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from langchain_huggingface import HuggingFaceEmbeddings
# embedding_instance = HuggingFaceEmbeddings(
# model_name="all-MiniLM-L6-v2",
# model_kwargs={"device": "cpu"},
# )
# def all_minilm_l6_v2():
# """
# Return the embedding instance.
# """
# return embedding_instance
# from sentence_transformers import SentenceTransformer
# from langchain.embeddings import HuggingFaceEmbeddings
# # Load the sentence-transformers model directly
# model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
# # Wrap it in LangChain HuggingFaceEmbeddings by passing the model instance
# embedding_instance = HuggingFaceEmbeddings(model=model)
# def all_minilm_l6_v2():
# """
# Return the embedding instance.
# """
# return embedding_instance
from langchain_huggingface import HuggingFaceEmbeddings
def all_minilm_l6_v2():
model_kwargs = {"device":"cpu"}
encode_kwargs = {"normalize_embeddings": True}
all_minilm_object = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
return all_minilm_object
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