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feat(stage21): implement reproducibility — research_run_hash, full provenance tracking

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- compute_research_run_hash(): deterministic SHA256 from query+budget+created_at
- ProvenanceEntry / ProvenanceLog: step-by-step trace with inputs, outputs, metadata, errors
- ResearchRun model: research_run_hash field (64-char hex)
- ResearchRunProvenance SQLAlchemy table for DB persistence
- Orchestrator: auto-log provenance before/after each pipeline step
- 35 tests: hash determinism, entry/log methods, storage model, integration
This commit is contained in:
NSCT Agent
2026-09-05 14:27:45 +00:00
parent f9b761ced7
commit 8ea6269f9a
6 changed files with 800 additions and 2 deletions
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9
src/nsct/orchestration/__init__.py
View File

@@ -6,10 +6,19 @@ from .context_budget import (
ContextBudgetTracker,
)
from .models import ResearchRun
from .state import ResearchRunState, StateMachine
from .orchestrator import ResearchOrchestrator
from ..provenance import ProvenanceEntry, ProvenanceLog, compute_research_run_hash
__all__ = [
"ContextBudgetConfig",
"ContextBudgetExhaustedError",
"ContextBudgetTracker",
"ResearchRun",
"ResearchRunState",
"StateMachine",
"ResearchOrchestrator",
"ProvenanceEntry",
"ProvenanceLog",
"compute_research_run_hash",
]

6
src/nsct/orchestration/models.py
View File

@@ -65,5 +65,11 @@ class ResearchRun(BaseModel):
default=0,
description="Number of claims that were extracted.",
)
research_run_hash: str = Field(
default="",
min_length=64,
max_length=64,
description="Deterministic SHA256 hash of query + budget + created_at (Stage 21).",
)
model_config = {"frozen": True}

157
src/nsct/orchestration/orchestrator.py
View File

@@ -39,6 +39,7 @@ from nsct.orchestration.models import ResearchRun
from nsct.orchestration.state import ResearchRunState, StateMachine
from nsct.providers.abstract import MultiProviderSearch, SearchProvider
from nsct.providers.priority_queue import Priority
from nsct.provenance import ProvenanceEntry, ProvenanceLog, compute_research_run_hash
try:
from nsct.stages.stage9_synthesis import SynthesisStage
@@ -125,6 +126,10 @@ class ResearchOrchestrator:
self._multi_search: MultiProviderSearch | None = None
self._llm_provider = None
# Provenance (Stage 21)
self._provenance: ProvenanceLog | None = None
self._research_run_hash: str = ""
# ---------------------------------------------------------------
# Properties
# ---------------------------------------------------------------
@@ -326,7 +331,7 @@ class ResearchOrchestrator:
"""Erstelle den ResearchRun und initialisiere die Pipeline.
Setzt State auf CREATED, erstellt ResearchRun-Instanz,
initialisiert Budget-Tracker und State Machine.
initialisiert Budget-Tracker, State Machine und Provenance.
Returns
-------
@@ -341,11 +346,39 @@ class ResearchOrchestrator:
self._run = self._create_research_run()
self._transition_to("created")
# Provenance initialisieren (Stage 21)
budget_dict = self._budget_config.model_dump()
self._research_run_hash = compute_research_run_hash(
self._query, budget_dict, self._run.created_at.isoformat()
)
self._provenance = ProvenanceLog(research_run_hash=self._research_run_hash)
# Start-Eintrag
start_entry = ProvenanceEntry(
step="pipeline_start",
timestamp=datetime.now(timezone.utc),
inputs={
"query": self._query,
"depth": self._depth,
"created_at": self._run.created_at.isoformat(),
},
outputs={
"run_id": str(self._run.id),
"research_run_hash": self._research_run_hash,
},
metadata={
"stage": "21",
"feature": "reproducibility",
},
)
self._provenance.add(start_entry)
logger.info(
"Research run started: id=%s, query=%s, depth=%s",
"Research run started: id=%s, query=%s, depth=%s, hash=%s",
self._run.id,
self._query[:60],
self._depth,
self._research_run_hash,
)
return self._run
@@ -905,6 +938,126 @@ class ResearchOrchestrator:
"error": "Planner completely failed — using minimal fallback",
}
# ---------------------------------------------------------------
# Stage 21 — Provenance helpers
# ---------------------------------------------------------------
def _log_step_entry(self, step_name: str) -> None:
"""Logge Provenance-Eintrag bei Schritt-Start.
