Get Started with Vector Search using Vertex AI

Google Cloud Tech
1 Nov 202309:22

TLDRThis video introduces vector search, highlighting its importance in AI/ML services and its applications in businesses for various types of content, such as products and user activities. It explains the concept of embeddings and how they differ from traditional databases, offering a more nuanced way to organize and search for content. The video showcases Google's ScaNN algorithm and its role in enabling fast, scalable vector search, and outlines how Google Cloud Vertex AI Search allows developers to leverage this technology for building production-quality vector search services with ease and efficiency.

Takeaways

  • 🔍 Vector search is an essential AI/ML technology used in various services like Google Search and YouTube.
  • 📈 It is becoming increasingly important for businesses to adopt vector search for search and recommendation systems.
  • 🌐 Google uses vector search to organize data and provide relevant search results and recommendations to users.
  • 🛠️ Vector search relies on 'embeddings', a data structure representing the meaning of content in a multi-dimensional space.
  • 📊 Traditional databases use structured data, whereas vector search uses embeddings to capture subtle differences in content.
  • 🚀 In the age of generative AI, vector search plays a crucial role in multimodal search, like finding products by analyzing images.
  • 🌟 Google's ScaNN algorithm is a highly efficient and scalable solution for vector search, foundational to many Google services.
  • 🛠️ Google Cloud Vertex AI Search allows developers to leverage ScaNN for building production-ready vector search services.
  • 🔧 It integrates with Google Cloud Services for a seamless MLOps pipeline from data extraction to embedding search.
  • 🏢 Enterprises can use vector search to provide semantic search and significantly enhance user experience for item exploration.
  • 🌐 Mercari, a marketplace app, successfully implemented vector search technology to improve product recommendations.

Q & A

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

    -Vector search is a technology that uses embeddings, which are vectors representing the meaning of content, to quickly find and recommend relevant items based on their semantic similarity rather than just keywords. It's important for businesses because it enhances user experience by providing more relevant search results and recommendations, thus becoming a crucial component in AI/ML services.

  • 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 allows Google to provide search results and recommendations that are more relevant to users by understanding the semantic meaning behind the content.

  • 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 high-dimensional vectors that capture the semantic meaning of content, allowing for more nuanced and accurate search and recommendation.

  • How do embeddings work in vector search?

    -Embeddings in vector search are created by AI after training with specific content like text or images. They represent the content's meaning in a high-dimensional space, allowing AI to identify the location of each content and sort them into categories based on their semantic similarity.

  • 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 calculating the distance between embeddings and selecting the closer ones. It uses vector quantization to divide the embedding space into a tree structure, enabling 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 for major Google services like Google Search and YouTube, enabling them to provide fast and accurate search results and recommendations.

  • How does Google Cloud Vertex AI Search make use of ScaNN?

    -Google Cloud Vertex AI Search is a fully-managed service that leverages Google's ScaNN-based technology to allow developers to easily add embeddings to an index and perform fast vector searches with a simple query. This service offers high performance, scalability, and reliability, making it suitable for enterprise-level applications.

  • What are the steps to build a production service using vector search in an enterprise?

    -To build a production service using vector search, one must first obtain embeddings for each item, create an index on Vector Search with the embeddings, and then deploy the index on an index endpoint. This endpoint will receive queries and execute the vector search to return relevant results, such as similar items based on their meanings.

  • How does vector search enhance the user experience in comparison to keyword-based search?

    -Vector search provides a semantic search experience by understanding the meaning behind the content rather than just matching keywords. This results in more accurate and relevant recommendations, leading to a better user experience as users can find items that truly match their interests and needs.

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

    -Mercari, a popular marketplace app, is an example of a company that has successfully implemented vector search technology. They use it to show product recommendations and 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, highlighting 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 aids in providing relevant search results and recommendations. The explanation extends to the concept of multimodal search, which is made possible by combining vector search with generative AI models, allowing for searches based on item images rather than just text. The paragraph also contrasts traditional databases with vector search technology, emphasizing the use of 'embeddings' in AI-based services to capture the essence of content through a mapped space, as opposed to the structured data approach of traditional IT systems.

05:00

🚀 Google's ScaNN and its Role in Vector Search

This paragraph delves into the specifics of Google's ScaNN algorithm, which is considered a leading solution in the industry for vector search. It underscores ScaNN's foundational role in major Google services, such as Google Search and YouTube, and its contribution to Google's mission of organizing the world's information. The paragraph then transitions to discuss how Google Cloud developers can leverage ScaNN-based web search technology through Vertex AI Search, a fully-managed service that simplifies the process of adding embeddings to an index and performing fast vector searches. The summary also touches on the integration of Vertex AI Search with other Google Cloud Services, which facilitates the construction and operation of a production MLOps pipeline. Lastly, it provides a practical example of how vector search can be used in enterprises to recommend similar items, outlining the steps from obtaining embeddings to deploying the index and executing searches.

Mindmap

Keywords

💡Vector Search

Vector search is a technology that utilizes vector representations, known as embeddings, to find items or content that are similar in meaning or context. Unlike traditional keyword-based search, vector search can understand the semantic meaning behind words or images, allowing for more accurate and relevant results. In the video, it's highlighted as an essential component in AI/ML services, used by Google to organize data across its various services like Search, YouTube, and Google Play, providing users with meaningful search results and recommendations.

