Text Embeddings Inference

Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.

To use it within langchain, first install huggingface-hub.

!pip install huggingface-hub -q

Then expose an embedding model using TEI. For instance, using Docker, you can serve BAAI/bge-large-en-v1.5 as follows:

model=BAAI/bge-large-en-v1.5
revision=refs/pr/5
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:0.6 --model-id $model --revision $revision

Finally, instantiate the client and embed your texts.

from langchain.embeddings import HuggingFaceHubEmbeddings

from getpass import getpass

huggingfacehub_api_token = getpass("Enter your HF API Key:\n\n")

Enter your HF API Key:

 ········

embeddings = HuggingFaceHubEmbeddings(
    model="http://localhost:8080", huggingfacehub_api_token=huggingfacehub_api_token
)

text = "What is deep learning?"

query_result = embeddings.embed_query(text)
query_result[:3]

[0.018113142, 0.00302585, -0.049911194]

doc_result = embeddings.embed_documents([text])