Nexus Graph
A tool that indexes academic papers and extracts their structure — authors, institutions, methodology, and materials — using an LLM.
The problem
Working through a literature review by hand is slow: cross-referencing authors and affiliations, and pulling out methodology and materials from each paper, is repetitive work that a language model can help structure.
Why it mattered
Turns a pile of PDFs into something you can actually query — who wrote what, with whom, using which methods and materials.
Topics
Built with
Architecture
The pipeline pulls in papers, then runs them through an LLM-driven extraction step that pulls out authors, institutional affiliations, methodology, and any materials or tools referenced. The results are cross-referenced into an indexed database so a paper, an author, or an institution can each be looked up on its own.
Engineering
The system architecture, indexing pipeline, and organisational modules are built in Python, with the OpenAI API doing the extraction work — turning unstructured paper text into structured records rather than leaving it as prose to be read one paper at a time.
Results
The tool works end to end: it categorises papers by field and topic, builds cross-referenced author/institution records, and structures methodology and materials information per paper. Precise before/after numbers aren’t published here yet — ask if you want the detail.
Lessons
Extraction quality depends heavily on how a paper is structured — papers with non-standard formatting or scanned figures need more handling than the straightforward cases. Widening database coverage and improving the extraction prompts are the two clearest next steps.