VectorDBZ

A modern desktop application for exploring, managing, and analyzing vector databases

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Supported Databases

Database	Status	Minimum Version
Qdrant	✅ Supported	v1.7+
Weaviate	✅ Supported	v1.19+
Milvus	✅ Supported	v2.3+
ChromaDB	✅ Supported	v0.4+
Pinecone	✅ Supported	Latest
pgvector (PostgreSQL)	✅ Supported	PostgreSQL 11+ with pgvector extension

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Features

• Multiple Database Support — Connect to Qdrant, Weaviate, Milvus, ChromaDB, Pinecone, and pgvector
• Data Explorer — Browse collections, view documents with pagination, and inspect payloads
• Vector Search — Find similar vectors with advanced filtering and multi-vector support
• Embedding Functions — Generate embeddings from text or files using custom JavaScript functions with pre-built templates for OpenAI, Cohere, Hugging Face, and more
• Visualization — 2D/3D scatter plots with PCA, t-SNE, and UMAP dimensionality reduction
• Collection Management — View collection info, drop/truncate collections, and delete documents
• Dark/Light Themes — Modern UI with multi-tab interface and responsive layout
• Cross-Platform — Windows, macOS, and Linux support

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Installation

Download the latest release for your platform from the Releases page.

Platform	Download
Windows	VectorDBZ-x.x.x-Setup.exe
macOS (Intel)	VectorDBZ-darwin-x64-x.x.x.zip
macOS (Apple Silicon)	VectorDBZ-darwin-arm64-x.x.x.zip
Linux	vectordbz_x.x.x_amd64.deb or .rpm

macOS Installation Note

The macOS app is not code-signed (requires Apple Developer account). After downloading:

1. Extract the .zip file
2. Move VectorDBZ.app to your Applications folder
3. First launch: Right-click → "Open" → Click "Open" in the dialog

If you see "VectorDBZ is damaged" error, open Terminal and run:

xattr -cr /Applications/VectorDBZ.app

Then try opening the app again.

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Feedback

This project is in active development. Contributions, bug reports, and feature requests are welcome.

Contributing

• Bug Reports — Open an issue
• Feature Requests — Start a discussion

Roadmap

We are actively working on:

• Additional vector database integrations
• Enhanced analysis capabilities
• Improved visualization tools
• Performance optimizations for large datasets