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#!/usr/bin/env python
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
# from langchain.chains import RetrievalQA
# from langchain import PromptTemplate
DB_FAISS_PATH = "mylib/vector_db"
# Initialize embeddings and database outside of functions
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"}
)
db = FAISS.load_local(DB_FAISS_PATH, embeddings)
# Initialize the LLM model once
llm = CTransformers(
model="TheBloke/Llama-2-7B-Chat-GGML",
# model= "meta-llama/Llama-2-7b-chat-hf".
model_type="llama",
max_new_tokens=512,
temperature=0.5,
)
# Initialize the QA chain once
# qa = RetrievalQA.from_chain_type(llm=llm, chain_type='stuff', retriever=db.as_retriever(search_kwargs={'k': 10}), return_source_documents=True)
# retriever = db.as_retriever(search_kwargs={"k": 10})
def final_result(query):
docs_and_scores = db.similarity_search_with_score(query, k=10)
# response = retriever.get_relevant_documents(query)
return docs_and_scores
if __name__ == "__main__":
while True:
user_query = input(
"Please enter Retailer, Brand, or Category (type 'exit' to quit): "
)
if user_query == "exit":
break
llm_response = final_result(user_query)
print(llm_response)
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