Intel® Extension for Transformers Quantized Text Embeddings

Load quantized BGE embedding models generated by Intel® Extension for Transformers (ITREX) and use ITREX Neural Engine, a high-performance NLP backend, to accelerate the inference of models without compromising accuracy.

Refer to our blog of Efficient Natural Language Embedding Models with Intel Extension for Transformers and BGE optimization example for more details.

from langchain_community.embeddings import QuantizedBgeEmbeddings

model_name = "Intel/bge-small-en-v1.5-sts-int8-static-inc"
encode_kwargs = {"normalize_embeddings": True}  # set True to compute cosine similarity

model = QuantizedBgeEmbeddings(
    model_name=model_name,
    encode_kwargs=encode_kwargs,
    query_instruction="Represent this sentence for searching relevant passages: ",
)

API Reference:QuantizedBgeEmbeddings

/home/yuwenzho/.conda/envs/bge/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
2024-03-04 10:17:17 [INFO] Start to extarct onnx model ops...
2024-03-04 10:17:17 [INFO] Extract onnxruntime model done...
2024-03-04 10:17:17 [INFO] Start to implement Sub-Graph matching and replacing...
2024-03-04 10:17:18 [INFO] Sub-Graph match and replace done...

usage

text = "This is a test document."
query_result = model.embed_query(text)
doc_result = model.embed_documents([text])

Related

• Embedding model conceptual guide
• Embedding model how-to guides