markitect-main/markitect/infospace/cli.py

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"""
CLI commands for infospace lifecycle management.
Provides ``markitect infospace`` subcommands for initialising,
inspecting, and evaluating infospaces.
"""
from __future__ import annotations
from pathlib import Path
from typing import Optional
import click
from markitect.infospace.config import (
DisciplineBinding,
InfospaceConfig,
SchemaRegistry,
TopicConfig,
find_infospace_config,
load_infospace_config,
save_infospace_config,
)
from markitect.infospace.entity_parser import parse_entity_directory
from markitect.infospace.state import build_state
def _load_config_or_exit(config_path: Optional[str] = None) -> tuple:
"""Resolve and load infospace.yaml, or exit with an error."""
if config_path:
p = Path(config_path)
else:
p = find_infospace_config()
if p is None:
click.echo("Error: No infospace.yaml found. Run 'markitect infospace init' first.", err=True)
raise SystemExit(1)
cfg = load_infospace_config(p)
return cfg, p
@click.group(name="infospace")
def infospace_commands():
"""Manage infospaces — create, inspect, evaluate."""
pass
# ── init ─────────────────────────────────────────────────────────────
@infospace_commands.command()
@click.option("--topic", required=True, help="Topic name for the infospace.")
@click.option("--domain", default="", help="Knowledge domain.")
@click.option("--sources", default="", help="Path to source material directory.")
@click.option("--discipline", multiple=True, help="Discipline name (repeatable).")
@click.option("--output", "-o", default="infospace.yaml", help="Output config file path.")
def init(topic: str, domain: str, sources: str, discipline: tuple, output: str):
"""Initialise a new infospace configuration file."""
out_path = Path(output)
if out_path.exists():
click.echo(f"Error: {out_path} already exists.", err=True)
raise SystemExit(1)
disciplines = [DisciplineBinding(name=d) for d in discipline]
config = InfospaceConfig(
topic=TopicConfig(name=topic, domain=domain, sources=sources),
disciplines=disciplines,
)
save_infospace_config(config, out_path)
click.echo(f"Created {out_path}")
# ── status ───────────────────────────────────────────────────────────
@infospace_commands.command()
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
def status(config_path: Optional[str]):
"""Show infospace status — entity count, domains, evaluation state."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
# Parse entities
entities_dir = root / cfg.entities_dir
entities = []
if entities_dir.is_dir():
entities = parse_entity_directory(entities_dir)
# Load latest snapshot if available
snapshot = None
history_path = root / cfg.metrics_dir / "history.yaml"
if history_path.is_file():
from markitect.infospace.evaluation_io import read_history
history = read_history(history_path)
if history:
snapshot = history[-1]
state = build_state(cfg, entities=entities, snapshot=snapshot)
click.echo(f"Infospace: {state.topic_name}")
if cfg.topic.domain:
click.echo(f"Domain: {cfg.topic.domain}")
click.echo(f"Entities: {state.entity_count}")
if state.domains:
click.echo(f"Domains: {', '.join(state.domains)}")
if cfg.disciplines:
names = [d.name for d in cfg.disciplines]
click.echo(f"Disciplines: {', '.join(names)}")
if state.has_evaluations:
click.echo(f"Last evaluated: {state.latest_snapshot.created_at.isoformat()}")
else:
click.echo("Evaluations: none")
# ── entities ─────────────────────────────────────────────────────────
@infospace_commands.command()
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
@click.option(
"--sort-by", "sort_key",
type=click.Choice(["slug", "domain", "words"]),
default="slug",
help="Sort entities by field.",
)
@click.option("--by-type", "by_type", is_flag=True, default=False,
help="Group entities by L2 entity type.")
def entities(config_path: Optional[str], sort_key: str, by_type: bool):
"""List entities with metadata summary."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
entities_dir = root / cfg.entities_dir
if not entities_dir.is_dir():
click.echo("No entities directory found.")
return
entity_list = parse_entity_directory(entities_dir)
if not entity_list:
click.echo("No entities found.")
return
if by_type:
_entities_by_type(cfg, root, entity_list)
return
# Sort
if sort_key == "domain":
entity_list.sort(key=lambda e: (e.domain or "", e.slug))
elif sort_key == "words":
entity_list.sort(key=lambda e: e.total_word_count, reverse=True)
else:
entity_list.sort(key=lambda e: e.slug)
# Format as table
click.echo(f"{'Slug':<40} {'Domain':<20} {'Words':>6}")
click.echo("-" * 68)
for e in entity_list:
click.echo(f"{e.slug:<40} {(e.domain or '-'):<20} {e.total_word_count:>6}")
click.echo(f"\nTotal: {len(entity_list)} entities")
def _entities_by_type(cfg, root: "Path", entity_list: list) -> None:
"""Print entities grouped by L2 entity type."""
