Book Chat (LC)
==============

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

::

  import streamlit as st
  import os
  import pyperclip
  from langchain.chains import RetrievalQA
  from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
  from langchain_community.document_loaders import UnstructuredHTMLLoader
  from langchain_community.vectorstores import FAISS

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

::

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

  print_banner()
  st.logo("https://ea-books.netlify.app/lit/book_lc.svg")

Get ``GEMINI_API_KEY``

::

  g_key = os.getenv("GEMINI_API_KEY")

Select Embeddings

::

  embed_model_names = [
      "gemini-embedding-exp-03-07", # March 2025
      "text-embedding-004", # April 2024
      "embedding-001", # December 2023
  ]
  embed_model_name = st.sidebar.selectbox("Embedding", embed_model_names)

  embedding = GoogleGenerativeAIEmbeddings(model=f"models/{embed_model_name}", google_api_key=g_key)

Folder to save index

::

  index_folder = f"vectors/book-lc-{embed_model_name}"

Input HTML file with the book's contents and a log of the questions asked.

::

  book_html = "html/index.html"
  history_file = "vectors/history.txt"

Print current folder name as a title

::

  current_folder = os.path.basename(os.getcwd())
  st.write(f"### {current_folder}")

Select LLM

::

  llm_models = [
      "gemini-2.5-pro-exp-03-25",
      "gemini-2.0-flash",
      "gemma-3-27b-it",
  ]
  llm_model = st.sidebar.selectbox("LLM", llm_models)

  llm = ChatGoogleGenerativeAI(model=llm_model, google_api_key=g_key)

Load history

::

  history = ""

  def update_history(new_text):
      with open(history_file, 'w', encoding="utf-8") as file:
          file.write(new_text + history)
        
  if os.path.exists(history_file):
      with open(history_file, "r", encoding="utf-8") as fin:
          history = fin.read()
    
  history = st.sidebar.text_area(f"History", value=history.strip(), height=200)

  if st.sidebar.button(":recycle: &nbsp; Update history", use_container_width=True):
      update_history("")
      st.toast(f'History updated')   
    
Create or load index

::

  def create_index(input_file, persist_dir):
      loader = UnstructuredHTMLLoader(input_file)
      documents = loader.load()
      vectorstore = FAISS.from_documents(documents, embedding)
      vectorstore.save_local(persist_dir)
      st.session_state.vstore = vectorstore

  def load_index(persist_dir):
      try:
          vectorstore = FAISS.load_local(persist_dir, embedding, allow_dangerous_deserialization=True)
          st.session_state.vstore = vectorstore
      except Exception as e:
          st.error(f"Error loading index: {e}")

Handle indexing logic

::

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

Setup QA chain

::

  if "qa" not in st.session_state:
      retriever = st.session_state.vstore.as_retriever()
      st.session_state.qa = RetrievalQA.from_chain_type(
          llm=llm,
          retriever=retriever,
          chain_type="stuff"
      )

Ask a question

::

  question = st.text_area(f"Question", height=200)

  if st.button(":question: &nbsp; Ask", use_container_width=True):
      update_history(question + "\n\n---\n")
      st.session_state.response = st.session_state.qa.invoke(question)
      st.rerun()

  if "response" in st.session_state:
      st.write(st.session_state.response["result"])
      if st.sidebar.button(":clipboard: &nbsp; Copy to clipboard", use_container_width=True):
          pyperclip.copy(st.session_state.response["result"])
          st.toast(f'Copied to clipboard')