探索ChatGroq模型:使用LangChain进行语言转换

探索ChatGroq模型:使用LangChain进行语言转换

引言

近年来,AI语言模型在自然语言处理领域取得了显著的发展。ChatGroq是一个强大的语言模型,专注于提供高效的语言翻译和交互功能。本文旨在帮助您了解如何使用LangChain库中的ChatGroq模型进行基本的语言翻译任务。

主要内容

1. 概述

ChatGroq模型提供了一系列功能,包括工具调用、结构化输出和JSON模式等。它支持异步运行和令牌级别的流式处理。然而,图像、音频和视频输入目前并不支持。

2. 设置

要使用ChatGroq模型,您需要创建一个Groq账户并获取API密钥。此外,您还需要安装langchain-groq包。

  • 注册和获取API密钥
    前往Groq控制台注册并生成API密钥。然后设置环境变量:

    import getpass
    import os
    
    os.environ["GROQ_API_KEY"] = getpass.getpass
def _init_chat_model_helper( model: str, *, model_provider: Optional[str] = None, **kwargs: Any ) -> BaseChatModel: model, model_provider = _parse_model(model, model_provider) if model_provider == "openai": _check_pkg("langchain_openai") from langchain_openai import ChatOpenAI return ChatOpenAI(model=model, **kwargs) elif model_provider == "anthropic": _check_pkg("langchain_anthropic") from langchain_anthropic import ChatAnthropic return ChatAnthropic(model=model, **kwargs) # type: ignore[call-arg] elif model_provider == "azure_openai": _check_pkg("langchain_openai") from langchain_openai import AzureChatOpenAI return AzureChatOpenAI(model=model, **kwargs) elif model_provider == "cohere": _check_pkg("langchain_cohere") from langchain_cohere import ChatCohere return ChatCohere(model=model, **kwargs) elif model_provider == "google_vertexai": _check_pkg("langchain_google_vertexai") from langchain_google_vertexai import ChatVertexAI return ChatVertexAI(model=model, **kwargs) elif model_provider == "google_genai": _check_pkg("langchain_google_genai") from langchain_google_genai import ChatGoogleGenerativeAI return ChatGoogleGenerativeAI(model=model, **kwargs) elif model_provider == "fireworks": _check_pkg("langchain_fireworks") from langchain_fireworks import ChatFireworks return ChatFireworks(model=model, **kwargs) elif model_provider == "ollama": try: _check_pkg("langchain_ollama") from langchain_ollama import ChatOllama except ImportError: # For backwards compatibility try: _check_pkg("langchain_community") from langchain_community.chat_models import ChatOllama except ImportError: # If both langchain-ollama and langchain-community aren't available, # raise an error related to langchain-ollama _check_pkg("langchain_ollama") return ChatOllama(model=model, **kwargs) elif model_provider == "together": _check_pkg("langchain_together") from langchain_together import ChatTogether return ChatTogether(model=model, **kwargs) elif model_provider == "mistralai": _check_pkg("langchain_mistralai") from langchain_mistralai import ChatMistralAI return ChatMistralAI(model=model, **kwargs) # type: ignore[call-arg] elif model_provider == "huggingface": _check_pkg("langchain_huggingface") from langchain_huggingface import ChatHuggingFace return ChatHuggingFace(model_id=model, **kwargs) elif model_provider == "groq": _check_pkg("langchain_groq") from langchain_groq import ChatGroq return ChatGroq(model=model, **kwargs) elif model_provider == "bedrock": _check_pkg("langchain_aws") from langchain_aws import ChatBedrock # TODO: update to use model= once ChatBedrock supports return ChatBedrock(model_id=model, **kwargs) elif model_provider == "bedrock_converse": _check_pkg("langchain_aws") from langchain_aws import ChatBedrockConverse return ChatBedrockConverse(model=model, **kwargs) elif model_provider == "google_anthropic_vertex": _check_pkg("langchain_google_vertexai") from langchain_google_vertexai.model_garden import ChatAnthropicVertex return ChatAnthropicVertex(model=model, **kwargs) elif model_provider == "deepseek": _check_pkg("langchain_deepseek", pkg_kebab="langchain-deepseek") from langchain_deepseek import ChatDeepSeek return ChatDeepSeek(model=model, **kwargs) elif model_provider == "nvidia": _check_pkg("langchain_nvidia_ai_endpoints") from langchain_nvidia_ai_endpoints import ChatNVIDIA return ChatNVIDIA(model=model, **kwargs) elif model_provider == "ibm": _check_pkg("langchain_ibm") from langchain_ibm import ChatWatsonx return ChatWatsonx(model_id=model, **kwargs) elif model_provider == "xai": _check_pkg("langchain_xai") from langchain_xai import ChatXAI return ChatXAI(model=model, **kwargs) else: supported = ", ".join(_SUPPORTED_PROVIDERS) raise ValueError( f"Unsupported {model_provider=}.\n\nSupported model providers are: " f"{supported}" ) _SUPPORTED_PROVIDERS = { "openai", "anthropic", "azure_openai", "cohere", "google_vertexai", "google_genai", "fireworks", "ollama", "together", "mistralai", "huggingface", "groq", "bedrock", "bedrock_converse", "google_anthropic_vertex", "deepseek", "ibm", "xai", }
