ChatGroq

这将帮助您开始使用 Groq 聊天模型。有关所有 ChatGroq 功能和配置的详细文档，请访问 API 参考。有关所有 Groq 模型的列表，请访问此 链接。

概述

集成详情

类	包	本地	可序列化	JS 支持	包下载	包最新版本
ChatGroq	langchain-groq	❌	测试版	✅

模型特性

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

要访问 Groq 模型，您需要创建一个 Groq 帐户，获取 API 密钥，并安装 langchain-groq 集成包。

凭据

前往 Groq 控制台 注册 Groq 并生成 API 密钥。完成此操作后，设置 GROQ_API_KEY 环境变量

import getpass
import os

if "GROQ_API_KEY" not in os.environ:
    os.environ["GROQ_API_KEY"] = getpass.getpass("Enter your Groq API key: ")

如果您希望自动跟踪您的模型调用，您也可以通过取消以下注释来设置您的 LangSmith API 密钥

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

安装

LangChain Groq 集成位于 langchain-groq 包中

%pip install -qU langchain-groq

[notice] A new release of pip is available: 24.0 -> 24.1.2
[notice] To update, run: pip install --upgrade pip
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完成

from langchain_groq import ChatGroq

llm = ChatGroq(
    model="mixtral-8x7b-32768",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    # other params...
)

API 参考：ChatGroq

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='I enjoy programming. (The French translation is: "J\'aime programmer.")\n\nNote: I chose to translate "I love programming" as "J\'aime programmer" instead of "Je suis amoureux de programmer" because the latter has a romantic connotation that is not present in the original English sentence.', response_metadata={'token_usage': {'completion_tokens': 73, 'prompt_tokens': 31, 'total_tokens': 104, 'completion_time': 0.1140625, 'prompt_time': 0.003352463, 'queue_time': None, 'total_time': 0.117414963}, 'model_name': 'mixtral-8x7b-32768', 'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop', 'logprobs': None}, id='run-64433c19-eadf-42fc-801e-3071e3c40160-0', usage_metadata={'input_tokens': 31, 'output_tokens': 73, 'total_tokens': 104})

print(ai_msg.content)

I enjoy programming. (The French translation is: "J'aime programmer.")

Note: I chose to translate "I love programming" as "J'aime programmer" instead of "Je suis amoureux de programmer" because the latter has a romantic connotation that is not present in the original English sentence.

链接

我们可以像这样 链接 我们的模型与提示模板

from langchain_core.prompts import ChatPromptTemplate

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='That\'s great! I can help you translate English phrases related to programming into German.\n\n"I love programming" can be translated as "Ich liebe Programmieren" in German.\n\nHere are some more programming-related phrases translated into German:\n\n* "Programming language" = "Programmiersprache"\n* "Code" = "Code"\n* "Variable" = "Variable"\n* "Function" = "Funktion"\n* "Array" = "Array"\n* "Object-oriented programming" = "Objektorientierte Programmierung"\n* "Algorithm" = "Algorithmus"\n* "Data structure" = "Datenstruktur"\n* "Debugging" = "Fehlersuche"\n* "Compile" = "Kompilieren"\n* "Link" = "Verknüpfen"\n* "Run" = "Ausführen"\n* "Test" = "Testen"\n* "Deploy" = "Bereitstellen"\n* "Version control" = "Versionskontrolle"\n* "Open source" = "Open Source"\n* "Software development" = "Softwareentwicklung"\n* "Agile methodology" = "Agile Methodik"\n* "DevOps" = "DevOps"\n* "Cloud computing" = "Cloud Computing"\n\nI hope this helps! Let me know if you have any other questions or if you need further translations.', response_metadata={'token_usage': {'completion_tokens': 331, 'prompt_tokens': 25, 'total_tokens': 356, 'completion_time': 0.520006542, 'prompt_time': 0.00250165, 'queue_time': None, 'total_time': 0.522508192}, 'model_name': 'mixtral-8x7b-32768', 'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop', 'logprobs': None}, id='run-74207fb7-85d3-417d-b2b9-621116b75d41-0', usage_metadata={'input_tokens': 25, 'output_tokens': 331, 'total_tokens': 356})

API 参考

有关所有 ChatGroq 功能和配置的详细文档，请访问 API 参考： https://python.langchain.ac.cn/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html

相关

• 聊天模型 概念指南
• 聊天模型 操作指南