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#!/usr/bin/env python
import streamlit as st
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_type="llama",
max_new_tokens=512,
temperature=0.1,
)
custom_prompt_template = """
This is a search engine that returns relevant results based on users query.Users will be able to input prompts based on categories,
brands, and retailers and receive a list of offers that match their query, along with a similarity score. Always retun offers as a list only give offers available
Context: {context}
Question: {question}
Only return the helpful OFFERS below as a list.
OFFERS:
"""
def set_custom_prompt():
"""
Prompt template for QA retrieval for each vectorstore
"""
prompt = PromptTemplate(
template=custom_prompt_template, input_variables=["context", "question"]
)
return prompt
def similar_offer(llm, prompt, db):
"""
Create a RetrievalQA chain for similarity-based offer retrieval
"""
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=db.as_retriever(search_kwargs={"k": 5}),
return_source_documents=True,
chain_type_kwargs={"prompt": prompt},
)
return qa_chain
def generate_response(query):
"""
Generate a response based on the user query
"""
qa_prompt = set_custom_prompt()
qa_result = similar_offer(llm, qa_prompt, db)
response = qa_result({"query": query}) # Pass the query in a dictionary
return response
def main():
st.title("🦜🔗 Fetch Reward Search ChatBot")
query = st.text_input("Find offers you love!")
# "Enter" button
if st.button("Enter"):
if query:
response = generate_response(query)
st.text("Answer:")
st.write(response["result"])
sources = response["source_documents"]
if sources:
st.text("Sources:")
st.write(sources)
else:
st.text("No sources found")
if __name__ == "__main__":
main() |