This repository contains everything needed to deploy a production-ready service for computing sentence similarity embeddings using the model all-MiniLM-L6-v2 and TorchServe. Those embeddings can then be used in combination with a vector database like Pinecone, Milvus, Weaviate or Qdrant.

This repository has been created because there was no simple example in the torchserve repository for deploying a huggingface model for sentence similarity. The closest I could find was resources for sequence classification, generation, question answering, and token classification as you can check here.

More details about it is provided in this article.

There are two ways to test the server, by running it as a process or as a docker container.

First, make sure you have docker installed.

docker run -p 8080:8080 -it ghcr.io/alexgseymour/torch-serve-embedder-encoder-x86:latest

Then, go to Usage to check how to use the service.

Note that the command can be further optimized for production by using settings documented in the TorchServe documentation

Also note that by using this docker image and even if you have a GPU available, the inferences will be done on CPU with the basic capabilities, i.e., without leveraging any CPU extension. If you want to leverage your GPU or some CPU capabilities, you need to adapt the Dockerfile according to the documentation of TorchServe.

First, make sure you have Python 3 and Java 11+ installed.

make serve

Then, go to Usage to check how to use the service. If you have a GPU available, it will use it for faster inferences.

This should start a server localy that you can query with a curl like the following:

curl --location 'http://127.0.0.1:8080/predictions/my_model' \
--header 'Content-Type: application/json' \
--data '{
    "input": ["hello, how are you?", "hi, what is up?"]
}'

You should get an output similar to

[
  [
    0.019096793606877327,
    0.03446517512202263,
    0.09162796288728714,
    0.0701652243733406,
    -0.029946573078632355,
    ...
  ],
  [
    -0.06470940262079239,
    -0.03830110654234886,
    0.013061972334980965,
    -0.0003482792235445231,
    ...
  ]
]

• TochServe uses a Java server to expose the API which is heavy. Checking the stats from Docker, the service takes about 4GB of RAM. I will check how I can optimize the RAM usage with Go bindings to reduce the footprint.

Many thanks to:

• Stane Aurelius who wrote a great post about the details on how to deploy a model with tochserve. I highly recommend reading it.

• The community publishing models on HuggingFace and particularly to the team who have produced the all-MiniLM-L6-v2 model and have shared it.

• The Microsoft Research team who produced the paper about MiniLM.

• The HuggingFace team who hosts the models and make them easily available to everyone.

The code in this repository is licensed under the MIT license.