app.py

# Adapted from https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps#build-a-simple-chatbot-gui-with-streaming
import os
import openai
import base64
import gc
import random
import tempfile
import time
import uuid

from IPython.display import Markdown, display
from dotenv import load_dotenv
from llama_index.core import SimpleDirectoryReader
from rag_code import EmbedData, QdrantVDB_QB, Retriever, RAG

import streamlit as st

if "id" not in st.session_state:
    st.session_state.id = uuid.uuid4()
    st.session_state.file_cache = {}

session_id = st.session_state.id
collection_name = "chat with docs"
batch_size = 32

load_dotenv()

def reset_chat():
    st.session_state.messages = []
    st.session_state.context = None
    gc.collect()


def display_pdf(file):
    # Opening file from file path

    st.markdown("### PDF Preview")
    base64_pdf = base64.b64encode(file.read()).decode("utf-8")

    # Embedding PDF in HTML
    pdf_display = f"""<iframe src="data:application/pdf;base64,{base64_pdf}" width="400" height="100%" type="application/pdf"
                        style="height:100vh; width:100%"
                    >
                    </iframe>"""

    # Displaying File
    st.markdown(pdf_display, unsafe_allow_html=True)


with st.sidebar:
    st.header(f"Add your documents!")
    
    uploaded_file = st.file_uploader("Choose your `.pdf` file", type="pdf")

    if uploaded_file:
        try:
            with tempfile.TemporaryDirectory() as temp_dir:
                file_path = os.path.join(temp_dir, uploaded_file.name)
                
                with open(file_path, "wb") as f:
                    f.write(uploaded_file.getvalue())
                
                file_key = f"{session_id}-{uploaded_file.name}"
                st.write("Indexing your document...")

                if file_key not in st.session_state.get('file_cache', {}):

                    if os.path.exists(temp_dir):
                            loader = SimpleDirectoryReader(
                                input_dir = temp_dir,
                                required_exts=[".pdf"],
                                recursive=True
                            )
                    else:    
                        st.error('Could not find the file you uploaded, please check again...')
                        st.stop()
                    
                    docs = loader.load_data()
                    documents = [doc.text for doc in docs]

                    # embed data    
                    embeddata = EmbedData(embed_model_name="BAAI/bge-large-en-v1.5", batch_size=batch_size)
                    embeddata.embed(documents)

                    # set up vector database
                    qdrant_vdb = QdrantVDB_QB(collection_name=collection_name,
                                              batch_size=batch_size,
                                              vector_dim=1024)
                    qdrant_vdb.define_client()
                    qdrant_vdb.create_collection()
                    qdrant_vdb.ingest_data(embeddata=embeddata)

                    # set up retriever
                    retriever = Retriever(vector_db=qdrant_vdb, embeddata=embeddata)

                    # set up rag
                    query_engine = RAG(retriever=retriever, llm_name="Meta-Llama-3.3-70B-Instruct")

                    st.session_state.file_cache[file_key] = query_engine

                else:
                    query_engine = st.session_state.file_cache[file_key]

                # Inform the user that the file is processed and Display the PDF uploaded
                st.success("Ready to Chat!")
                display_pdf(uploaded_file)
        except Exception as e:
            st.error(f"An error occurred: {e}")
            st.stop()     

col1, col2 = st.columns([6, 1])

with col1:
    st.header(f"Fastest RAG Stack with SambaNova and Llama-3.3")

with col2:
    st.button("Clear ↺", on_click=reset_chat)

# Initialize chat history
if "messages" not in st.session_state:
    reset_chat()


# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])


# Accept user input
if prompt := st.chat_input("What's up?"):
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)

    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        message_placeholder = st.empty()
        full_response = ""
        
        # Simulate stream of response with milliseconds delay
        streaming_response = query_engine.query(prompt)
        
        for chunk in streaming_response:
            try:
                new_text = chunk.raw["choices"][0]["delta"]["content"]
                full_response += new_text
                message_placeholder.markdown(full_response + "▌")
            except:
                pass

        message_placeholder.markdown(full_response)

    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": full_response})