license	apache-2.0
datasets	issdandavis/scbe-aethermoore-knowledge-base
language	en
tags	embeddings hyperbolic-geometry poincare-ball 21-dimensional ai-safety

PHDM 21D Embedding Model

Custom embedding model for the SCBE-AETHERMOORE framework. Maps text inputs into a 21-dimensional Poincare Ball manifold for hyperbolic AI safety governance.

Architecture

• Embedding Dimension: 21D (6D hyperbolic + 6D phase + 3D flux + 6D audit)
• Geometry: Poincare Ball B^n with Harmonic Wall containment
• Polyhedral Lattice: 16 cognitive polyhedra (5 Platonic + 3 Archimedean + 2 Kepler-Poinsot + 2 Toroidal + 4 Johnson/Rhombic)
• Neurotransmitter Weights: Six Sacred Tongues (KO=1.0, AV=1.62, RU=2.62, CA=4.24, UM=6.85, DR=11.09)

Training Data

• Notion knowledge base exports (SCBE technical docs, PHDM specs)
• Perplexity interaction logs (filtered via GeoSeal privacy layer)
• Sacred Tongue tokenized corpora

Deployment

• GCP: Vertex AI Model Registry + GKE Autopilot (test-scbecluser)
• AWS: Lambda functions for intent classification
• HuggingFace: Model weights and inference API

Usage

from phdm_embedding import PHDMEmbedder

embedder = PHDMEmbedder.from_pretrained("issdandavis/phdm-21d-embedding")
vector = embedder.encode("Book a flight from SFO to NYC")
# Returns: 21D numpy array in Poincare Ball coordinates

Dataset Setup (PowerShell)

Use this repo as a working directory for Hugging Face datasets:

cd C:\Users\issda\hf-repos\phdm-21d-embedding
python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install -r requirements-datasets.txt

Set your token in the current shell session:

$env:HF_TOKEN="hf_your_token_here"

Load and preview a dataset split:

python scripts/load_hf_dataset.py --dataset-id issdandavis/scbe-aethermoore-knowledge-base --split train --limit 3

Push local JSONL files to a dataset repo:

python scripts/push_jsonl_dataset.py --dataset-id issdandavis/scbe-aethermoore-knowledge-base --train .\data\train.jsonl --validation .\data\validation.jsonl

Convert Perplexity/Markdown exports into JSONL splits:

python scripts/markdown_to_jsonl.py --input-dir C:\path\to\perplexity-export --output-dir .\data --train-ratio 0.9 --validation-ratio 0.1

One-shot flow (convert then push):

python scripts/markdown_to_jsonl.py --input-dir C:\path\to\perplexity-export --output-dir .\data
python scripts/push_jsonl_dataset.py --dataset-id issdandavis/your-central-knowledge-base --train .\data\train.jsonl --validation .\data\validation.jsonl --test .\data\test.jsonl

Expected JSONL row format example:

{"id":"6e4fcd3f34f5b021","source":"perplexity_space_export","space":"SCBE GitHub Deployment","relative_path":"SCBE GitHub Deployment/notes.md","title":"Deployment Notes","text":"Example source content","meta":{"author":"issdandavis"}}

Related

• SCBE-AETHERMOORE GitHub
• Knowledge Base Dataset
• Interaction Logs Dataset