Hugging Face

让我们加载 Hugging Face 嵌入类。

%pip install --upgrade --quiet  langchain sentence_transformers

from langchain_huggingface.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings()

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])

Hugging Face 推理 API

我们还可以通过 Hugging Face 推理 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("输入你的 HF 推理 API 密钥:\n\n")

输入你的 HF 推理 API 密钥:
 ········

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]

[-0.038338541984558105, 0.1234646737575531, -0.028642963618040085]

Hugging Face Hub

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

embeddings = HuggingFaceEndpointEmbeddings()

text = "This is a test document."

query_result = embeddings.embed_query(text)

query_result[:3]