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# 是否启用Langsmith监控功能
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LANGSMITH_TRACING=true
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# Langsmith监控WebUI地址
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LANGSMITH_ENDPOINT=https://api.smith.LangChain.com
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# 创建的API_KEY
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LANGSMITH_API_KEY=<YOUR_API_KEY>
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# 自定义项目名称，可以在Langsmith WebUI监控页面根据名称查看对应的运行记录
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LANGSMITH_PROJECT="pr-clear-harmony-32"

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import os
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from dotenv import load_dotenv
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from langchain_deepseek import ChatDeepSeek
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# 将env文件中的变量加载为环境变量
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#override=True：表示.env优先
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load_dotenv(override=True)
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DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
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DEEPSEEK_BASE_URL = os.getenv("DEEPSEEK_BASE_URL")
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model = ChatDeepSeek(
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api_key=DEEPSEEK_API_KEY,
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api_base=DEEPSEEK_BASE_URL,
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model_name="deepseek-v4-flash"
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)
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print(model.invoke("你好"))

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from langchain.chat_models import init_chat_model
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from dotenv import load_dotenv
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import os
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load_dotenv(override=True)
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CLOSEAI_API_KEY=os.getenv("CLOSEAI_API_KEY")
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CLOSEAI_BASE_URL=os.getenv("CLOSEAI_BASE_URL")
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model = init_chat_model(model="deepseek-v4-flash",
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model_provider="openai",
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api_key=CLOSEAI_API_KEY,
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base_url=CLOSEAI_BASE_URL)
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print(model.invoke("你好，用一句话回答"))

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from langchain.chat_models import init_chat_model
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from dotenv import load_dotenv
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import os
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from rich import print as rprint
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# 从.env文件中加载环境变量
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load_dotenv(override=True)
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DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
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DEEPSEEK_BASE_URL = os.getenv("DEEPSEEK_BASE_URL")
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# 1. 初始化模型
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model = init_chat_model(
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model="deepseek-v4-flash",
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model_provider="deepseek",
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api_key=DEEPSEEK_API_KEY,
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base_url=DEEPSEEK_BASE_URL,
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temperature=0.2,
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max_tokens=500,
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# 指定可调整参数
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configurable_fields=("model", "model_provider", "temperature",
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"max_tokens"),
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)
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# 2. 准备 config 字典
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config = {
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"run_name": "joke_generation", # 在LangSmith中这次运行会显示为"joke_generation"
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"tags": ["my_tag1", "my_tag2"], # 打上标签便于分类查找
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"metadata": {
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"user_id": "shkstart", # 记录用户ID
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"session_id": "sess_123" # 记录会话ID
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},
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"configurable": {
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"model": "deepseek-v4-pro", # 配置模型参数
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"model_provider": "openai", # 配置模型提供商参数
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"temperature": 0.7, # 配置温度参数
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"max_tokens": 1000 # 配置最大令牌数
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}
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}
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# 3. 调用模型并传入config
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response = model.invoke(
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"1 + 2 = ？",
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config=config
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)
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rprint(response)