Upload 2 files

app.py

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+import streamlit as st
+import os
+import tempfile
+
+from dotenv import load_dotenv
+load_dotenv()
+
+from streamlit_functions import RAGChain
+def main():
+    st.title("RAG Application")
+    
+    if "openai_api_key" not in st.session_state:
+        st.session_state.openai_api_key = os.getenv("OPENAI_API_KEY")
+    if "RAGChatbot" not in st.session_state:
+        st.session_state.RAGChatbot = None
+
+    if st.session_state.openai_api_key is None:
+        st.session_state.openai_api_key = st.text_input("OPENAI_API Key", type="password")
+    else:
+        with st.sidebar:
+            if uploaded_file := st.file_uploader("Choose a file", type=["pdf"]):
+                with tempfile.TemporaryDirectory() as tmpdirname:
+                    with open(os.path.join(tmpdirname, uploaded_file.name), "wb") as f:
+                        f.write(uploaded_file.getbuffer())
+                    st.session_state.RAGChatbot = RAGChain(pdf_file_path=os.path.join(tmpdirname, uploaded_file.name), api_key=st.session_state.openai_api_key)
+
+    if st.session_state.RAGChatbot is not None:
+        for chat_message in st.session_state.RAGChatbot.get_chat_history():
+            with st.chat_message("user"):
+                st.write(chat_message["user"])
+            with st.chat_message("assistant"):
+                st.write(chat_message["assistant"])
+        if user_query := st.chat_input("Ask a question:"):
+            with st.chat_message("user"):
+                st.write(user_query)
+            with st.spinner("Waiting for response..."):
+                answer, context_list = st.session_state.RAGChatbot.ask_question(user_query)
+            with st.chat_message("assistant"):
+                st.write(answer)
+            with st.sidebar:
+                st.subheader("Context")
+                for context in context_list:
+                    st.write(context)
+                    st.write("-"*25)
+
+if __name__ == "__main__":
+    main()

streamlit_functions.py

@@ -0,0 +1,72 @@
+import PyPDF2
+import os
+from langchain_openai import ChatOpenAI
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_openai import OpenAIEmbeddings
+
+from dotenv import load_dotenv
+load_dotenv()
+
+class RAGChain:
+    def __init__(self, pdf_file_path, api_key=os.getenv("OPENAI_API_KEY")):
+        pdf_text = self.extract_text_from_pdf_with_pypdf2(pdf_file_path)
+        chunked_documents = self.create_chunks_for_pypdf2_parse(pdf_text)
+        vectorstore = self.create_vectorstore_with_faiss(chunked_documents)
+        # Creating LLM
+        self.llm = ChatOpenAI(model="gpt-4o-mini", api_key=api_key)
+        # Creating Retriever from vectorstore
+        self.retriever = vectorstore.as_retriever()
+        # Create a chat history to store the conversation history
+        self.chat_history = []
+    
+    def ask_question(self, question):
+        # Create a chat history text to pass to LLM to create a single question
+        chat_history_text = "\n".join([f"User:{f['user']}\nAssistant:{f['assistant']}" for f in self.chat_history[:-10]])
+        # Prompt to create a single question that will help us retrieve relevant context
+        single_question_prompt = f"You will be given chat history and the user question. Your task is to reply with a single question that accurately represents the user question based on the context of the chat history. \n\nChat history:\n{chat_history_text}\n\nUser question: {question}\n\n Reply with the single question and nothing else.\n\nSingle Answer:"
+        # Use LLM to create a single question
+        single_question = self.llm.invoke(single_question_prompt).content
+        # Retrieve the relevant context from the vectorstore
+        context = self.retriever.invoke(single_question)
+        context = [f.page_content for f in context]
+        context_text = "\n\n".join(context)
+        # Prompt to answer the single question
+        answer_prompt = f"You will be given a context and a question. Your task is to answer the question based on the context. \n\nContext:\n{context_text}\n\nQuestion: {single_question}\n\n Answer:"
+        # Use LLM to answer the question
+        answer = self.llm.invoke(answer_prompt).content
+        # Update the chat history
+        self.chat_history.append({"user": question, "assistant": answer})
+        # Return the answer
+        return answer, context
+
+    def clear_history(self):
+        self.chat_history = []
+
+    def get_chat_history(self):
+        return self.chat_history
+    
+    def extract_text_from_pdf_with_pypdf2(self, file_path):
+        pdf_reader = PyPDF2.PdfReader(file_path)
+        
+        full_text = ""
+        for page_num in range(len(pdf_reader.pages)):
+            page = pdf_reader.pages[page_num]
+            text = page.extract_text()
+            full_text += text + "\n"
+        
+        return full_text
+
+    def create_chunks_for_pypdf2_parse(self, pdf_text):
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+        chunks = text_splitter.split_text(pdf_text)
+        return chunks
+
+    def create_vectorstore_with_faiss(self, chunked_documents):
+        embedding_function = OpenAIEmbeddings()
+        if type(chunked_documents[0]) == str:
+            vectorstore = FAISS.from_texts(chunked_documents, embedding_function)
+        else:
+            vectorstore = FAISS.from_documents(chunked_documents, embedding_function)
+        # Save the vectorstore to local path
+        return vectorstore