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app.py

@@ -0,0 +1,130 @@
+## RAG Q&A Conversation With PDF Including Chat History
+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_chroma import Chroma
+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()
+
+os.environ['HF_TOKEN'] = os.getenv("HF_TOKEN")
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+## Set up Streamlit
+st.title("Conversational RAG With PDF uploads and chat history")
+st.write("Upload PDFs and chat with their content")
+
+## Input the Groq API Key
+api_key = st.text_input("Enter your Groq API key:", type="password")
+
+## Check if Groq API key is provided
+if api_key:
+    llm = ChatGroq(groq_api_key=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 = Chroma.from_documents(documents=splits, embedding=embeddings)
+        retriever = vectorstore.as_retriever()
+
+        contextualize_q_system_prompt = (
+            "Given a chat history and the latest user question "
+            "which might reference context in the chat history, "
+            "formulate a standalone question which can be understood "
+            "without the chat history. Do NOT answer the question, "
+            "just reformulate it if needed and otherwise return it as is."
+        )
+        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 for question-answering tasks. "
+            "Use the following pieces of retrieved context to answer "
+            "the question. If you don't know the answer, say that you "
+            "don't know. Use three sentences maximum and keep the "
+            "answer concise."
+            "\n\n"
+            "{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 the Groq API Key")
+
+
+
+
+

requirements.txt

@@ -1,3 +1,12 @@
-altair
-pandas
-streamlit
+streamlit
+langchain
+langchain-community
+langchain-chroma
+langchain-core
+langchain-huggingface
+langchain-groq
+chromadb
+pypdf
+python-dotenv
+sentence-transformers
+huggingface-hub