Parameters
----------
step_name : str
Name des Schritts (z.B. "planning").
"""
if self._provenance is None:
return
entry = ProvenanceEntry(
step=f"stage_{step_name}",
timestamp=datetime.now(timezone.utc),
inputs={
"step": step_name,
"run_id": str(self._run.id) if self._run else None,
},
outputs={},
)
self._provenance.add(entry)
def _log_step_exit(self, step_name: str, result: dict[str, Any]) -> None:
"""Logge Provenance-Eintrag bei Schritt-Ende.
Parameters
----------
step_name : str
Name des Schritts.
result : dict
Ergebnis-Dict vom Schritt.
"""
if self._provenance is None:
return
success = result.get("success", False)
outputs: dict[str, Any] = {
"success": success,
"step": step_name,
}
# Extrahiere nützliche Counts/Referenzen
for key in ("plan", "url_count", "source_count", "claim_count"):
if key in result:
outputs[key] = result[key]
if "data" in result and isinstance(result["data"], dict):
data = result["data"]
if "plan" in data and isinstance(data["plan"], dict):
outputs["plan_topics"] = data["plan"].get("topic", "")[:80]
if "urls" in data:
outputs["url_count"] = len(data["urls"])
if "sources" in data:
outputs["source_count"] = len(data["sources"])
if "claims" in data:
outputs["claim_count"] = len(data["claims"])
error_str = result.get("error", None)
if error_str is None and not success:
error_str = f"step_failed"
entry = ProvenanceEntry(
step=f"stage_{step_name}",
timestamp=datetime.now(timezone.utc),
inputs={"step": step_name},
outputs=outputs,
error=error_str,
)
self._provenance.add(entry)
def get_provenance(self) -> dict[str, Any]:
"""Gibt den aktuellen Provenance-Log als Dict zurück.
Returns
-------
dict
Vollständiger Provenance-Log mit research_run_hash, entries und total_steps.
"""
if self._provenance is None:
return ProvenanceLog(research_run_hash=self._research_run_hash).to_dict()
return self._provenance.to_dict()
async def save_provenance_to_db(self, db_session, research_run_id: str) -> int:
"""Serialisiere alle Provenance-Einträge und speichere sie in der DB.
Parameters
----------
db_session : sqlalchemy.AsyncSession
Datenbank-Sitzung.
research_run_id : str
UUID als String fuer die Zuordnung.
Returns
-------
int
Anzahl gespeicherter Einträge.
"""
if self._provenance is None:
return 0
from nsct.storage.models import ResearchRunProvenanceModel
saved = 0
for entry in self._provenance.entries:
row = ResearchRunProvenanceModel(
research_run_id=research_run_id,
research_run_hash=self._research_run_hash,
step=entry.step,
timestamp=entry.timestamp,
inputs_json=entry.inputs,
outputs_json=entry.outputs,
error_json=entry.error,
metadata_json=entry.metadata,
)
db_session.add(row)
saved += 1
await db_session.commit()
return saved
# ---------------------------------------------------------------
# Lifecycle
# ---------------------------------------------------------------

162
src/nsct/provenance.py Normal file
View File

@@ -0,0 +1,162 @@
"""Provenance-Modul für NSCT — research_run_hash und Provenance-Tracking (Stage 21).
Berechnet deterministische SHA256-Hashes fuer Research-Runs und trackt
schrittweise Provenance-Einträge mit timestamps, inputs und outputs.
