UdaPlay - AI Game Research Agent Project

Project Overview

This is one of my projects for the Agentic AI Nanodegree at Udacity.

UdaPlay is intended to be an AI-powered research agent for the video game industry. This project is divided into two main parts that help us build a sophisticated AI agent capable of answering questions about video games using both local knowledge and web searches.

Project Structure

Part 1: Offline RAG (Retrieval-Augmented Generation)

In this part, I built a Vector Database using ChromaDB to store and retrieve video game information efficiently.

Key tasks:

• Set up ChromaDB as a persistent client
• Create a collection with appropriate embedding functions
• Process and index game data from JSON files
• Each game document contains:
  • Name
  • Platform
  • Genre
  • Publisher
  • Description
  • Year of Release

Part 2: AI Agent Development

Built an intelligent agent that combines local knowledge with web search capabilities.

The agent had the following capabilities:

1. Answer questions using internal knowledge (RAG)
2. Search the web when needed
3. Maintain conversation state
4. Return structured outputs
5. Store useful information for future use

Required Tools to Implement:

1. retrieve_game: Search the vector database for game information
2. evaluate_retrieval: Assess the quality of retrieved results
3. game_web_search: Perform web searches for additional information

Requirements

Environment Setup

To reproduce this work, it is necessary to create a .env file with the following API keys:

OPENAI_API_KEY="YOUR_KEY"
CHROMA_OPENAI_API_KEY="YOUR_KEY"
TAVILY_API_KEY="YOUR_KEY"

Project Dependencies

• Python 3.11+
• ChromaDB
• OpenAI
• Tavily
• dotenv

Directory Structure

project/
├── starter/
│   ├── games/           # JSON files with game data
│   ├── lib/             # Custom library implementations
│   │   ├── llm.py       # LLM abstractions
│   │   ├── messages.py  # Message handling
│   │   ├── ...
│   │   └── tooling.py   # Tool implementations
│   ├── Udaplay_01_starter_project.ipynb  # Part 1 implementation
│   └── Udaplay_02_starter_project.ipynb  # Part 2 implementation

Steps

1. Create and activate a virtual environment
2. Install required dependencies
3. Set up your .env file with necessary API keys
4. Follow the notebooks in order:
   • Complete Part 1 to set up your vector database
   • Complete Part 2 to implement the AI agent

Testing the Implementation

After completing both parts, I tested this agent with questions such as:

• "When was Pokémon Gold and Silver released?"
• "Which one was the first 3D platformer Mario game?"
• "Was Mortal Kombat X released for PlayStation 5?"