Get Started with Vector Search using Vertex AI

Google Cloud Tech
1 Nov 202309:22

TLDRThis video introduces vector search, highlighting its growing importance in businesses and AI/ML services. It explains how Google uses vector search across services like Search, YouTube, and Google Play to deliver relevant results and recommendations. The video also discusses the concept of embeddings and how they differ from traditional databases. Google's ScaNN algorithm is presented as a solution for fast and scalable vector search, underpinning major services. The video concludes by demonstrating how Google Cloud Vertex AI Search, a fully-managed service, can be utilized to build production-ready vector search services, enhancing user experience and setting a new standard for IT systems.

Takeaways

  • 🔍 Vector search is an essential AI/ML technology used in various services like Google Search and YouTube for providing relevant search results and recommendations.
  • 📈 Vector search is becoming increasingly important in the age of generative AI, enhancing the search and recommendation capabilities for a wide range of items and activities in businesses.
  • 🌐 Unlike traditional databases, vector search uses 'embeddings', a special type of vector that maps the meaning of content, allowing for more nuanced and accurate searches.
  • 🎯 Google utilizes vector search to organize data across its services, demonstrating the technology's potential for businesses to improve user experiences and interactions.
  • 📊 The embedding space created by AI can have hundreds or thousands of dimensions, capturing subtle differences and sorting content into numerous categories.
  • 🚀 Google's ScaNN algorithm is a leading industry solution for approximate nearest neighbor searches, enabling fast and scalable searches with billions of embeddings.
  • 🛠️ Google Cloud Vertex AI Search is a fully-managed service that simplifies the process of adding embeddings to an index and performing rapid vector searches.
  • 🔗 Vertex AI Search integrates seamlessly with other Google Cloud services like BigQuery, Vertex AI Embeddings API, and Feature Store, facilitating a streamlined MLOps pipeline.
  • 🛍️ Businesses can leverage vector search to provide semantic search for items, understanding the meaning behind names and descriptions, and significantly enhancing user experience.
  • 🏢 Companies like Mercari have successfully implemented vector search technology, achieving efficient AI service development and deployment within a short timeframe and with minimal resources.

Q & A

  • What is vector search and why is it important for businesses?

    -Vector search is a technology that uses embeddings, which are data structures representing the meaning of content, to quickly find and recommend relevant items based on their semantic meaning rather than just keywords. It's becoming crucial for businesses as it significantly enhances user experience and is a key component in AI/ML services, enabling the creation of new, more intuitive ways of human-computer interaction.

  • How does Google utilize vector search in its services?

    -Google uses vector search to organize data across its various services like Google Search, YouTube, and Google Play. This technology allows Google to provide search results and recommendations that are relevant to users by understanding the semantic meaning of the content, rather than relying solely on keywords.

  • What is the difference between traditional databases and vector search technology?

    -Traditional databases organize data as structured or tabular data using simple keywords, labels, and categories. In contrast, vector search technology uses embeddings, which are vectors that capture the semantic meaning of content, allowing for more nuanced and accurate search and recommendation.

  • How does the embedding space created by AI work?

    -AI creates an embedding space, which is essentially a map of the content's meaning, by assigning specific values to each piece of content it has trained with. This allows AI to identify the location of each content on the map, grouping similar meanings closely together, thus enabling efficient and accurate vector search.

  • What is the approximate nearest neighbor (ANN) technique and how does it benefit vector search?

    -The approximate nearest neighbor (ANN) technique is a method used to speed up vector search by using vector quantization to divide the embedding space into multiple spaces with a tree structure, similar to an index in a database. This allows for extremely fast and scalable search, even with billions of embeddings.

  • What is ScaNN and how does it contribute to Google's services?

    -ScaNN is an ANN algorithm developed by Google research that is considered one of the best in the industry for search and recommendation. It forms the foundation of Google's business and user experience, enabling major Google services like Google Search and YouTube to provide relevant results and recommendations.

  • What is Vertex AI Search and how does it benefit developers?

    -Vertex AI Search is a fully-managed service by Google Cloud that allows developers to easily add embeddings to an index and perform fast vector searches with simple queries. It offers high performance, scalability, and reliability, making it a solid foundation for enterprise services at Google-scale.

  • How can businesses use vector search to improve their user experience?

