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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) | |