feat(opt): three efficiency levers for NL→SQL workflow

Lever 1: Join-graph via FK logics in annotations + suggest-fks command - TableAnnotation.foreign_keys field stores curated logical FKs (DWH has no FK constraints) - tht schema suggest-fks: mine from approved SQL, heuristics (time_key → dim_time), same-name discovery + explicit --assume flag for multi-owner PKs - mschema renders 【Foreign keys】 section populated; validation in merge.py - SKILL.md F4 now reads FKs from mschema-text, no custom data_time_key logic Lever 2: Context-pack consolidation at kickoff (tht search pack) - Single embedding of question, reused for schema + evidence + solved searches - One command: tht search pack <question> --session <id> → retrieval_pack.md - Graceful degradation when Ollama/vector store unreachable (exit 0, empty sections) - SKILL.md F1 prescribes as first call; reduces model thinking turns via pre-retrieval Lever 3: Phase-summary recap v2 auto-construction from session ledger - tht session show --json includes full decisions ledger - tht phase meta --json exports 'emits' (substantive decision types per phase) - Gate appends deterministic 【Decisioni registrate in questa fase】 section (appendLedgerSection) - Model authors only summary + checks; recap table comes from persisted state (exact by construction) - SKILL.md Disciplina 6: brief model output, gate fills the rest Tests: 358 Python (including 10 FK + 3 pack + 1 session-ledger tests) + 111 JS gate tests, all pass. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-07 17:43:08 +02:00
parent 87e875bc81
commit e24b41b156
19 changed files with 936 additions and 29 deletions
@@ -0,0 +1,223 @@
+from datetime import datetime
+
+import yaml
+from typer.testing import CliRunner
+
+from tht.cli import app
+from tht.mschema.merge import find_orphans
+from tht.mschema.models import (
+    Annotations,
+    ColumnPhysical,
+    ForeignKey,
+    PhysicalSchema,
+    TableAnnotation,
+    TablePhysical,
+)
+from tht.mschema.render import to_mschema_text, to_schema_dict
+
+
+def _physical():
+    return PhysicalSchema(
+        database="d", schema="s", introspected_at=datetime(2026, 1, 1),
+        tables={
+            "dim_patient": TablePhysical(
+                columns={"cod_paz": ColumnPhysical(type="bigint", pk=True)},
+            ),
+            "dim_time": TablePhysical(
+                columns={"day_key": ColumnPhysical(type="integer", pk=True)},
+            ),
+            "fact_ablazione": TablePhysical(
+                columns={
+                    "cod_paz": ColumnPhysical(type="bigint"),
+                    "data_time_key": ColumnPhysical(type="integer"),
+                    "esito": ColumnPhysical(type="text"),
+                },
+            ),
+        },
+    )
+
+
+def _annotations_with_fks():
+    return Annotations(
+        tables={
+            "fact_ablazione": TableAnnotation(
+                foreign_keys=[
+                    ForeignKey(columns=["cod_paz"], ref_table="dim_patient",
+                               ref_columns=["cod_paz"]),
+                    ForeignKey(columns=["data_time_key"], ref_table="dim_time",
+                               ref_columns=["day_key"]),
+                ],
+            )
+        }
+    )
+
+
+def test_mschema_text_renders_annotation_fks():
+    text = to_mschema_text(_physical(), _annotations_with_fks())
+    assert "fact_ablazione.cod_paz=dim_patient.cod_paz" in text
+    assert "fact_ablazione.data_time_key=dim_time.day_key" in text
