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Bring AI-Based Search to Your Web App – Sebastian Witalec, JSNation 2023
Discover how to bring AI-powered search to your web app, exploring machine learning models, vector embeddings, and practical applications using Weaviate and Cohere APIs.
- AI-based search can be easily integrated into web applications using machine learning models.
- Traditional search methods can be limiting, and AI-based search can provide more accurate results.
- AI-based search can be used to query images and text in a single step, without the need for manual processing.
- The concept of vector embeddings is key to AI-based search, allowing for the creation of semantic search queries.
- The author of the talk, Sebastian Witalec, shares his personal journey of learning about machine learning and how he was able to implement AI-based search in his web application.
- The talk covers the use of Weaviate, an open-source vector database, and Cohere, an API that provides vector embeddings.
- The author explains that AI-based search is not limited to text, but can also be used to search images and other forms of media.
- The talk also covers the concept of CLIP, a model that can generate text and images based on a given prompt.
- The author shares several examples of how AI-based search can be used in practical applications, such as searching for celebrities or finding information on Wikipedia.
- The talk concludes with the author encouraging developers to explore the possibilities of AI-based search and to not be intimidated by the complexity of machine learning.