Live Link : https://ragchatbot-huqpb4rg7pjuu5ewptqfsk.streamlit.app/

🤖 RAG ChatBot Help & Support

An AI-powered PDF Question Answering Chatbot built using Streamlit, LangChain, Google Gemini, and FAISS.

The application allows users to upload PDF documents and ask questions based on the uploaded content. The chatbot retrieves the most relevant information from the document and generates accurate answers using Google Gemini.

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🚀 Features

• Upload PDF files
• Extract text from PDF documents
• Intelligent text chunking
• Vector embeddings using Google Gemini
• FAISS vector database for semantic search
• Context-aware question answering
• Fast and interactive Streamlit interface
• Retrieval Augmented Generation (RAG)

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🛠️ Tech Stack

Frontend

• Streamlit

Backend

• Python

AI / LLM

• Google Gemini 2.5 Flash
• LangChain

Vector Database

• FAISS

Embeddings

• Google Generative AI Embeddings

PDF Processing

• PyPDF2

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📂 Project Structure

ChatBot-main/
│
├── assets/
│   ├── Syllabus_Theory_of_Automata.pdf
│   ├── question_1.png
│   └── question_2.png
│
├── faiss_index/
│   ├── index.faiss
│   └── index.pkl
│
├── app.py
├── requirements.txt
├── .env
└── README.md

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⚙️ Installation

Clone Repository

git clone https://github.com/your-username/RAG-ChatBot.git
cd RAG-ChatBot

Create Virtual Environment

python -m venv venv

Activate Environment

Windows

venv\Scripts\activate

Linux / Mac

source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

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🔑 API Configuration

Create a .env file:

GOOGLE_API_KEY=your_google_api_key

Get your Gemini API Key from:

https://aistudio.google.com/app/apikey

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▶️ Run Application

streamlit run app.py

Open:

http://localhost:8501

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🔄 Workflow

PDF Upload
      │
      ▼
Text Extraction
      │
      ▼
Text Chunking
      │
      ▼
Generate Embeddings
      │
      ▼
Store in FAISS
      │
      ▼
User Question
      │
      ▼
Similarity Search
      │
      ▼
Relevant Context
      │
      ▼
Gemini LLM
      │
      ▼
Final Answer

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📄 Sample PDF

The project was tested using:

Syllabus_Theory_of_Automata.pdf

This PDF contains course syllabus and academic content used for chatbot testing.

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📸 Screenshots

Question Answering Example 1

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Question Answering Example 2

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🎯 Learning Outcomes

Through this project I learned:

• LangChain Fundamentals
• RAG (Retrieval Augmented Generation)
• FAISS Vector Databases
• Google Gemini Integration
• Embeddings and Semantic Search
• Streamlit Application Development
• PDF Processing using Python

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🔮 Future Enhancements

• Chat History
• Conversation Memory
• Document Summarization
• Voice Assistant Integration
• Cloud Deployment

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👨‍💻 Author : Yatendra Gupta