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app.py

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+from io import BytesIO
+import streamlit as st
+import requests
+import os
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_openai import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+
+def getpdfdoc():
+    with st.spinner("Loading PDF..."):
+        filename = '48lawsofpower.pdf'
+        if os.path.exists(filename):
+            with open(filename, 'rb') as f:
+                pdf_doc = f.read()
+            return pdf_doc
+        else:
+            url = 'https://pgcag.files.wordpress.com/2010/01/48lawsofpower.pdf'
+            response = requests.get(url)
+
+            with open(filename, 'wb') as f:
+                f.write(response.content)
+            
+            return getpdfdoc()
+
+    
+def extract_text_from_pdf(pdf_file_obj):
+    with st.spinner("Extracting text from PDF..."):
+        pdf_reader = PdfReader(BytesIO(pdf_file_obj))
+        text = ""
+        for page_num in range(len(pdf_reader.pages)):
+            page_obj = pdf_reader.pages[page_num]
+            text += page_obj.extract_text()
+        return text
+
+def get_text_chunks(text):
+    with st.spinner("Splitting text into chunks..."):
+        text_splitter = CharacterTextSplitter(
+            separator="\n",
+            chunk_size=1000,
+            chunk_overlap=200,
+            length_function=len
+        )
+        chunks = text_splitter.split_text(text)
+        return chunks
+
+
+def get_vectorstore(text_chunks):
+    with st.spinner("Creating vectorstore..."):
+        metadatas = [{"source": f"{i}-pl"} for i in range(len(text_chunks))]
+        embeddings = OpenAIEmbeddings()
+        vectorstore = Chroma.from_texts(texts=text_chunks, embedding=embeddings, persist_directory="./chroma_db", metadatas=metadatas)
+        return vectorstore
+
+def get_conversation_chain(vectorstore):
+    with st.spinner("Loading LLM..."):
+        llm = ChatOpenAI()
+
+        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 handle_userinput(user_question):
+    response = st.session_state.conversation({'question': user_question})
+    st.session_state.chat_history = response['chat_history']
+
+    for i, message in enumerate(st.session_state.chat_history):
+        if i % 2 == 0:
+            st.markdown(("User: "+message.content))
+        else:
+            st.markdown(("AI: "+message.content))
+
+
+def main():
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = None
+    
+    if st.session_state.conversation is None:
+        if os.path.isdir("./chroma_db"):
+            if os.listdir("./chroma_db"):
+                with st.spinner("Loading vector store..."):
+                    vectorstore = Chroma(persist_directory="./chroma_db", embedding_function=OpenAIEmbeddings())
+                    st.session_state.conversation = get_conversation_chain(vectorstore)
+
+    if st.session_state.conversation is None:
+        pdf_doc = getpdfdoc()
+
+        # get pdf text
+        raw_text = extract_text_from_pdf(pdf_doc)
+
+        # get the text chunks
+        text_chunks = get_text_chunks(raw_text)
+
+        # create vector store
+        vectorstore = get_vectorstore(text_chunks)
+
+        # create conversation chain
+        st.session_state.conversation = get_conversation_chain(vectorstore)
+
+    if st.session_state.conversation is not None:
+        st.header("Ask questions from 48 Laws of Power:books:")
+        user_question = st.chat_input("Ask a question about your documents:")
+        if user_question:
+            handle_userinput(user_question)
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+langchain
+langchain-community
+langchain-openai
+pypdf2
+chromadb
+streamlit