refactor(memory): extract F2 recall path (#23)

harness/tests/memory/test_recall.py

@@ -0,0 +1,168 @@
+import json
+from datetime import UTC, datetime
+from types import SimpleNamespace
+import uuid
+
+from typer.testing import CliRunner
+
+from tht.cli import app
+from tht.decisions import DecisionInput
+from tht.memory import MemoryRecord, recall_memories, save_registry
+from tht.phase import current_phase
+from tht.session.filesystem_repository import FilesystemSessionRepository
+from tht.session.models import PrincipalContext, SessionManifest
+
+
+def _memory(id_: str, type_: str = "concept_clarified") -> MemoryRecord:
+    return MemoryRecord(
+        id=id_,
+        ts=datetime(2026, 8, 24, tzinfo=UTC),
+        session_id="source-session",
+        decision_seq=int(id_.split("-")[1]),
+        type=type_,
+        subject=f"subject {id_}",
+        detail=f"detail {id_}",
+        rationale=f"rationale {id_}",
+        question_context="source question",
+        concepts=[f"concept {id_}"],
+    )
+
+
+class Embedder:
+    def __init__(self):
+        self.questions = []
+
+    def embed_query(self, question):
+        self.questions.append(question)
+        return [0.1, 0.2]
+
+
+class Searcher:
+    def __init__(self, hits):
+        self.hits = hits
+        self.calls = []
+
+    def search(self, embedding, *, top_n, kinds):
+        self.calls.append((embedding, top_n, kinds))
+        return self.hits
+
+
+def test_recall_preserves_rank_and_public_payload_for_reusable_memories():
+    records = [_memory("mem-0001"), _memory("mem-0002", "table_promoted"), _memory("mem-0003")]
+    searcher = Searcher([
+        SimpleNamespace(ref="mem-0003", similarity=0.93456),
+        SimpleNamespace(ref="mem-0002", similarity=0.92345),
+        SimpleNamespace(ref="orphan", similarity=0.91234),
+        SimpleNamespace(ref="mem-0001", similarity=0.87654),
+    ])
+    embedder = Embedder()
+
+    results = recall_memories(
+        "active patients",
+        records=records,
+        decisions=[],
+        searcher=searcher,
+        embedder=embedder,
+        top=5,
+    )
+
+    assert [result["id"] for result in results] == ["mem-0003", "mem-0001"]
+    assert results[0] == {
+        "id": "mem-0003",
+        "type": "concept_clarified",
+        "subject": "subject mem-0003",
+        "detail": "detail mem-0003",
+        "rationale": "rationale mem-0003",
+        "question_context": "source question",
+        "tables": [],
+        "concepts": ["concept mem-0003"],
+        "session_id": "source-session",
+        "score": 0.9346,
+    }
+    assert embedder.questions == ["active patients"]
+    assert searcher.calls == [([0.1, 0.2], 5, ["memory"])]
+
+
+def _workspace_config(tmp_path):
+    config = tmp_path / "workspace.yaml"
+    config.write_text(
+        f"""
+runtime_identity:
+  workspace_id: psd-clinical
+  workspace_revision: {'a' * 40}
+dwh:
+  type: postgres_direct
+  connection: {{database: analytics, schema: mart, user: reader, password: secret}}
+vectors:
+  type: qdrant
+  base_url: http://qdrant:6333
+  collection: psd-clinical
+roots:
+  sessions: {tmp_path / 'sessions'}
+  artifacts: {tmp_path / 'artifacts'}
+  indexes: {tmp_path / 'indexes'}
+embeddings:
+  provider: ollama_internal
+  base_url: http://embedding:11434
+  model: qwen3-embedding:0.6b
+  dim: 1024
+"""
+    )
+    return config
+
+
+def test_recall_cli_reconstructs_applied_and_rejected_memory_from_persisted_f2_session(
+    tmp_path, monkeypatch
+):
+    monkeypatch.setenv("THT_HOME", str(tmp_path / "home"))
+    repository = FilesystemSessionRepository(
+        tmp_path / "home",
+        "psd-clinical",
+        PrincipalContext(issuer="local", subject="reviewer"),
+        root=tmp_path / "sessions",
+    )
+    session_id = str(uuid.uuid4())
+    repository.create(SessionManifest(
+        id=session_id,
+        created_at=datetime(2026, 8, 24, tzinfo=UTC),
+        question="active patients",
+        database="analytics",
+        schema="mart",
+    ))
+    repository.append_decisions(session_id, [
+        DecisionInput(type="phase_approved", subject="phase:1"),
+        DecisionInput(
+            type="concept_clarified",
+            subject="active patient",
+            rationale="Applied from mem-0003",
+        ),
+        DecisionInput(
+            type="memory_rejected",
+            subject="mem-0001",
+            rationale="Not relevant to the resumed question",
+        ),
+    ])
+
+    records = [_memory("mem-0001"), _memory("mem-0003")]
+    searcher = Searcher([
+        SimpleNamespace(ref="mem-0003", similarity=0.9),
+        SimpleNamespace(ref="mem-0001", similarity=0.8),
+    ])
+    embedder = Embedder()
+    save_registry(records, tmp_path / "artifacts" / "memory" / "registry.jsonl")
+    monkeypatch.setattr("tht.cli.vector_cmd.open_searcher", lambda cfg: searcher)
+    monkeypatch.setattr("tht.cli.vector_cmd.make_embedder", lambda cfg: embedder)
+
+    response = CliRunner().invoke(
+        app,
+        [
+            "memory", "search", "active patients", "--session", session_id,
+            "--json", "-c", str(_workspace_config(tmp_path)),
+        ],
+    )
+
+    assert response.exit_code == 0, response.output
+    assert json.loads(response.stdout) == []
+    assert current_phase(repository.get(session_id)) == 2
+    assert embedder.questions == ["active patients"]
+    assert searcher.calls == [([0.1, 0.2], 5, ["memory"])]