Metadata-Version: 2.4
Name: ramish-explorer
Version: 0.2.0
Summary: Explore .ramish knowledge graph files — query, validate, predict, and audit geometric truth.
Author-email: "A.I. Sciences LLC" <support@ramish.io>
License: MIT
Project-URL: Homepage, https://ramish.io
Project-URL: Documentation, https://ramish.io/docs
Project-URL: Repository, https://github.com/LWMartin/ramish-explorer
Keywords: knowledge-graph,quaternion,embeddings,data-quality,audit
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.20
Requires-Dist: click>=8.0
Requires-Dist: rich>=12.0
Dynamic: license-file

# 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](https://ramish.io) — a geometric knowledge engine that maps structural integrity in relational databases using quaternion embeddings.

## Install

```bash
pip install ramish-explorer
```

## Quick Start

```bash
# File overview
ramish-explorer stats engine.ramish

# Hybrid geometric + graph query
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 (frozen key rotation)
ramish-explorer predict engine.ramish "Sunset Condo" "serviced_by"

# Recommend based on neighborhood averaging
ramish-explorer recommend engine.ramish "AC/DC" "has_genre"

# Interactive exploration
ramish-explorer explore engine.ramish
```

## Commands

| Command | Description |
|---------|-------------|
| `stats` | File structure, geometry, and trust weight distribution |
| `query` | Hybrid geometric retrieval with graph reranking |
| `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) |
| `recommend` | Neighborhood averaging — entities similar to known targets |
| `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](https://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
- **Geometric search** — `query` combines embedding-space retrieval with graph structure for discovery

## Requirements

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

## Changelog

### v0.2.0 (2026-03-23)

**Geometric query engine** — `query()` now uses hybrid lexical-seed → geometric retrieval → graph rerank, replacing the v0.1 wave-propagation path. Geometry provides recall, graph edges provide precision. Results without graph support are marked with `~similar`.

**Relation indexes** — Pre-built lookup tables (`edges_by_rel_type`, `out_edges`, `in_edges`, compound indexes) replace full linear scans in `predict`, `compose`, `keys`, and REPL commands. Single O(n) build pass during `load()`.

**Sign-aligned frozen keys** — Frozen key extraction now aligns quaternion signs before averaging, eliminating partial cancellation from sign ambiguity. Keys are cached per session (13,000x speedup on subsequent access). Stability score included.

**Canonical quaternion layout** — Single `embedding_to_quats()` helper replaces all inline reshape/transpose patterns. One source of truth for the component-major → (n, dim, 4) conversion.

**Predict/recommend split** — `predict` now always uses frozen key rotation (geometric inference). The old neighborhood-averaging behavior moves to a new `recommend` command.

**File safety hardening** — Header validation rejects files with entity/relation counts exceeding sanity bounds or file sizes too small for declared contents. Entity name length capped.

**Test suite expansion** — 90 tests covering indexes, geometric query, sign alignment, quaternion layout, file safety, predict semantics, and name resolution. All 33 original v0.1 regression tests pass unchanged.

### v0.1.2 (2026-02-24)

Frozen rotation key infrastructure (`.rkey` files), spectral coherence scoring fix, Chinook audit time 16+ min → ~20 sec, exact edge hash storage.

### v0.1.1 (2026-02-22)

Fix repository URL in package metadata.

### v0.1.0 (2026-02-22)

Initial release. Reader, CLI, REPL, quaternion math, trust weight validation, audit.

## License

MIT

## Links

- [Ramish.io](https://ramish.io) — Generate .ramish files
- [Documentation](https://ramish.io/docs)
- [GitHub](https://github.com/LWMartin/ramish-explorer)