from markitect.infospace.classification import ENTITY_TYPES
from markitect.infospace.classification_io import read_classifications_directory
from markitect.infospace.evaluation_io import read_entity_evaluation
# Load classifications
cls_dir = root / cfg.classifications_dir
cls_map: dict = {}
if cls_dir.is_dir():
from markitect.infospace.classification_io import read_classifications_directory
for c in read_classifications_directory(cls_dir):
cls_map[c.entity_slug] = c
# Load evaluation scores (best-effort)
eval_dir = root / cfg.evaluations_dir
eval_scores: dict = {} # slug → overall_score
if eval_dir.is_dir():
for ef in eval_dir.glob("*.md"):
try:
ev = read_entity_evaluation(ef)
eval_scores[ev.entity_slug] = ev.overall_score
except Exception:
pass
# Build index: entity_type → list of (entity, classification)
entity_index = {
t: [] for t in ENTITY_TYPES
}
entity_index["Unclassified"] = []
entity_map = {e.slug: e for e in entity_list}
for e in entity_list:
cls = cls_map.get(e.slug)
if cls is None:
entity_index["Unclassified"].append((e, None))
else:
bucket = cls.entity_type if cls.entity_type in entity_index else "Unclassified"
entity_index[bucket].append((e, cls))
# Print each type group
type_order = list(ENTITY_TYPES) + ["Unclassified"]
total = 0
for etype in type_order:
group = entity_index.get(etype, [])
if not group:
continue
click.echo(f"\n=== {etype} ({len(group)} entities) ===")
group.sort(key=lambda x: x[0].slug)
for e, cls in group:
vsm = cls.vsm_system if cls else ""
domain = (e.domain or "-")[:18]
score = eval_scores.get(e.slug)
score_str = f" \u2605{score:.1f}" if score is not None else ""
slug_col = f"{e.slug:<40}"
click.echo(f" {slug_col} {domain:<18} {vsm:<4}{score_str}")
if cls and cls.entity_type == "Relation" and cls.links_mechanism:
subj = cls.links_subject or cls.links_subject_slug or "?"
obj = cls.links_object or cls.links_object_slug or "?"
click.echo(f" \u2192 links: {subj} \u2194 {obj}")
mech = cls.links_mechanism
if len(mech) > 80:
mech = mech[:77] + "..."
click.echo(f" \u2192 mechanism: {mech}")
total += len(group)
click.echo(f"\nTotal: {total} entities")
# ── evaluate ─────────────────────────────────────────────────────────
@infospace_commands.command()
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
@click.option("--provider", default="openrouter", help="LLM provider (openrouter, openai, etc.).")
@click.option("--model", default=None, help="LLM model name.")
@click.option("--entity", "entity_slug", default=None, help="Evaluate a single entity by slug.")
@click.option("--chapter", default=None, help="Evaluate entities from a specific chapter.")
def evaluate(config_path, provider, model, entity_slug, chapter):
"""Evaluate entities using LLM-based quality assessment."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
entities_dir = root / cfg.entities_dir
if not entities_dir.is_dir():
click.echo("Error: No entities directory found.", err=True)
raise SystemExit(1)
entity_list = parse_entity_directory(entities_dir)
if not entity_list:
click.echo("No entities to evaluate.")
return
# Filter
if entity_slug:
entity_list = [e for e in entity_list if e.slug == entity_slug]
if not entity_list:
click.echo(f"Error: Entity '{entity_slug}' not found.", err=True)
raise SystemExit(1)
elif chapter:
entity_list = [e for e in entity_list if chapter in e.source_chapter]
if not entity_list:
click.echo(f"No entities found for chapter '{chapter}'.")
return
# Skip entities that already have evaluation files (incremental resume)
from markitect.infospace.evaluate import run_entity_evaluation
output_dir = root / cfg.evaluations_dir
if not entity_slug and not chapter and output_dir.is_dir():
previous_digests = {
p.stem: "" # non-empty sentinel → triggers skip in BatchEvaluator
for p in output_dir.glob("*.md")
}
entity_list = [e for e in entity_list if e.slug not in previous_digests]
if not entity_list:
click.echo("All entities already evaluated. Nothing to do.")
return
if previous_digests:
click.echo(f"Skipping {len(previous_digests)} already-evaluated entities.")
# Create adapter
from markitect.llm import create_adapter
from markitect.prompts.execution.models import RunConfig
adapter = create_adapter(provider, model=model)
run_config = RunConfig(model_name=model, temperature=0.3, max_tokens=2000)
# Progress callback
def on_progress(done, total, result):
status = result.status.upper()
click.echo(f" [{done}/{total}] {result.key}: {status}")
click.echo(f"Evaluating {len(entity_list)} entities via {provider}...")
summary = run_entity_evaluation(
config=cfg,
entities=entity_list,
adapter=adapter,
run_config=run_config,
output_dir=output_dir,
progress_callback=on_progress,
)
click.echo(f"\nDone: {summary.succeeded} succeeded, {summary.failed} failed, {summary.skipped} skipped")
if summary.total_tokens > 0:
click.echo(f"Tokens used: {summary.total_tokens}")
# ── eval-summary ──────────────────────────────────────────────────────
@infospace_commands.command(name="eval-summary")
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
@click.option("--update-metrics", is_flag=True, default=False,
help="Merge per_entity_mean into metrics.yaml for viability checks.")
