ChatCerebras

此笔记本提供有关如何开始使用 Cerebras 聊天模型的快速概述。有关所有 ChatCerebras 功能和配置的详细文档，请访问 API 参考。

在 Cerebras，我们开发了世界上最大、最快的 AI 处理器，即 Wafer-Scale Engine-3 (WSE-3)。由 WSE-3 提供支持的 Cerebras CS-3 系统代表了新型 AI 超级计算机，它以无与伦比的性能和可扩展性为生成式 AI 训练和推理设定了标准。

使用 Cerebras 作为您的推理提供商，您可以

为 AI 推理工作负载实现前所未有的速度
以高吞吐量构建商业应用
利用我们无缝的集群技术轻松扩展您的 AI 工作负载

我们的 CS-3 系统可以快速轻松地进行集群，以创建世界上最大的 AI 超级计算机，从而简化最大模型的部署和运行。领先的企业、研究机构和政府机构已经开始使用 Cerebras 解决方案来开发专有模型和训练流行的开源模型。

想要体验 Cerebras 的强大功能？访问我们的网站获取更多资源，并探索通过 Cerebras Cloud 或本地部署访问我们技术的选项！

有关 Cerebras Cloud 的更多信息，请访问 cloud.cerebras.ai。我们的 API 参考可在 inference-docs.cerebras.ai 获取。

概述

集成详细信息

类	包	本地	可序列化	JS 支持	包下载	包最新版本
ChatCerebras	langchain-cerebras	❌	beta	❌

模型功能

工具调用	结构化输出	JSON 模式	图像输入	音频输入	视频输入	令牌级流	原生异步	令牌使用情况	Logprobs
✅	✅	✅	❌	❌	❌	✅	✅	✅	❌

设置

pip install langchain-cerebras

凭据

从 cloud.cerebras.ai 获取 API 密钥并将其添加到您的环境变量中

export CEREBRAS_API_KEY="your-api-key-here"

import getpass
import os

os.environ["CEREBRAS_API_KEY"] = getpass.getpass("Enter your Cerebras API key: ")

Enter your Cerebras API key:  ········

如果您希望自动跟踪您的模型调用，也可以通过取消下面的注释来设置您的 LangSmith API 密钥

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

安装

LangChain Cerebras 集成位于 langchain-cerebras 包中

%pip install -qU langchain-cerebras

实例化

现在我们可以实例化我们的模型对象并生成聊天完成

from langchain_cerebras import ChatCerebras

llm = ChatCerebras(
    model="llama3.1-70b",
    # other params...
)

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='Je adore le programmation.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 35, 'total_tokens': 42}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-e5d66faf-019c-4ac6-9265-71093b13202d-0', usage_metadata={'input_tokens': 35, 'output_tokens': 7, 'total_tokens': 42})

链接

我们可以使用提示模板来链接我们的模型，如下所示

from langchain_cerebras import ChatCerebras
from langchain_core.prompts import ChatPromptTemplate

llm = ChatCerebras(
    model="llama3.1-70b",
    # other params...
)

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "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 参考：ChatPromptTemplate

AIMessage(content='Ich liebe Programmieren!\n\n(Literally: I love programming!)', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 30, 'total_tokens': 44}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-e1d2ebb8-76d1-471b-9368-3b68d431f16a-0', usage_metadata={'input_tokens': 30, 'output_tokens': 14, 'total_tokens': 44})

流式传输

from langchain_cerebras import ChatCerebras
from langchain_core.prompts import ChatPromptTemplate

llm = ChatCerebras(
    model="llama3.1-70b",
    # other params...
)

system = "You are an expert on animals who must answer questions in a manner that a 5 year old can understand."
human = "I want to learn more about this animal: {animal}"
prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])

chain = prompt | llm

for chunk in chain.stream({"animal": "Lion"}):
    print(chunk.content, end="", flush=True)

API 参考：ChatPromptTemplate

OH BOY! Let me tell you all about LIONS!

Lions are the kings of the jungle! They're really big and have beautiful, fluffy manes around their necks. The mane is like a big, golden crown!

Lions live in groups called prides. A pride is like a big family, and the lionesses (that's what we call the female lions) take care of the babies. The lionesses are like the mommies, and they teach the babies how to hunt and play.

Lions are very good at hunting. They work together to catch their food, like zebras and antelopes. They're super fast and can run really, really fast!

