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Hugging Face

Let's load the Hugging Face Embedding class.

%pip install --upgrade --quiet  langchain 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 API​

We can also access embedding models via the Hugging Face Inference API, which does not require us to install sentence_transformers and download models locally.

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]
[-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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