Book Chat (LI)
==============

Use Calibre to convert EPUB files into HTML format. 
This script will then analyze the HTML content using RAG (Retrieval-Augmented Generation) with LlamaIndex.


::

  import streamlit as st
  import os

  from llama_index.llms.google_genai import GoogleGenAI
  from llama_index.embeddings.google_genai import GoogleGenAIEmbedding
  from llama_index.core import (
      VectorStoreIndex, 
      SimpleDirectoryReader, 
      Settings, 
      StorageContext, 
      load_index_from_storage
  )

Prints a stylized banner to the console when the application starts.

::

  @st.cache_data
  def print_banner():
      print("""
          ___.                  __                    .__            __   
          \\_ |__   ____   ____ |  | __           ____ |  |__ _____ _/  |_ 
           | __ \\ /  _ \\ /  _ \\|  |/ /  ______ _/ ___\\|  |  \\\\__  \\\\   __\\
           | \\_\\ (  <_> |  <_> )    <  /_____/ \\  \\___|   Y  \\/ __ \\|  |  
           |___  /\\____/ \\____/|__|_ \\          \\___  >___|  (____  /__|  
               \\/                   \\/              \\/     \\/     \\/                                                    
      """)
      return 1

  print_banner()

Folder to save index

::

  index_folder = "vectors/html"
  book_html = "html/index.html"

Select LLM

::

  g_key = os.getenv("GEMINI_API_KEY")

  llm_models = [
      "gemini-2.0-flash",
      "gemma-3-27b-it",
  ]

  llm_model = st.sidebar.selectbox(
     "LLM Model",
     llm_models,
  )

  llm = GoogleGenAI(
      model=llm_model,
      api_key=g_key
  )

  Settings.llm = llm

Select Embeddings

::

  embed_model_names = [
      "text-embedding-004",  
      "gemini-embedding-exp-03-07",
  ]

  embed_model_name = st.sidebar.selectbox(
     "Embedding Model",
     embed_model_names,
  )

  embed_model = GoogleGenAIEmbedding(
      model_name=embed_model_name,
      embed_batch_size=100,
      api_key=g_key,
  )

  Settings.embed_model = embed_model

Create Index
------------

Creates a new index from input document and persists it.

::

  def create_index(input_file, persist_dir):
      reader = SimpleDirectoryReader(input_files=[input_file])
      documents = reader.load_data()
      print(f"Creating index from {len(documents)} document(s)...")
      st.session_state.index = VectorStoreIndex.from_documents(documents)
      st.session_state.index.storage_context.persist(persist_dir=persist_dir)

Loads an existing index and refreshes it with current documents.

::

  def load_index(input_dir, persist_dir):
      try:
          storage_context = StorageContext.from_defaults(persist_dir=persist_dir)
          st.session_state.index = load_index_from_storage(storage_context)
      except Exception as e:
          st.error(f"Error loading or refreshing index: {e}")

Update or create index

::

  if os.path.exists(index_folder):
      if "index" not in st.session_state:
          load_index(book_html, index_folder)
  
  else:
      if st.sidebar.button('Create Index', type='primary', use_container_width=True):
          create_index(book_html, index_folder)
      else:
          st.stop()

  
Query
-----

::

  if "query_engine" not in st.session_state:
      st.session_state.query_engine = st.session_state.index.as_query_engine()
  
  question = st.text_area(f"Question", height=200)
  
  if st.button('Ask', type='primary', use_container_width=True):
      response = st.session_state.query_engine.query(question)
      st.write(response.response)