Learn how to create Genkit flows that power your app's backend and frontend, and discover the essential security steps needed to protect your AI endpoints. Nohe covers everything from initial setup to implementing rate limits and caching to keep your project secure and cost-effective.
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The wait is over! Nohe walks you through how to implement full text search in Firestore Enterprise. Previously, if you wanted to search your Firestore data with keywords, you had to export your data to a third-party provider or deal with the overhead of generating embeddings for Vector Search. Now, everything is integrated directly into the Firestore ecosystem.
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Learn how to use Firestore's new Vector Embedding support to power semantic search and recommendations in your Firebase app! In this episode of Firebase Deep Dives, Nohe covers: -What vector embeddings are and how they work -Why you would want to use vector embeddings in your app -How to generate vector embeddings using Vertex AI and Cloud Functions -How to query your Firestore data using vector embeddings and k-nearest neighbors search
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