Hugging Face

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])

from getpass import getpass

huggingfacehub_api_token = getpass()

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]

!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]