def eval_summary(config_path: Optional[str], update_metrics: bool):
"""Show aggregate statistics from per-entity evaluation files."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
evaluations_dir = root / cfg.evaluations_dir
if not evaluations_dir.is_dir():
click.echo("No evaluations directory found. Run 'markitect infospace evaluate' first.")
return
from markitect.infospace.evaluation_io import read_entity_evaluation
eval_files = sorted(evaluations_dir.glob("*.md"))
if not eval_files:
click.echo("No evaluation files found.")
return
overall_scores: list = []
dim_scores: dict = {}
failed: list = []
for ef in eval_files:
try:
ev = read_entity_evaluation(ef)
overall_scores.append(ev.overall_score)
for s in ev.scores:
dim_scores.setdefault(s.name, []).append(s.value)
except Exception as exc:
failed.append((ef.stem, str(exc)))
n = len(overall_scores)
if n == 0:
click.echo("No evaluations could be read.")
return
mean_overall = sum(overall_scores) / n
click.echo(f"Evaluation summary — {n} entities evaluated")
if failed:
click.echo(f" (failed to read: {len(failed)})")
click.echo()
click.echo(f" {'Dimension':<30} {'Mean':>6}")
click.echo(" " + "-" * 38)
click.echo(f" {'overall':<30} {mean_overall:>6.3f}")
for dim, vals in sorted(dim_scores.items()):
click.echo(f" {dim:<30} {sum(vals)/len(vals):>6.3f}")
score_min = min(overall_scores)
score_max = max(overall_scores)
click.echo()
click.echo(f" Range: {score_min:.2f} {score_max:.2f}")
if update_metrics:
from markitect.infospace.history import read_metrics_file, write_metrics_file
metrics_file = root / cfg.metrics_dir / "metrics.yaml"
existing = read_metrics_file(metrics_file)
existing["per_entity_mean"] = round(mean_overall, 6)
write_metrics_file(existing, metrics_file)
click.echo(f"\nUpdated metrics.yaml: per_entity_mean = {mean_overall:.4f}")
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
# ── relations ─────────────────────────────────────────────────────────
@infospace_commands.command()
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
@click.option("--entity", "entity_slug", default=None,
help="Show only relations involving this entity slug.")
@click.option("--vsm", "vsm_filter", default=None,
help="Show only relations whose VSM channel contains this string (e.g. S2, S3).")
@click.option("--loops", "loops_only", is_flag=True, default=False,
help="Show only feedback loops (cycles in the relation graph).")
@click.option("--stats", "stats_only", is_flag=True, default=False,
help="Show aggregate statistics only, no individual relations.")
def relations(config_path: Optional[str], entity_slug: Optional[str],
vsm_filter: Optional[str], loops_only: bool, stats_only: bool):
"""Show the L3 relation graph — triplets, feedback loops, and VSM channels."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
from markitect.infospace.relation_parser import parse_relations_directory
relations_dir = root / cfg.relations_dir
if not relations_dir.is_dir():
click.echo("No relations directory found. Create output/relations/ and add relation files.")
return
all_relations = parse_relations_directory(relations_dir)
if not all_relations:
click.echo("No relation files found in " + str(relations_dir))
return
# Build directed graph for cycle detection
try:
import networkx as nx
G = nx.DiGraph()
for r in all_relations:
G.add_edge(r.subject_slug, r.object_slug,
predicate=r.predicate,
relation_type=r.relation_type,
vsm_channel=r.vsm_channel,
slug=r.slug)
except ImportError:
G = None
# Find feedback loops
loops = []
if G is not None:
try:
loops = list(nx.simple_cycles(G))
except Exception:
loops = []
# Stats summary
import re as _re
def _vsm_code(channel: str) -> str:
"""Strip parenthetical description, returning just the system code (e.g. 'S3 → S1')."""
return _re.sub(r'\s*\(.*', '', channel).strip() or channel
n = len(all_relations)
vsm_counts: dict = {}
type_counts: dict = {}
for r in all_relations:
vsm_counts[_vsm_code(r.vsm_channel)] = vsm_counts.get(_vsm_code(r.vsm_channel), 0) + 1
type_counts[r.relation_type] = type_counts.get(r.relation_type, 0) + 1
click.echo(f"Relation graph — {n} relations")
if G is not None:
click.echo(f" Entities in graph: {G.number_of_nodes()}")
click.echo(f" Feedback loops: {len(loops)}")
click.echo()
if stats_only:
click.echo("Relation types:")
for rt, count in sorted(type_counts.items(), key=lambda x: -x[1]):
click.echo(f" {rt:<25} {count:>4}")
click.echo()
click.echo("VSM channels:")
for ch, count in sorted(vsm_counts.items(), key=lambda x: -x[1]):
click.echo(f" {ch:<20} {count:>4}")
return
# Feedback loops section
if loops or loops_only:
if loops:
click.echo(f"Feedback loops ({len(loops)}):")
for i, cycle in enumerate(loops, 1):
click.echo(f" Loop {i}: {''.join(cycle)}{cycle[0]}")
click.echo()
elif loops_only:
click.echo("No feedback loops detected in current relation set.")
