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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，请访问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​

类包本地可序列化JS支持包下载包最新
ChatHuggingFacelangchain-huggingface✅测试版❌PyPI - 下载量PyPI - 版本

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式传输原生异步令牌使用Logprobs
✅✅❌✅✅✅❌✅✅❌

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​

类包本地可序列化JS支持包下载包最新
ChatHuggingFacelangchain_huggingface✅❌❌PyPI - VersionPyPI - Version

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式传输原生异步令牌使用Logprobs
✅✅❌❌❌❌❌❌❌❌

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​

你需要将Hugging Face 访问令牌保存为环境变量：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​

你可以通过两种不同的方式实例化一个ChatHuggingFace模型，要么从HuggingFaceEndpoint，要么从HuggingFacePipeline。

HuggingFaceEndpoint​

from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint

llm = HuggingFaceEndpoint(
    repo_id="HuggingFaceH4/zephyr-7b-beta",
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)

chat_model = ChatHuggingFace(llm=llm)
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

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)
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使用量化进行实例化​

要运行量化版本的模型，您可以指定一个bitsandbytes量化配置，如下所示：

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 Reference: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，请访问API参考：https://python.langchain.com/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，请访问API参考：https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html

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