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.