return
if loops_only:
return
# Filter relations
filtered = all_relations
if entity_slug:
filtered = [r for r in filtered
if entity_slug in (r.subject_slug, r.object_slug)]
if not filtered:
click.echo(f"No relations found involving '{entity_slug}'.")
return
if vsm_filter:
filtered = [r for r in filtered if vsm_filter in r.vsm_channel]
if not filtered:
click.echo(f"No relations with VSM channel containing '{vsm_filter}'.")
return
# Display relations
click.echo(f"{'Subject':<35} {'Predicate':<30} {'Object':<35} {'VSM'}")
click.echo("-" * 110)
for r in filtered:
subj = r.subject[:33] + ".." if len(r.subject) > 35 else r.subject
obj = r.object[:33] + ".." if len(r.object) > 35 else r.object
pred = r.predicate[:28] + ".." if len(r.predicate) > 30 else r.predicate
click.echo(f"{subj:<35} {pred:<30} {obj:<35} {r.vsm_channel}")
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
# ── classify ─────────────────────────────────────────────────────────
@infospace_commands.command()
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
@click.option("--entity", "entity_slug", default=None,
help="Classify a single entity by slug.")
@click.option("--provider", default="openrouter",
help="LLM provider (openrouter, gemini, openai, …).")
@click.option("--model", default=None, help="Model name override.")
@click.option("--rpm", default=0, type=int,
help="Max requests per minute (0 = unlimited). Use 10 for Gemini free tier.")
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
def classify(config_path: Optional[str], entity_slug: Optional[str],
provider: str, model: Optional[str], rpm: int):
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
"""Classify entities with Entity Type and VSM System (L2)."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
from markitect.infospace.classifier import run_entity_classification
from markitect.llm import create_adapter
from markitect.prompts.execution.models import RunConfig
entity_list = parse_entity_directory(root / cfg.entities_dir)
if not entity_list:
click.echo("No entities found in " + str(root / cfg.entities_dir), err=True)
return
output_dir = root / cfg.classifications_dir
if entity_slug:
entity_list = [e for e in entity_list if e.slug == entity_slug]
if not entity_list:
click.echo(f"Entity '{entity_slug}' not found.", err=True)
return
else:
# Incremental skip — entities already classified are omitted
if output_dir.is_dir():
done_slugs = {p.stem for p in output_dir.glob("*.md")}
before = len(entity_list)
entity_list = [e for e in entity_list if e.slug not in done_slugs]
skipped = before - len(entity_list)
if skipped:
click.echo(f"Skipping {skipped} already-classified entities.")
if not entity_list:
click.echo("All entities already classified. Nothing to do.")
return
delay = (60.0 / rpm) if rpm > 0 else 0.0
click.echo(f"Classifying {len(entity_list)} entities …" +
(f" (rate: {rpm} RPM, {delay:.1f}s delay)" if delay else ""))
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
output_dir.mkdir(parents=True, exist_ok=True)
adapter = create_adapter(provider, model=model)
run_config = RunConfig(model_name=model, temperature=0.1, max_tokens=2000)
def _progress(done: int, total: int, result) -> None:
if result.status == "success":
click.echo(f" [{done}/{total}] {result.key}")
else:
click.echo(f" [{done}/{total}] {result.key} — FAILED: {result.error}")
summary = run_entity_classification(
config=cfg,
entities=entity_list,
adapter=adapter,
run_config=run_config,
output_dir=output_dir,
progress_callback=_progress,
delay_seconds=delay,
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
)
click.echo(f"\nDone: {summary.succeeded} classified, {summary.failed} failed.")
# ── classify-summary ──────────────────────────────────────────────────
@infospace_commands.command(name="classify-summary")
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
@click.option("--update-metrics", "update_metrics", is_flag=True, default=False,
help="Write type_distribution metrics to metrics.yaml.")
def classify_summary(config_path: Optional[str], update_metrics: bool):
"""Show type × VSM distribution across all classified entities (L2)."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
from markitect.infospace.classification import ENTITY_TYPES, VSM_SYSTEMS
from markitect.infospace.classification_io import read_classifications_directory
cls_dir = root / cfg.classifications_dir
if not cls_dir.is_dir():
click.echo("No classifications directory found. Run 'classify' first.")
return
all_cls = read_classifications_directory(cls_dir)
if not all_cls:
click.echo("No classification files found.")
