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I built this after noticing that existing tools for comparing constitutional law either have steep learning curves or only support keyword search. By combining Gemini embeddings with UMAP projection, you can navigate 30,828 constitutional articles from 188 countries in 3D and find conceptually related provisions even when the wording differs. Feedback welcome, especially from legal researchers or comparative law folks.
Source and pipeline: github.com/joaoli13/constitutional-map-ai
Show HN: Semantic atlas of 188 constitutions in 3D (30k articles, embeddings)
A tool for comparing constitutional law that overcomes the limitations of existing tools (steep learning curves, keyword-only search) by enabling navigation of conceptually related provisions across 30,828 articles from 188 countries using Gemini embeddings and UMAP projection.
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AI Executive Synthesis
A tool for comparing constitutional law that overcomes the limitations of existing tools (steep learning curves, keyword-only search) by enabling navigation of conceptually related provisions across 30,828 articles from 188 countries using Gemini embeddings and UMAP projection.
This project addresses a significant pain point in legal research: the complexity and inefficiency of comparing constitutional law across jurisdictions. By leveraging Gemini embeddings and UMAP projection, it transforms a traditionally text-heavy, keyword-dependent process into a navigable 3D semantic space. This capability to identify conceptually related provisions, irrespective of exact wording, represents a substantial leap beyond conventional search tools. The market implication is a potential for increased efficiency and accuracy in comparative legal analysis, impacting legal tech, academic research, and policy development. This demonstrates the power of advanced natural language processing and dimensionality reduction techniques to unlock insights from vast, unstructured datasets, creating specialized tools that cater to niche, high-value professional domains.
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