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README.md

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+---
+license: mit
+language:
+- en
+tags:
+- knowledge-graph
+- causal-inference
+- rag
+- zero-hallucination
+- triplets
+pretty_name: dotcausal Dataset Loader
+size_categories:
+- n<1K
+---
+
+# dotcausal - HuggingFace Dataset Loader
+
+Load `.causal` binary knowledge graph files as HuggingFace Datasets.
+
+## What is .causal?
+
+The `.causal` format is a binary knowledge graph with **embedded deterministic inference**. It solves the fundamental problem of AI-assisted discovery: **LLMs hallucinate, databases don't reason**.
+
+| Technology | What it does | What's missing |
+|------------|--------------|----------------|
+| **SQLite** | Stores facts | No reasoning |
+| **Vector RAG** | Finds similar text | No logic |
+| **LLMs** | Reasons creatively | Hallucination risk |
+| **.causal** | Stores + Reasons | **Zero hallucination** |
+
+### Key Features
+
+- **30-40x faster queries** than SQLite
+- **50-200% fact amplification** through transitive chains
+- **Zero hallucination** - pure deterministic logic
+- **Full provenance** - trace every inference
+
+## Installation
+
+```bash
+pip install datasets dotcausal
+```
+
+## Usage
+
+### Load from local .causal file
+
+```python
+from datasets import load_dataset
+
+# Load your .causal file
+ds = load_dataset("chkmie/dotcausal", data_files="knowledge.causal")
+
+print(ds["train"][0])
+# {'trigger': 'SARS-CoV-2', 'mechanism': 'damages', 'outcome': 'mitochondria',
+#  'confidence': 0.9, 'is_inferred': False, 'source': 'paper_A.pdf', 'provenance': []}
+```
+
+### With configuration
+
+```python
+# Only explicit triplets (no inferred)
+ds = load_dataset(
+    "chkmie/dotcausal",
+    "explicit_only",
+    data_files="knowledge.causal",
+)
+
+# High confidence only (>= 0.8)
+ds = load_dataset(
+    "chkmie/dotcausal",
+    "high_confidence",
+    data_files="knowledge.causal",
+)
+```
+
+### Multiple files / splits
+
+```python
+ds = load_dataset(
+    "chkmie/dotcausal",
+    data_files={
+        "train": "train_knowledge.causal",
+        "test": "test_knowledge.causal",
+    },
+)
+```
+
+## Dataset Schema
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `trigger` | string | The cause/trigger entity |
+| `mechanism` | string | The relationship type |
+| `outcome` | string | The effect/outcome entity |
+| `confidence` | float32 | Confidence score (0-1) |
+| `is_inferred` | bool | Whether derived or explicit |
+| `source` | string | Original source (e.g., paper) |
+| `provenance` | list[string] | Source triplets for inferred facts |
+
+## Creating .causal Files
+
+```python
+from dotcausal import CausalWriter
+
+writer = CausalWriter()
+writer.add_triplet(
+    trigger="SARS-CoV-2",
+    mechanism="damages",
+    outcome="mitochondria",
+    confidence=0.9,
+    source="paper_A.pdf",
+)
+writer.save("knowledge.causal")
+```
+
+## References
+
+- **PyPI**: https://pypi.org/project/dotcausal/
+- **GitHub**: https://github.com/DT-Foss/dotcausal
+- **Whitepaper**: https://doi.org/10.5281/zenodo.18326222
+
+## Citation
+
+```bibtex
+@article{foss2026causal,
+  author = {Foss, David Tom},
+  title = {The .causal Format: Deterministic Inference for AI-Assisted Hypothesis Amplification},
+  journal = {Zenodo},
+  year = {2026},
+  doi = {10.5281/zenodo.18326222}
+}
+```
+
+## License
+
+MIT