return
n = len(all_cls)
type_counts: dict = {}
vsm_counts: dict = {}
matrix: dict = {} # (entity_type, vsm_system) → count
for c in all_cls:
type_counts[c.entity_type] = type_counts.get(c.entity_type, 0) + 1
vsm_counts[c.vsm_system] = vsm_counts.get(c.vsm_system, 0) + 1
key = (c.entity_type, c.vsm_system)
matrix[key] = matrix.get(key, 0) + 1
click.echo(f"Classification summary — {n} entities\n")
click.echo("Entity types:")
for t, count in sorted(type_counts.items(), key=lambda x: -x[1]):
pct = 100 * count / n if n else 0.0
click.echo(f" {t:<15} {count:>4} ({pct:.1f}%)")
click.echo()
vsm_order = ["S1", "S2", "S3", "S3*", "S4", "S5"]
click.echo("VSM systems:")
for v in vsm_order:
if v in vsm_counts:
count = vsm_counts[v]
pct = 100 * count / n if n else 0.0
click.echo(f" {v:<6} {count:>4} ({pct:.1f}%)")
click.echo()
# Type × VSM matrix
header = f"{'':15}" + "".join(f"{v:>7}" for v in vsm_order)
sep = "-" * (15 + 7 * len(vsm_order))
click.echo(header)
click.echo(sep)
for t in ENTITY_TYPES:
row = f"{t:<15}"
for v in vsm_order:
c = matrix.get((t, v), 0)
row += f"{c if c else '.':>7}"
click.echo(row)
click.echo()
filled_cells = len(matrix)
total_cells = len(ENTITY_TYPES) * len(vsm_order)
click.echo(f"Matrix fill: {filled_cells}/{total_cells} cells occupied")
click.echo()
if update_metrics:
import math
from markitect.infospace.history import read_metrics_file, write_metrics_file
metrics_dir = root / cfg.metrics_dir
metrics_dir.mkdir(parents=True, exist_ok=True)
# Type entropy
type_entropy = 0.0
for count in type_counts.values():
p = count / n
if p > 0:
type_entropy -= p * math.log2(p)
existing = read_metrics_file(metrics_dir / "metrics.yaml")
new_metrics = {
"type_distribution": type_counts,
"vsm_type_matrix_cells": filled_cells,
"type_entropy": round(type_entropy, 4),
}
merged = {**existing, **new_metrics}
write_metrics_file(merged, metrics_dir / "metrics.yaml")
click.echo(
f"Updated metrics.yaml: type_entropy={type_entropy:.4f}, "
f"vsm_type_matrix_cells={filled_cells}"
)
# ── classify-links ────────────────────────────────────────────────────
@infospace_commands.command(name="classify-links")
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
@click.option("--provider", default="openrouter",
help="LLM provider (openrouter, gemini, openai, …).")
@click.option("--model", default=None, help="Model name override.")
def classify_links(config_path: Optional[str], provider: str, model: Optional[str]):
"""Capture relation endpoint data (subject, object, mechanism) for Relation-type entities."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
from markitect.infospace.classification import ENTITY_TYPES
from markitect.infospace.classification_io import read_classifications_directory
from markitect.infospace.classifier import run_relation_link_capture
from markitect.llm import create_adapter
from markitect.prompts.execution.models import RunConfig
cls_dir = root / cfg.classifications_dir
if not cls_dir.is_dir():
click.echo("No classifications directory found. Run 'classify' first.", err=True)
raise SystemExit(1)
all_cls = read_classifications_directory(cls_dir)
cls_map = {c.entity_slug: c for c in all_cls}
# Filter to Relation-type entities that are missing links_mechanism
relation_slugs = [
c.entity_slug for c in all_cls
if c.entity_type == "Relation" and not c.links_mechanism
]
if not relation_slugs:
click.echo("All Relation-type entities already have endpoint data. Nothing to do.")
return
# Load entity metadata for these slugs
entity_list = parse_entity_directory(root / cfg.entities_dir)
entity_map = {e.slug: e for e in entity_list}
relation_entities = [entity_map[s] for s in relation_slugs if s in entity_map]
missing_from_entities = [s for s in relation_slugs if s not in entity_map]
if missing_from_entities:
click.echo(f"Warning: {len(missing_from_entities)} Relation-type slugs not found in "
f"entities directory and will be skipped.")
if not relation_entities:
click.echo("No Relation-type entities found to enrich.")
return
click.echo(f"Capturing relation links for {len(relation_entities)} Relation-type entities …")
adapter = create_adapter(provider, model=model)
run_config = RunConfig(model_name=model, temperature=0.1, max_tokens=512)
def _progress(done: int, total: int, result) -> None:
if result.status == "success":
click.echo(f" [{done}/{total}] {result.key}")
else:
click.echo(f" [{done}/{total}] {result.key} — FAILED: {result.error}")
summary = run_relation_link_capture(
config=cfg,
relation_entities=relation_entities,
classifications=cls_map,
adapter=adapter,
run_config=run_config,
output_dir=cls_dir,
progress_callback=_progress,
)
click.echo(f"\nDone: {summary.succeeded} enriched, {summary.failed} failed.")
