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import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
def get_pdf_text(pdf_docs):
text=""
for pdf in pdf_docs:
pdf_reader= PdfReader(pdf)
for page in pdf_reader.pages:
text+= page.extract_text()
return text
def get_text_chunks(text):
text_splitter= CharacterTextSplitter( separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
chunks= text.splitter.split_text(text)
return chunks
def get_vectorstores(text_chunks):
embeddings= OpenAIEmbeddings()
# embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
vectorstore= FAISS.from_text(texts=text_chunks, embedding=embeddings)
def get_conversation_chain(vectorstore):
llm = ChatOpenAI()
# llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
memory = ConversationBufferMemory(
memory_key='chat_history', return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory
)
return conversation_chain
def main():
st.set_page_config(page_title="Chat", page_icon=":books:")
if "conversation" not in st.session_state:
st.session_state.conversation = None
st.header("Chat with multiple PDFs :books:")
st.text_input("Ask a question about your documents:")
with st.sidebar:
st.subheader("Your documents")
pdf_docs = st.file_uploader("Upload your docs here", accept_multiple_files=True)
if st.button("Process"):
with st.spinner("Processing"):
# get pdf text, contents
raw_text = get_pdf_text(pdf_docs)
# st.write(raw_text)
# get text chunks
text_chunks=get_text_chunks(raw_text)
# create vector store
vectorstore = get_vectorstore(text_chunks)
# create conversation chain
# conversation=get_conversation_chain(vectorstore)
st.session_state.conversation = get_conversation_chain(vectorstore)
if __name__ == "__main__":
main()
# Instructor embeddings
# InstructorEmbedding==1.0.1
# sentence-transformers==2.2.2 |