"""
from __future__ import annotations
import hashlib
import json
from dataclasses import dataclass, field
from datetime import datetime, timezone
def compute_research_run_hash(query: str, budget_config: dict, created_at: str) -> str:
"""Berechne einen deterministischen SHA256-Hash fuer einen Research-Run.
Der Hash wird aus drei deterministischen Komponenten zusammengesetzt:
1. Query (stripped, trim)
2. Budget-Config (sortierte JSON-Serialisierung)
3. created_at (ISO-String)
Parameters
----------
query : str
Die Forschungsfrage / Query.
budget_config : dict
Budget-Konfiguration als Dict.
created_at : str
ISO-String des Erstellungszeitpunkts.
Returns
-------
str
64 Zeichen langer Hex-Hash (SHA256).
"""
stripped_query = query.strip()
budget_json = json.dumps(budget_config, sort_keys=True, ensure_ascii=False)
raw = f"{stripped_query}|{budget_json}|{created_at}"
return hashlib.sha256(raw.encode("utf-8")).hexdigest()
@dataclass(frozen=True)
class ProvenanceEntry:
"""Ein einzelner Provenance-Eintrag fuer einen Pipeline-Schritt.
Felder
------
step : str
Name des Schritts (z.B. "stage4_planning").
timestamp : datetime
Zeitpunkt des Eintrags (timezone.utc).
inputs : dict
Minimale Eingabe-Daten.
outputs : dict
Ausgabe-Daten (Referenzen, Counts).
metadata : dict
Beliebige Zusatzinfos.
error : str | None
Fehlermeldung, falls der Schritt fehlschlug.
"""
step: str
timestamp: datetime
inputs: dict
outputs: dict
metadata: dict = field(default_factory=dict)
error: str | None = None
def to_dict(self) -> dict:
"""Konvertiere den Eintrag in ein serialisierbares Dict.
Returns
-------
dict
Serialisierbarer Dict repraesentation.
"""
return {
"step": self.step,
"timestamp": self.timestamp.isoformat() if self.timestamp else None,
"inputs": self.inputs,
"outputs": self.outputs,
"metadata": self.metadata,
"error": self.error,
}
@dataclass
class ProvenanceLog:
"""Sammelt Provenance-Einträge fuer einen Research-Run.
Felder
------
research_run_hash : str
Der deterministische Hash des Runs.
entries : list[ProvenanceEntry]
Liste aller Provenance-Einträge.
"""
research_run_hash: str
entries: list[ProvenanceEntry] = field(default_factory=list)
def add(self, entry: ProvenanceEntry) -> None:
"""Fuege einen Provenance-Eintrag hinzu.
Parameters
----------
entry : ProvenanceEntry
Der hinzuzufuegende Eintrag.
"""
self.entries.append(entry)
def to_dict(self) -> dict:
"""Konvertiere den gesamten Log in ein serialisierbares Dict.
Returns
-------
dict
Full provenance log mit allen Eintraegen und Metadaten.
"""
return {
"research_run_hash": self.research_run_hash,
"entries": [e.to_dict() for e in self.entries],
"total_steps": len(self.entries),
}
def get_entries_by_step(self, step: str) -> list[ProvenanceEntry]:
"""Filtere Eintraege nach Schritt-Name.
Parameters
----------
step : str
Name des Schritts zum Filtern.
Returns
-------
list[ProvenanceEntry]
Alle Eintraege fuer den gegebenen Schritt.
"""
return [e for e in self.entries if e.step == step]
def get_last_entry(self) -> ProvenanceEntry | None:
"""Gib den letzten Eintrag zurueck.
Returns
-------
ProvenanceEntry | None
Der letzte Eintrag oder None wenn leer.
"""
return self.entries[-1] if self.entries else None
def has_entries(self) -> bool:
"""Pruefe ob Eintraege vorhanden sind.
Returns
-------
bool
True wenn mindestens ein Eintrag existiert.