💡Embeddings

Embeddings are numerical representations of words, phrases, images, or any content that capture their semantic meaning in a multi-dimensional space. These vectors allow AI systems to identify and organize content based on its contextual relevance rather than just its explicit keywords. In the context of the video, embeddings are fundamental to how vector search works, as they enable the AI to map and locate content with similar meanings closely together, facilitating quick and accurate searching.

💡AI/ML Services

AI/ML Services refer to applications and platforms that leverage Artificial Intelligence (AI) and Machine Learning (ML) to perform tasks, analyze data, and provide insights. These services can range from simple chatbots to complex recommendation systems like those used by Google. In the video, vector search is presented as one of the most essential components of AI/ML services, underpinning the ability to deliver relevant search results and content recommendations.

💡Generative AI

Generative AI refers to AI systems that can create new content, such as images, text, or music, based on patterns learned from existing data. It is a subset of AI that focuses on the creation of novel items rather than just analyzing or categorizing existing content. In the video, generative AI is mentioned as playing an increasingly crucial role in the age of AI, particularly in combination with vector search to enable multimodal search capabilities that go beyond traditional keyword-based searches.

💡Multimodal Search

Multimodal search is a type of search technology that can handle and process different types of data inputs, such as text, images, and audio, to provide more comprehensive search results. This approach allows for a richer understanding of the query and the content, as it can analyze multiple aspects of the data simultaneously. In the video, multimodal search is made possible through the combination of vector search and generative AI models, enabling the search for items based on both their visual appearance and other relevant attributes.

💡Approximate Nearest Neighbor (ANN)

Approximate Nearest Neighbor (ANN) is a technique used in computer science and machine learning to quickly find the closest data points in a high-dimensional space. It is particularly useful for vector search as it allows for fast and efficient searching among millions of embeddings without the need for exact calculations. ANN algorithms, like Google's ScaNN, use vector quantization to divide the embedding space into a tree structure, enabling rapid and scalable search across billions of embeddings.

💡ScaNN

ScaNN, developed by Google Research, is an Approximate Nearest Neighbor (ANN) algorithm that is highly optimized for speed and accuracy in vector search. It is considered one of the best in the industry and forms the backbone of search and recommendation systems in many Google services. ScaNN uses a tree structure to divide the embedding space, allowing for efficient searching of similar embeddings even among vast datasets.

💡Google Cloud Vertex AI

Google Cloud Vertex AI is a fully-managed service provided by Google Cloud that enables developers to build and deploy AI models with ease. It offers various tools and services for data extraction, embedding generation, and vector search. Vertex AI Search, a part of Vertex AI, allows users to add embeddings to an index and perform fast vector searches, leveraging the power of ScaNN. This service is designed to be scalable and reliable, making it suitable for enterprise-level applications.

💡BigQuery

Google BigQuery is a serverless data warehouse solution that allows users to analyze and process large datasets in the cloud. It integrates with various Google Cloud services and can be used to extract data, generate embeddings, and perform queries to support AI-driven applications. In the video, BigQuery is used to obtain embeddings for text, showcasing its role in the MLOps pipeline for data extraction and preparation.

💡MLOps

MLOps, a portmanteau of Machine Learning and Operations, is a set of practices and tools that facilitate the deployment, monitoring, and maintenance of machine learning models in production environments. It aims to streamline the transition from model development to model operations, ensuring that models can be efficiently and reliably used in real-world applications. In the video, the process of building and operating a production MLOps pipeline for data extraction, embedding generation, and embedding search is discussed, highlighting the role of Google Cloud services in this process.

💡Semantic Search

Semantic search is a type of search technology that goes beyond keyword matching to understand the actual meaning and context of a query. It uses natural language processing and machine learning to deliver more relevant and accurate results by comprehending the intent behind the search. In the video, semantic search is exemplified by the ability of vector search to find items based on the meaning of their names, rather than just matching keywords, which leads to a significantly improved user experience.

Highlights

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

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

Vector search can be applied to various types of content including products, users, conversations, music, videos, and IoT sensor data.

Generative AI and vector search can be combined for multimodal searches, such as finding products based on images rather than text.

Traditional databases use structured data, while AI organizes data using embeddings, a special type of vector.

Embedding space is a map of content's meaning, where AI can identify the location of each content.

Embeddings can have hundreds or thousands of dimensions to capture subtle differences in content.

Google's ScaNN algorithm is a highly efficient and scalable approach for vector search.

Google Cloud Vertex AI Search is a fully-managed service that leverages ScaNN for fast vector searches.

Vector search enables a new level of user experience by understanding the meaning behind content.

Google Cloud's integration with services like BigQuery and Vertex AI Embeddings API simplifies the MLOps pipeline.

Vector search can be used to show similar items to users based on semantic search rather than just keywords.

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

Vector search is essential for creating production-ready services and enhancing user experience.

Google's infrastructure for vector search underpins major services and is now accessible to developers through Vertex AI.

Using vector search, items can be found by their meanings in milliseconds, transforming human-computer interaction.

Vector search services are crucial for businesses to stay competitive in the age of AI and data-driven operations.

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