🏦 BankBot AI

An AI-powered banking chatbot built using Python, Streamlit, and Groq LLMs. BankBot AI acts as a virtual banking assistant capable of answering banking-related questions through a hybrid approach that combines a predefined FAQ knowledge base with AI-generated responses.

🚀 Features

• Banking-focused AI chatbot
• Predefined FAQ knowledge base
• FAQ alias mapping for better query matching
• Domain restriction to banking-related queries only
• Groq-powered LLM responses
• Streamlit web interface
• Fast and interactive user experience

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🛠️ Tech Stack

• Python
• Streamlit
• Groq API
• Natural Language Processing (NLP)

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📂 Project Structure

BankBot-AI/
│
├── app.py
├── chatbot.py
├── faqs.py
├── faq_aliases.py
├── domain_filter.py
├── requirements.txt
├── .gitignore
│
└── .streamlit/
    └── secrets.toml

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⚙️ Installation

1. Clone the repository

git clone https://github.com/debugwithsushant/BankBot-AI.git
cd BankBot-AI

2. Create a virtual environment

py -3.13 -m venv venv

3. Activate the virtual environment

Windows

venv\Scripts\activate

Linux / macOS

source venv/bin/activate

4. Install dependencies

pip install -r requirements.txt

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🔑 Configure Groq API Key

Create:

.streamlit/secrets.toml

Add:

GROQ_API_KEY = "your_groq_api_key_here"

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▶️ Run the Application

streamlit run app.py

Open your browser and visit:

http://localhost:8501

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💬 Example Queries

• What is a savings account?
• What is a fixed deposit?
• How does a credit card work?
• What is the interest rate on loans?
• How can I open a bank account?

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🛡️ Domain Restriction

BankBot AI is designed to answer banking-related questions only.

Examples:

✅ "What is a savings account?"

✅ "Explain fixed deposits."

❌ "Who won the FIFA World Cup?"

❌ "Write a Python program."

This helps keep chatbot responses relevant and reliable.

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📸 Screenshots

Add screenshots of:

• Main chatbot interface
• FAQ response example
• AI-generated response example
• Domain restriction example

inside a /screenshots folder and reference them here.

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🎯 Project Objectives

• Provide instant answers to banking FAQs
• Reduce dependency on manual customer support
• Demonstrate practical use of AI and NLP
• Build a domain-specific conversational assistant

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🔮 Future Enhancements

• Multi-language support
• Voice-enabled interactions
• Database-backed knowledge base
• Conversation history storage
• User authentication
• Retrieval-Augmented Generation (RAG)

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👨‍💻 Author

Sushant Pawar

Infosys Springboard Virtual Internship Project

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📄 License

This project is licensed under the MIT License.