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
集成细节
集成细节
| 类 | 包 | 本地 | 可序列化 | JS 支持 | 包下载 | 包最新版本 |
|---|---|---|---|---|---|---|
| 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 | 令牌使用情况 | Logprobs |
|---|---|---|---|---|---|---|---|---|---|
| ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
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 | 包最新版本 |
|---|---|---|---|---|---|---|
| ChatHuggingFace | langchain_huggingface | ✅ | ❌ | ❌ | | |
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 | 令牌使用情况 | Logprobs |
|---|---|---|---|---|---|---|---|---|---|
| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
设置
要访问langchain_huggingface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
您需要将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
[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.
实例化
您可以通过两种不同的方式实例化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)
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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使用量化进行实例化
要运行模型的量化版本，您可以指定如下所示的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，请访问 API 参考：https://python.langchain.ac.cn/v0.2/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.ac.cn/v0.2/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html