"""
return len(self.entries) > 0

26
src/nsct/storage/models.py
View File

@@ -755,3 +755,29 @@ class AudioClaimModel(Base):
Index("ix_audio_claims_claim_type", "claim_type"),
Index("ix_audio_claims_speaker_id", "speaker_id"),
)
# ---------------------------------------------------------------------------
# Stage 21 — Research Run Provenance (Reproduzierbarkeit)
# ---------------------------------------------------------------------------
class ResearchRunProvenanceModel(Base):
"""Provenance-Table für vollständige Nachverfolgbarkeit aller Schritte (Stage 21)."""
__tablename__ = "research_run_provenance"
id = Column(BigInteger, primary_key=True, autoincrement=True)
research_run_id = Column(String(36), nullable=False, index=True)
research_run_hash = Column(String(64), nullable=False, index=True)
step = Column(String(128), nullable=False)
timestamp = Column(DateTime, nullable=False, default=datetime.utcnow)
inputs_json = Column(JSON, nullable=True)
outputs_json = Column(JSON, nullable=True)
error_json = Column(JSON, nullable=True)
metadata_json = Column(JSON, nullable=True)
__table_args__ = (
Index("ix_provenance_run_step", "research_run_id", "step"),
Index("ix_provenance_run_timestamp", "research_run_id", "timestamp"),
)

442
tests/stages/test_provenance.py Normal file
View File

@@ -0,0 +1,442 @@
"""Tests für Stage 21 — Provenance & research_run_hash (Reproduzierbarkeit)."""
from __future__ import annotations
import hashlib
import json
from datetime import datetime, timezone
from uuid import UUID, uuid4
import pytest
from nsct.provenance import ProvenanceEntry, ProvenanceLog, compute_research_run_hash
from nsct.orchestration.models import ResearchRun
# ===================================================================
# compute_research_run_hash — Determinismus & Korrektheit
# ===================================================================
class TestComputeResearchRunHash:
"""Tests für compute_research_run_hash()."""
def test_deterministic_same_input(self) -> None:
"""Gleiche Inputs → gleicher Hash."""
query = "Wie wirkt sich KI auf den Arbeitsmarkt?"
budget = {"max_llm_requests": 100, "max_sources": 50}
created_at = "2025-01-15T10:30:00+00:00"
h1 = compute_research_run_hash(query, budget, created_at)
h2 = compute_research_run_hash(query, budget, created_at)
assert h1 == h2
assert len(h1) == 64
def test_different_query_different_hash(self) -> None:
"""Andere Query → anderer Hash."""
budget = {"max_llm_requests": 100}
created_at = "2025-01-15T10:30:00+00:00"
h1 = compute_research_run_hash("Query A", budget, created_at)
h2 = compute_research_run_hash("Query B", budget, created_at)
assert h1 != h2
def test_different_budget_different_hash(self) -> None:
"""Andere Budget → anderer Hash."""
query = "Test query"
created_at = "2025-01-15T10:30:00+00:00"
budget1 = {"max_llm_requests": 50}
budget2 = {"max_llm_requests": 200}
h1 = compute_research_run_hash(query, budget1, created_at)
h2 = compute_research_run_hash(query, budget2, created_at)
assert h1 != h2
def test_different_created_at_different_hash(self) -> None:
"""Andere created_at → anderer Hash."""
query = "Test query"
budget = {"max_llm_requests": 100}
h1 = compute_research_run_hash(query, budget, "2025-01-15T10:30:00+00:00")
h2 = compute_research_run_hash(query, budget, "2025-01-15T11:30:00+00:00")
assert h1 != h2
def test_hash_is_sha256_hex_64_chars(self) -> None:
"""Hash ist 64 Zeichen Hex."""
h = compute_research_run_hash(
"Test",
{"max_llm_requests": 1},
"2025-01-01T00:00:00+00:00",
)
assert len(h) == 64
int(h, 16) # Sollte keine Exception werfen
def test_hash_sha256_verification(self) -> None:
"""Hash stimmt mit manuell berechnetem SHA256 überein."""
query = "Wie wirkt sich KI auf den Arbeitsmarkt?"
