Implement web crawling in Knowledge Bases for Amazon Bedrock

Amazon Bedrock is a fully managed service that offers access to a variety of high-performing foundation models (FMs) from leading AI companies via a single API. It provides capabilities to build secure, private, and responsible AI applications. Users can experiment with and customize FMs using their enterprise data and build agents for tasks utilizing their systems and data sources. Knowledge Bases for Amazon Bedrock allows aggregation of data sources into a comprehensive repository, facilitating applications that use Retrieval Augmented Generation (RAG). Customers can extend the capability to crawl and index their public-facing websites by integrating web crawlers into the knowledge base, enhancing the accuracy and relevance of AI applications. The web crawler fetches data from provided URLs, traversing child links within the same primary domain. It supports various file types like PDFs and CSVs but adheres to robots.txt directives and set crawling boundaries. Different sync scopes control the inclusion of webpages, such as Default, Host only, and Subdomains, each defining specific paths for the crawler. Filters using regex can refine the scope further, excluding or including URLs based on set patterns. For example, excluding URLs ending in .pdf or including URLs containing "products". To create a knowledge base with a web crawler, users can follow a step-by-step process on the Amazon Bedrock console, specifying configurations like source URLs, sync scope, and inclusion/exclusion patterns. They can select embedding models and vector databases, using the Quick create option for Amazon OpenSearch Serverless vector search collections. Testing the knowledge base involves syncing the data source and querying the model with specific prompts. Citations in responses link to source webpages, ensuring response accuracy. The setup can also be done programmatically using the AWS SDK for Python (Boto3), specifying embedding models and web crawler configurations. Monitoring the web crawl status is possible through Amazon CloudWatch logs, which report URLs being visited. To clean up resources, users need to delete the knowledge base, vector database, and IAM service role. Amazon Bedrock enhances generative AI applications by incorporating diverse, up-to-date web data efficiently.