Get Started with Vector Search using Vertex AI

Step-by-Step Tutorial: Getting Started with Vector Search using Google Cloud Vertex AI

Step 1: Understanding Vector Search

1. Vector search is a crucial component in AI/ML services, organizing data efficiently.
2. It is used in various services like Google Search, YouTube, and Google Play for relevant search results and recommendations.
3. Vector search can be applied to businesses for searching and recommending products, users, activities, conversations, music, videos, and IoT sensor signals.

Step 2: Difference Between Traditional Databases and Vector Search

1. Traditional IT systems use structured data with keywords, labels, and categories.
2. Modern AI services use embeddings, a special type of vector, to represent content meaningfully.
3. Embeddings help AI models organize data efficiently and identify similarities between different content items.

Step 3: Benefits of Vector Search and Embeddings

1. Vector search enables fast and accurate search results based on content meanings.
2. Embeddings help in creating a new level of user experience and interaction with AI systems.
3. Google Cloud offers Vertex AI Search, a fully-managed service for efficient vector searches.

Step 4: Building a Production-Ready Vector Search Service with Google Cloud Vertex AI

1. Utilize Google Cloud's highly scalable and reliable vector search infrastructure for efficient searches.
2. Use Vertex AI Search to add embeddings to an index and perform fast vector searches with simple queries.
3. Integrate with Google Cloud Services like BigQuery, Vertex AI Embeddings API, and Feature Store for a complete MLOps pipeline.

Step 5: Implementing Vector Search in Enterprises

1. Obtain embeddings for each item you want to search for.
2. Build an index on Vector Search using the obtained embeddings.
3. Run queries on Vector Search to find similar items based on their embeddings.

Step 6: Deploying Vector Search in Production Systems

1. Export embeddings as a JSON file on Cloud Storage to represent the meaning of item names.
2. Create an index on Vector Search by specifying the Cloud Storage path of the JSON file and relevant parameters.
3. Deploy the index on an index endpoint to execute vector searches and receive similar items within milliseconds.

Step 7: Enhancing User Experience with Semantic Search

1. Vector Search enables semantic search, understanding the meaning of item names for better user experience.
2. Expect improved exploration and relevance spotting of items using the power of AI and embeddings.

Step 8: Success Stories and Implementation

1. Many production systems, like Mercari, use Vector Search for product recommendations successfully.
2. Implementing Vector Search reduces time and cost for AI service development significantly.

Step 9: Getting Started with Vector Search

1. Visit the provided URL to begin your journey with Vector Search using Google Cloud Vertex AI.

By following these steps, you can understand, implement, and leverage the power of Vector Search using Google Cloud Vertex AI for your business or project.