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safishamsi
5779767fd3
Projects the verdicts `graphify reflect` already distills (preferred / tentative / contested, exponential time-decayed) into a derived experiential layer the read surfaces consume, so accumulated agent experience actually shows up where you look — without polluting the structural graph. Design (grounded in agent-memory + provenance literature; a redesign of the #1542 approach): - SIDECAR, not graph.json stamping. `reflect` writes `.graphify_learning.json` next to graph.json (an additional output, so the git hooks produce it automatically). graph.json stays purely structural; nothing leaks into GraphML; no graph.json churn. Mirrors the named-graph / event-sourcing separation of durable truth from a derived layer. - Reuses the existing reflect aggregate (its `_decay` is the recency-weighted exponential model; `_finalize_sources` the classification) — no new scoring. - PROVENANCE: each verdict carries the source questions/dates that produced it (cap 5, most-recent first). - STALENESS: each verdict stores the node's file fingerprint; on read, a changed source file flags the verdict stale ("code changed since — re-verify") rather than presenting a confident lesson on rewritten code. - CONTESTED surfaced distinctly (useful N / dead-end M), not averaged away. - DEAD-ENDS stay QUERY-SCOPED — never a node-level status; they appear only in the report as question -> nodes. - Read surfaces (explain / query+MCP / GRAPH_REPORT / graph.html) merge the overlay at read time, sanitized; un-annotated graphs are byte-identical. Deferred (logged): letting verdicts influence query/seed traversal — the recommender feedback-loop / Matthew-effect risk means that needs propensity correction + exploration, not naive biasing. Builds on the idea in #1441/#1542 (thanks @TPAteeq). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
108 lines
5.3 KiB
Python
108 lines
5.3 KiB
Python
import json
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from pathlib import Path
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from graphify.build import build_from_json
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from graphify.cluster import cluster, score_all
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from graphify.analyze import god_nodes, surprising_connections
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from graphify.report import generate
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FIXTURES = Path(__file__).parent / "fixtures"
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def make_inputs():
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extraction = json.loads((FIXTURES / "extraction.json").read_text())
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G = build_from_json(extraction)
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communities = cluster(G)
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cohesion = score_all(G, communities)
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labels = {cid: f"Community {cid}" for cid in communities}
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gods = god_nodes(G)
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surprises = surprising_connections(G)
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detection = {"total_files": 4, "total_words": 62400, "needs_graph": True, "warning": None}
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tokens = {"input": extraction["input_tokens"], "output": extraction["output_tokens"]}
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return G, communities, cohesion, labels, gods, surprises, detection, tokens
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def test_report_contains_header():
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G, communities, cohesion, labels, gods, surprises, detection, tokens = make_inputs()
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report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, "./project")
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assert "# Graph Report" in report
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def test_report_contains_corpus_check():
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G, communities, cohesion, labels, gods, surprises, detection, tokens = make_inputs()
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report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, "./project")
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assert "## Corpus Check" in report
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def test_report_contains_god_nodes():
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G, communities, cohesion, labels, gods, surprises, detection, tokens = make_inputs()
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report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, "./project")
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assert "## God Nodes" in report
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def test_report_contains_surprising_connections():
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G, communities, cohesion, labels, gods, surprises, detection, tokens = make_inputs()
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report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, "./project")
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assert "## Surprising Connections" in report
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def test_report_contains_communities():
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G, communities, cohesion, labels, gods, surprises, detection, tokens = make_inputs()
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report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, "./project")
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assert "## Communities" in report
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def test_report_contains_ambiguous_section():
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G, communities, cohesion, labels, gods, surprises, detection, tokens = make_inputs()
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report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, "./project")
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assert "## Ambiguous Edges" in report
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def test_report_shows_token_cost():
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G, communities, cohesion, labels, gods, surprises, detection, tokens = make_inputs()
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report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, "./project")
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assert "Token cost" in report
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assert "1,200" in report
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def test_report_shows_raw_cohesion_scores():
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G, communities, cohesion, labels, gods, surprises, detection, tokens = make_inputs()
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report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, "./project", min_community_size=1)
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assert "Cohesion:" in report
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assert "✓" not in report
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assert "⚠" not in report
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# --- work-memory lessons section ----------------------------------------------
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def test_report_work_memory_section_present_with_overlay_and_dead_ends():
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"""When a work-memory overlay (preferred sources) and query-scoped dead-ends
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are supplied, the report grows a `## Work-memory lessons` section listing the
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preferred sources and, separately, the dead-ends as question -> nodes."""
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G, communities, cohesion, labels, gods, surprises, detection, tokens = make_inputs()
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learning = {
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"overlay": {
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"auth_login": {"status": "preferred", "uses": 3, "score": 2.4,
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"label": "login()", "stale": False},
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"redis": {"status": "tentative", "uses": 1, "score": 0.5,
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"label": "RedisClient", "stale": False},
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},
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"dead_ends": [
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{"question": "does it use websockets?", "nodes": ["WSServer"], "date": "2026-05-01"},
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],
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}
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report = generate(G, communities, cohesion, labels, gods, surprises, detection,
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tokens, "./project", learning=learning)
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assert "## Work-memory lessons" in report
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assert "**Preferred sources**" in report
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assert "`login()`" in report
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# Tentative is not listed in the report's preferred block.
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assert "RedisClient" not in report
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# Dead-ends are query-scoped: question -> nodes, NOT a node-level status.
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assert "**Known dead ends**" in report
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assert "does it use websockets?" in report
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assert "`WSServer`" in report
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def test_report_work_memory_section_absent_without_overlay():
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"""No learning input => no section; report identical to pre-feature."""
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G, communities, cohesion, labels, gods, surprises, detection, tokens = make_inputs()
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before = generate(G, communities, cohesion, labels, gods, surprises, detection,
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tokens, "./project")
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assert "## Work-memory lessons" not in before
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# Explicit empty learning also omits the section.
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empty = generate(G, communities, cohesion, labels, gods, surprises, detection,
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tokens, "./project", learning={"overlay": {}, "dead_ends": []})
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assert "## Work-memory lessons" not in empty
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assert before == empty
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