# EmbeddingBuddy

A Python Dash application for interactive exploration and visualization of
embedding vectors through dimensionality reduction techniques.

## 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.

## Features

- **Dimensionality Reduction**: Support for PCA, t-SNE, and UMAP algorithms
- **Interactive Visualizations**: 2D and 3D plots using Plotly
- **Web Interface**: Built with Python Dash for easy accessibility
- **Vector Analysis**: Tools for exploring embedding vector relationships and
  patterns

## Data Format

EmbeddingBuddy accepts newline-delimited JSON (NDJSON) files where each line contains an embedding document with the following structure:

```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"]}
```

**Required Fields:**

- `embedding`: Array of floating-point numbers representing the vector
- `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

## Features (Initial Version)

- **Drag-and-drop file upload** for NDJSON embedding datasets
- **PCA dimensionality reduction** (automatically applied)
- **Interactive 2D/3D visualizations** with toggle between views
- **Color coding options** by category, subcategory, or tags
- **Point inspection** - click points to view full document content
- **Real-time visualization** optimized for small to medium datasets

## 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 app.py
   ```

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

4. **Test with sample data** by dragging and dropping the included `sample_data.ndjson` file

## Tech Stack

- **Python Dash**: Web application framework
- **Plotly**: Interactive plotting and visualization
- **scikit-learn**: PCA implementation
- **NumPy/Pandas**: Data manipulation and analysis
- **uv**: Modern Python package and project manager