ChatHuggingFace

这将帮助您开始使用 langchain_huggingface 聊天模型。有关所有 ChatHuggingFace 功能和配置的详细文档，请参阅 API 参考。有关 Hugging Face 支持的模型列表，请查看 此页面。

概述

集成详情

集成详情

类别	包	本地	可序列化	JS 支持	包下载量	最新包版本
ChatHuggingFace	langchain-huggingface	✅	测试版	❌

模型特性

工具调用	结构化输出	JSON 模式	图片输入	音频输入	视频输入	逐令牌流式传输	原生异步	令牌使用量	对数概率
✅	✅	❌	✅	✅	✅	❌	✅	✅	❌

设置

要访问 Hugging Face 模型，您需要创建一个 Hugging Face 账户，获取一个 API 密钥，并安装 langchain-huggingface 集成包。

凭证

生成一个 Hugging Face 访问令牌 并将其存储为环境变量：HUGGINGFACEHUB_API_TOKEN。

import getpass
import os

if not os.getenv("HUGGINGFACEHUB_API_TOKEN"):
    os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass("Enter your token: ")

安装

类别	包	本地	可序列化	JS 支持	包下载量	最新包版本
ChatHuggingFace	langchain_huggingface	✅	❌	❌

模型特性

工具调用	结构化输出	JSON 模式	图片输入	音频输入	视频输入	逐令牌流式传输	原生异步	令牌使用量	对数概率
✅	✅	❌	❌	❌	❌	❌	❌	❌	❌

设置

要访问 langchain_huggingface 模型，您需要创建一个 Hugging Face 账户，获取一个 API 密钥，并安装 langchain_huggingface 集成包。

凭证

您需要拥有一个 Hugging Face 访问令牌 并将其保存为环境变量：HUGGINGFACEHUB_API_TOKEN。

import getpass
import os

os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass(
    "Enter your Hugging Face API key: "
)

%pip install --upgrade --quiet  langchain-huggingface text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2 bitsandbytes accelerate

[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

您可以通过两种不同的方式实例化一个 ChatHuggingFace 模型：通过 HuggingFaceEndpoint 或通过 HuggingFacePipeline。

HuggingFaceEndpoint

from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint

llm = HuggingFaceEndpoint(
    repo_id="deepseek-ai/DeepSeek-R1-0528",
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
    provider="auto",  # let Hugging Face choose the best provider for you
)

chat_model = ChatHuggingFace(llm=llm)

API 参考：ChatHuggingFace | HuggingFaceEndpoint

The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.
Token is valid (permission: fineGrained).
Your token has been saved to /Users/isaachershenson/.cache/huggingface/token
Login successful

现在让我们利用 推理提供商 在特定的第三方提供商上运行模型

llm = HuggingFaceEndpoint(
    repo_id="deepseek-ai/DeepSeek-R1-0528",
    task="text-generation",
    provider="hyperbolic",  # set your provider here
    # provider="nebius",
    # provider="together",
)

chat_model = ChatHuggingFace(llm=llm)

HuggingFacePipeline

from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline

llm = HuggingFacePipeline.from_model_id(
    model_id="HuggingFaceH4/zephyr-7b-beta",
    task="text-generation",
    pipeline_kwargs=dict(
        max_new_tokens=512,
        do_sample=False,
        repetition_penalty=1.03,
    ),
)

chat_model = ChatHuggingFace(llm=llm)

API 参考：ChatHuggingFace | HuggingFacePipeline

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使用量化实例化

要运行模型的量化版本，您可以按如下方式指定一个 bitsandbytes 量化配置

from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_use_double_quant=True,
)

并将其作为其 model_kwargs 的一部分传递给 HuggingFacePipeline

llm = HuggingFacePipeline.from_model_id(
    model_id="HuggingFaceH4/zephyr-7b-beta",
    task="text-generation",
    pipeline_kwargs=dict(
        max_new_tokens=512,
        do_sample=False,
        repetition_penalty=1.03,
        return_full_text=False,
    ),
    model_kwargs={"quantization_config": quantization_config},
)

chat_model = ChatHuggingFace(llm=llm)

调用

from langchain_core.messages import (
    HumanMessage,
    SystemMessage,
)

messages = [
    SystemMessage(content="You're a helpful assistant"),
    HumanMessage(
        content="What happens when an unstoppable force meets an immovable object?"
    ),
]

ai_msg = chat_model.invoke(messages)

API 参考：HumanMessage | SystemMessage

print(ai_msg.content)

According to the popular phrase and hypothetical scenario, when an unstoppable force meets an immovable object, a paradoxical situation arises as both forces are seemingly contradictory. On one hand, an unstoppable force is an entity that cannot be stopped or prevented from moving forward, while on the other hand, an immovable object is something that cannot be moved or displaced from its position. 

In this scenario, it is un

API 参考

有关所有 ChatHuggingFace 功能和配置的详细文档，请参阅 API 参考：https://python.langchain.ac.cn/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html

API 参考

有关所有 ChatHuggingFace 功能和配置的详细文档，请参阅 API 参考：https://python.langchain.ac.cn/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html

相关

• 聊天模型概念指南
• 聊天模型操作指南