# EmbeddingBuddy

A modular Python Dash web application for interactive exploration and visualization of embedding
vectors through dimensionality reduction techniques. Compare documents and prompts
in the same embedding space to understand semantic relationships.

![Screenshot of 3d graph and UI for Embedding Buddy](./embedding-buddy-screenshot.png)

## Overview

EmbeddingBuddy provides an intuitive web interface for analyzing high-dimensional
embedding vectors by applying various dimensionality reduction algorithms and
visualizing the results in interactive 2D and 3D plots. The application features
a clean, modular architecture that makes it easy to test, maintain, and extend
with new features. It supports dual dataset visualization, allowing you to compare 
documents and prompts to understand how queries relate to your content.

## Features

- **Dual file upload** - separate drag-and-drop for documents and prompts
- **Multiple dimensionality reduction methods**: PCA, t-SNE, and UMAP
- **Interactive 2D/3D visualizations** with toggle between views
- **Color coding options** by category, subcategory, or tags
- **Visual distinction**: Documents appear as circles, prompts as diamonds with desaturated colors
- **Prompt visibility toggle** - show/hide prompts to reduce visual clutter
- **Point inspection** - click points to view full content and identify document vs prompt
- **Reset functionality** - clear all data to start fresh
- **Sidebar layout** with controls on left, large visualization area on right
- **Real-time visualization** optimized for small to medium datasets

## Data Format

EmbeddingBuddy accepts newline-delimited JSON (NDJSON) files for both documents
and prompts. Each line contains an embedding with the following structure:

**Documents:**

```json
{"id": "doc_001", "embedding": [0.1, -0.3, 0.7, ...], "text": "Sample text content", "category": "news", "subcategory": "politics", "tags": ["election", "politics"]}
{"id": "doc_002", "embedding": [0.2, -0.1, 0.9, ...], "text": "Another example", "category": "review", "subcategory": "product", "tags": ["tech", "gadget"]}
```

**Prompts:**

```json
{"id": "prompt_001", "embedding": [0.15, -0.28, 0.65, ...], "text": "Find articles about machine learning applications", "category": "search", "subcategory": "technology", "tags": ["AI", "research"]}
{"id": "prompt_002", "embedding": [0.72, 0.18, -0.35, ...], "text": "Show me product reviews for smartphones", "category": "search", "subcategory": "product", "tags": ["mobile", "reviews"]}
```

**Required Fields:**

- `embedding`: Array of floating-point numbers representing the vector (must be same dimensionality for both documents and prompts)
- `text`: String content associated with the embedding

**Optional Fields:**

- `id`: Unique identifier (auto-generated if missing)
- `category`: Primary classification
- `subcategory`: Secondary classification
- `tags`: Array of string tags for flexible labeling

**Important:** Document and prompt embeddings must have the same number of dimensions to be visualized together.

## Installation & Usage

This project uses [uv](https://docs.astral.sh/uv/) for dependency management.

1. **Install dependencies:**

```bash
uv sync
```

2. **Run the application:**

```bash
uv run python main.py
```

3. **Open your browser** to http://127.0.0.1:8050

4. **Test with sample data**:
   - Upload `sample_data.ndjson` (documents)
   - Upload `sample_prompts.ndjson` (prompts) to see dual visualization
   - Use the "Show prompts" toggle to compare how prompts relate to documents

## Development

### Project Structure

The application follows a modular architecture for improved maintainability and testability:

```text
src/embeddingbuddy/
├── config/          # Configuration management
│   └── settings.py  # Centralized app settings
├── data/           # Data parsing and processing
│   ├── parser.py   # NDJSON parsing logic
│   └── processor.py # Data transformation utilities
├── models/         # Data schemas and algorithms
│   ├── schemas.py  # Pydantic data models
│   └── reducers.py # Dimensionality reduction algorithms
├── visualization/ # Plot creation and styling
│   ├── plots.py   # Plot factory and creation logic
│   └── colors.py  # Color mapping utilities
├── ui/            # User interface components
│   ├── layout.py  # Main application layout
│   ├── components/ # Reusable UI components
│   └── callbacks/ # Organized callback functions
└── utils/         # Utility functions
```

### Testing

Run the test suite to verify functionality:

```bash
# Install test dependencies
uv sync --extra test

# Run all tests
uv run pytest tests/ -v

# Run specific test file
uv run pytest tests/test_data_processing.py -v

# Run with coverage
uv run pytest tests/ --cov=src/embeddingbuddy
```

### Development Tools

Install development dependencies for linting, type checking, and security:

```bash
# Install all dev dependencies
uv sync --extra dev

# Or install specific groups
uv sync --extra test        # Testing tools
uv sync --extra lint        # Linting and formatting
uv sync --extra security    # Security scanning tools

# Run linting
uv run ruff check src/ tests/
uv run ruff format src/ tests/

# Run type checking
uv run mypy src/embeddingbuddy/

# Run security scans
uv run bandit -r src/
uv run safety check
```

### Adding New Features

The modular architecture makes it easy to extend functionality:

- **New reduction algorithms**: Add to `models/reducers.py`
- **New plot types**: Extend `visualization/plots.py`
- **UI components**: Add to `ui/components/`
- **Configuration options**: Update `config/settings.py`

## Tech Stack

- **Python Dash**: Web application framework
- **Plotly**: Interactive plotting and visualization
- **scikit-learn**: PCA implementation
- **UMAP-learn**: UMAP dimensionality reduction
- **openTSNE**: Fast t-SNE implementation
- **NumPy/Pandas**: Data manipulation and analysis
- **pytest**: Testing framework
- **uv**: Modern Python package and project manager