But lions are also very sleepy. They like to take long naps in the sun, and they can sleep for up to 20 hours a day! Can you imagine sleeping that much?

Lions are also very loud. They roar really loudly to talk to each other. It's like they're saying, "ROAR! I'm the king of the jungle!"

And guess what? Lions are very social. They like to play and cuddle with each other. They're like big, furry teddy bears!

So, that's lions! Aren't they just the coolest?

异步

from langchain_cerebras import ChatCerebras
from langchain_core.prompts import ChatPromptTemplate

llm = ChatCerebras(
    model="llama3.1-70b",
    # other params...
)

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "human",
            "Let's play a game of opposites. What's the opposite of {topic}? Just give me the answer with no extra input.",
        )
    ]
)
chain = prompt | llm
await chain.ainvoke({"topic": "fire"})

API 参考：ChatPromptTemplate

AIMessage(content='Ice', response_metadata={'token_usage': {'completion_tokens': 2, 'prompt_tokens': 36, 'total_tokens': 38}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-7434bdde-1bec-44cf-827b-8d978071dfe8-0', usage_metadata={'input_tokens': 36, 'output_tokens': 2, 'total_tokens': 38})

异步流式传输

from langchain_cerebras import ChatCerebras
from langchain_core.prompts import ChatPromptTemplate

llm = ChatCerebras(
    model="llama3.1-70b",
    # other params...
)

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "human",
            "Write a long convoluted story about {subject}. I want {num_paragraphs} paragraphs.",
        )
    ]
)
chain = prompt | llm

async for chunk in chain.astream({"num_paragraphs": 3, "subject": "blackholes"}):
    print(chunk.content, end="", flush=True)

API 参考：ChatPromptTemplate

In the distant reaches of the cosmos, there existed a peculiar phenomenon known as the "Eclipse of Eternity," a swirling vortex of darkness that had been shrouded in mystery for eons. It was said that this blackhole, born from the cataclysmic collision of two ancient stars, had been slowly devouring the fabric of space-time itself, warping the very essence of reality. As the celestial bodies of the galaxy danced around it, they began to notice a strange, almost imperceptible distortion in the fabric of space, as if the blackhole's gravitational pull was exerting an influence on the very course of events itself.

As the centuries passed, astronomers from across the galaxy became increasingly fascinated by the Eclipse of Eternity, pouring over ancient texts and scouring the cosmos for any hint of its secrets. One such scholar, a brilliant and reclusive astrophysicist named Dr. Elara Vex, became obsessed with unraveling the mysteries of the blackhole. She spent years pouring over ancient texts, deciphering cryptic messages and hidden codes that hinted at the existence of a long-lost civilization that had once thrived in the heart of the blackhole itself. According to legend, this ancient civilization had possessed knowledge of the cosmos that was beyond human comprehension, and had used their mastery of the universe to create the Eclipse of Eternity as a gateway to other dimensions.

As Dr. Vex delved deeper into her research, she began to experience strange and vivid dreams, visions that seemed to transport her to the very heart of the blackhole itself. In these dreams, she saw ancient beings, their faces twisted in agony as they were consumed by the void. She saw stars and galaxies, their light warped and distorted by the blackhole's gravitational pull. And she saw the Eclipse of Eternity itself, its swirling vortex of darkness pulsing with an otherworldly energy that seemed to be calling to her. As the dreams grew more vivid and more frequent, Dr. Vex became convinced that she was being drawn into the heart of the blackhole, and that the secrets of the universe lay waiting for her on the other side.

API 参考

有关所有 ChatCerebras 功能和配置的详细文档，请访问 API 参考：https://python-api.langchain.ac.cn/en/latest/chat_models/langchain_cerebras.chat_models.ChatCerebras.html

聊天模型概念指南
聊天模型操作指南

ChatCerebras

概述

集成详细信息

模型功能

设置

凭据

安装

实例化

调用

链接

流式传输

异步

异步流式传输

API 参考

此页面是否有帮助？

您也可以在 GitHub 上留下详细的反馈 GitHub.

ChatCerebras

概述​

集成详细信息​

模型功能​

设置​

凭据​

安装​

实例化​

调用​

链接​

流式传输​

异步​

异步流式传输​

API 参考​

相关​

此页面是否有帮助？

您也可以在 GitHub 上留下详细的反馈 GitHub.

概述

集成详细信息

模型功能

设置

凭据

安装

实例化

调用

链接

流式传输

异步

异步流式传输

API 参考

相关