The text discusses integrating Amazon Comprehend and Amazon Kendra to enrich enterprise search capabilities. Structured and unstructured data are rapidly growing, and using custom metadata helps categorize information. Amazon Comprehend can identify document types and entities, which Amazon Kendra then uses to filter search results, including facets for better searching. The solution is particularly applied to insurance claim processing, where it classifies documents and extracts entities to improve search and routing. The process involves training Comprehend models, creating endpoints, deploying Lambda functions, and ingesting data into Kendra for enhanced searching. Security and IAM concerns are also addressed, and users are advised to implement the solution in non-production before production.
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Data Growth and Custom Metadata
Organizations are dealing with a surge in both structured and unstructured data. The amount of data is expected to grow tenfold by 2025, making it essential to categorize and search through this information efficiently. Custom metadata, such as document types and various entity types, can be added to extend intelligent search capabilities.
Amazon Comprehend & Amazon Kendra
Amazon Comprehend uses natural language processing to extract insights from documents and create custom metadata. This metadata can then be ingested into Amazon Kendra, a machine learning-powered enterprise search service, to enrich content for better filtering and search results.
Practical Solutions for Insurance Companies
Insurance companies can process claims more effectively by using custom content enrichment. By adding custom entities and classes specific to their business domain, they can filter content based on these custom parameters.
Solution Overview
The proposed solution includes classifying insurance claims and retrieving specific entities. This process routes documents to the right department and enhances search capabilities.
Steps to Implement the Solution
- Train Amazon Comprehend with custom classification and entity recognition models.
- Create and deploy a Lambda function for post-extraction enrichment.
- Populate the Amazon Kendra index with enriched data.
- Use the enriched metadata to filter searches in Amazon Kendra.
Data Security and IAM
Following the least privilege principle, the solution prioritizes security by restricting permissions to necessary services and features.
Training and Deployment
Train your custom models using labeled data and deploy them for real-time classification or asynchronous batch jobs.
Post Extraction Enrichment
A Lambda function processes text extracted by Amazon Kendra, invoking Amazon Comprehend to detect custom entities and classify documents, updating the metadata for search facets.
Creating the Amazon Kendra Index
After creating the index and data source, you can ingest data and filter searches based on custom metadata fields.
Filtering Searches in Kendra
With the index populated, users can perform searches and filter results based on entities and classifications identified by Amazon Comprehend.
Clean Up
To avoid unwanted charges, delete the provisioned infrastructure after experimentation.
Conclusion
Amazon Comprehend and Amazon Kendra can significantly enhance search capabilities for structured/unstructured data, making it a valuable asset for various use cases.
About the Authors
The authors are experts in AI/ML at Amazon Web Services, providing insights into generative AI, large language models, and prompt engineering.
Takeaway for Middle Managers
Stay ahead of the curve by leveraging AI to intelligently process data and enhance search capabilities. Implement AI solutions like Amazon Comprehend and Amazon Kendra to streamline operations and improve data management.
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