AI Agent with multiple Tools & Movie Search

A sophisticated AI Agent that combines multiple tools such as reddit search, image generation, movie search capabilities, and evaluation frameworks.

Demo

ai-agent.mp4

Generated Images and results

├── src/
│ ├── ai/
│ │ ├── tools/ # AI tool implementations
│ │ ├── agent.ts # Main AI agent logic
│ │ ├── llm.ts # LLM integration (Gemini)
│ │ └── memory.ts # Conversation memory management
│ ├── evals/ # Evaluation framework
│ ├── rag/ # Movie search & retrieval
│ └── dashboard/ # Visualization interface

Features

1. AI Tools Integration

• Dad Jokes: Fetches random dad jokes
• Image Generation: Creates images from text descriptions
• Location Services: Provides geographical coordinates
• Weather Information: Retrieves weather data
• Reddit Integration: Fetches Reddit content
• Movie Search: Searches movie information from IMDB dataset
• Human In loop: Ask for human permission to proceed further

2. RAG (Retrieval Augmented Generation)

• Movie database integration using IMDB dataset
• Vector search capabilities via Upstash
• Efficient movie information retrieval system

3. Evaluation Framework

• Tool performance measurement
• Automated testing of AI responses
• Scoring system for tool accuracy
• Multiple experiment configurations

4. Dashboard

• React-based visualization interface
• Experiment results display
• Performance metrics graphs
• Interactive data exploration

Technical Stack

• AI/LLM: Google Gemini (via OpenAI-compatible API)
• Vector Database: Upstash
• Frontend: React + Vite
• Backend: Node.js + TypeScript
• Data Storage: JSON-based file storage
• Testing: Custom evaluation framework

Key Components

AI Tools

Each tool is modular and follows a consistent interface:

• Tool definition with Zod schema validation
• Standardized input/output handling
• Error management
• API integrations where needed

RAG System

• IMDB movie dataset integration
• Vector embeddings for efficient search
• Query optimization for movie information retrieval

Evaluation System

• Automated tool testing
• Performance metrics collection
• Experiment tracking
• Result visualization

Dashboard

• Experiment results visualization
• Performance metrics display
• Interactive data exploration
• Real-time updates

Getting Started

1. Install dependencies:

npm install

2. Configure environment variables:

GEMINI_API_KEY=your_key
GEO_CODE_API_KEY=your_key
# Add other required API keys

3. Start the application:

npm start

4. Run tests:

npm run test

5. Start dashboard:

cd dashboard
npm run dev

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request