ramish-explorer 0.1.1

Explore .ramish knowledge graph files — query, validate, predict, and audit geometric truth.

Ramish Explorer

Read, query, and explore .ramish knowledge graph files.

Ramish Explorer is the free, open-source reader for .ramish engine files generated by Ramish.io — a geometric knowledge engine that maps structural integrity in relational databases using quaternion embeddings.

Install

pip install ramish-explorer

Quick Start

# File overview
ramish-explorer stats engine.ramish

# Natural language search
ramish-explorer query engine.ramish "What relates to AC/DC?"

# Show all connections for an entity
ramish-explorer edges engine.ramish "Iron Maiden"

# Find similar entities (duplicate detection)
ramish-explorer similar engine.ramish "ABC Plumbing"

# Validate a specific relationship
ramish-explorer validate engine.ramish "Led Zeppelin" "genre" "Rock"

# Predict missing links
ramish-explorer predict engine.ramish "Sunset Condo" "serviced_by"

# Interactive exploration
ramish-explorer explore engine.ramish

Commands

Command	Description
stats	File structure, geometry, and trust weight distribution
query	Natural language search with wave propagation
validate	Check a specific triple (subject, relation, object)
edges	Show all edges for an entity with trust weights
similar	Find nearest neighbors by embedding distance
predict	Frozen key rotation inference (head + relation → tail)
compose	Multi-hop quaternion composition (relation1 ⊗ relation2)
keys	Extract frozen relational keys with stability metrics
audit	Structural integrity report
requantize	Convert between fp32/fp16/int8 quantization
explore	Interactive REPL

What is a .ramish file?

A .ramish file is a portable, binary knowledge graph with truth weights. It contains:

• Entities mapped from your database tables (rows, columns, values)
• Relations connecting entities with typed edges
• Quaternion embeddings encoding geometric structure
• Truth weights measuring confidence in each relationship

Generate .ramish files at ramish.io.

Use Cases

• Data quality auditing — find orphaned records, low-confidence relationships, structural anomalies
• Duplicate detection — similar finds near-duplicates in embedding space
• Relationship exploration — edges shows the full connection web for any entity
• Link prediction — predict finds missing connections using frozen key rotation
• Drift monitoring — compare .ramish files over time to track data quality changes

Requirements

• Python 3.8+
• numpy, click, rich (installed automatically)

License

MIT

Links

• Ramish.io — Generate .ramish files
• Documentation