Commit Graph

11 Commits

Author SHA1 Message Date
d1f57272a4 feat(example): add L2 classifications for 823/988 WoN entities (S3.4)
Batch classification via OpenRouter (claude-sonnet-4). 165 entities
remain unclassified due to credit exhaustion; incremental skip means
a follow-up run will complete them automatically.

Type × VSM matrix (823 entities):
                  S1   S2   S3  S3*   S4   S5
  Element         86   75   58   21   43   32  (315 total, 38%)
  Process         39   42   37   17   67   24  (226 total, 28%)
  Institution      4   12   30   24    .   52  (122 total, 15%)
  Principle        3    7   15    2   43   32  (102 total, 12%)
  Relation         2   14    5    5   22   10   (58 total,  7%)
  Matrix fill: 29/30 cells (Institution/S4 empty — expected)

Metrics updated: type_entropy=2.0936, vsm_type_matrix_cells=29

Also:
- BatchEvaluator gains delay_seconds param for rate-limited providers
- classify CLI gains --rpm option (--rpm 10 for Gemini free tier)
- history.write_metrics_file now handles non-float metric values
  (type_distribution is a dict, was crashing round())
- run_entity_classification forwards delay_seconds to BatchEvaluator
- classify-links and graph commands added by user (entities --by-type,
  graph --format mermaid/dot, classify-links for Relation enrichment)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-02-23 12:49:11 +01:00
81a4c8796a feat(infospace): add L2 entity classification with type × VSM matrix (S2.9)
Implements the L2 typed-entities layer — each entity is assigned an
Entity Type (Element, Process, Relation, Principle, Institution) and a
VSM System (S1–S5) by an LLM, with one-sentence rationales for each.

New modules:
- markitect/infospace/classification.py — EntityClassification dataclass
  + ENTITY_TYPES / VSM_SYSTEMS controlled vocabularies
- markitect/infospace/classification_io.py — write/read classification
  files (YAML frontmatter + markdown body, mirrors evaluation_io)
- markitect/infospace/classifier.py — build_classification_prompt(),
  parse_classification_response(), run_entity_classification(); batch
  runner writes files incrementally (same resumable pattern as evaluate)

CLI: markitect infospace classify [--entity SLUG] [--provider P] [--model M]
  - Incremental skip: checks output/classifications/ for existing files
  - Defaults to openrouter provider; 2000 max_tokens (Gemini 2.5 Flash
    uses ~787 thinking tokens, so 800 was too low)

CLI: markitect infospace classify-summary [--update-metrics]
  - Entity type counts + VSM system counts with percentages
  - 5 × 6 type × VSM matrix (spots structural blind spots at a glance)
  - --update-metrics writes type_distribution, type_entropy,
    vsm_type_matrix_cells to metrics.yaml

Config: InfospaceConfig gains classifications_dir (default output/classifications)
Schema: schemas/typed-entity-schema-v1.0.md — type/VSM vocabulary tables,
  rationale format rules, validation rules, metrics enabled at L2
infospace.yaml: schemas.typed_entity references typed-entity-schema-v1.0.md

Seed classifications (3): division_of_labour (Process/S1),
  natural_price_as_central_price (Principle/S2),
  invisible_hand_mechanism (Principle/S4)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-02-23 09:35:58 +01:00
2d45425b25 feat(infospace): add L3 relation graph with VSM-aware triplets (S2.8)
Implements the L3 relation graph layer — a directed graph of (Subject,
Predicate, Object) triplets annotated with VSM channel codes and feedback
roles. Triplets are authored as markdown files under output/relations/,
parsed into RelationMeta dataclasses, and analysed with networkx.

New modules:
- markitect/infospace/relation_models.py — RelationMeta dataclass +
  RELATION_TYPES controlled vocabulary (15 relation classes → VSM codes)
- markitect/infospace/relation_parser.py — parse_relation_file() and
  parse_relations_directory()

New schema: examples/infospace-with-history/schemas/relation-schema-v1.0.md
  — file naming convention, required sections, controlled vocabulary table

15 seed relation files covering the three core WoN feedback loops:
  - Capital Accumulation loop (positive reinforcement, S1/S3)
  - Market Price Balancing loop (negative feedback, S2/S3)
  - Market Extent mutual dependency (S1/S2)
  Plus structural relations: wages regulation, rent residual, price
  decomposition, invisible hand coordination

