digitai

an interactive AI system developed to equip a small, localized AI model with access to XML resources through Retrieval-Augmented Generation (RAG) resource to be queried with XML-aware tools (XPath, XQuery, XSLT). Agentic access to XML-aware queries to be developed with the Model Context Protocol (MCP).

🛠 Current Developers

Alexander C. Fisher

Role: Project Lead, Pipeline Developer, and Literature Review Co-Lead
Affiliation: DIGIT Major @ Penn State Behrend
GitHub: @afish2003

• Designed and built the full Python pipeline: embeddings, FAISS indexing, Neo4j integration, and RAG prompting
• Leads configuration design, system architecture, and interface logic
• Will lead the upcoming fine-tuning phase to improve LLM performance
• Co-leads the literature review, analyzing scholarly sources to inform system design

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Hadleigh Jae Bills

Role: Data Pipeline Lead and Graph Architect
Affiliation: DIGIT Major @ Penn State Behrend
GitHub: @HadleighJae

• Prepares and structures TEI-derived JSON for both vector and graph pipelines
• Builds and maintains the full Neo4j graph with custom Cypher logic
• Designs the data model the pipeline relies on and supports structural debugging
• Leads research on TEI schema logic and contributes key sources for literature review

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Michael Simons

Role: Battle-Tester and Documentation
Affiliation: DIGIT Major @ Penn State Behrend
GitHub: @mrs7068

• Recent addition to the project as an additional battle-tester
• Maintains project documentation

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Dr. Elisa Beshero-Bondar

Role: Faculty Advisor, XSLT Architect, and Research Lead
Affiliation: Faculty @ Penn State Behrend
GitHub: @ebeshero

• Authored the XSLT transformation for converting TEI P5 XML into structured JSON
• Provides core expertise in TEI, digital editing, and scholarly infrastructure
• Leads the literature review and guides the team’s research direction

This project is part of an ongoing digital humanities research initiative at Penn State Behrend. Please cite responsibly.

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Dependencies

During active development, requirements.txt lists required packages without pinned versions. This is intentional.

Exact version pinning will be added once the inference stack stabilizes.