Marton Kodok

Traditional search engines rely heavily on exact keyword matching, often failing to capture the true semantic intent behind user queries. To build next-generation applications, developers must bridge the gap between text, images, and other unstructured media.

In this practical, code-driven session, we will demystify Multimodal Embeddings-the core technology that translates different data types into a shared vector space where semantic relationships can be calculated. We will explore how Google's Gemini Multimodal API can be leveraged to represent both visual and textual information in a way that computer algorithms can instantly compare.

Step-by-step, we will build a fully functional similarity search using Python. We will cover:
The Foundation: How multimodal embeddings represent diverse data formats in a unified vector space.
The Pipeline: Connecting to the Gemini API, generating embeddings, and storing them for rapid retrieval.
The Search: Executing complex, multi-faceted queries (such as matching an image with a descriptive text modifier).

Whether you are an AI enthusiast, a backend developer, or a data engineer, you will walk away with a solid understanding of vector embeddings, practical Python code templates, and the architectural knowledge to deploy multimodal search in your own projects.