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from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.embeddings import HuggingFaceEmbeddings
def get_embeddings_function(version = "v1.2",query_instruction = "Represent this sentence for searching relevant passages: "):
if version == "v1.2":
# https://huggingface.co/BAAI/bge-base-en-v1.5
# Best embedding model at a reasonable size at the moment (2023-11-22)
model_name = "BAAI/bge-base-en-v1.5"
encode_kwargs = {'normalize_embeddings': True,"show_progress_bar":False} # set True to compute cosine similarity
print("Loading embeddings model: ", model_name)
embeddings_function = HuggingFaceBgeEmbeddings(
model_name=model_name,
encode_kwargs=encode_kwargs,
query_instruction=query_instruction,
)
else:
embeddings_function = HuggingFaceEmbeddings(model_name = "sentence-transformers/multi-qa-mpnet-base-dot-v1")
return embeddings_function
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