# ── viability ────────────────────────────────────────────────────────
@infospace_commands.command()
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
def viability(config_path: Optional[str]):
"""Show viability dashboard — threshold checks and pass/fail."""
cfg, cfg_path = _load_config_or_exit(config_path)
if not cfg.viability:
click.echo("No viability thresholds configured in infospace.yaml.")
return
# Try to load latest metrics
root = cfg_path.parent
metrics: dict = {}
metrics_file = root / cfg.metrics_dir / "metrics.yaml"
if metrics_file.is_file():
import yaml
raw = yaml.safe_load(metrics_file.read_text(encoding="utf-8"))
if isinstance(raw, dict):
metrics = {k: float(v) for k, v in raw.items() if isinstance(v, (int, float))}
state = build_state(cfg, metrics=metrics if metrics else None)
if not state.viability_results:
click.echo("No metrics available. Run evaluations first.")
click.echo("\nConfigured thresholds:")
for name, t in cfg.viability.items():
bounds = []
if t.min is not None:
bounds.append(f"min={t.min}")
if t.max is not None:
bounds.append(f"max={t.max}")
click.echo(f" {name}: {', '.join(bounds)}")
return
click.echo(f"{'Metric':<30} {'Value':>8} {'Threshold':>15} {'Status':>8}")
click.echo("-" * 63)
for r in state.viability_results:
bounds = []
if r.threshold.min is not None:
bounds.append(f"min={r.threshold.min}")
if r.threshold.max is not None:
bounds.append(f"max={r.threshold.max}")
status_str = "PASS" if r.passed else "FAIL"
click.echo(
f"{r.metric:<30} {r.value:>8.4f} {', '.join(bounds):>15} {status_str:>8}"
)
click.echo()
if state.is_viable:
click.echo(f"Viable: YES ({state.viability_pass_count}/{state.viability_total_count} thresholds met)")
else:
click.echo(f"Viable: NO ({state.viability_pass_count}/{state.viability_total_count} thresholds met)")
# ── check ───────────────────────────────────────────────────────────
@infospace_commands.command()
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
@click.option(
"--concern", "concerns", multiple=True,
type=click.Choice(["redundancy", "coverage", "coherence", "consistency", "granularity"]),
help="Run specific concern(s). Omit to run all five.",
)
@click.option("--json", "as_json", is_flag=True, help="Output results as JSON.")
def check(config_path: Optional[str], concerns: tuple, as_json: bool):
"""Run collection-level quality checks (C1C5)."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
entities_dir = root / cfg.entities_dir
if not entities_dir.is_dir():
click.echo("Error: No entities directory found.", err=True)
raise SystemExit(1)
entity_list = parse_entity_directory(entities_dir)
if not entity_list:
click.echo("No entities to check.")
return
from markitect.infospace.checks import run_all_checks
checks_list = list(concerns) if concerns else None
report = run_all_checks(
entities=entity_list,
checks=checks_list,
)
if as_json:
import json
click.echo(json.dumps(report.to_dict(), indent=2))
else:
click.echo(f"Collection checks — {len(entity_list)} entities\n")
d = report.to_dict()
for concern_name, concern_data in d.items():
label = concern_data.get("concern", concern_name.upper())
click.echo(f" {label}{concern_name}")
for k, v in concern_data.items():
if k == "concern":
continue
click.echo(f" {k}: {v}")
click.echo()
# Show summary metrics
m = report.metrics()
if m and not as_json:
click.echo("Metrics summary:")
for k, v in sorted(m.items()):
click.echo(f" {k}: {v:.4f}")
# Record to history
if m:
from markitect.infospace.history import record_check_results
snap = record_check_results(report, cfg, root, entity_count=len(entity_list))
if not as_json:
click.echo(f"\nRecorded snapshot {snap.snapshot_id}")
# ── history ─────────────────────────────────────────────────────────
@infospace_commands.command()
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
@click.option("--metric", default=None, help="Show trend for a specific metric.")
@click.option("--json", "as_json", is_flag=True, help="Output as JSON.")
def history(config_path: Optional[str], metric: Optional[str], as_json: bool):
"""Show metrics history — snapshots over time."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
from markitect.infospace.history import get_history, metric_trend
snapshots = get_history(cfg, root)
if not snapshots:
click.echo("No history found. Run 'markitect infospace check' first.")
return
if metric:
trend = metric_trend(snapshots, metric)
if not trend:
click.echo(f"No data for metric '{metric}'.")
return
if as_json:
import json
click.echo(json.dumps(trend, indent=2))
else:
click.echo(f"Trend: {metric}\n")
for entry in trend:
click.echo(f" {entry['date'][:19]} {entry['value']:.4f}")
return
if as_json:
import json
click.echo(json.dumps([s.to_dict() for s in snapshots], indent=2, default=str))
return
click.echo(f"History: {len(snapshots)} snapshot(s)\n")
click.echo(f"{'#':<4} {'Date':<20} {'Entities':>8} {'Metrics':>8}")
click.echo("-" * 42)
for i, snap in enumerate(snapshots, 1):
date_str = snap.created_at.isoformat()[:19]
n_metrics = len(snap.collection_metrics)
click.echo(f"{i:<4} {date_str:<20} {snap.entity_count:>8} {n_metrics:>8}")
@infospace_commands.command(name="history-diff")
@click.argument("date_a")
@click.argument("date_b")
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
def history_diff(date_a: str, date_b: str, config_path: Optional[str]):
"""Compare two history snapshots by date (YYYY-MM-DD)."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
from markitect.infospace.history import find_snapshot_by_date, get_history
from markitect.infospace.evaluation_io import diff_snapshots
snapshots = get_history(cfg, root)
if len(snapshots) < 2:
click.echo("Need at least two snapshots to diff.")
return
snap_a = find_snapshot_by_date(snapshots, date_a)
snap_b = find_snapshot_by_date(snapshots, date_b)
if snap_a is None:
click.echo(f"No snapshot found near '{date_a}'.")
return
if snap_b is None:
click.echo(f"No snapshot found near '{date_b}'.")
return
if snap_a.snapshot_id == snap_b.snapshot_id:
click.echo("Both dates resolve to the same snapshot.")
return
diff = diff_snapshots(snap_a, snap_b)
click.echo(diff.summary())
# ── bind-discipline ─────────────────────────────────────────────────
@infospace_commands.command(name="bind-discipline")
@click.argument("discipline_path")
@click.option("--name", required=True, help="Name for the discipline.")
