Semantic Search with Vertex AI: Embeddings, Vector Indexing & Real Queries

Description

. In this video, we build a complete Semantic Search Engine on Google Cloud using Vertex AI, Gemini Embeddings, and Vector Search. You’ll see how modern AI search works — not by matching keywords, but by understanding meaning through high-dimensional embeddings. We start by loading real Stack Overflow data from BigQuery, generate semantic embeddings using the gemini-embedding-001 model, store the vectors in Cloud Storage, create a Tree-AH Vector Index, deploy it to a Vertex AI Index Endpoint, and finally run real semantic queries to find the most similar questions. 🧷 If you like, you can walk through the project's materials: https://github.com/M3hrdad-Dehghan/GenAI-Application/tree/main/Semantic%20Search%20with%20Vertex%20AI%20Embeddings%2C%20Vector%20Indexing%20%26%20Real%20Queries ------------------------------------------------------------------- Also, you can find more videos like this in the below playlists: ✔️ https://www.youtube.com/playlist?list=PLJMAQelT_Fct4dfvN985ztuQbjWpCug4O ------------------------------------------------------------------- I'd be pleased if you see my profile: 🔶 https://github.com/M3hrdad-Dehghan 🔶 https://www.linkedin.com/in/mehrdad-dehghan