A Semantic Search Engine that understands the Content & Context of your Queries.
Use Multi-Modal inputs like Text-Image or a Reverse Image Search to Query a Vector Database of over 15k Images. Try it Out!

At its core, the Search Engine is built upon the concept of Vector Similarity Search. All the Images are encoded into vector embeddings based on their semantic meaning using a Transformer Model, which are then stored in a vector space. When searched with a query, it returns the nearest neighbors to the input query which are the relevant search results.

We use the Contrastive Language-Image Pre-Training (CLIP) Model by OpenAI which is a Pre-trained Multi-Modal Vision Transformer that can semantically encode Words, Sentences & Images into a 512 Dimensional Vector. This Vector encapsulates the meaning & context of the entity into a Mathematically Measurable format.

2-D Visualization of 500 Images in a 512-D Vector Space

The Images are stored as vector embeddings in a Qdrant Collection which is a Vector Database. The Search Term is encoded and run as a query to Qdrant, which returns the Nearest Neighbors based on their Cosine-Similarity to the Search Query.

The Dataset: All images are sourced from the Open Images Dataset by Common Visual Data Foundation.

• Python
• Jupyter Notebooks
• Qdrant - Vector Database
• Sentence-Transformers - Library
• CLIP by OpenAI - ViT Model
• Gradio - UI
• HuggingFace Spaces - Deployment