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:

{"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

• Drag-and-drop file upload for NDJSON embedding datasets
• 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
• Point inspection - click points to view full document content
• Sidebar layout with controls on left, large visualization area on right
• Real-time visualization optimized for small to medium datasets

Installation & Usage

This project uses uv for dependency management.

1. Install dependencies:

uv sync

2. Run the application:

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
• UMAP-learn: UMAP dimensionality reduction
• openTSNE: Fast t-SNE implementation
• NumPy/Pandas: Data manipulation and analysis
• uv: Modern Python package and project manager