Added files

.gitignore

@@ -7,6 +7,7 @@ __pycache__/
 *.so
 
 # Distribution / packaging
+research/
 .Python
 build/
 develop-eggs/
@@ -122,6 +123,7 @@ celerybeat.pid
 *.sage.py
 
 # Environments
+pdfsensevenv/
 .env
 .venv
 env/

app.py

@@ -0,0 +1,117 @@
+# Importing libraries
+import streamlit as st
+from langchain.chains.history_aware_retriever import create_history_aware_retriever
+from langchain.chains.retrieval import 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
+from langchain_core.output_parsers import StrOutputParser
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+# API and model setting
+os.environ['HF_TOKEN']=os.getenv('HF_TOKEN')
+os.environ['GROQ_API_KEY']=os.getenv('GROQ_API_KEY')
+embeddings=HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+# Streamlit app
+st.title("𝖯𝖣𝖥𝖲𝖾𝗇𝗌𝖾 : 𝖯𝖣𝖥 𝖰𝗎𝖾𝗌𝗍𝗂𝗈𝗇 𝖺𝗇𝖽 𝖠𝗇𝗌𝗐𝖾𝗋𝗂𝗇𝗀 𝗐𝗂𝗍𝗁 𝗌𝖾𝗌𝗌𝗂𝗈𝗇 𝖼𝗁𝖺𝗍 𝗁𝗂𝗌𝗍𝗈𝗋𝗒")
+st.write("upload pdfs and ask questions related to pdfs")
+llm=ChatGroq(model="Gemma2-9b-It")
+session_id=st.text_input("Session id",value="common_session")
+
+# manage chat history
+if 'store' not in st.session_state:
+    st.session_state.store={}
+
+# Upload files and documents loading
+uploaded_files=st.file_uploader("Drop the pdf files here",type="pdf",accept_multiple_files=True)
+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
+        docs=PyPDFLoader(temppdf).load()
+        documents.extend(docs)
+    # Delete the temp file as we no longer need it
+    if os.path.exists("./temp.pdf"):
+        os.remove("./temp.pdf")
+    # Text splitting and embedding and storing in chromadb
+    text_splitter=RecursiveCharacterTextSplitter(chunk_size=5000,chunk_overlap=500)
+    splits=text_splitter.split_documents(documents)
+    faiss_index = FAISS.from_documents(splits, embeddings)
+    retriever=faiss_index.as_retriever()
+
+    # Prompts
+    context_system_prompt=(
+        "Given a chat history and 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 it is"
+    )
+    context_prompt=ChatPromptTemplate.from_messages([
+        ("system",context_system_prompt),
+        MessagesPlaceholder("chat_history"),
+        ("human","{input}")]
+    )
+
+    history_aware_ret=create_history_aware_retriever(llm,retriever,context_prompt)
+
+    system_prompt=(
+        "You are 'PDFSense' a PDF reading and answering assistant. "
+        "Use the following pieces of retrieved context to answer "
+        "the question. If you don't know the answer, say that you dont know."
+        "Answer the questions nicely."
+        "\n\n"
+        "{context}"
+    )
+
+    prompt=ChatPromptTemplate.from_messages(
+        [
+            ("system",system_prompt),
+            MessagesPlaceholder("chat_history"),
+            ("human","{input}")
+        ]
+    )
+    # Chain for the chatbot
+    qa_chain=create_stuff_documents_chain(llm,prompt)
+    rag_chain=create_retrieval_chain(history_aware_ret,qa_chain)
+
+    # Session Id storing in chat history
+    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]
+    
+    # RAG with history
+    conversation_rag=RunnableWithMessageHistory(
+        rag_chain,
+        get_session_history,
+        input_messages_key="input",
+        history_messages_key="chat_history",
+        output_messages_key="answer"                                        
+        )
+    
+    user_input=st.text_input("Enter question")
+    if user_input:
+        session_history=get_session_history(session_id)
+        response=conversation_rag.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)

requirements.txt

@@ -0,0 +1,14 @@
+ipykernel
+streamlit
+faiss-cpu
+langchain
+langchain_huggingface
+langchain_groq
+gradio 
+typing-extensions
+python-dotenv
+langchain_community
+pypdf
+pymupdf
+langchain-text-splitters
+langchain_huggingface