Vector Intelligence Toolset (Vector Toolset)

Vector Intelligence Toolset is an innovative exploration platform designed to harness the power of embeddings and high-dimensional vector spaces. This tool provides a dynamic playground for understanding, visualizing, and interacting with word embeddings in new and creative ways.

With Vector Toolset, users can:

• Visualize Similarities: Type words and explore their relationships in an interactive 3D space, where distance represents similarity.
• Combine Ideas: Select and combine words to generate new embeddings, uncovering unexpected connections and possibilities.
• Define Functions: Perform operations like opposites, transformations, or simple calculations directly within the embedding space, pushing the boundaries of vector reasoning.
• Experiment and Innovate: Use the tool to play with concepts, analyze relationships, and develop new ways of thinking about embeddings.

This project is not just a tool but a gateway to deeper insights into the structure of language and meaning in vector spaces. Whether you're an AI enthusiast, a researcher, or just curious about how embeddings work, Vector Toolset offers a hands-on way to engage with the possibilities of vector reasoning and intelligence.

Overview

VectorToolset provides an intuitive interface for working with word embeddings and vector operations. It features a powerful playground that allows users to:

• Perform vector arithmetic operations
• Find analogies between concepts
• Visualize high-dimensional embeddings
• Define custom logical operations
• Explore semantic relationships

The Analogical Reasoning interface allows users to discover relationships between concepts

The Embedding Playground provides an interactive environment for exploring vector spaces

Features

Core Capabilities

• Vector Operations: Basic arithmetic, normalization, and similarity measures
• Analogical Reasoning: Solve analogies using vector arithmetic (e.g., king - man + woman ≈ queen)
• Embedding Management: Import and work with various embedding models (Word2Vec, BERT)
• Visualization: 2D and 3D visualization of embedding spaces
• Custom Operations: Define and apply custom logical operations

Interactive Playground

• Natural language interface using Given-When-Then syntax
• Real-time visualization of vector operations
• Interactive token selection and manipulation
• Support for multiple embedding models
• Vector space exploration tools

Installation

# Clone the repository
git clone https://github.com/TensoriumAi/VectorToolset.git

# Install dependencies
cd VectorToolset
npm install

# Start the development server
npm start

Quick Start

1. Launch the playground interface
2. Select your embedding model (Simple Embedding Model or BERT)
3. Enter words in the input fields for analogical reasoning
4. Use the buttons to:
   • Solve analogies
   • Find similar concepts
   • Evaluate analogical strength

Project Documentation

Technical Specification

For detailed information about the project's architecture, components, and implementation details, see the Technical Specification. Key highlights include:

• Comprehensive embedding manipulation module
• Advanced tracing and visualization capabilities
• Sophisticated association and analogy processing
• Natural language interface with GWT integration
• Detailed API documentation and usage examples

Project Status

Track the current development progress in the Project Status document. Current highlights:

• Core embedding and operations modules implemented
• Visualization components complete with D3.js and Three.js integration
• GWT processor and analogical reasoning engine operational
• Comprehensive test coverage for critical components
• Active development on enhanced features

Mid-Project Review

Read our detailed technical assessment in the Mid-Project Review. Key findings:

• Strong modular architecture with clear separation of concerns
• Robust test coverage across components
• Successful integration of advanced concepts
• Areas identified for performance optimization
• Roadmap for UI/UX improvements

Development

Prerequisites

• Node.js >= 14.0.0
• npm >= 6.0.0

Building

# Build the project
npm run build

# Run tests
npm test

Project Structure

VectorToolset/
├── src/                 # Source code
│   ├── embeddings/     # Embedding models
│   ├── operations/     # Vector operations
│   └── visualization/  # Visualization components
├── playground/         # Interactive UI
├── test/              # Test suites
└── dist/              # Built files

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat: Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with modern JavaScript tooling
• Visualization powered by D3.js and Three.js
• Embedding support from various open-source models