Unlock the true potential of your data with Zeta DB, a powerful, Python-based vector database designed to bridge the gap between vision and language.
Whether you are building intelligent image search engines, content recommendation systems, or AI-driven asset managers, Zeta DB provides the semantic backbone you need.

Why Zeta DB?

Traditional databases search by keywords whereas Zeta DB searches by meaning. By leveraging state-of-the-art CLIP models (like jina-clip-v2), Zeta DB transforms both images and text into high-dimensional vectors, enabling you to find exactly what you’re looking for based on context, not just metadata.

Key Features

• Multimodal Power: Seamlessly generate, store, and query embeddings for both images and text in a unified space.
• Blazing Fast Search: Built on top of LanceDB, ensuring scalable and efficient nearest neighbor search performance.
• Persistent & Reliable: Your index is automatically saved to disk, ensuring your data is safe and ready for instant retrieval.
• Fully Configurable: Tailor the experience to your needs. Swap models, adjust embedding dimensions, and manage storage paths effortlessly via a central configuration.
• Smart Indexing: Includes built-in duplicate prevention and vector normalization to keep your database clean and your results accurate.

The Tech Stack

Zeta DB combines the simplicity of Python with the raw power of modern AI infrastructure:

• Embedding Engine: Jina AI CLIP models (1024-dim full-size embeddings).
• Vector Indexing: LanceDB for high-performance on-disk storage.
• Interface: Gradio-ready entry point for immediate interaction.