Temperature控制输出的准确性和创造性
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from langchain_core.messages import HumanMessage, SystemMessage
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_ollama.chat_models import ChatOllama
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llm = ChatOllama(
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model="qwen2.5:7b"
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)
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# ==================== 方式1: 直接使用 llm.stream() ====================
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print("=" * 50)
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print("方式1: 直接使用 llm.stream()")
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print("=" * 50)
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messages = [
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SystemMessage(content="你是一个Python专家，回答要简洁，不超过50字"),
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HumanMessage(content="什么是列表推导式？")
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]
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print("回答: ", end="", flush=True)
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for chunk in llm.stream(messages):
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print(chunk.content, end="", flush=True)
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print("\n")
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# ==================== 方式2: 在链中使用 stream() ====================
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print("=" * 50)
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print("方式2: 在链中使用 chain.stream()")
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print("=" * 50)
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# 创建链：prompt | llm | parser
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prompt = ChatPromptTemplate.from_template("用一句话解释{concept}")
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parser = StrOutputParser()
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chain = prompt | llm | parser
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# 使用 stream() 方法进行流式输出
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print("回答: ", end="", flush=True)
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for chunk in chain.stream({"concept": "列表推导式"}):
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print(chunk, end="", flush=True)
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print("\n")
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# ==================== 方式3: 链中不使用 parser，直接流式输出 LLM ====================
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print("=" * 50)
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print("方式3: 链中不使用 parser，直接流式输出 LLM 的 content")
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print("=" * 50)
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# 不使用 parser，直接输出 LLM 的 AIMessage
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chain_without_parser = prompt | llm
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print("回答: ", end="", flush=True)
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for chunk in chain_without_parser.stream({"concept": "装饰器"}):
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# chunk 是 AIMessageChunk，需要访问 content 属性
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if hasattr(chunk, 'content'):
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print(chunk.content, end="", flush=True)
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print("\n")
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"""
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Temperature 参数详解
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==================
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Temperature 是控制 LLM 输出随机性的重要参数。
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作用原理：
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- Temperature 值越高，模型输出的随机性越大，创造性越强
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- Temperature 值越低，模型输出越确定，更倾向于选择概率最高的词
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取值范围：
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- 通常范围：0.0 - 2.0
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- 推荐范围：0.0 - 1.0
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- 默认值：通常为 0.7 或 1.0（取决于模型）
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使用场景：
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- temperature=0: 需要确定性答案（如代码生成、翻译、问答）
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- temperature=0.3-0.7: 平衡创造性和准确性（如内容创作、对话）
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- temperature=0.8-1.5: 需要创造性（如创意写作、头脑风暴）
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"""
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from langchain_ollama import ChatOllama
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question = "用一句话解释什么是Python"
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print("=" * 70)
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print("Temperature 参数对比演示")
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print("=" * 70)
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print(f"\n问题: {question}\n")
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# ==================== Temperature = 0 (完全确定性) ====================
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print("-" * 70)
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print("Temperature = 0 (完全确定性，每次输出相同)")
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print("-" * 70)
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llm_temp0 = ChatOllama(model="qwen2.5:7b", temperature=0)
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print("第1次调用:")
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response1 = llm_temp0.invoke(question)
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print(f" {response1.content}\n")
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print("第2次调用:")
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response2 = llm_temp0.invoke(question)
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print(f" {response2.content}\n")
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print("第3次调用:")
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response3 = llm_temp0.invoke(question)
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print(f" {response3.content}\n")
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# ==================== Temperature = 0.7 (平衡) ====================
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print("-" * 70)
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print("Temperature = 0.7 (平衡创造性和准确性)")
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print("-" * 70)
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llm_temp07 = ChatOllama(model="qwen2.5:7b", temperature=0.7)
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print("第1次调用:")
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response1 = llm_temp07.invoke(question)
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print(f" {response1.content}\n")
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print("第2次调用:")
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response2 = llm_temp07.invoke(question)
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print(f" {response2.content}\n")
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print("第3次调用:")
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response3 = llm_temp07.invoke(question)
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print(f" {response3.content}\n")
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# ==================== Temperature = 1.0 (高随机性) ====================
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print("-" * 70)
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print("Temperature = 1.0 (高随机性，每次输出可能不同)")
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print("-" * 70)
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llm_temp1 = ChatOllama(model="qwen2.5:7b", temperature=1.0)
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print("第1次调用:")
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response1 = llm_temp1.invoke(question)
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print(f" {response1.content}\n")
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print("第2次调用:")
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response2 = llm_temp1.invoke(question)
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print(f" {response2.content}\n")
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print("第3次调用:")
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response3 = llm_temp1.invoke(question)
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print(f" {response3.content}\n")
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# ==================== 不同场景的推荐值 ====================
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print("=" * 70)
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print("不同场景的 Temperature 推荐值")
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print("=" * 70)
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scenarios = [
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("代码生成", 0.0, "需要准确、可执行的代码"),
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("翻译任务", 0.0, "需要准确、一致的翻译"),
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("问答系统", 0.0, "需要准确、事实性的答案"),
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("内容总结", 0.3, "需要准确但略有变化的总结"),
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("对话助手", 0.7, "需要自然、多样化的回复"),
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("创意写作", 0.9, "需要创造性、多样化的表达"),
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("头脑风暴", 1.0, "需要最大化的创意和多样性"),
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]
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print("\n场景\t\t\tTemperature\t说明")
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print("-" * 70)
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for scenario, temp, desc in scenarios:
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print(f"{scenario:15}\t{temp}\t\t{desc}")
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print("\n" + "=" * 70)
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print("总结")
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print("=" * 70)
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print("""
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1. Temperature = 0:
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- 优点：输出稳定、可预测、适合需要准确性的任务
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- 缺点：缺乏创造性、可能显得机械化
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2. Temperature = 0.3-0.7:
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- 优点：平衡准确性和创造性
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- 适用：大多数通用场景
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3. Temperature = 0.8-1.5:
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- 优点：输出多样化、有创造性
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- 缺点：可能不够准确、输出不稳定
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- 适用：创意任务、需要多样性的场景
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提示：
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- 对于相同输入，temperature=0 时多次调用结果相同
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- temperature>0 时，每次调用结果可能不同
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- 根据任务类型选择合适的 temperature 值
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""")
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