Reference implementations and example code for the BigHugeBrain (BHB) document analysis API by Correlation Concepts.
BHB is a neural correlation engine that transforms unstructured text into structured JSON "DNA" — rich graphs of identities, sentiments, topics, and entity relationships. This repository provides working examples in multiple languages to help you get started.
bhb-examples/
├── react/ # Next.js interactive visualization dashboard
├── python/ # Python scripts for analysis & visualization
├── LICENSE
└── README.md
A full Next.js application with D3.js and Recharts visualizations including entity networks, correlation matrices, sentiment timelines, topic clusters, and more.
cd react
npm install
npm run devSee the React README for full documentation.
Standalone Python scripts demonstrating how to authenticate, analyze documents, and generate visualizations using matplotlib, networkx, and seaborn.
cd python
pip install -r requirements.txt
python examples/analyze_document.pySee the Python README for full documentation.
BHB (BigHugeBrain) is a document analysis API that performs:
- Identity Resolution — Enterprise-grade named entity recognition for people, organizations, places, and things
- Correlation Mapping — Entity co-occurrence analysis across document segments
- Sentiment Chronology — Emotional flow tracking across the document timeline
- Topological Clustering — Semantic concept grouping and theme extraction
- Linguistic Profiling — Token-level analysis of plurality, gender, possessiveness, and more
The API returns structured JSON that can be ingested into graph databases, used for predictive modeling, or rendered as interactive visualizations.
To use the BHB API, you need OAuth2 client credentials (Client ID + Client Secret). Visit docs.proggor.com for details on obtaining access.
- Interactive Docs: docs.proggor.com
- API Playground: docs.proggor.com/playground