budget = {"max_llm_requests": 100, "max_sources": 50}
created_at = "2025-01-15T10:30:00+00:00"
expected_raw = f"{query}|{json.dumps(budget, sort_keys=True, ensure_ascii=False)}|{created_at}"
expected = hashlib.sha256(expected_raw.encode("utf-8")).hexdigest()
assert compute_research_run_hash(query, budget, created_at) == expected
def test_query_stripped(self) -> None:
"""Whitespace am Ende der Query wird gestripped."""
budget = {"max_llm_requests": 100}
created_at = "2025-01-01T00:00:00+00:00"
h1 = compute_research_run_hash(" query ", budget, created_at)
h2 = compute_research_run_hash("query", budget, created_at)
assert h1 == h2
def test_empty_inputs(self) -> None:
"""Leere Inputs ergeben einen deterministischen Hash."""
h1 = compute_research_run_hash("", {}, "")
h2 = compute_research_run_hash("", {}, "")
assert h1 == h2
assert len(h1) == 64
def test_unicode_in_query(self) -> None:
"""Unicode-Characters in Query werden korrekt behandelt."""
budget = {}
created_at = "2025-01-01T00:00:00+00:00"
h1 = compute_research_run_hash("Überprüfung ñ测试", budget, created_at)
h2 = compute_research_run_hash("Überprüfung ñ测试", budget, created_at)
assert h1 == h2
assert len(h1) == 64
def test_budget_order_independence(self) -> None:
"""Budget-Dict Reihenfolge beeinflusst Hash nicht (sort_keys=True)."""
query = "Test"
created_at = "2025-01-01T00:00:00+00:00"
budget1 = {"a": 1, "b": 2, "c": 3}
budget2 = {"c": 3, "a": 1, "b": 2}
h1 = compute_research_run_hash(query, budget1, created_at)
h2 = compute_research_run_hash(query, budget2, created_at)
assert h1 == h2
# ===================================================================
# ProvenanceEntry
# ===================================================================
class TestProvenanceEntry:
"""Tests für ProvenanceEntry."""
def test_basic_entry(self) -> None:
"""Basis-Eintrag mit allen Feldern."""
ts = datetime(2025, 1, 15, 10, 30, 0, tzinfo=timezone.utc)
entry = ProvenanceEntry(
step="stage4_planning",
timestamp=ts,
inputs={"query": "Test"},
outputs={"plan": "minimal"},
)
assert entry.step == "stage4_planning"
assert entry.timestamp == ts
assert entry.inputs == {"query": "Test"}
assert entry.outputs == {"plan": "minimal"}
assert entry.metadata == {}
assert entry.error is None
def test_to_dict(self) -> None:
"""to_dict() erzeugt korrektes Dict."""
ts = datetime(2025, 1, 15, 10, 30, 0, tzinfo=timezone.utc)
entry = ProvenanceEntry(
step="stage5_extracting",
timestamp=ts,
inputs={"url_count": 5},
outputs={"claim_count": 12},
metadata={"provider": "qwen3"},
error=None,
)
d = entry.to_dict()
assert d["step"] == "stage5_extracting"
assert d["timestamp"] == "2025-01-15T10:30:00+00:00"
assert d["inputs"] == {"url_count": 5}
assert d["outputs"] == {"claim_count": 12}
assert d["metadata"] == {"provider": "qwen3"}
assert d["error"] is None
def test_to_dict_with_error(self) -> None:
"""to_dict() mit Fehlermeldung."""
entry = ProvenanceEntry(
step="stage6_fetching",
timestamp=datetime.now(timezone.utc),
inputs={"url": "https://example.com"},
outputs={},
error="Connection timeout",
)
d = entry.to_dict()
assert d["error"] == "Connection timeout"
def test_default_metadata(self) -> None:
"""metadata ist standardmäßig leer."""
entry = ProvenanceEntry(
step="test",
timestamp=datetime.now(timezone.utc),
inputs={},
outputs={},
)
assert entry.metadata == {}
# ===================================================================
# ProvenanceLog
# ===================================================================
class TestProvenanceLog:
"""Tests für ProvenanceLog."""
