A C++ library for text (and maybe image) embeddings, focusing on efficient inference of BERT-like (and maybe clip-like) models.
Many existing GGML-based text embedding libraries have limited support for Chinese text processing due to their custom tokenizer implementations. This project addresses this limitation by leveraging Hugging Face's Rust tokenizer implementation, wrapped with a C++ API to ensure consistency with the Python transformers library while providing native performance.
While currently focused on BERT-like text embedding models, the project aims to support image embedding models in the future (Work in Progress).
Note: This is an experimental and educational project. It is not recommended for production use at this time.
The following models have been tested and verified:
- BAAI/bge-m3
- BAAI/bge-base-zh-v1.5
- shibing624/text2vec-base-multilingual
The C++ implementation shows high accuracy compared to the Python implementation, with differences in the order of 10^-9. For detailed comparison results, please refer to alignment.ipynb
.
First, install the required dependencies:
pip install -r scripts/requirements.txt
Then convert the models to GGUF format:
# Convert BGE-M3 model
python scripts/convert.py BAAI/bge-m3 ./models/bge-m3.fp16.gguf f16
# Convert BGE-Base Chinese v1.5 model
python scripts/convert.py BAAI/bge-base-zh-v1.5 ./models/bge-base-zh-v1.5.fp16.gguf f16
# Convert Text2Vec multilingual model
python scripts/convert.py shibing624/text2vec-base-multilingual ./models/text2vec-base-multilingual.fp16.gguf f16
Execute the embedding tests:
./build/test_embedding
Before running the notebooks, install embeddings.cpp:
# Install the package
pip install .
# Generate Python stub files
cd build && make stub
Configure and build with Metal support:
cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
-DGGML_METAL=ON \
-DGGML_METAL_EMBED_LIBRARY=ON \
-DEMBEDDINGS_CPP_ENABLE_PYBIND=ON ..