Chatbot Assistant Template

A comprehensive project template for building AI-powered chatbot assistants with multiple LLM integrations, a modern web interface, and a plug-and-play chatbot widget powered by Retrieval-Augmented Generation (RAG) using llama_index for context retrieval.

🚀 Project Overview

This project serves as a practical playground and implementation template for integrating Large Language Models (LLMs) into web applications. It consists of four main components:

1. LLM Playground (practices.ipynb) - A Jupyter notebook for experimenting with different LLM APIs
2. Backend Server - FastAPI-based chatbot server with LLM and RAG integration (leveraging llama_index for retrieval)
3. Frontend Template - AI-generated modern web interface for testing the chatbot
4. Customizable Chatbot Widget - Embeddable real-time AI chat powered by RAG

🖼️ Chatbot Widget Overview

Above: Example of the chatbot widget interface embedded into a static web page. The chatbot leverages Retrieval-Augmented Generation (RAG), which means it intelligently searches for relevant information from your content (such as documentation or company data) and uses it to provide accurate, context-aware answers to user questions.
In this project, we use llama_index for the underlying document indexing, semantic search, and context retrieval powering RAG.

How the Chatbot Works (RAG Powered with llama_index)

• When a user sends a question via the widget, the message is sent to the backend server.
• The server retrieves the most relevant context passages from your document collection using an embedding-based similarity search powered by llama_index.
• The context, along with the question, is sent to the LLM (e.g., GPT-4 via OpenAI, or OSS LLMs via Hugging Face).
• The LLM generates an answer, strictly conditioned on the provided context. If the answer cannot be confidently given from the context, it will say "I don't know".
• The response is sent back in real time and shown in the chat widget.

This enables your chatbot to give safe, reliable, and on-topic answers from your own business or project knowledge!

📁 Project Structure

chatbot_assistant_template/
├── practices.ipynb          # LLM experimentation playground
├── backend/
│   └── server.py           # FastAPI chatbot server (RAG logic with llama_index inside)
├── frontend/
│   ├── static_pages/       # HTML pages (AI-generated, includes chat widget)
│   ├── scripts/           # JavaScript for chat functionality and widget
│   └── styles/            # CSS styling
├── requirements.txt        # Python dependencies
├── setup.sh              # Setup script
└── README.md             # This file

🧪 LLM Playground (practices.ipynb)

The Jupyter notebook serves as a comprehensive learning environment for working with different LLM APIs and frameworks:

Features:

• Hugging Face Integration: Direct model usage with transformers library
• OpenAI API: Text generation, image understanding, and conversational AI
• Langchain Framework: Chain-based LLM applications and sequential processing
• Practical Examples: Text generation, sentiment analysis, translation, and more

Supported APIs:

• 🤗 Hugging Face Transformers
• 🧠 OpenAI GPT models
• 🔗 Langchain framework
• 🎯 Custom pipeline implementations

🤖 Backend Server

A FastAPI-based server designed to handle chatbot interactions, with full end-to-end Retrieval-Augmented Generation (RAG) powered by llama_index:

Current Features:

• CORS-enabled API endpoints
• Chat message processing endpoint (/chat)
• Environment variable management
• Vector document retrieval (semantic search over your docs, using llama_index)
• LLM integration and prompt construction
• Error handling and logging

Planned Implementation:

• Additional model options (OpenAI, Hugging Face, or custom models)
• Conversation memory and context management
• Response streaming capabilities
• Authentication and rate limiting

💬 Chatbot Widget Implementation

The frontend comes with a reusable chat widget that you can embed in any page of your business website or internal tool.

Key Widget Features:

• Instant live chat interface with smooth UX
• User question input with support for multi-turn dialogue
• Shows streaming/loading status while awaiting AI response
• Context-aware answers using real business knowledge (RAG)
• Fully responsive and mobile friendly
• Easy to adapt to brand colors and layout

How it works:
The JavaScript in frontend/scripts/ handles real-time communication with the backend API, sending user messages and displaying AI responses. The widget is designed in a modular way so it can be dropped into any static or dynamic website.

Try it! Open frontend/static_pages/index.html and start chatting with your AI assistant, powered by your own documents and knowledge base.

🎨 Frontend Template

Note: The entire frontend codebase was generated by AI for the purpose of testing the chatbot integration.

Features:

• Modern, responsive design
• Interactive chat widget (see above)
• Real-time messaging interface
• Professional business website template
• Mobile-friendly layout

Pages:

• Home: Hero section with company overview
• Services: Service offerings and features
• Products: Product catalog
• About: Company information
• Contact: Contact form and information

🛠️ Setup Instructions

Prerequisites

• Python 3.8+
• pip package manager
• Git

Installation

1. Clone the repository:

   git clone <repository-url>
   cd chatbot_assistant_template

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables: Create a .env file in the project root:

   OPENAI_API_KEY=your_openai_api_key_here
   HF_TOKEN=your_huggingface_token_here

4. Run the setup script (optional):

   chmod +x setup.sh
   ./setup.sh

Running the Application

1. Start the backend server:

   cd backend
   python server.py

   The server will be available at http://localhost:8000

2. Open the frontend: Navigate to frontend/static_pages/index.html in your browser and try chatting with the chat assistant bot

3. Experiment with LLMs: Open practices.ipynb in Jupyter Notebook to explore different LLM APIs

🔧 Development

Backend Development

The server is built with FastAPI and includes:

• RESTful API endpoints
• CORS middleware for frontend integration
• Environment variable management
• Vector-based retrieval (RAG) via llama_index
• Structured error handling

Frontend Development

The frontend includes:

• Vanilla JavaScript for chat functionality and widget implementation
• CSS Grid and Flexbox for responsive design
• Font Awesome icons
• Modern UI/UX patterns

LLM Integration

The notebook provides examples for:

• Direct API calls to various LLM providers
• Framework-based implementations (Langchain)
• Custom pipeline development
• Model comparison and testing

📚 Learning Resources

This project is designed as a learning tool for:

• LLM API integration
• Retrieval-Augmented Generation (RAG) in practice (including usage of llama_index)
• Web development with AI
• Chatbot architecture
• Modern frontend development
• Backend API design

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Note: This project serves as both a learning resource and a practical template for building AI-powered applications. The frontend template was entirely generated by AI and the chatbot widget is designed for seamless RAG-powered integration in any project, providing a realistic testing environment for chatbot-assisted automation and customer experience.
All semantic retrieval and context passage selection in the backend is powered by llama_index.