国内外大模型 API 调用#

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BaseChatModel（抽象基类）
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├── ChatOpenAI          → OpenAI GPT 系列
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├── ChatAnthropic       → Anthropic Claude 系列
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├── ChatTongyi          → 阿里通义千问
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├── ChatOllama          → 本地 Ollama 模型
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├── ChatZhipuAI         → 智谱 GLM 系列
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├── QianfanChatEndpoint → 百度文心一言
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└── ...更多社区集成

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# pip install langchain-openai
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from langchain_openai import ChatOpenAI
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llm = ChatOpenAI(
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    model="deepseek-chat",    # deepseek-chat 或 deepseek-reasoner
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    base_url="https://api.deepseek.com",
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    api_key="sk-xxxxxxxx",
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    temperature=0,
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    max_tokens=2048,
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)
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response = llm.invoke("解释梯度下降算法的直觉理解。")
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print(response.content)

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# pip install langchain-community qianfan
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from langchain_community.chat_models import QianfanChatEndpoint
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from langchain_core.messages import HumanMessage
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llm = QianfanChatEndpoint(
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    model="ERNIE-4.0-8K",
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    temperature=0.7,
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)
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response = llm.invoke([HumanMessage(content="介绍一下深度学习的发展历程。")])
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print(response.content)

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# pip install langchain-openai
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from langchain_openai import ChatOpenAI
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def create_llm(provider: str) -> ChatOpenAI:
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    """工厂函数：根据 provider 创建对应的 LLM 实例"""
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    configs = {
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        "openai": {
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            "model": "gpt-4o",
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            "base_url": "https://api.openai.com/v1",
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            "api_key": "sk-xxx",
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        },
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        "deepseek": {
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            "model": "deepseek-chat",
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            "base_url": "https://api.deepseek.com",
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            "api_key": "sk-xxx",
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        },
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        "qwen": {
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            "model": "qwen-max",
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            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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            "api_key": "sk-xxx",
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        },
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        "glm": {
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            "model": "glm-4-plus",
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            "base_url": "https://open.bigmodel.cn/api/paas/v4",
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            "api_key": "xxx",
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        },
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    }
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    cfg = configs[provider]
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    return ChatOpenAI(
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        model=cfg["model"],
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        base_url=cfg["base_url"],
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        api_key=cfg["api_key"],
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        temperature=0,
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        max_tokens=1024,
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    )
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# 切换模型只需改一个字符串
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llm = create_llm("deepseek")
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response = llm.invoke("什么是向量数据库？")
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print(response.content)

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# pip install langchain-openai langchain-core
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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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prompt = ChatPromptTemplate.from_messages([
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    ("system", "你是一位{role}。"),
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    ("human", "{question}"),
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])
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# 构建 Chain —— 模型部分可随时替换
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chain = prompt | create_llm("deepseek") | StrOutputParser()
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result = chain.invoke({"role": "物理学家", "question": "为什么天空是蓝色的？"})
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print(result)
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# 切换到 Qwen，Chain 结构完全不变
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chain_qwen = prompt | create_llm("qwen") | StrOutputParser()
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result_qwen = chain_qwen.invoke({"role": "物理学家", "question": "为什么天空是蓝色的？"})

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# pip install langchain-openai tenacity
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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from tenacity import retry, stop_after_attempt, wait_exponential
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# 方式一：内置参数
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llm = ChatOpenAI(model="gpt-4o", max_retries=3, timeout=30)
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# 方式二：手动指数退避
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@retry(
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    stop=stop_after_attempt(3),
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    wait=wait_exponential(multiplier=1, min=2, max=30),
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)
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def safe_invoke(llm, messages):
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    return llm.invoke(messages)
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response = safe_invoke(llm, [HumanMessage(content="Hello")])

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# 简单问答：短超时
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llm_fast = ChatOpenAI(model="gpt-4o-mini", timeout=15)
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# 长文档生成：长超时
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llm_slow = ChatOpenAI(model="gpt-4o", timeout=120, max_tokens=4096)

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# pip install langchain-openai langchain-community
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from langchain_openai import ChatOpenAI
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from langchain_community.callbacks import get_openai_callback
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from langchain_core.messages import HumanMessage
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llm = ChatOpenAI(model="gpt-4o", temperature=0)
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with get_openai_callback() as cb:
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    response = llm.invoke([HumanMessage(content="什么是 LangChain？")])
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    print(f"输入 Tokens:  {cb.prompt_tokens}")
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    print(f"输出 Tokens:  {cb.completion_tokens}")
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    print(f"总 Tokens:    {cb.total_tokens}")
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    print(f"总费用 (USD): ${cb.total_cost:.6f}")

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# pip install tiktoken
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import tiktoken
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def estimate_tokens(text: str, model: str = "gpt-4o") -> int:
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    """估算文本的 token 数量（仅适用于 OpenAI 模型）"""
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    encoding = tiktoken.encoding_for_model(model)
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    return len(encoding.encode(text))
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tokens = estimate_tokens("这是一段需要估算 token 的文本。")
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print(f"预估 token 数: {tokens}")