Intel® Extension for Transformers Quantized Text Embeddings
加载由 Intel® Extension for Transformers (ITREX) 生成的量化 BGE 嵌入模型，并使用 ITREX Neural Engine（一种高性能 NLP 后端）加速模型推理，且不影响准确性。
有关更多详细信息，请参阅我们的博客文章《Efficient Natural Language Embedding Models with Intel Extension for Transformers》和《BGE optimization example》。
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 参考：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...
使用方法
text = "This is a test document."
query_result = model.embed_query(text)
doc_result = model.embed_documents([text])