WikiCharacterBot

Intelligent Wikipedia-Powered RAG Chatbot
Jan 2026
An intelligent chatbot that fetches Wikipedia content and uses RAG (Retrieval-Augmented Generation) to answer questions about people, characters, and topics. Built with OpenAI's function calling, ChromaDB vector database, and Gradio web interface.

Python OpenAI API GPT-4o-mini RAG ChromaDB Vector Database Gradio Wikipedia API Embeddings Function Calling LLM Hugging Face Spaces VS Code GitHub

Overview

WikiCharacterBot demonstrates Retrieval-Augmented Generation (RAG) combined with OpenAI’s function calling capabilities. The chatbot intelligently fetches Wikipedia content, indexes it in a vector database, and uses semantic search to provide accurate, sourced answers.

Key Features

  • Wikipedia Integration: Automatically fetches and embeds Wikipedia articles
  • RAG Architecture: ChromaDB vector database for intelligent context retrieval
  • Function Calling: Leverages OpenAI’s function calling for automated tool use
  • Web Interface: Beautiful Gradio UI for seamless interaction
  • Persistent Storage: ChromaDB maintains indexed content locally
  • Multi-turn Conversations: Maintains chat history for coherent dialogues

Technical Stack

  • LLM: OpenAI GPT-4o-mini with function calling
  • Vector DB: ChromaDB with persistent local storage
  • Embeddings: ChromaDB’s DefaultEmbeddingFunction
  • Web Framework: Gradio for interactive UI
  • Data Source: Wikipedia API for content retrieval

How It Works

  1. User submits a question via the web interface
  2. OpenAI GPT-4o-mini uses function calling to decide which tools to use
  3. fetch_wiki_content() retrieves relevant Wikipedia articles
  4. Content is embedded and stored in ChromaDB collection
  5. query_rag() performs semantic search for relevant context
  6. GPT-4o-mini generates an answer grounded in retrieved context
  7. Response appears in chat interface with full conversation history

Usage

Run the web application:

python app.py

Launch the CLI version:

python WikiCharacterBot.py

Setup

  1. Install dependencies:
pip install -r requirements.txt
  1. Create .env file:
OPENAI_API_KEY=your_api_key_here
  1. Run the application and start asking questions about your favorite people and characters!

Learning Value

This project is ideal for understanding:

  • OpenAI Function Calling: Real-world tool integration with LLMs
  • RAG Patterns: Building reliable systems with external knowledge
  • Vector Databases: Semantic search and embedding management
  • Multi-turn Conversations: Managing stateful LLM interactions
  • API Integration: Combining multiple APIs for cohesive solutions