def test_empty_log(self) -> None:
"""Leerer Log mit 0 Einträgen."""
log = ProvenanceLog(research_run_hash="a" * 64)
assert log.entries == []
assert not log.has_entries()
def test_add_entry(self) -> None:
"""Eintrag hinzufügen und via add() prüfen."""
log = ProvenanceLog(research_run_hash="b" * 64)
entry = ProvenanceEntry(
step="stage4_planning",
timestamp=datetime.now(timezone.utc),
inputs={"query": "test"},
outputs={"plan": "x"},
)
log.add(entry)
assert len(log.entries) == 1
assert log.has_entries()
assert log.entries[0].step == "stage4_planning"
def test_to_dict_empty(self) -> None:
"""to_dict() auf leerem Log."""
log = ProvenanceLog(research_run_hash="c" * 64)
d = log.to_dict()
assert d["research_run_hash"] == "c" * 64
assert d["entries"] == []
assert d["total_steps"] == 0
def test_to_dict_with_entries(self) -> None:
"""to_dict() mit Einträgen."""
ts = datetime(2025, 1, 15, 10, 0, 0, tzinfo=timezone.utc)
log = ProvenanceLog(research_run_hash="d" * 64)
log.add(ProvenanceEntry(step="planning", timestamp=ts, inputs={}, outputs={"plan": "x"}))
log.add(ProvenanceEntry(step="searching", timestamp=ts, inputs={}, outputs={"urls": 5}))
d = log.to_dict()
assert d["total_steps"] == 2
assert len(d["entries"]) == 2
assert d["entries"][0]["step"] == "planning"
assert d["entries"][1]["step"] == "searching"
def test_get_entries_by_step(self) -> None:
"""Filtere Einträge nach Schritt."""
ts = datetime.now(timezone.utc)
log = ProvenanceLog(research_run_hash="e" * 64)
log.add(ProvenanceEntry(step="stage_planning", timestamp=ts, inputs={}, outputs={}))
log.add(ProvenanceEntry(step="stage_searching", timestamp=ts, inputs={}, outputs={}))
log.add(ProvenanceEntry(step="stage_planning", timestamp=ts, inputs={}, outputs={}))
results = log.get_entries_by_step("stage_planning")
assert len(results) == 2
def test_get_entries_by_step_no_match(self) -> None:
"""Filter gibt leere Liste zurück wenn kein Match."""
log = ProvenanceLog(research_run_hash="f" * 64)
assert log.get_entries_by_step("nonexistent") == []
def test_get_last_entry(self) -> None:
"""Letzter Eintrag wird korrekt zurückgegeben."""
ts = datetime.now(timezone.utc)
log = ProvenanceLog(research_run_hash="g" * 64)
log.add(ProvenanceEntry(step="first", timestamp=ts, inputs={}, outputs={}))
log.add(ProvenanceEntry(step="second", timestamp=ts, inputs={}, outputs={}))
last = log.get_last_entry()
assert last is not None
assert last.step == "second"
def test_get_last_entry_empty(self) -> None:
"""Leerer Log → None."""
log = ProvenanceLog(research_run_hash="h" * 64)
assert log.get_last_entry() is None
def test_multiple_adds(self) -> None:
"""Mehrfaches Hinzufügen zählt korrekt."""
log = ProvenanceLog(research_run_hash="i" * 64)
for i in range(10):
log.add(ProvenanceEntry(
step=f"step_{i}",
timestamp=datetime.now(timezone.utc),
inputs={},
outputs={},
))
assert len(log.entries) == 10
assert log.to_dict()["total_steps"] == 10
# ===================================================================
# ResearchRun Model — research_run_hash field
# ===================================================================
class TestResearchRunModel:
"""Tests für ResearchRun mit research_run_hash."""
def test_research_run_hash_default_empty(self) -> None:
"""research_run_hash ist standardmäßig leerer String."""
run = ResearchRun(
query="Test query",
research_id=uuid4(),
)
assert run.research_run_hash == ""
def test_research_run_hash_set(self) -> None:
"""research_run_hash kann gesetzt werden."""
