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import streamlit as st
# from langchain_helper import get_qa_chain, create_vector_db
from langchain.vectorstores import FAISS
from langchain.llms import GooglePalm
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from dotenv import load_dotenv
load_dotenv()
import os
# get this free api key from https://makersuite.google.com/
llm = GooglePalm(google_api_key=os.environ["GOOGLE_API_KEY"], temperature=0.1)
instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-large")
vectordb_file_path = "faiss_index"
def get_qa_chain():
# Load the vector database from the local folder
vectordb = FAISS.load_local(vectordb_file_path, instructor_embeddings)
# Create a retriever for querying the vector database
retriever = vectordb.as_retriever(score_threshold=0.7)
prompt_template = """Given the following context and a question, generate an answer based on this context only.
In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
If the answer is not found in the context, kindly state "I don't know." Don't try to make up an answer.
CONTEXT: {context}
QUESTION: {question}"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
chain = RetrievalQA.from_chain_type(llm=llm,
chain_type="stuff",
retriever=retriever,
input_key="query",
return_source_documents=True,
chain_type_kwargs={"prompt": PROMPT})
return chain
st.title("Q&A for My Courses ")
# btn = st.button("Create Knowledgebase")
# if btn:
# create_vector_db()
question = st.text_input("Question: ")
if question:
chain = get_qa_chain()
response = chain(question)
st.header("Answer")
st.write(response["result"])
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