+
+
+def test_schema_dict_merges_annotation_fks():
+    d = to_schema_dict(_physical(), _annotations_with_fks())
+    fks = d["fact_ablazione"]["foreign_keys"]
+    assert {"columns": ["cod_paz"], "ref_table": "dim_patient",
+            "ref_columns": ["cod_paz"]} in fks
+
+
+def test_find_orphans_flags_broken_annotation_fk():
+    ann = Annotations(
+        tables={
+            "fact_ablazione": TableAnnotation(
+                foreign_keys=[
+                    ForeignKey(columns=["cod_paz"], ref_table="dim_sparita",
+                               ref_columns=["x"]),
+                    ForeignKey(columns=["colonna_sparita"], ref_table="dim_time",
+                               ref_columns=["day_key"]),
+                ],
+            )
+        }
+    )
+    orphans = find_orphans(_physical(), ann)
+    assert "fact_ablazione.fk(cod_paz)->dim_sparita" in orphans
+    assert "fact_ablazione.fk(colonna_sparita)->dim_time" in orphans
+
+
+def test_find_orphans_ok_with_valid_fk():
+    assert find_orphans(_physical(), _annotations_with_fks()) == []
+
+
+def _write_workspace(tmp_path):
+    _physical().to_yaml(tmp_path / "artifacts" / "mschema" / "physical.yaml")
+    cfg = tmp_path / "workspace.yaml"
+    cfg.write_text(
+        "database: {database: d, schema: s, user: u, password: p, transport: direct}\n"
+        f"paths: {{artifacts: {tmp_path/'artifacts'}, indexes: {tmp_path/'i'}, sessions: {tmp_path/'s'}}}\n"
+    )
+    return cfg
+
+
+def test_suggest_fks_prints_candidates(tmp_path):
+    cfg = _write_workspace(tmp_path)
+    res = CliRunner().invoke(app, ["schema", "suggest-fks", "-c", str(cfg)])
+    assert res.exit_code == 0, res.output
+    data = yaml.safe_load(res.output.rsplit("\n", 2)[0].split("FK candidate")[0])
+    fks = data["tables"]["fact_ablazione"]["foreign_keys"]
+    assert {"columns": ["cod_paz"], "ref_table": "dim_patient",
+            "ref_columns": ["cod_paz"]} in fks
+    assert {"columns": ["data_time_key"], "ref_table": "dim_time",
+            "ref_columns": ["day_key"]} in fks
+
+
+def test_mine_join_pairs_from_approved_sql():
+    from tht.mschema.fkmine import mine_join_pairs
+
+    sql = """
+    WITH abl AS (
+        SELECT sea.cod_paz, dt.year
+        FROM datawarehouse.fact_ablazione AS sea
+        JOIN datawarehouse.dim_time AS dt ON sea.data_time_key = dt.day_key
+    )
+    SELECT * FROM abl JOIN abl b ON abl.year = b.year;
+    """
+    pairs = mine_join_pairs(sql, _physical())
+    assert pairs[("fact_ablazione", "data_time_key", "dim_time", "day_key")] == 1
+    # il join CTE-CTE (abl.year=b.year) non produce coppie
+    assert len(pairs) == 1
+
+
+def test_mine_join_pairs_ignores_non_pk_pairs_and_bad_sql():
+    from tht.mschema.fkmine import mine_join_pairs
+
+    # esito=esito: nessun lato e' PK -> scartato
+    sql = ("SELECT * FROM fact_ablazione a JOIN fact_ablazione b "
+           "ON a.esito = b.esito")
+    assert len(mine_join_pairs(sql, _physical())) == 0
+    assert len(mine_join_pairs("WITH broken (", _physical())) == 0
+
+
+def test_suggest_fks_skips_generic_and_ambiguous_pks(tmp_path):
+    phys = PhysicalSchema(
+        database="d", schema="s", introspected_at=datetime(2026, 1, 1),
+        tables={
+            "dim_a": TablePhysical(columns={"id": ColumnPhysical(type="int", pk=True)}),
+            "dim_b": TablePhysical(columns={"id": ColumnPhysical(type="int", pk=True)}),
+            "dim_c1": TablePhysical(columns={"cod_x": ColumnPhysical(type="int", pk=True)}),
