- EmbeddingClient implementation that computes, locally, sentence embeddings with SBERT transformers.
- Uses pre-trained transformer models, serialized into Open Neural Network Exchange (ONNX) format.
- Deep Java Library and the Microsoft ONNX Java Runtime are used to run
the ONNX models and compute the embeddings efficiently.
- Add default tokenizer.json and model.onnx for sentence-transformers/all-MiniLM-L6-v2.
- Add, configurable resource caching service to allow caching
remote (http/https) resources to the local FS.
- README.md provides information on how to serialize ONNX models.
- add Git LFS configuration for large onnx model files.
