dotcausal 0.1.0

Binary Knowledge Graph Format with Embedded Inference for AI Applications

dotcausal

Binary Knowledge Graph Format with Embedded Inference

The .causal format is a binary storage format for causal knowledge graphs with embedded deterministic inference rules. Designed for AI applications requiring zero-hallucination guarantees.

Features

• 72% smaller than SQLite for equivalent data
• 1.9x fact amplification through 3-pass inference
• 38x faster queries than traditional databases
• Zero hallucination - pure deterministic logic
• Single-file portable - like SQLite, but for knowledge graphs

Installation

pip install dotcausal

Quick Start

Python API

from dotcausal import CausalFile, CausalWriter, CausalReader

# Create a new .causal file
writer = CausalWriter()
writer.add_triplet(
    trigger="SARS-CoV-2 infection",
    mechanism="causes",
    outcome="mitochondrial dysfunction",
    confidence=0.9,
    source="paper1.pdf"
)
writer.add_triplet(
    trigger="mitochondrial dysfunction",
    mechanism="leads to",
    outcome="chronic fatigue",
    confidence=0.85,
    source="paper2.pdf"
)
stats = writer.save("knowledge.causal")
print(f"Saved {stats['triplets']} triplets")

# Read and query with inference
reader = CausalReader("knowledge.causal")

# Get explicit facts only
explicit = reader.get_all_triplets(include_inferred=False)
print(f"Explicit: {len(explicit)}")

# Get amplified facts (explicit + inferred)
all_facts = reader.get_all_triplets(include_inferred=True)
print(f"Total with inference: {len(all_facts)}")

# Search
results = reader.search("COVID", field="all")
for r in results:
    print(f"{r['trigger']} → {r['mechanism']} → {r['outcome']}")

Command Line

# Show file statistics
dotcausal stats knowledge.causal

# Query the graph
dotcausal query knowledge.causal "COVID" --limit 10

# Convert SQLite to .causal
dotcausal convert pipeline.db output.causal

# Export to JSON
dotcausal export knowledge.causal -o output.json

# Validate file integrity
dotcausal validate knowledge.causal

The 3-Pass Inference Engine

The .causal format includes a deterministic inference engine that derives new facts from explicit ones:

Pass	Method	Example
1	Exact keyword matching	A→activates→B + B→activates→C = A→activates→C
2	Semantic direction	positive×positive = positive chain
3	Jaro-Winkler fuzzy	"COVID-19" ↔ "SARS-CoV-2" (similarity 0.82)

All inference is deterministic - same input always produces same output. Zero hallucination guaranteed.

File Format

[HEADER 64 bytes]  → Magic "CAUSAL01", Version, CRC
[OFFSET TABLE]     → Section offsets
[ENTITIES]         → Deduplicated dictionary
[TRIPLETS]         → Explicit facts with metadata
[RULES]            → Inference rules
[CLUSTERS]         → Semantic groupings
[GAPS]             → Knowledge gaps

Use Cases

• RAG Enhancement: Ground LLM responses in verified causal chains
• Scientific Discovery: Amplify weak signals through transitive inference
• Knowledge Bases: Portable, compressed storage for triplet data
• AI Safety: Zero-hallucination fact retrieval

Citation

If you use .causal in your research, please cite:

@software{foss2026dotcausal,
  author = {Foss, David Tom},
  title = {dotcausal: Binary Knowledge Graph Format with Embedded Inference},
  year = {2026},
  url = {https://github.com/dotcausal/dotcausal}
}

Links

• Documentation: https://dotcausal.com/docs
• Whitepaper: Zenodo DOI
• GitHub: https://github.com/dotcausal/dotcausal

License

MIT License - see LICENSE for details.