new commit

app.py

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+import streamlit as st
+from langchain.chains import create_history_aware_retriever, create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_groq import ChatGroq
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader
+import os
+
+from dotenv import load_dotenv
+load_dotenv()
+
+## Set up Streamlit
+st.title("RAG-based Conversational Chatbot")
+st.write("Upload PDFs and chat with their content")
+
+## Input the Groq API Key and Hugging Face API Key
+groq_api_key = st.text_input("Enter your Groq API key:", type="password")
+hf_api_key = st.text_input("Enter your Hugging Face API key:", type="password")
+
+## Check if both API keys are provided
+if groq_api_key and hf_api_key:
+    os.environ['HF_TOKEN'] = hf_api_key
+    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+    llm = ChatGroq(groq_api_key=groq_api_key, model_name="Gemma2-9b-It")
+
+    ## Chat interface
+    session_id = st.text_input("Session ID", value="default_session")
+
+    ## Statefully manage chat history
+    if 'store' not in st.session_state:
+        st.session_state.store = {}
+
+    uploaded_files = st.file_uploader("Choose a PDF file", type="pdf", accept_multiple_files=True)
+
+    ## Process uploaded PDFs
+    if uploaded_files:
+        documents = []
+        for uploaded_file in uploaded_files:
+            temppdf = f"./temp.pdf"
+            with open(temppdf, "wb") as file:
+                file.write(uploaded_file.getvalue())
+                file_name = uploaded_file.name
+
+            loader = PyPDFLoader(temppdf)
+            docs = loader.load()
+            documents.extend(docs)
+
+        # Split and create embeddings for the documents
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
+        splits = text_splitter.split_documents(documents)
+
+        vectorstore = FAISS.from_documents(documents, embeddings)
+
+        retriever = vectorstore.as_retriever()
+
+        contextualize_q_system_prompt = ("""
+            Note: this is very important and high priority, If the human prompt is looking for an answer which is out of 
+            context given, clearly state that "you don't know and tell it's out of context".
+            You are provided with a chat history and the latest user question, 
+            which may refer to previous messages. Your task is to rewrite the 
+            latest user question into a standalone question that does not rely 
+            on prior context for understanding. Ensure the reformulated question 
+            is clear and concise. If no rephrasing is needed, return the question 
+            unchanged. Do not provide an answer.
+        """)
+
+        contextualize_q_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", contextualize_q_system_prompt),
+                MessagesPlaceholder("chat_history"),
+                ("human", "{input}"),
+            ]
+        )
+
+        history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
+
+        # Answer question
+        system_prompt = """
+            You are an assistant specialized in answering questions. 
+            Utilize the provided retrieved context to formulate your response. 
+            Note: this is very important and high priority, If the human prompt is looking for an answer which is out of 
+            context given, clearly state that "you don't know and tell it's out of context".
+
+            {context}
+        """
+
+        qa_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", system_prompt),
+                MessagesPlaceholder("chat_history"),
+                ("human", "{input}"),
+            ]
+        )
+
+        question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
+        rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
+
+        def get_session_history(session: str) -> BaseChatMessageHistory:
+            if session_id not in st.session_state.store:
+                st.session_state.store[session_id] = ChatMessageHistory()
+            return st.session_state.store[session_id]
+
+        conversational_rag_chain = RunnableWithMessageHistory(
+            rag_chain, get_session_history,
+            input_messages_key="input",
+            history_messages_key="chat_history",
+            output_messages_key="answer"
+        )
+
+        user_input = st.text_input("Your question:")
+        if user_input:
+            session_history = get_session_history(session_id)
+            response = conversational_rag_chain.invoke(
+                {"input": user_input},
+                config={
+                    "configurable": {"session_id": session_id}
+                },
+            )
+            st.write(st.session_state.store)
+            st.write("Assistant:", response['answer'])
+            st.write("Chat History:", session_history.messages)
+else:
+    st.warning("Please enter both the Groq API Key and Hugging Face API Key.")

requirements.txt

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+langchain
+ipykernel
+langchain_community
+python-dotenv
+pypdf
+bs4
+arxiv
+pymupdf
+wikipedia
+lxml
+langchain-openai
+langchain-text-splitters
+chromadb
+sentence_transformers
+langchain_huggingface
+faiss-cpu
+langchain_chroma
+langchain-groq
+fastapi
+uvicorn
+langserve
+streamlit
+langchain-core