Hugging Face

让我们加载 Hugging Face Embedding 类。

%pip install --upgrade --quiet  langchain langchain-huggingface sentence_transformers

from langchain_huggingface.embeddings import HuggingFaceEmbeddings

API 参考：HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

text = "This is a test document."

query_result = embeddings.embed_query(text)

query_result[:3]

[-0.04895168915390968, -0.03986193612217903, -0.021562768146395683]

doc_result = embeddings.embed_documents([text])

拥抱人脸推理 API

我们还可以通过 Hugging Face Inference API 访问嵌入模型，这不需要我们安装sentence_transformers并在本地下载模型。

import getpass

inference_api_key = getpass.getpass("Enter your HF Inference API Key:\n\n")

Enter your HF Inference API Key:

 ········

from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings

embeddings = HuggingFaceInferenceAPIEmbeddings(
    api_key=inference_api_key, model_name="sentence-transformers/all-MiniLM-l6-v2"
)

query_result = embeddings.embed_query(text)
query_result[:3]

API 参考：HuggingFaceInferenceAPIEmbeddings

[-0.038338541984558105, 0.1234646737575531, -0.028642963618040085]

Hugging Face 集线器

我们还可以通过 Hugging Face Hub 包在本地生成嵌入，这需要我们安装huggingface_hub

!pip install huggingface_hub

from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings

API 参考：HuggingFaceEndpointEmbeddings

embeddings = HuggingFaceEndpointEmbeddings()

text = "This is a test document."

query_result = embeddings.embed_query(text)

query_result[:3]

嵌入模型概念指南
嵌入模型操作指南

拥抱人脸推理 API

Hugging Face 集线器

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