History Reimagined: Library Of Congress

The Arizona State University Artificial Intelligence Cloud Innovation Center (ASU AI CIC), powered by Amazon Web Services (AWS), collaborated with the Library of Congress (LOC) on an experiment for an innovative AI-driven solution that transforms public access to foundational U.S. historical documents. This initiative aims to expand access with new approaches for users to search, interpret, and engage with complex materials like the U.S. Constitution by enabling natural language interaction and providing deep, contextual understanding grounded in the Library’s vast collections.

The Library of Congress holds over 200 petabytes of historical and cultural data, yet making this information engaging for a broad audience presents a significant challenge. Individuals seeking to understand documents like the U.S. Constitution often rely on traditional boolean search tools or unverified internet sources, which can lead to fragmented and decontextualized information. The existing Constitution Annotated website, while comprehensive and accessible, can benefit from additional AI-powered search capabilities to help users explore the rich historical context surrounding the nation's founding documents in new ways. A solution that provides accurate, user-friendly, and engaging responses through natural language would enhance both the experience and understanding of these important U.S. historical documents.

The ASU AI CIC team designed an AI-powered solution that enables users to explore U.S. historical documents from 1760–1820 through an intuitive, persona-driven interface. The prototype allows users to select a role—such as a Law Student, Policy Analyst, or Research Journalist—and receive answers tailored to their specific needs and level of expertise.

The solution leverages a powerful combination of AWS services to deliver historically accurate, context-aware responses grounded in primary source documents:

• GraphRAG Architecture: The core of the solution is a GraphRAG (Retrieval-Augmented Generation) system built on AWS Bedrock Knowledge Base. This combines the semantic search capabilities of Amazon OpenSearch Serverless with the contextual relationship mapping of Amazon Neptune Analytics. This allows the system to not only find relevant text but also understand the connections between different historical documents, events, and figures.
• Persona-Driven AI Responses: User queries are enhanced by Amazon Bedrock's Claude 3.5 Sonnet, which analyzes conversation history and persona instructions to generate tailored, accurate responses. The system is designed to use only the information from the retrieved documents, preventing AI hallucinations and ensuring historical integrity.
• Scalable Data Ingestion: The architecture uses Amazon ECS on AWS Fargate to run containerized data collection tasks, pulling information from sources like Congress.gov and the Chronicling America newspaper archives. These documents are processed and stored in Amazon S3, with specialized AWS Lambda functions managing the data transformation and synchronization with the knowledge base.
• Modern User Interface: The frontend, built with Next.js and hosted on AWS Amplify, provides a responsive and interactive chat experience. It features a clean design inspired by the America 250 initiative, with clear source citations and a user-friendly layout that encourages exploration.

This project provides a powerful new model for how cultural heritage institutions can use AI to democratize access to complex historical collections. By enabling natural language search and persona-based responses, the solution makes dense constitutional and legislative information accessible to a wider audience, from casual learners to expert researchers.

The GraphRAG architecture demonstrates how to move beyond simple keyword searching to deliver deeper, more contextual insights. For the Library of Congress, this creates a pathway to unlock the full value of its digital archives, fostering a more profound public understanding of American history. It also streamlines the research process for professionals like congressional staffers and journalists, who can now receive accurate, verified information with full source attribution in seconds.

The creativity and inquiry that the students brought to this experiment was priceless. Legislative data, especially historical legislation, is very dense and hard to interpret. They did a deep dive into our collections and found the connections to build context. The ASU CIC team was incredibly knowledgeable and eager to solve problems, they even invited a Law School student to conduct quality reviews of the outcomes. As AI uses matures in real-world applications, it is exciting to see this very high level of expertise coming out of schools like Arizona State University.  

Natalie Buda Smith, Director of Digital Strategy, Library of Congress

The architecture designed for the Library of Congress is highly adaptable and can be applied across numerous sectors that rely on deep archival research and knowledge discovery. This approach provides new horizons for discovery that supercharges the work of subject matter experts and researchers. 

• Legal and Academic Research: Law firms and universities can use this framework to build intelligent search tools for case law, scholarly articles, and other extensive document repositories.
• Government and Policy: Federal agencies can deploy similar solutions to help policy analysts and the public navigate complex regulatory and legislative databases.
• Medical and Scientific Research: The GraphRAG model can be adapted to sift through vast libraries of scientific papers and clinical trial data, identifying connections and accelerating discovery.
• Corporate Knowledge Management: Large enterprises can use this architecture to create internal chatbots that provide employees with contextual answers from technical manuals, HR policies, and project documentation.