Customer Support ChatBot

An AI-powered customer support chatbot designed to answer user queries in real-time using modern LLM technologies and retrieval-based approaches.

This project demonstrates how to build an intelligent assistant capable of understanding user questions and providing accurate, context-aware responses.

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Features

• AI-powered conversational chatbot
• Real-time responses
• Context-aware answers using LLMs
• Custom knowledge base support
• Retrieval-Augmented Generation (RAG) approach
• Clean and interactive chat interface

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How It Works

The chatbot follows a modern AI pipeline:

1. User sends a question
2. Text is processed and converted into embeddings
3. Relevant information is retrieved from the knowledge base
4. LLM generates a contextual response
5. Answer is returned to the user

Tech Stack

• Python
• LangChain
• OpenAI / LLM APIs
• Vector Database (Pinecone / ChromaDB)
• Flask / Streamlit (depending on your implementation)
• Embeddings & Semantic Search

Installation & Setup

1. Clone the repository

git clone https://github.com/Ostap200488/CustomerSupportChatBot.git
cd CustomerSupportChatBot

Create virtual environment
conda create -n chatbot python=3.10 -y
conda activate chatbot

Install dependencies
pip install -r requirements.txt
Create .env file
Add your API keys:
OPENAI_API_KEY="your_openai_key"
PINECONE_API_KEY="your_pinecone_key"
Run the application
python app.py
OR (if Streamlit is used):
streamlit run app.py
Open in browser
http://localhost:5000
OR (Streamlit):
http://localhost:8501

Author
Ostap Demchuk
Junior Software Developer
GitHub: https://github.com/Ostap200488
Portfolio: https://portfolio21345.netlify.app