--- 启动反射模式 ---")
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print(f"用户查询: {query}")
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# LLM 生成初步响应
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print("\n生成初步响应...")
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initial_response = initial_response_chain.invoke({"question": query})
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print(f"LLM 初步响应:\n{initial_response}")
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# 模拟用户反馈
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print(f"\n用户反馈:\n{feedback}")
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# LLM 进行反思，并生成新的回答
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print("\nLLM 正在反思并生成新响应...")
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refined_response = reflection_chain.invoke({
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"previous_response": initial_response,
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"user_feedback": feedback
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})
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print("\n--- LLM 经过反思后的新响应 ---")
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print(refined_response)
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return refined_response
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# 6.运行并测试
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if __name__ == "__main__":
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# 模拟用户查询
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initial_question = "请用一句话介绍一下 LangChain。"
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# 模拟用户反馈，指出初步回答的不足
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user_feedback_text = "你的回答太简单了，请更详细地解释一下 LangChain 的核心概念，比如 Agent 和 Chain 的区别。"
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# 运行反射过程
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reflect_and_refine(initial_question, user_feedback_text)
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