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app.py

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+import streamlit as st
+from dotenv import load_dotenv
+import sys
+from PyPDF2 import PdfReader
+from langchain_community.llms import OpenAI
+from langchain_community.chat_models import ChatOpenAI
+from langchain_text_splitters import CharacterTextSplitter
+from langchain_openai.embeddings import OpenAIEmbeddings
+from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain.retrievers.document_compressors import LLMChainExtractor
+from langchain.retrievers import MultiQueryRetriever
+from langchain.chains import RetrievalQA
+from langchain.llms import OpenAI , Cohere
+
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    pdf_reader = PdfReader(pdf_docs)
+
+    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,
+    is_separator_regex=False,)
+
+    chunks = text_splitter.split_text(text)
+
+    return chunks
+
+def get_vectorstore(text_chunks):
+    embeddings = OpenAIEmbeddings()
+    # embeddings = HuggingFaceInstructEmbeddings(model_name = "hkunlp/instructor-large")
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+def ll_retriver(vectorstore):
+    llm = OpenAI(temperature=0)
+    llm_based_retriver=MultiQueryRetriever.from_llm(
+        retriever=vectorstore.as_retriever(),
+        llm=llm
+    )
+    return llm_based_retriver
+
+def chain(llm_based_retriever):
+    llm = OpenAI(temperature=0)
+    QA_Chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=llm_based_retriever
+    )
+    return QA_Chain
+
+
+def main():
+    load_dotenv()
+
+    st.set_page_config(page_title = "Chat with a PDFs",page_icon=":books:")
+
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+
+    if "Q_A_Chain" not in st.session_state:
+        st.session_state.Q_A_Chain = None
+
+    st.header("Chat with PDF :books:")
+    # question = st.text_input("Ask a Question about your document:")
+
+    with st.sidebar:
+        st.subheader("Upload your PDF")
+        pdf_docs = st.file_uploader("Upload your PDF here then Process")
+        
+        if st.button("Process"):
+            with st.spinner("Processing"):
+
+                # get the raw PDF context
+                raw_text = get_pdf_text(pdf_docs)
+                # st.write(raw_text)
+
+                # get the chunks
+                text_chunks = get_text_chunks(raw_text)
+                # st.write(text_chunks)
+
+                #Create Vector Store
+                vectorstore = get_vectorstore(text_chunks)
+
+                # Conversation chain
+                llm_based_retriver = ll_retriver(vectorstore)
+                st.session_state.Q_A_Chain = chain(llm_based_retriver)
+                st.success("PDF processed successfully, you can now ask Questions.")
+    
+    if st.session_state.Q_A_Chain:
+        question = st.text_input("Ask a Question about your document:")
+        if st.button("Submit Question"):
+            if question:
+                with st.spinner("Getting answer..."):
+                    docs = st.session_state.Q_A_Chain({"query":question})
+                    st.write(docs['result'])
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+openai
+langchain
+cohere
+tiktoken
+langchain-community
+pypdf
+langchain-openai
+chromadb
+streamlit
+PyPDF2
+langchain-text-splitters
+faiss-cpu
+sentence-transformers
+InstructorEmbedding