Hugging Face integration

Integrate with the Hugging Face embedding model using LangChain Python.

Let’s load the Hugging Face Embedding class.

pip install -qU  langchain langchain-huggingface sentence_transformers

from langchain_huggingface.embeddings import 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]

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