Create app.py

app.py

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+import streamlit as st
+import os
+api_token = os.getenv("HF_TOKEN")
+
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings 
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+
+list_llm = ["meta-llama/Llama-3.2-3B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]  
+
+# Load and split PDF document
+def load_doc(list_file_path):
+    loaders = [PyPDFLoader(x) for x in list_file_path]
+    pages = []
+    for loader in loaders:
+        pages.extend(loader.load())
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)  
+    return text_splitter.split_documents(pages)
+
+# Create vector database
+def create_db(splits):
+    embeddings = HuggingFaceEmbeddings()
+    return FAISS.from_documents(splits, embeddings)
+
+# Initialize langchain LLM chain
+def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
+    llm = HuggingFaceEndpoint(
+        repo_id=llm_model,
+        huggingfacehub_api_token=api_token,
+        temperature=temperature,
+        max_new_tokens=max_tokens,
+        top_k=top_k,
+    )
+
+    memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True)
+    retriever = vector_db.as_retriever()
+
+    return ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=retriever,
+        chain_type="stuff", 
+        memory=memory,
+        return_source_documents=True,
+        verbose=False,
+    )
+
+st.title("RAG PDF Chatbot")
+
+uploaded_files = st.file_uploader("Upload PDF files", accept_multiple_files=True, type="pdf")
+
+if uploaded_files:
+    # Save uploaded files to local disk
+    file_paths = []
+    for uploaded_file in uploaded_files:
+        file_path = os.path.join("temp", uploaded_file.name)
+        os.makedirs("temp", exist_ok=True)
+        with open(file_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        file_paths.append(file_path)
+
+    st.session_state["doc_splits"] = load_doc(file_paths)
+    st.success("Documents successfully loaded and split!")
+
+if 'vector_db' not in st.session_state and 'doc_splits' in st.session_state:
+    st.session_state['vector_db'] = create_db(st.session_state['doc_splits'])
+
+llm_option = st.selectbox("Select LLM", list_llm)
+
+temperature = st.slider("Temperature", 0.01, 1.0, 0.5, 0.1)
+max_tokens = st.slider("Max Tokens", 128, 9192, 4096, 128)
+top_k = st.slider("Top K", 1, 10, 3, 1)
+
+if 'qa_chain' not in st.session_state and 'vector_db' in st.session_state:
+    st.session_state['qa_chain'] = initialize_llmchain(llm_option, temperature, max_tokens, top_k, st.session_state['vector_db'])
+
+if "chat_history" not in st.session_state:
+    st.session_state["chat_history"] = []
+
+user_input = st.text_input("Ask a question")
+
+if st.button("Submit") and user_input:
+    qa_chain = st.session_state['qa_chain']
+    response = qa_chain.invoke({"question": user_input, "chat_history": st.session_state["chat_history"]})
+
+    st.session_state["chat_history"].append((user_input, response["answer"]))
+
+    st.write("### Response:")
+    st.write(response["answer"])
+
+    st.write("### Sources:")
+    for doc in response["source_documents"][:3]:
+        st.write(f"Page {doc.metadata['page'] + 1}: {doc.page_content[:300]}...")
+
+st.write("### Chat History")
+for user_msg, bot_msg in st.session_state["chat_history"]:
+    st.text(f"User: {user_msg}")
+    st.text(f"Assistant: {bot_msg}")