ChatHuggingFace
这将帮助您入门langchain_huggingface 聊天模型。有关所有ChatHuggingFace功能和配置可参考 API 参考。有关 Hugging Face 支持的模型列表，请查看此页面。
概述
集成详细信息
集成详细信息
| 类 | 包 | 本地化 | 序列 化 | JS 支持 | 软件包下载 | 最新包装 |
|---|---|---|---|---|---|---|
| ChatHuggingFace | langchain-huggingface | ✅ | beta | ❌ |  |  |
模型特点
| 工具调用 | 结构化输出 | JSON 模式 | 图像输入 | 音频输入 | 视频输入 | 令牌级流式处理 | 本机异步 | Token 使用情况 | 日志 |
|---|---|---|---|---|---|---|---|---|---|
| ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
设置
要访问 Hugging Face 模型，您需要创建一个 Hugging Face 帐户，获取 API 密钥，并安装langchain-huggingface集成包。
凭据
生成 Hugging Face Access Token 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 | 本地化 | 序列 化 | JS 支持 | 软件包下载 | 最新包装 |
|---|---|---|---|---|---|---|
| ChatHuggingFace | langchain_huggingface | ✅ | ❌ | ❌ | | |
模型特点
| 工具调用 | 结构化输出 | JSON 模式 | 图像输入 | 音频输入 | 视频输入 | 令牌级流式处理 | 本机异步 | Token 使用情况 | 日志 |
|---|---|---|---|---|---|---|---|---|---|
| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
设置
要访问langchain_huggingface创建 A/AN 所需的模型Hugging Faceaccount 中，获取 API 密钥，并安装langchain_huggingface集成包。
凭据
您需要将 Hugging Face Access Token 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
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m24.0[0m[39;49m -> [0m[32;49m24.1.2[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m
Note: you may need to restart the kernel to use updated packages.
实例
您可以实例化ChatHuggingFace模型，从HuggingFaceEndpoint或从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)
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
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 Quantization 进行实例化
要运行模型的量化版本，您可以指定bitsandbytes量化配置如下：
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,
)
并将其传递给HuggingFacePipeline作为其model_kwargs:
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 系统
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功能和配置可参考 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