CLI: markitect infospace relations [--entity SLUG] [--vsm FILTER]
     [--loops] [--stats]
  - Builds directed graph from parsed files
  - Detects feedback loops via nx.simple_cycles()
  - 6 loops found from 15 seed relations (3 intended + 3 emergent)
  - --stats aggregates by VSM system code (strips parentheticals)

Config: InfospaceConfig gains relations_dir (default output/relations)
infospace.yaml: schemas.relation references relation-schema-v1.0.md

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-02-23 06:04:28 +01:00
7f1eecbdb2 feat(infospace): add eval-summary command and improve evaluate pipeline (S3.3)
- Fix evaluate dimensions to match template file:
  definition_precision, source_grounding, domain_placement,
  vsm_relevance, explanatory_value (was domain_relevance,
  discipline_alignment, conceptual_clarity)
- Add VSM background context to evaluation prompt so LLM can
  score vsm_relevance without macro injection
- Fix model_name bug: was sending literal "default" to API (HTTP 400)
- Refactor run_entity_evaluation to write files incrementally via
  callback rather than all at once after the batch — long runs are
  now resumable if interrupted
- Add incremental skip in CLI: entities with existing eval files
  are skipped automatically on re-run (acts as resume)
- Add eval-summary command: reads all eval files, shows per-dimension
  means, optionally writes per_entity_mean to metrics.yaml
- Fix record_check_results to merge rather than overwrite metrics.yaml
  so per_entity_mean survives subsequent check runs
- Add per_entity_mean viability threshold (min: 3.5) to infospace.yaml

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-02-23 01:26:45 +01:00
df1fdf1842 feat(pipeline): per-stage max_tokens, LLM provenance, processing log
- PipelineStage now supports max_tokens to override the 4096 default
- SourcePipeline records provider/model on each entity file as HTML comment
- output/processing-log.yaml tracks tokens, cost, duration, retries, errors
- _call_llm returns (content, metadata) for downstream traceability
- _http.py wraps JSON parse errors with body preview for debugging
- infospace.yaml stages: extract/map=6000 tokens, synthesize=3000 tokens

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-02-19 14:50:49 +01:00
72d9904485 feat(infospace): add process command for batch source file processing
- Extend PipelineStage with name, output_dir, output_macro,
  split_entities, and macros fields for declarative pipeline config
- Add SourcePipeline class (pipeline.py) using simple @{macro}
  substitution — no SQLite dependency, skip-if-exists per stage,
  LLM retry on rate limits, git commit per source
- Add `markitect infospace process [GLOB_PATTERN]` CLI command with
  --all, --provider, --model, --check-after-each, --no-commit flags
- Update infospace.yaml with output_dir, output_macro, split_entities,
  and macros for each pipeline stage in the WoN example

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-02-19 13:29:50 +01:00
b76d6d38c1 feat(infospace): add composition model for discipline binding (S2.6)
Discipline resolution, viability checking, entity access, stale
mapping detection, and binding management. CLI commands: bind-discipline,
disciplines, stale-mappings.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-19 02:03:54 +01:00
ce7f78d57d feat(infospace): add metrics history and viability tracking (S2.5)
History module with snapshot creation from check results, metrics file
I/O, auto-append to history after checks, date-based snapshot lookup,
and metric trend extraction. CLI commands: history, history-diff.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-19 02:01:00 +01:00
11585e6968 feat(infospace): add collection-level quality checks C1–C5 (S2.4)
Five concern checks: Redundancy (embedding/word overlap), Coverage
(FCA gap analysis), Coherence (graph connectivity), Consistency
(cycle detection), Granularity (Shannon entropy). Orchestrator runs
all or selected checks, CLI `markitect infospace check` command added.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-19 01:54:22 +01:00
3461d2f354 feat(infospace): add per-entity evaluation pipeline and CLI command (S2.3)
Evaluation pipeline builds prompts from entity metadata, delegates
to BatchEvaluator, parses structured LLM responses into ScoreEntry
objects, and writes evaluation files. CLI: 'markitect infospace evaluate'
with --provider, --entity, --chapter filters.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-19 01:48:34 +01:00
3726503adb feat(infospace): add lifecycle CLI commands — init, status, entities, viability (S2.2)
Adds 'markitect infospace' command group with init (create config),
status (entity count/domains/disciplines), entities (list with sort),
and viability (threshold dashboard with pass/fail).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-19 01:46:54 +01:00