    -Businesses can use vector search to provide semantic search, understanding the meaning behind item names, descriptions, or images to find and recommend similar items. This enhances the user experience by offering more relevant and meaningful content to users, making it easier for them to explore and discover items of interest.

  • How does the process of using vector search for a production service in enterprises look like?

    -The process involves getting embeddings for each item, building an index on Vector Search with the embeddings, and deploying the index on an index endpoint. Queries can then be run on Vector Search to find similar items by their names or other attributes, providing results within milliseconds.

  • How does the use of embeddings from BigQuery and Vertex AI Search simplify the process of creating a semantic search system?

    -BigQuery's ML.EMBED_TEXT function allows for easy generation of embeddings for text data. These embeddings can then be exported and used in Vertex AI Search to create an index for semantic search. This streamlined process simplifies the creation of a semantic search system and reduces the time and cost associated with AI service development.

  • Can you provide an example of a company that has successfully implemented vector search technology?

    -Mercari, a popular marketplace app, has successfully implemented vector search technology to show product recommendations. They were able to build and deploy the system within a month with a single person, significantly reducing the time and cost for AI service development.

Outlines

00:00

🔍 Introduction to Vector Search and its Importance

This paragraph introduces the concept of vector search, emphasizing its growing significance in businesses due to its ability to handle AI/ML services efficiently. It explains how vector search is utilized in everyday life through various Google services like Search, YouTube, and Google Play, and how it provides relevant search results and recommendations. The paragraph also touches on the use of vector search in businesses for different purposes, such as product recommendations and IoT sensor data analysis. The introduction of generative AI and its role in enhancing vector search capabilities is highlighted, showcasing an example of how it can identify product items based on images rather than text. The difference between traditional databases and vector search technology is outlined, with a focus on the use of 'embeddings' as a data structure in AI systems to map and organize content meaningfully. The concept of embedding space is simplified with an example and the potential of vector search in revolutionizing human-computer interaction is discussed.

05:00

🚀 Google's ScaNN and its Role in Vector Search

This paragraph delves into the specifics of Google's contributions to vector search, particularly the development of ScaNN (Sparse Coder Nearest Neighbors), an algorithm that stands as an industry-leading solution for search and recommendation systems. ScaNN's foundational role in major Google services is underscored, as well as its impact on Google's mission to make information universally accessible and useful. The paragraph introduces Google Cloud Vertex AI Search, a managed service that leverages ScaNN to facilitate fast and scalable vector searches. The integration of Vertex AI Search with other Google Cloud services is highlighted, showcasing its potential for streamlined MLOps pipelines. A practical example is provided on how vector search can be implemented in enterprises for tasks such as showing similar items to users, with a step-by-step guide on using BigQuery and Vector Search to achieve this. The effectiveness of semantic search over keyword search is emphasized, and real-world applications of vector search technology, such as in the Mercari app, are cited. The paragraph concludes with an invitation for users to explore vector search further through a provided URL.

Mindmap

Keywords

💡Vector Search

Vector search is a technology that enables the efficient and accurate retrieval of items based on their content meaning rather than just keywords. It uses embeddings, which are vector representations of data, to map similar content close together in a high-dimensional space. In the context of the video, vector search is crucial for businesses to provide relevant search results and recommendations, as seen with Google's services like Search, YouTube, and Google Play. The technology allows for semantic search, understanding the meaning behind item names, descriptions, or images, and has been deployed in production systems like Mercari's marketplace app for product recommendations.

💡Embeddings

Embeddings are numerical representations of data, such as text, images, or any content, that capture their semantic meaning in a high-dimensional vector space. They are the foundation of vector search, allowing AI to identify and locate content based on its meaning rather than relying on keywords or categories. In the video, embeddings are created by training AI with specific content, resulting in a vector that represents the essence of that content. These embeddings are then used in vector search to find and recommend items that are semantically similar to a given query, providing a more intuitive and relevant user experience.

💡AI/ML Services

AI/ML (Artificial Intelligence/Machine Learning) services refer to the applications and systems that utilize AI and machine learning algorithms to perform tasks that would typically require human intelligence. These services can include data analysis, pattern recognition, prediction, and decision-making. In the video, AI/ML services are highlighted as being dependent on vector search technology, which is becoming an essential component for organizing data and providing relevant search results and recommendations across various platforms, such as Google Search and YouTube.