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
def bind_discipline_cmd(discipline_path: str, name: str, config_path: Optional[str]):
"""Bind a discipline infospace to the current infospace."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
from markitect.infospace.composition import bind_discipline
status = bind_discipline(cfg, name=name, path=discipline_path, root=root)
if status.error:
click.echo(f"Error: {status.error}", err=True)
raise SystemExit(1)
# Persist updated config
save_infospace_config(cfg, cfg_path)
click.echo(f"Bound discipline '{name}' from {discipline_path}")
click.echo(f" Entities: {status.entity_count}")
if status.has_config:
viable_str = "YES" if status.is_viable else "NO"
click.echo(f" Viable: {viable_str}")
# ── disciplines ─────────────────────────────────────────────────────
@infospace_commands.command()
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
def disciplines(config_path: Optional[str]):
"""List bound disciplines and their viability status."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
if not cfg.disciplines:
click.echo("No disciplines bound.")
return
from markitect.infospace.composition import check_discipline_status
click.echo(f"{'Name':<30} {'Entities':>8} {'Viable':>8} {'Path'}")
click.echo("-" * 70)
for binding in cfg.disciplines:
status = check_discipline_status(binding, root)
viable_str = "YES" if status.is_viable else ("NO" if status.has_config else "?")
click.echo(
f"{status.name:<30} {status.entity_count:>8} {viable_str:>8} {status.path}"
)
if status.error:
click.echo(f" Error: {status.error}")
# ── process ─────────────────────────────────────────────────────
@infospace_commands.command()
@click.argument("glob_pattern", default=None, required=False)
@click.option("--all", "process_all", is_flag=True, help="Process all source files.")
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
@click.option("--provider", default=None, help="LLM provider (openrouter, openai, etc.).")
@click.option("--model", default=None, help="LLM model name.")
@click.option(
"--check-after-each",
is_flag=True,
help="Run collection checks (C1C5) after each source file.",
)
@click.option("--no-commit", is_flag=True, help="Skip git commits.")
def process(
glob_pattern: Optional[str],
process_all: bool,
config_path: Optional[str],
provider: Optional[str],
model: Optional[str],
check_after_each: bool,
no_commit: bool,
):
"""Process source files through the pipeline defined in infospace.yaml.
GLOB_PATTERN is matched against the sources directory declared in
infospace.yaml (default ``*.md``). Use ``--all`` to process every
source file.
\b
Examples:
# Process chapters 1-3 from book 1
markitect infospace process "book-1-chapter-0[1-3].md" --provider openrouter
# Process all source files and check metrics after each
markitect infospace process --all --provider openrouter --check-after-each
# Dry run — load existing outputs only, no LLM calls
markitect infospace process --all
"""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
if not cfg.pipeline or not cfg.pipeline.stages:
click.echo(
"Error: No pipeline stages defined in infospace.yaml.\n"
"Add a 'pipeline.stages' section with at least one stage.",
err=True,
)
raise SystemExit(1)
# Resolve sources directory
sources_dir = root / cfg.topic.sources if cfg.topic.sources else root
if not sources_dir.is_dir():
click.echo(
f"Error: Sources directory not found: {sources_dir}\n"
f"Set 'topic.sources' in infospace.yaml.",
err=True,
)
raise SystemExit(1)
# Collect source files
if process_all:
source_files = sorted(sources_dir.glob("*.md"))
else:
pattern = glob_pattern or "*.md"
source_files = sorted(sources_dir.glob(pattern))
if not source_files:
if process_all:
click.echo(f"No source files found in {sources_dir}")
else:
click.echo(
f"No files matched: {glob_pattern or '*.md'}\n"
f"Sources directory: {sources_dir}"
)
return
click.echo(f"Found {len(source_files)} source file(s) in {sources_dir.name}/")
# Create LLM adapter
adapter = None
if provider:
from markitect.llm import create_adapter
_PROVIDER_DEFAULTS = {"openrouter": "arcee-ai/trinity-large-preview:free"}
resolved_model = model or _PROVIDER_DEFAULTS.get(provider)
adapter = create_adapter(provider, model=resolved_model)
click.echo(f"LLM: {provider} ({resolved_model or 'default'})")
else:
click.echo("No LLM provider — will use existing outputs only (manual mode).")
