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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Resources:
Embeddings API → https://goo.gle/3Ni4r7g
Firestore Vector Support → https://goo.gle/3YgNAYO
Embeddings Gecko Documentation → https://goo.gle/3BzSlUu
Watch more Firebase Release Notes Deep Dive → https://goo.gle/frn-deepdive
Subscribe to Firebase → https://goo.gle/Firebase
#FirebaseReleaseNotes
Speaker: Alexander Nohe
Products Mentioned: Firebase