tools/__init__.py

# import os
# from langchain.embeddings import HuggingFaceEmbeddings
# from langchain.vectorstores.chroma import Chroma
# from langchain_community.cross_encoders import HuggingFaceCrossEncoder
# import time
# from langchain.retrievers import ContextualCompressionRetriever
# from langchain.retrievers.document_compressors import CrossEncoderReranker
# from bot.llm_client import Mistral


# os.environ["SERPER_API_KEY"] = 'TOKEN'
# CHROMA_PATH = "final_test/chroma_test"
# CHROMA_PATH = "final_test/chroma_test2"


# def load_embedding_model(model_path : str):
#     start_time = time.time()
#     encode_kwargs = {"normalize_embeddings": True}
#     local_embedding = HuggingFaceEmbeddings(
#         model_name=model_path,
#         cache_folder="./models",
#         encode_kwargs=encode_kwargs
#     )
#     end_time = time.time()
#     print(f'model load time {round(end_time - start_time, 0)} second')
#     return local_embedding

# embedding = load_embedding_model(model_path="intfloat/multilingual-e5-large")

# reranker_model = HuggingFaceCrossEncoder(model_name="BAAI/bge-reranker-v2-m3")

# db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding).as_retriever(search_kwargs={"k": 20})

# model_llm_rag = Mistral()