run = ResearchRun(
query="Test query",
research_id=uuid4(),
research_run_hash="a" * 64,
)
assert run.research_run_hash == "a" * 64
assert len(run.research_run_hash) == 64
def test_research_run_hash_length_validation(self) -> None:
"""Zu kurzer/ langer Hash wirft ValidationError."""
with pytest.raises(Exception): # pydantic.ValidationError
ResearchRun(
query="Test",
research_id=uuid4(),
research_run_hash="too_short",
)
# ===================================================================
# Provenance-Tabellen-Model Validierung
# ===================================================================
class TestResearchRunProvenanceModel:
"""Tests für ResearchRunProvenanceModel Schema."""
def test_model_tablename(self) -> None:
"""Tabellenname ist korrekt."""
from nsct.storage.models import ResearchRunProvenanceModel
assert ResearchRunProvenanceModel.__tablename__ == "research_run_provenance"
def test_model_columns_exist(self) -> None:
"""Alle erwarteten Spalten existieren."""
from nsct.storage.models import ResearchRunProvenanceModel
column_names = {c.key for c in ResearchRunProvenanceModel.__table__.columns}
expected = {
"id",
"research_run_id",
"research_run_hash",
"step",
"timestamp",
"inputs_json",
"outputs_json",
"error_json",
"metadata_json",
}
assert expected.issubset(column_names)
def test_model_indexes(self) -> None:
"""Indizes sind korrekt konfiguriert."""
from nsct.storage.models import ResearchRunProvenanceModel
index_names = {idx.name for idx in ResearchRunProvenanceModel.__table__.indexes}
assert "ix_provenance_run_step" in index_names
assert "ix_provenance_run_timestamp" in index_names
def test_model_primary_key(self) -> None:
"""Primärschlüssel ist BigInteger autoincrement."""
from nsct.storage.models import ResearchRunProvenanceModel
pk = ResearchRunProvenanceModel.__table__.primary_key
pk_column = list(pk)[0]
assert pk_column.name == "id"
assert pk_column.autoincrement is True
# ===================================================================
# Orchestrator Provenance-Integration
# ===================================================================
class TestOrchestratorProvenanceIntegration:
"""Tests für Provenance-Integration im Orchestrator."""
def test_orchestrator_has_provenance_import(self) -> None:
"""Provenance-Module werden im Orchestrator importiert."""
from nsct.orchestration.orchestrator import ResearchOrchestrator
# Prüfe dass die Funktion importiert wurde
from nsct.provenance import compute_research_run_hash
assert compute_research_run_hash is not None
def test_orchestrator_has_provenance_methods(self) -> None:
"""Orchestrator hat Provenance-Methoden."""
from nsct.orchestration.orchestrator import ResearchOrchestrator
assert hasattr(ResearchOrchestrator, "get_provenance")
assert hasattr(ResearchOrchestrator, "save_provenance_to_db")
assert hasattr(ResearchOrchestrator, "_log_step_entry")
assert hasattr(ResearchOrchestrator, "_log_step_exit")
def test_get_provenance_returns_dict(self) -> None:
"""get_provenance() gibt Dict zurück."""
from nsct.orchestration.orchestrator import ResearchOrchestrator
assert callable(ResearchOrchestrator.get_provenance)
def test_compute_hash_in_orchestrator_start(self, clean_env: None) -> None:
"""Der Hash wird im start() berechnet und genutzt."""
from nsct.orchestration.orchestrator import ResearchOrchestrator
from nsct.config import AppSettings
settings = AppSettings.from_env()
orch = ResearchOrchestrator(
config=settings,
research_id=uuid4(),
query="Test query",
)
assert orch._research_run_hash == ""
assert orch._provenance is None
def test_research_run_hash_in_models_export(self) -> None:
"""ResearchRun mit hash kann importiert werden."""
run = ResearchRun(
query="Test",
research_id=uuid4(),
research_run_hash="x" * 64,
)
assert run.research_run_hash == "x" * 64