Book Chat (LC)¶
Answer Questions About Book Contents Using RAG with LangChain and Chroma
Use Calibre to convert EPUB files into HTMLZ format.
Unpack the HTMLZ archive into an
htmlfolder.This script will analyze the
html/index.htmlfile and save the embeddings into a Chroma database.
pip install -U chromadb langchain-chroma
Name |
URL |
|---|---|
LangGraph Studio |
|
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/ |
OpenAI Embeddings Prices |
|
Gemini Models |
|
Gemini Rate Limits |
import streamlit as st
import os
import pyperclip
import time
from datetime import date
from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.document_loaders import UnstructuredHTMLLoader
from langchain_chroma import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.tracers.context import tracing_v2_enabled
from contextlib import nullcontext
import tiktoken
See: PersistedList
from PersistedList import PersistedList
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
embedding_models = [
"openai/text-embedding-3-small",
"google/text-embedding-004", # April 2024
"google/gemini-embedding-exp-03-07", # March 2025 # Exceeds rate limit when selected
"google/embedding-001", # December 2023
]
embedding_prices = {
"openai/text-embedding-3-small": 0.02,
"google/text-embedding-004": 0.0,
"google/gemini-embedding-exp-03-07": 0.0,
"google/embedding-001": 0.0,
}
embedding_models_persisted = PersistedList(".book-chat-embeddings")
embedding_models = embedding_models_persisted.sort_by_pattern(embedding_models)
embedding_model = st.sidebar.selectbox("Embedding", embedding_models)
# split `embedding_model` into `model_vendor` before "/" and `embed_model_name` after
embedding_model_vendor, embed_model_name = embedding_model.split("/", 1)
if embedding_model_vendor == "google":
embedding = GoogleGenerativeAIEmbeddings(model=f"models/{embed_model_name}", google_api_key=g_key)
elif embedding_model_vendor == "openai":
embedding = OpenAIEmbeddings(model=embed_model_name)
Folder to save index
index_folder = f"vectors/book-{embed_model_name}"
embedding_models_persisted.select(embedding_model)
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 = [
"google/gemini-2.5-flash-preview-04-17",
"google/gemini-2.0-flash",
"google/gemma-3-27b-it",
]
llm_prices = {
"gpt-4.1-mini": 0.4,
"gpt-4.1-nano": 0.1,
"gpt-4.1": 2.0,
"gpt-4o-mini": 0.15,
"gpt-4o": 2.5,
"o3-mini": 1.10,
"o3": 2.0,
"o3-pro": 20.0,
}
llm_models_persisted = PersistedList(".book-chat-models")
llm_models = llm_models_persisted.sort_by_pattern(llm_models)
llm_model_selected = st.sidebar.selectbox("LLM", llm_models)
llm_model_vendor, llm_model = llm_model_selected.split("/", 1)
if llm_model_vendor == "google":
llm = ChatGoogleGenerativeAI(model=llm_model, google_api_key=g_key)
elif llm_model_vendor == "openai":
llm = ChatOpenAI(model=llm_model, temperature=0.1)
llm_models_persisted.select(llm_model_selected)
Load history
history = ""
def update_history(prompt):
# Add current date in YYYY-MM-DD format
current_date = date.today().strftime("%Y-%m-%d")
new_text = f"{prompt}\n\n{current_date}\n---\n"
# If contents of history_file already starts with new_text then don't update history.
if history.startswith(new_text):
return
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("text-embedding-3-small")
total_tokens = sum(len(enc.encode(chunk.page_content)) for chunk in chunks)
cost = (total_tokens / 1_000_000) * embedding_prices[embedding_model]
cents = cost/100
st.sidebar.write("**Embeddings price**")
st.sidebar.write(f'''
| Chunks | Tokens | Cents |
|---|---|---|
| {len(chunks)} | {total_tokens} | {cents} |
''')
if st.sidebar.button(':construction: 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
prompt = ChatPromptTemplate.from_template("""
Answer the user's question based only on the following context.
If the context doesn't contain the answer, state that you don't have enough information.
Context:
{context}
Question: {input}
Answer:
""")
retriever = st.session_state.vstore.as_retriever()
doc_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, doc_chain)
Ask a question¶
question = st.text_area(f"Question")
if st.button(":question: Ask", use_container_width=True):
update_history(question)
start_time = time.time()
with tracing_context:
st.session_state.response = rag_chain.invoke({"input": 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["answer"])
if st.sidebar.button(":clipboard: Copy to clipboard", use_container_width=True):
pyperclip.copy(st.session_state.response["answer"])
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")