Commit Graph

5 Commits

Author SHA1 Message Date
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
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
b20fe4db68 feat(infospace): add infospace configuration model and state (S2.1)
InfospaceConfig (topic, disciplines, schemas, competency questions,
viability thresholds, pipeline) with YAML load/save and directory
discovery. InfospaceState aggregates entities, evaluations, and
viability checks for status reporting.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-19 01:44:14 +01:00