open-text-embeddings 1.0.1

Open Source Text Embedding Models with OpenAI API-Compatible Endpoint

Open Source Text Embedding Models with OpenAI API-Compatible Endpoint

Many open source projects support the compatibility of the completions and the chat/completions endpoints of the OpenAI API, but do not support the embeddings endpoint.

The goal of this project is to create an OpenAI API-compatible version of the embeddings endpoint, which serves open source sentence-transformers models and other models supported by the LangChain's HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings and HuggingFaceBgeEmbeddings class.

Supported Text Embeddings Models

Below is a compilation of open-source models that are tested via the embeddings endpoint:

• BAAI/bge-large-en
• intfloat/e5-large-v2
• sentence-transformers/all-MiniLM-L6-v2
• sentence-transformers/all-mpnet-base-v2
• universal-sentence-encoder-large/5 (Please refer to the universal_sentence_encoder branch for more details)

The models mentioned above have undergone personal testing and verification. It is worth noting that all sentence-transformers models are expected to perform seamlessly with the endpoint.

Standalone FastAPI Server

To run the embeddings endpoint locally as a standalone FastAPI server, follow these steps:

1. Install the dependencies by executing the following commands:

   pip install --no-cache-dir open-text-embeddings[server]
   

2. Run the server with the desired model using the following command which enabled normalize embeddings (Omit the NORMALIZE_EMBEDDINGS if the model don't support normalize embeddings):

   MODEL=intfloat/e5-large-v2 NORMALIZE_EMBEDDINGS=1 python -m open.text.embeddings.server
   

   If a GPU is detected in the runtime environment, the server will automatically execute using the cuba mode. However, you have the flexibility to specify the DEVICE environment variable to choose between cpu and cuba. Here's an example of how to run the server with your desired configuration:

   MODEL=intfloat/e5-large-v2 NORMALIZE_EMBEDDINGS=1 DEVICE=cpu python -m open.text.embeddings.server
   

   This setup allows you to seamlessly switch between CPU and GPU modes, giving you control over the server's performance based on your specific requirements.

3. You will see the following text from your console once the server has started:

   INFO:     Started server process [19705]
   INFO:     Waiting for application startup.
   INFO:     Application startup complete.
   INFO:     Uvicorn running on http://localhost:8000 (Press CTRL+C to quit)
   

AWS Lambda Function

To deploy the embeddings endpoint as an AWS Lambda Function using GitHub Actions, follow these steps:

1. Fork the repo.

2. Add your AWS credentials (AWS_KEY and AWS_SECRET) to the repository secrets. You can do this by navigating to https://github.com/username/open-text-embeddings/settings/secrets/actions.

3. Manually trigger the Deploy Dev or Remove Dev GitHub Actions to deploy or remove the AWS Lambda Function.

Testing the Embeddings Endpoint

To test the embeddings endpoint, the repository includes an embeddings.ipynb notebook with a LangChain-compatible OpenAIEmbeddings class.

To get started:

1. Install the dependencies by executing the following command:

   pip install --no-cache-dir open-text-embeddings openai tiktoken
   

2. Execute the cells in the notebook to test the embeddings endpoint.

Contributions

Thank you very much for the following contributions:

• Vokturz contributed #2: support for CPU/GPU choice and initialization before starting the app.