Hugging Face

Let’s load the Hugging Face Embedding class.

%pip install --upgrade --quiet  langchain sentence_transformers

from langchain_community.embeddings import HuggingFaceEmbeddings

API Reference:

• 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 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]

API Reference:

• HuggingFaceInferenceAPIEmbeddings

[-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_community.embeddings import HuggingFaceHubEmbeddings

API Reference:

• HuggingFaceHubEmbeddings

embeddings = HuggingFaceHubEmbeddings()

text = "This is a test document."

query_result = embeddings.embed_query(text)

query_result[:3]