marcopanandClaude Fable 5
e24b41b156
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>
116 lines
4.5 KiB
Python
116 lines
4.5 KiB
Python
import json
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from datetime import datetime
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from types import SimpleNamespace
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from typer.testing import CliRunner
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from tht.cli import app
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from tht.mschema.models import ColumnPhysical, PhysicalSchema, TablePhysical
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from tht.vectorstore.embeddings import EmbeddingsError
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class _FakeEmbedder:
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def __init__(self):
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self.calls = 0
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def embed_query(self, text):
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self.calls += 1
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return [0.1, 0.2, 0.3]
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class _FakeSearcher:
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def search(self, vec, top_n, kinds=None):
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if kinds == ["solved_question"]:
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return [SimpleNamespace(
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kind="memory", ref="s-1", id="m1", title="q solved",
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similarity=0.91, content="quanti pazienti nel 2024?",
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metadata={"session_id": "2026-01-01-000000-x", "sql": "SELECT 1",
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"tables": ["fact_ablazione"], "question": "quanti pazienti nel 2024?"},
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)]
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if kinds == ["schema_table", "schema_column"]:
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return [SimpleNamespace(
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kind="schema_table", ref="fact_ablazione", id="t1",
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title="Tabella fact_ablazione", similarity=0.88,
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content="Tabella fact_ablazione", metadata={},
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)]
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if kinds == ["evidence"]:
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return [SimpleNamespace(
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kind="evidence", ref="ev1", id="ev1", title="Dominio ablazione",
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similarity=0.8, content="L'ablazione e' una procedura...",
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metadata={"status": "approved"},
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)]
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return []
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def _workspace(tmp_path, with_session=None):
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PhysicalSchema(
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database="d", schema="s", introspected_at=datetime(2026, 1, 1),
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tables={"fact_ablazione": TablePhysical(
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comment="Ablazioni", columns={"cod_paz": ColumnPhysical(type="bigint")})},
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).to_yaml(tmp_path / "artifacts" / "mschema" / "physical.yaml")
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cfg = tmp_path / "workspace.yaml"
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cfg.write_text(
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"database: {database: d, schema: s, user: u, password: p, transport: direct}\n"
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"vector_db: {database: v, schema: public, user: u, password: p}\n"
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"embeddings: {base_url: 'http://localhost:11434', model: nomic-embed-text, dim: 8}\n"
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f"paths: {{artifacts: {tmp_path/'artifacts'}, indexes: {tmp_path/'i'}, "
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f"sessions: {tmp_path/'sessions'}}}\n"
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)
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if with_session:
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sdir = tmp_path / "sessions" / with_session
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sdir.mkdir(parents=True)
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(sdir / "session_manifest.yaml").write_text(
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f"id: {with_session}\nquestion: q\ndatabase: d\nschema: s\n"
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"created_at: 2026-01-01T00:00:00+00:00\nstatus: open\n"
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)
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return cfg
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def _patch(monkeypatch, embedder, searcher):
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import tht.cli.vector_cmd as vc
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monkeypatch.setattr(vc, "make_embedder", lambda _cfg: embedder)
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monkeypatch.setattr(vc, "open_searcher", lambda _cfg: searcher)
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def test_pack_single_embed_and_sections(tmp_path, monkeypatch):
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cfg = _workspace(tmp_path)
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emb = _FakeEmbedder()
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_patch(monkeypatch, emb, _FakeSearcher())
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res = CliRunner().invoke(app, ["search", "pack", "quanti pazienti", "-c", str(cfg)])
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assert res.exit_code == 0, res.output
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assert emb.calls == 1 # UN solo embedding per le tre ricerche
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assert "fact_ablazione" in res.output and "Ablazioni" in res.output
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assert "Dominio ablazione" in res.output
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assert "SELECT 1" in res.output
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def test_pack_json_and_session_file(tmp_path, monkeypatch):
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sid = "2026-01-01-000000-test"
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cfg = _workspace(tmp_path, with_session=sid)
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_patch(monkeypatch, _FakeEmbedder(), _FakeSearcher())
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res = CliRunner().invoke(
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app, ["search", "pack", "q", "-c", str(cfg), "--session", sid, "--json"]
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)
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assert res.exit_code == 0, res.output
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data = json.loads(res.output)
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assert data["tables"][0]["name"] == "fact_ablazione"
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pack = tmp_path / "sessions" / sid / "retrieval_pack.md"
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assert pack.exists()
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assert "Retrieval pack" in pack.read_text()
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def test_pack_degrades_gracefully(tmp_path, monkeypatch):
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cfg = _workspace(tmp_path)
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class _Broken:
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def embed_query(self, text):
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raise EmbeddingsError("ollama down")
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_patch(monkeypatch, _Broken(), _FakeSearcher())
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res = CliRunner().invoke(app, ["search", "pack", "q", "-c", str(cfg), "--json"])
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assert res.exit_code == 0, res.output
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data = json.loads(res.output[res.output.index("{"):])
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assert data["tables"] == [] and data["evidence"] == [] and data["solved"] == []
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assert any("retrieval non disponibile" in w for w in data["warnings"])
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