包阅导读总结
1. `Vertex AI、Search Engine、GKE、BigQuery、Agent Builder`
2. 本文主要介绍了利用 GKE、Vertex AI 等工具构建搜索引擎的相关内容，包括 Vertex AI 相关工具的作用、搜索功能特点、优势及后续步骤等。
3.
– 利用 Vertex AI 的 Agent Builder 和 Search 构建 AI 应用和搜索体验
– Agent Builder 简化集成过程，提供多种集成选项
– Vertex AI Search 用于构建网站搜索，支持不同数据类型，具备多种高级搜索能力
– 结合 BigQuery 构建搜索索引，可通过自然语言查询快速获取信息，并可利用 Agent Builder 定制搜索体验
– 考虑因素和后续步骤
– 系统架构可扩展，能处理多 RSS feeds 和大量数据
– 谷歌云托管服务简化管理和维护
– 微服务提高灵活性
– Vertex AI Search 支持高级搜索功能
– 可用于构建内部知识库等多种场景，提供综合构建指南和 github 资源
思维导图：
文章来源：cloud.google.com
作者：Krishna Chytanya Ayyagari,Samuel Andersen
发布时间：2024/7/23 0:00
语言：英文
总字数：1306字
预计阅读时间：6分钟
评分：81分
标签：搜索引擎,Google Cloud,GKE,Vertex AI,BigQuery
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Vertex AI Agent Builder and Vertex AI Search are two powerful tools that allow developers to easily create and deploy AI agents and applications. Agent Builder simplifies the process of integrating AI agents or apps with enterprise data, offering a range of options for seamless integration. Vertex AI Search, a part of Agent Builder, helps developers build Google-quality search experiences for websites, structured and unstructured data. It also provides an out-of-the-box grounding system and DIY grounding APIs for building generative AI agents and apps. By indexing data from various sources, including BigQuery, Vertex AI Search enables users to quickly find relevant information through natural language queries. Data stored in BigQuery can be used to create a search index using Vertex AI Search, and Agent Builder can be utilized to customize the search experience or integrate it with other Vertex AI features. Follow this documentation to create a generic “Search” experience with BigQuery as the data store.
The search index allows users to search through the extracted article content using keywords or phrases. Vertex AI Search provides advanced search capabilities, such as natural language processing, ranking, and relevance scoring. Throughout the entire process, logs are generated to capture information about events, errors, or performance. This is crucial for monitoring, debugging, and optimizing the system’s operation.
Additional considerations and next steps
This blog post presents a detailed guide on constructing a low-code search engine by leveraging the combined capabilities of GKE, Cloud Scheduler, BigQuery and vector search:
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Designed for scalability, the architecture handles multiple RSS feeds and large volumes of data.
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Google Cloud managed services simplify infrastructure management and maintenance.
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The use of microservices promotes modularity and flexibility for future enhancements or changes.
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Vertex AI Search provides a powerful foundation for implementing sophisticated search features.
This resulting search engine efficiently searches through RSS feeds and delivers relevant results, making it a valuable tool for users seeking specific information from various sources. For example, you could use it to construct internal knowledge bases, monitor evolving news and trends, or create customized search engines that meet specific requirements such as newsletters.
In this post, we offer a comprehensive guide to building a custom low-code search engine on Google Cloud, using BigQuery, Vertex AI Agent Builder, and Vertex AI Search. Take the chance to create a search engine that fits your needs precisely using our Google Cloud Generative AI github repository.