09-23
def _init_chat_model_helper( model: str, *, model_provider: Optional[str] = None, **kwargs: Any ) -> BaseChatModel: model, model_provider = _parse_model(model, model_provider) if model_provider == "openai": _check_pkg("langchain_openai") from langchain_openai import ChatOpenAI return ChatOpenAI(model=model, **kwargs) elif model_provider == "anthropic": _check_pkg("langchain_anthropic") from langchain_anthropic import ChatAnthropic return ChatAnthropic(model=model, **kwargs) # type: ignore[call-arg] elif model_provider == "azure_openai": _check_pkg("langchain_openai") from langchain_openai import AzureChatOpenAI return AzureChatOpenAI(model=model, **kwargs) elif model_provider == "cohere": _check_pkg("langchain_cohere") from langchain_cohere import ChatCohere return ChatCohere(model=model, **kwargs) elif model_provider == "google_vertexai": _check_pkg("langchain_google_vertexai") from langchain_google_vertexai import ChatVertexAI return ChatVertexAI(model=model, **kwargs) elif model_provider == "google_genai": _check_pkg("langchain_google_genai") from langchain_google_genai import ChatGoogleGenerativeAI return ChatGoogleGenerativeAI(model=model, **kwargs) elif model_provider == "fireworks": _check_pkg("langchain_fireworks") from langchain_fireworks import ChatFireworks return ChatFireworks(model=model, **kwargs) elif model_provider == "ollama": try: _check_pkg("langchain_ollama") from langchain_ollama import ChatOllama except ImportError: # For backwards compatibility try: _check_pkg("langchain_community") from langchain_community.chat_models import ChatOllama except ImportError: # If both langchain-ollama and langchain-community aren't available, # raise an error related to langchain-ollama _check_pkg("langchain_ollama") return ChatOllama(model=model, **kwargs) elif model_provider == "together": _check_pkg("langchain_together") from langchain_together import ChatTogether return ChatTogether(model=model, **kwargs) elif model_provider == "mistralai": _check_pkg("langchain_mistralai") from langchain_mistralai import ChatMistralAI return ChatMistralAI(model=model, **kwargs) # type: ignore[call-arg] elif model_provider == "huggingface": _check_pkg("langchain_huggingface") from langchain_huggingface import ChatHuggingFace return ChatHuggingFace(model_id=model, **kwargs) elif model_provider == "groq": _check_pkg("langchain_groq") from langchain_groq import ChatGroq return ChatGroq(model=model, **kwargs) elif model_provider == "bedrock": _check_pkg("langchain_aws") from langchain_aws import ChatBedrock # TODO: update to use model= once ChatBedrock supports return ChatBedrock(model_id=model, **kwargs) elif model_provider == "bedrock_converse": _check_pkg("langchain_aws") from langchain_aws import ChatBedrockConverse return ChatBedrockConverse(model=model, **kwargs) elif model_provider == "google_anthropic_vertex": _check_pkg("langchain_google_vertexai") from langchain_google_vertexai.model_garden import ChatAnthropicVertex return ChatAnthropicVertex(model=model, **kwargs) elif model_provider == "deepseek": _check_pkg("langchain_deepseek", pkg_kebab="langchain-deepseek") from langchain_deepseek import ChatDeepSeek return ChatDeepSeek(model=model, **kwargs) elif model_provider == "nvidia": _check_pkg("langchain_nvidia_ai_endpoints") from langchain_nvidia_ai_endpoints import ChatNVIDIA return ChatNVIDIA(model=model, **kwargs) elif model_provider == "ibm": _check_pkg("langchain_ibm") from langchain_ibm import ChatWatsonx return ChatWatsonx(model_id=model, **kwargs) elif model_provider == "xai": _check_pkg("langchain_xai") from langchain_xai import ChatXAI return ChatXAI(model=model, **kwargs) else: supported = ", ".join(_SUPPORTED_PROVIDERS) raise ValueError( f"Unsupported {model_provider=}.\n\nSupported model providers are: " f"{supported}" ) _SUPPORTED_PROVIDERS = { "openai", "anthropic", "azure_openai", "cohere", "google_vertexai", "google_genai", "fireworks", "ollama", "together", "mistralai", "huggingface", "groq", "bedrock", "bedrock_converse", "google_anthropic_vertex", "deepseek", "ibm", "xai", } 需要添加tongyi 怎么修改
最新发布
09-23
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