AlloyDB AI: Hybrid search & multimodal capabilities for retail product search

Key Concepts:

• AlloyDB: Google's Postgres-compatible database with fast vector search.
• Vector Search: Searching based on semantic similarity using embeddings.
• Text Embeddings: Numerical representations of text meaning.
• Image Embeddings: Numerical representations of image content.
• Vertex AI: Google's AI platform, used for generating embeddings.
• Scan Vector Indexing: Google's technology for fast vector search.
• Full Text Search: Searching based on keywords and lexical similarity.
• TF-IDF: Term Frequency-Inverse Document Frequency, a relevance ranking method.
• Hybrid Search: Combining multiple search techniques (SQL, full text, vector).
• AI.IF Operator: Using Gemini to tune the final result set.
• Multimodal AI: Using AI with multiple types of data (text, images).

1. Introduction to AlloyDB and Product Discovery

• Paul Ramsey introduces AlloyDB as a Postgres-compatible database with up to 10x faster vector search queries.
• AlloyDB empowers businesses to revolutionize product discovery using native AI integrations and vector search.
• Goal: Help customers find what they need, driving sales and boosting the bottom line.
• Demo focuses on Cymbal Shops, a fictional fashion retailer modernizing their online customer experience.

2. The Problem with Basic SQL Search

• Example: Shopper searches for "black belt" on CymbalShops.com.
• Basic SQL search returns irrelevant items at the top of the page.
• Reason: Lack of relevance ranking; query is arbitrarily ordered by name.
• Inefficient full table scans lead to slow performance and scalability issues.

3. Improvement with Full Text Search

• Cymbal Shops can improve search using built-in full text search in Postgres (supported by AlloyDB).
• Searching "black belt" with full text search gives better results, with relevant items listed first.
• Full text search offers basic relevance ranking based on TF-IDF.
• Uses efficient inverted indexes for better performance.
• Understands lexical variations with word stemming and stop word removal.
• Supports advanced features like exact phrase matching and negation.

4. Limitations of Full Text Search

• Example: Searching for "handbags" when the store uses "purses."
• Full text search returns irrelevant items because it doesn't understand semantic similarity.
• Full text search is great at lexical similarity search but struggles with user intent.

5. AlloyDB's Native AI Capabilities and Vector Search

• Searching for "handbags" with text embeddings in AlloyDB yields much better results.
• AlloyDB understands the meaning behind the search, not just the keywords.
• Query uses vector similarity search.
• AlloyDB integrates with Vertex AI to generate text embeddings directly within the database using the embedding function.
• Finds items with semantically similar embeddings in the catalog.
• AlloyDB leverages Google's cutting-edge scan indexing technology for fast and efficient vector searches.

6. Hybrid Search: Combining Multiple Techniques

• No single search technique is perfect.
• Example: Searching by SKU might not work well with embeddings alone.
• Leading retailers use hybrid search, combining the best of multiple techniques.
• Hybrid search combines traditional SQL for exact matches on SKUs, full text search for keyword relevance, and vector search for semantic understanding.
• AlloyDB retrieves results from each method and intelligently merges them into a single, highly relevant ranked list.
• Delivers precision, lexical matching, and deep semantic relevance.

7. Dynamic Facet Calculation and AI Query Engine

• AlloyDB's columnar engine powers fast aggregates for dynamic facet calculation.
• Filtering by facets is as simple as modifying a WHERE clause.
• Customers can further tune their final result set with the power of Gemini by leveraging the new AI.IF operator.

8. Multimodal AI: Image-Based Visual Search

• Example: Finding a replacement for a favorite worn-out jacket.
• AlloyDB leverages its native Vertex AI integration to generate embeddings for images.
• User uploads a picture of the jacket.
• AlloyDB generates an image embedding on the fly and performs a similarity search against image embeddings in the product catalog.
• Instantly, visually similar jackets are displayed.
• Google's multimodal embedding model discerns subtle differences (e.g., winter coat vs. puffer jacket).

9. Conclusion: AlloyDB as a Unified Platform

• AlloyDB provides a powerful, unified platform for modern product discovery.
• Supports simple keywords, nuanced semantic queries, and image-based visual search.
• Delivers superior relevance by understanding user intent through native AI and vector search.
• Offers high performance and scalability with optimized indexing (full text search and Google scan for vectors).
• Provides flexibility, seamlessly supporting SQL, full text search, vector, and hybrid search patterns.
• Simplified architecture integrates AI and vector capabilities directly within the database.
• Call to action: Reach out to Google Cloud account team to learn more and modernize product search using AlloyDB and Vertex AI.

Main Takeaways/Synthesis:

AlloyDB offers a significant upgrade to traditional search methods by integrating AI and vector search capabilities directly into the database. This allows for more relevant and nuanced search results, improving the customer experience and driving sales. The combination of full text search, vector search, and SQL in a hybrid approach provides a comprehensive solution for various search needs, while multimodal AI extends the capabilities to image-based searches. AlloyDB's integration with Vertex AI and Google's scan indexing technology ensures high performance and scalability, making it a powerful tool for modern product discovery.