Why Build a PDF Chatbot?

Extracting information from long PDF documents — research papers, technical manuals, business reports — can be time-consuming. Instead of manually scanning pages, imagine simply asking:

❝

“What are the key findings?”
“Summarize section 3.”
“Who is the author?”

This application solves that problem by integrating:

Streamlit — for building an interactive web UI
LangChain — for managing LLM workflows
FAISS — for efficient similarity search
Google Generative AI — for embeddings + Gemini model access
Gemini 2.0 Flash — for conversational responses

Together, these tools create a Retrieval-Augmented Generation (RAG) system.

How the System Works (Architecture Overview)

Step-by-Step Flow:

Upload PDF
Extract text
Split text into chunks
Convert chunks into embeddings
Store embeddings in FAISS
User asks a question
Relevant chunks are retrieved
Gemini generates a context-aware answer

Code Walkthrough

1️⃣ Setting Up the Environment

We load environment variables and configure access to Google’s Generative AI services.

from dotenv import load_dotenv
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai

load_dotenv()
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

This ensures secure API key management using a .env file.

2️⃣ Extracting Text from PDFs

We use PyPDF2 to read and extract text from uploaded files.

from PyPDF2 import PdfReader

def get_pdf_text(pdf_docs):
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text

3️⃣ Splitting Text into Chunks

Large text blocks are inefficient for embedding. So we divide them into smaller overlapping chunks.

from langchain.text_splitter import RecursiveCharacterTextSplitter

def get_text_chunks(text):
    splitter = RecursiveCharacterTextSplitter(
        chunk_size=10000,
        chunk_overlap=1000
    )
    return splitter.split_text(text)

This improves search relevance and retrieval accuracy.

4️⃣ Creating a FAISS Vector Store

Each chunk is converted into embeddings using Google’s embedding model and stored in FAISS.

from langchain.vectorstores import FAISS
from langchain_google_genai import GoogleGenerativeAIEmbeddings

def get_vector_store(text_chunks):
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
    vector_store.save_local("faiss_index")

Now the document is searchable using semantic similarity.

5️⃣ Building the Conversational Chain

We configure Gemini 2.0 Flash with a structured prompt to ensure grounded answers.

from langchain.prompts import PromptTemplate
from langchain.chains.question_answering import load_qa_chain
from langchain_google_genai import ChatGoogleGenerativeAI

def get_conversational_chain():
    prompt_template = """
    Answer the question as detailed as possible from the provided context.
    If the answer is not in the context, say:
    "answer is not available in the context."

    Context:
    {context}

    Question:
    {question}

    Answer:
    """

    model = ChatGoogleGenerativeAI(
        model="gemini-2.0-flash-exp",
        temperature=0.3
    )

    prompt = PromptTemplate(
        template=prompt_template,
        input_variables=["context", "question"]
    )

    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
    return chain

This ensures factual and context-aware responses.

6️⃣ Handling User Queries

When a question is asked:

Load FAISS index
Perform similarity search
Pass results to Gemini
Display answer

def user_input(user_question):
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    new_db = FAISS.load_local(
        "faiss_index",
        embeddings,
        allow_dangerous_deserialization=True
    )

    docs = new_db.similarity_search(user_question)
    chain = get_conversational_chain()

    response = chain(
        {"input_documents": docs, "question": user_question},
        return_only_outputs=True
    )

    st.markdown(f"### Reply:\n{response['output_text']}")

7️⃣ Building the Streamlit Interface

Streamlit provides:

PDF upload sidebar
Processing button
Chat input field
Response display

Run the app using:

streamlit run chat_pdf.py

Key Benefits of This Approach

✅ Turns static PDFs into interactive chat experiences

✅ Saves time when reviewing large documents

✅ Uses advanced AI + vector search

✅ Scalable and production-ready foundation

✅ Demonstrates a real-world RAG implementation

Final Thoughts

This project highlights how modern AI tools can transform traditional document interaction.

By combining:

Semantic search (FAISS)
Embeddings
LLMs like Gemini 2.0 Flash
An interactive Streamlit UI

We create a powerful system that enables natural, conversational knowledge extraction from PDFs.

This architecture can be extended to:

Legal document assistants
Research summarizers
Business intelligence tools
Educational study assistants

Chat with PDF Using Flash Gemini 2.0