# Run pipeline
from markitect.infospace.pipeline import SourcePipeline
pipeline = SourcePipeline(
cfg, root,
adapter=adapter,
provider=provider or "",
model=(model or _PROVIDER_DEFAULTS.get(provider or "", "")) if provider else "",
no_commit=no_commit,
)
total = len(source_files)
completed = 0
for i, source_file in enumerate(source_files, 1):
click.echo(f"\n[{i}/{total}] {source_file.name}")
success = pipeline.process_source(source_file)
if success:
completed += 1
if check_after_each:
pipeline.run_collection_check()
click.echo(f"\nDone: {completed}/{total} source file(s) fully processed.")
# ── stale-mappings ──────────────────────────────────────────────────
@infospace_commands.command(name="stale-mappings")
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
def stale_mappings(config_path: Optional[str]):
"""Check for stale mappings due to discipline changes."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
if not cfg.disciplines:
click.echo("No disciplines bound — no mappings to check.")
return
from markitect.infospace.composition import find_stale_mappings
# Try to load mapping references from output
mapping_refs = _load_mapping_references(cfg, root)
stale = find_stale_mappings(cfg, root, mapping_references=mapping_refs)
if not stale:
click.echo("No stale mappings detected.")
return
click.echo(f"Found {len(stale)} stale mapping(s):\n")
for s in stale:
click.echo(f" {s.entity_slug} -> {s.discipline_entity}")
click.echo(f" {s.reason}")
# ── graph ──────────────────────────────────────────────────────────────────
@infospace_commands.command()
@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
@click.option(
"--format", "output_format",
type=click.Choice(["mermaid", "dot"]),
default="mermaid",
show_default=True,
help="Output format.",
)
@click.option(
"--color-by",
type=click.Choice(["type", "vsm"]),
default="type",
show_default=True,
help="Color nodes by entity type or VSM system.",
)
@click.option("--type", "filter_type", default=None,
help="Show only entities with this entity type (e.g. Relation, Process).")
@click.option("--vsm", "filter_vsm", default=None,
help="Show only entities with this VSM system (e.g. S1, S3).")
@click.option("--entity", "filter_entity", default=None,
help="Show neighborhood of a specific entity slug.")
@click.option("--loops", "loops_only", is_flag=True, default=False,
help="Show only the feedback loop subgraph.")
@click.option("--output", "-o", default=None,
help="Write to file instead of stdout.")
@click.option("--classified-only/--all-entities", "classified_only",
default=True, show_default=True,
help="Only include classified entities (default: true).")
def graph(
config_path: Optional[str],
output_format: str,
color_by: str,
filter_type: Optional[str],
filter_vsm: Optional[str],
filter_entity: Optional[str],
loops_only: bool,
output: Optional[str],
classified_only: bool,
):
"""Render the entity-relation graph as Mermaid or DOT."""
cfg, cfg_path = _load_config_or_exit(config_path)
root = cfg_path.parent
from markitect.infospace.classification_io import read_classifications_directory
from markitect.infospace.relation_parser import parse_relations_directory
from markitect.infospace.graph_export import (
apply_filters,
build_entity_graph,
to_dot,
to_mermaid,
)
# Load classifications
cls_dir = root / cfg.classifications_dir
classifications = []
if cls_dir.is_dir():
classifications = read_classifications_directory(cls_dir)
classified_slugs = {c.entity_slug for c in classifications}
# Load relations
relations_dir = root / cfg.relations_dir
relations = []
if relations_dir.is_dir():
relations = parse_relations_directory(relations_dir)
if not classifications and not relations:
click.echo("No classifications or relations found. Run 'classify' and add relation files.")
return
# Detect feedback loops via networkx
feedback_cycles = []
if relations:
try:
import networkx as nx
G = nx.DiGraph()
for r in relations:
G.add_edge(r.subject_slug, r.object_slug)
feedback_cycles = list(nx.simple_cycles(G))
except ImportError:
pass
# Build graph
g = build_entity_graph(classifications, relations, feedback_cycles)
# Apply filters
filtered = apply_filters(
g,
filter_type=filter_type,
filter_vsm=filter_vsm,
filter_entity=filter_entity,
loops_only=loops_only,
classified_only=classified_only,
classified_slugs=classified_slugs,
)
if not filtered.nodes:
click.echo("No nodes match the given filters.")
return
# Export
if output_format == "dot":
result = to_dot(filtered, color_by=color_by)
else:
result = to_mermaid(filtered, color_by=color_by)
if output:
out_path = Path(output)
out_path.write_text(result, encoding="utf-8")
click.echo(
f"Wrote {output_format} graph ({len(filtered.nodes)} nodes, "
f"{sum(len(v) for v in filtered.edges.values())} edges) to {out_path}"
)
else:
click.echo(result, nl=False)
def _load_mapping_references(
cfg: InfospaceConfig, root: Path
) -> Optional[dict]:
"""Try to load mapping references from YAML file in output dir."""
mapping_file = root / cfg.metrics_dir / "mapping-references.yaml"
if not mapping_file.is_file():
return None
import yaml
data = yaml.safe_load(mapping_file.read_text(encoding="utf-8"))
if isinstance(data, dict):
return data
return None