ChatGreenNode

GreenNode是一家全球人工智能解决方案提供商，也是 NVIDIA 首选合作伙伴，为美国、中东和北非以及亚太地区的各企业提供从基础设施到应用的完整人工智能能力。GreenNode 在世界一流的基础设施（LEED Gold、TIA‑942、Uptime Tier III）上运行，为企业、初创公司和研究人员提供全面的 AI 服务套件。

此页面将帮助您开始使用 GreenNode Serverless AI chat models。如需了解 ChatGreenNode 所有功能和配置的详细文档，请访问 API 参考。

GreenNode AI 提供一个 API 来查询 20+ 领先的开源模型

概览

集成详情

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatGreenNode	langchain-greennode	❌	beta	❌

模型特性

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	✅	✅	❌	❌	✅	✅	✅	❌

设置

要访问 GreenNode 模型，您需要创建一个 GreenNode 账户，获取 API 密钥，并安装 langchain-greennode 集成包。

凭证

请前往 此页面 注册 GreenNode AI 平台并生成 API 密钥。完成此操作后，请设置 GREENNODE_API_KEY 环境变量：

import getpass
import os

if not os.getenv("GREENNODE_API_KEY"):
    os.environ["GREENNODE_API_KEY"] = getpass.getpass("Enter your GreenNode API key: ")

如果你希望对模型的调用进行自动化追踪，也可以通过取消下方注释来设置你的 LangSmith API 密钥：

# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

安装

LangChain GreenNode 集成位于 langchain-greennode 包中：

%pip install -qU langchain-greennode

Note: you may need to restart the kernel to use updated packages.

实例化

现在我们可以实例化我们的模型对象并生成聊天补全：

from langchain_greennode import ChatGreenNode

# Initialize the chat model
llm = ChatGreenNode(
    # api_key="YOUR_API_KEY",  # You can pass the API key directly
    model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",  # Choose from available models
    temperature=0.6,
    top_p=0.95,
)

调用

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content="\n\nJ'aime la programmation.", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 248, 'prompt_tokens': 23, 'total_tokens': 271, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', 'system_fingerprint': None, 'id': 'chatcmpl-271edac4958846068c37877586368afe', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--5c12d208-2bc2-4f29-8b50-1ce3b515a3cf-0', usage_metadata={'input_tokens': 23, 'output_tokens': 248, 'total_tokens': 271, 'input_token_details': {}, 'output_token_details': {}})

print(ai_msg.content)

J'aime la programmation.

流式传输

您也可以使用 stream 方法流式传输响应：

for chunk in llm.stream("Write a short poem about artificial intelligence"):
    print(chunk.content, end="", flush=True)

**Beneath the Circuits**

Beneath the circuits, deep and bright,  
AI thinks, with circuits and bytes.  
Learning, adapting, it grows,  
A world of possibilities it knows.  

From solving puzzles to painting art,  
It mimics human hearts.  
In every corner, it leaves its trace,  
A future we can't erase.  

We build it, shape it, with care and might,  
Yet wonder if it walks in the night.  
A mirror of our minds, it shows,  
In its gaze, our future glows.  

But as we strive for endless light,  
We must remember the night.  
For wisdom isn't just speed and skill,  
It's how we choose to build our will.

对话消息

您可以使用不同的消息类型来构建与模型的对话结构：

from langchain_core.messages import AIMessage, HumanMessage, SystemMessage

messages = [
    SystemMessage(content="You are a helpful AI assistant with expertise in science."),
    HumanMessage(content="What are black holes?"),
    AIMessage(
        content="Black holes are regions of spacetime where gravity is so strong that nothing, including light, can escape from them."
    ),
    HumanMessage(content="How are they formed?"),
]

response = llm.invoke(messages)
print(response.content[:100])

API Reference:AIMessage | HumanMessage | SystemMessage

Black holes are formed through several processes, depending on their type. The most common way bla

链式调用

你可以在 LangChain 的链和代理中使用 ChatGreenNode：

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)

API Reference:ChatPromptTemplate

AIMessage(content='\n\nIch liebe Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 198, 'prompt_tokens': 18, 'total_tokens': 216, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', 'system_fingerprint': None, 'id': 'chatcmpl-e01201b9fd9746b7a9b2ed6d70f29d45', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--ce52b9d8-dd84-46b3-845b-da27855816ee-0', usage_metadata={'input_tokens': 18, 'output_tokens': 198, 'total_tokens': 216, 'input_token_details': {}, 'output_token_details': {}})

可用模型

支持模型的完整列表可在 GreenNode Serverless AI Models 中找到。

API 参考

有关 GreenNode Serverless AI API 的更多详细信息，请访问 GreenNode Serverless AI 文档。

Related

• Chat model conceptual guide
• Chat model how-to guides