+            "dim_c2": TablePhysical(columns={"cod_x": ColumnPhysical(type="int", pk=True)}),
+            "fact_f": TablePhysical(
+                columns={
+                    "id": ColumnPhysical(type="int"),
+                    "cod_x": ColumnPhysical(type="int"),
+                },
+            ),
+        },
+    )
+    phys.to_yaml(tmp_path / "artifacts" / "mschema" / "physical.yaml")
+    cfg = tmp_path / "workspace.yaml"
+    cfg.write_text(
+        "database: {database: d, schema: s, user: u, password: p, transport: direct}\n"
+        f"paths: {{artifacts: {tmp_path/'artifacts'}, indexes: {tmp_path/'i'}, sessions: {tmp_path/'s'}}}\n"
+    )
+    res = CliRunner().invoke(app, ["schema", "suggest-fks", "-c", str(cfg)])
+    assert res.exit_code == 0, res.output
+    assert "nessuna FK da suggerire" in res.output  # id generico, cod_x ambigua
+    assert "cod_x" in res.output  # segnalata come ambigua saltata
+
+    # --assume disambigua la PK multi-proprietario
+    res2 = CliRunner().invoke(
+        app, ["schema", "suggest-fks", "-c", str(cfg), "--assume", "cod_x=dim_c1"]
+    )
+    assert res2.exit_code == 0, res2.output
+    yaml_text = "\n".join(
+        line for line in res2.output.splitlines() if "FK candidate" not in line
+    )
+    data = yaml.safe_load(yaml_text)
+    fact_fks = data["tables"]["fact_f"]["foreign_keys"]
+    assert {"columns": ["cod_x"], "ref_table": "dim_c1",
+            "ref_columns": ["cod_x"]} in fact_fks
+    # dim_c2.cod_x -> dim_c1 (estensione 1:1), ma NON dim_c1 -> se stessa
+    assert "dim_c1" not in data["tables"] or all(
+        fk["ref_table"] != "dim_c1" for fk in data["tables"].get("dim_c1", {}).get("foreign_keys", [])
+    )
+
+    # --assume con tabella inesistente -> errore chiaro
+    res3 = CliRunner().invoke(
+        app, ["schema", "suggest-fks", "-c", str(cfg), "--assume", "cod_x=nope"]
+    )
+    assert res3.exit_code == 1
+    assert "non valido" in res3.output
+
+
+def test_suggest_fks_from_sql_mines_joins(tmp_path):
+    cfg = _write_workspace(tmp_path)
+    sqldir = tmp_path / "approved"
+    sqldir.mkdir()
+    (sqldir / "q1.sql").write_text(
+        "SELECT f.esito FROM datawarehouse.fact_ablazione f "
+        "JOIN datawarehouse.dim_patient p ON f.cod_paz = p.cod_paz"
+    )
+    res = CliRunner().invoke(
+        app, ["schema", "suggest-fks", "-c", str(cfg), "--from-sql", str(sqldir)]
+    )
+    assert res.exit_code == 0, res.output
+    assert "Minati 1 equi-join da 1 file SQL" in res.output
+    assert "ref_table: dim_patient" in res.output
+
+
+def test_suggest_fks_write_merges_and_is_idempotent(tmp_path):
+    cfg = _write_workspace(tmp_path)
+    ann_path = tmp_path / "artifacts" / "mschema" / "annotations.yaml"
+    Annotations(
+        tables={"fact_ablazione": TableAnnotation(description="Ablazioni")}
+    ).to_yaml(ann_path)
+
+    res = CliRunner().invoke(app, ["schema", "suggest-fks", "-c", str(cfg), "--write"])
+    assert res.exit_code == 0, res.output
+    ann = Annotations.from_yaml(ann_path)
+    assert ann.tables["fact_ablazione"].description == "Ablazioni"  # non distrutta
+    assert len(ann.tables["fact_ablazione"].foreign_keys) == 2
+
+    res2 = CliRunner().invoke(app, ["schema", "suggest-fks", "-c", str(cfg), "--write"])
+    assert "nessuna FK da suggerire" in res2.output
+    ann2 = Annotations.from_yaml(ann_path)
+    assert len(ann2.tables["fact_ablazione"].foreign_keys) == 2