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Hugging Face

Let's load the Hugging Face Embedding class.

%pip install --upgrade --quiet  langchain langchain-huggingface sentence_transformers
from langchain_huggingface.embeddings import HuggingFaceEmbeddings
API Reference: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])

Hugging Face Inference Providers​

We can also access embedding models via the Inference Providers, which let's us use open source models on scalable serverless infrastructure.

First, we need to get a read-only API key from Hugging Face.

from getpass import getpass

huggingfacehub_api_token = getpass()

Now we can use the HuggingFaceInferenceAPIEmbeddings class to run open source embedding models via Inference Providers.

from langchain_huggingface import HuggingFaceInferenceAPIEmbeddings

embeddings = HuggingFaceInferenceAPIEmbeddings(
    api_key=huggingfacehub_api_token,
    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​

We can also generate embeddings locally via the Hugging Face Hub package, which requires us to install 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]