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Rainvare/organizacional-knowledge-harness

sourceHugging Faceupdated 6mo agoView on Hugging Face

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App README

Organizational Knowledge Harness

A living context engine that learns from use. EOF

OKH

A living context engine that learns from use.

Architecture

Built on Harness Engineering (Böckeler, Thoughtworks 2026):

Harness layer	Implementation
Context engineering	Extraction Agent + Graph Engine
Architectural constraints	NM_graph + Coherence Analyzer
Garbage collection	Evidence Accumulator + Proposal Queue

Sprints delivered

Sprint	What it does
1	Ingesta → Grafo → Generación con trace de nodos
2	Coherence Analyzer + Evidence Accumulator + Proposal Queue
3	Input externo — aprende de outputs de otras IAs, diferencia fuente
4	Exportación en 4 formatos: prompt, markdown, JSON, CSV
5	Input multimodal: PDF, PPTX, DOCX, imágenes, URL

Stack

Component	Technology
Frontend + Backend	Streamlit
LLM	Gemini Flash (free tier)
Graph persistence	JSON + git versioning
Hosting	Hugging Face Spaces

Setup

bash

git clone https://github.com/your-username/organizational-knowledge-harness
cd organizational-knowledge-harness
pip install -r requirements.txt

# Optional: multimodal parsers
pip install PyMuPDF python-pptx python-docx

export GEMINI_API_KEY=your_key_here
streamlit run frontend/app.py

Theoretical basis

—Harness Engineering — Böckeler (Thoughtworks, Feb 2026)
—NM_graph — adapted from Muñoz Number / UFAL (DOI: 10.5281/zenodo.18653104)
—AI as reasoning partner — Knuth / Claude's Cycles (Stanford, Feb 2026)
—Multimodal input — MANGO (NeurIPS 2025): per-modality preprocessing prevents signal dominance

Portfolio context

context-graph-engine              → concept demo
context-curator                   → ingestion + extraction
organizational-knowledge-harness  → full harness platform

R. Indira Valentina Réquiz