Files
creator/backend/tests/conftest.py
2026-07-04 12:21:45 +02:00

85 lines
2.8 KiB
Python

import sys
from pathlib import Path
import pytest
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
import database # noqa: E402
@pytest.fixture
async def testdb(tmp_path, monkeypatch):
"""Fresh sqlite file per test; resets the module-global connection."""
monkeypatch.setattr(database, "DB_PATH", tmp_path / "test.db")
database._db = None
await database.init_db()
yield database
await database.close_db()
@pytest.fixture
async def fake_welt(testdb, tmp_path, monkeypatch):
"""E2E ohne LLM: run_agent überall durch die Fake-Welt ersetzt, Tempo-Bremsen raus.
Alle echten Schichten (_race, Quorum, Panels, Producer, QA-Gate) laufen mit."""
import agents
import blocks
import board_inventory as bi
import guide
import kanban
import pipeline
import qa
import repair
from fake_agents import Welt
welt = Welt()
async def fake_run_agent(agent_key, prompt, timeout, provider="claude", role="fast",
capabilities="none", lane="batch", scope=None, on_line=None, label=""):
return welt.respond(agent_key, prompt, capabilities)
for mod in (agents, pipeline, blocks, guide, repair):
monkeypatch.setattr(mod, "run_agent", fake_run_agent)
# Tempo: grace/poll/backoff bremsen echte Läufe, nicht den Fake
monkeypatch.setattr(blocks, "CONSENSUS_GRACE", 0)
monkeypatch.setattr(bi, "_QA_GATE_POLL", 0.05)
monkeypatch.setattr(kanban, "RETRY_BACKOFF", 0.05)
monkeypatch.setattr(qa, "QA_DIR", tmp_path / "qa")
import guide_board
monkeypatch.setattr(guide_board, "READABILITY_ACTIVE", False) # kein Modell-Load im Test
import asyncio as _aio
monkeypatch.setattr(bi, "_ingest_lock", _aio.Lock()) # Modul-Lock klebt sonst am Vortest-Loop
class _FakeEmb: # identischer Text → cos 1.0, sonst 0.0 (deterministisch, ohne Modell)
@staticmethod
def available():
return True
@staticmethod
def embed_sims(texts):
import numpy as np
uniq = {t: k for k, t in enumerate(dict.fromkeys(texts))}
arr = np.zeros((len(texts), max(len(uniq), 1)))
for r, t in enumerate(texts):
arr[r, uniq[t]] = 1.0
return arr @ arr.T
@staticmethod
def embed(texts):
import numpy as np
uniq = {t: k for k, t in enumerate(dict.fromkeys(texts))}
arr = np.zeros((len(texts), max(len(uniq), 1)))
for r, t in enumerate(texts):
arr[r, uniq[t]] = 1.0
return arr
import board_artefacts as ba
for mod in (blocks, ba, qa):
monkeypatch.setattr(mod, "embedding", _FakeEmb)
async def emb_ok(flow): # Board-1-Vektorpfade aus (wie board_env) — Judge-Wellen reichen
return False
monkeypatch.setattr(bi, "_emb_ok", emb_ok)
return welt