Book Chat (LC)

Answer Questions About Book Contents Using RAG with LangChain and Chroma

  1. Use Calibre to convert EPUB files into HTMLZ format.

  2. Unpack the HTMLZ archive into an html folder.

  3. This script will analyze the html/index.html file and save the embeddings into a Chroma database.

pip install -U chromadb langchain-chroma
Useful Links

Name

URL

LangGraph Studio

https://studio.langchain.com/

Trace with LangSmith

https://docs.smith.langchain.com/observability/how_to_guides/trace_with_langchain

tracers - LangChain documentation

https://python.langchain.com/api_reference/core/tracers.html

Using Chroma in LangChain

https://python.langchain.com/docs/integrations/vectorstores/chroma/

 
import streamlit as st
import os
import pyperclip
import time

from langchain.chains import RetrievalQA
from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from langchain_community.document_loaders import UnstructuredHTMLLoader
from langchain_chroma import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter

from langchain_core.tracers.context import tracing_v2_enabled
from contextlib import nullcontext
import tiktoken

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

st.set_page_config(
    page_title="Book-Chat",
)

@st.cache_data
def print_banner():
    print("""
        ,---,.                           ,-.             ,----..    ,---,                   ___
      ,'  .'  \\                      ,--/ /|            /   /   \\ ,--.' |                 ,--.'|_
    ,---.' .' |   ,---.     ,---.  ,--. :/ |     ,---,.|   :     :|  |  :                 |  | :,'
    |   |  |: |  '   ,'\\   '   ,'\\ :  : ' /    ,'  .' |.   |  ;. /:  :  :                 :  : ' :
    :   :  :  / /   /   | /   /   ||  '  /   ,---.'   ,.   ; /--` :  |  |,--.  ,--.--.  .;__,'  /
    :   |    ; .   ; ,. :.   ; ,. :'  |  :   |   |    |;   | ;    |  :  '   | /       \\ |  |   |
    |   :     \\'   | |: :'   | |: :|  |   \\  :   :  .' |   : |    |  |   /' :.--.  .-. |:__,'| :
    |   |   . |'   | .; :'   | .; :'  : |. \\ :   |.'   .   | '___ '  :  | | | \\__\\/: . .  '  : |__
    '   :  '; ||   :    ||   :    ||  | ' \\ \\`---'     '   ; : .'||  |  ' | : ,\" .--.; |  |  | '.'|
    |   |  | ;  \\   \\  /  \\   \\  / '  : |--'           '   | '/  :|  :  :_:,'/  /  ,.  |  ;  :    ;
    |   :   /    `----'    `----'  ;  |,'              |   :    / |  | ,'   ;  :   .'   \\ |  ,   /
    |   | ,'                       '--'                 \\   \\ .'  `--''     |  ,     .-./  ---`-'
    `----'                                               `---`               `--`---'
    """)
    return 1

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

LangSmith tracing

langsmith_tracing = st.sidebar.toggle("LangSmith Tracing", value=False)
tracing_context = tracing_v2_enabled() if langsmith_tracing else nullcontext()

Get GEMINI_API_KEY

g_key = os.getenv("GEMINI_API_KEY")

Select Embeddings

Useful Links

Name

URL

Gemini Models

https://ai.google.dev/gemini-api/docs/models

Gemini Rate Limits

https://ai.google.dev/gemini-api/docs/rate-limits

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

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

Folder to save index

index_folder = f"vectors/book-{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/questions.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:   Update history", use_container_width=True):
    update_history("")
    st.toast(f'History updated')

Chroma

Create or load index

def create_doc_chunks(input_file):
    loader = UnstructuredHTMLLoader(input_file)
    docs = loader.load()

    # split into 1,000‐char chunks with 200‐char overlap
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,
    )

    chunks = text_splitter.split_documents(docs)
    return chunks

def create_index(input_file, persist_dir, chunks):
    # Create a *persistent* Chroma collection in one step
    vectorstore = Chroma.from_documents(
        chunks,
        embedding,
        persist_directory=persist_dir,     # <- where it lives on disk
        collection_name="book"             # any name you like
    )

    st.session_state.vstore = vectorstore

def load_index(persist_dir):
    try:
        # Load an existing collection directly
        vectorstore = Chroma(
            persist_directory=persist_dir,
            embedding_function=embedding,  # same embedder you used to build it
            collection_name="book",        # must match create_index
        )
        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:
    # No index folder
    chunks = create_doc_chunks(book_html)

    enc = tiktoken.encoding_for_model("gpt-4o-nano")
    total_tokens = sum(len(enc.encode(chunk.page_content)) for chunk in chunks)
    cents = 0

    st.sidebar.write(f'''
        | Chunks | Tokens | Cents |
        |---|---|---|
        | {len(chunks)} | {total_tokens} | {cents} |
        ''')

    if st.sidebar.button(':construction: &nbsp; Create Index', type='primary', use_container_width=True):
        with tracing_context:
            create_index(book_html, index_folder, chunks)
        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")

if st.button(":question: &nbsp; Ask", use_container_width=True):
    update_history(question + "\n\n---\n")
    start_time = time.time()
    with tracing_context:
        st.session_state.response = st.session_state.qa.invoke(question)
    end_time = time.time()
    st.session_state.execution_time = end_time - start_time
    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')

Show last execution time

if "execution_time" in st.session_state:
    st.sidebar.write(f"Execution time: `{round(st.session_state.execution_time, 1)}` sec")