💡Generative AI

Generative AI refers to AI systems that can create new content, such as text, images, or music, that resembles the content they were trained on. In the context of the video, generative AI is used in conjunction with vector search to enable multimodal search capabilities. This means the AI can understand and retrieve items based not only on text descriptions but also on visual elements, such as images, by using embeddings derived from the visual content. This enhances the search experience by allowing users to find items that match their queries in a more intuitive and comprehensive way.

💡Approximate Nearest Neighbor (ANN)

Approximate Nearest Neighbor (ANN) is a technique used in machine learning and vector search to quickly find the closest data points in a high-dimensional space. Instead of exact matches, ANN provides close approximations, which is sufficient for many applications and significantly speeds up the search process. In the video, ANN is mentioned as a critical technology for improving the performance of vector search, allowing for fast and scalable search across millions of embeddings, which is essential for services like Google Search and YouTube.

💡ScaNN

ScaNN is an ANN algorithm developed by Google Research that is considered one of the best in the industry for search and recommendation. It is used as the foundation for major Google services, providing a highly efficient and scalable way to perform vector search. ScaNN allows for the organization of the world's information in a way that is accessible and useful by enabling fast and accurate vector search across billions of embeddings, which is crucial for user experience and business operations.

💡Google Cloud Vertex AI

Google Cloud Vertex AI is a suite of AI services and tools provided by Google Cloud that enables developers to build, deploy, and manage machine learning models. It offers various capabilities, including Vertex AI Search, which is a fully-managed vector search service. This service allows users to add embeddings to an index and perform fast vector searches, providing a simple and efficient way for businesses to leverage the power of AI for search and recommendation tasks, enhancing user experience and driving business value.

💡BigQuery

BigQuery is a serverless data warehouse solution provided by Google Cloud that enables scalable analysis over petabytes of data. It allows users to run SQL queries and extract insights from large datasets efficiently. In the video, BigQuery is used to demonstrate how easy it is to get embeddings for text, using the ML.EMBED_TEXT function, which is then used in vector search for semantic search capabilities, enhancing the user experience by providing relevant and meaningful search results.

💡Semantic Search

Semantic search is a type of search technology that focuses on understanding the meaning and intent behind a user's query, rather than just matching keywords. It uses AI and natural language processing to provide more relevant and contextually appropriate results. In the video, semantic search is enabled by vector search, which understands the meaning of item names or descriptions through embeddings, allowing for a more intuitive and user-friendly search experience that goes beyond traditional keyword-based searches.

💡Mercari

Mercari is a popular marketplace app that allows users to buy and sell various items. In the context of the video, Mercari uses vector search technology to provide product recommendations to its users. By leveraging the power of AI and semantic search, Mercari can offer a more personalized and engaging shopping experience, making it easier for users to find items they are interested in.

Highlights

Vector search is becoming increasingly important for businesses due to its role in AI/ML services.

Google utilizes vector search to organize data across its services like Search, YouTube, and Google Play.

Vector search can be applied to various types of content including products, users, conversations, and IoT signals.

Generative AI enhances vector search by enabling multimodal searches, such as finding products based on images rather than text.

Traditional databases use structured data, whereas vector search relies on embeddings, a special type of vector.

Embeddings are high-dimensional representations that capture the meaning of content, organizing similar content closely together.

Google's ScaNN algorithm is a leading industry solution for fast and scalable vector search.

Vertex AI Search, powered by ScaNN, is a fully-managed service for fast vector searches with a simple query.

Vector search enables a new level of user experience by finding items by their meanings in milliseconds.

Google Cloud's integration with services like BigQuery and Feature Store simplifies the MLOps pipeline for data extraction and embedding search.

Enterprises can easily use vector search to show similar items by creating an index and querying it with item names.

Semantic search with vector search provides a better user experience by understanding the meaning of item names.

Mercari, a popular marketplace app, successfully implemented vector search technology for product recommendations.

Vector search is a foundational technology for creating modern, user-centric IT systems.

Google Cloud Vertex AI offers a platform for businesses to leverage vector search and improve their services.

Vector search services are essential for businesses to meet the new standard of user experience.