15c542d92c
Add the analysis role's execution path: a two-phase orchestration
(per-scenario diagnosis gathered in parallel, then a synthesis pass)
that reads the existing campaign report aggregation plus capped failure
samples, validates the LLM's JSON against the report schema, and strips
fabricated run/scenario references before persisting. Results upsert one
row per campaign (generating/completed/failed) with the model config
snapshot; GET/POST /api/campaigns/{id}/analysis expose the state machine,
guarding non-terminal campaigns and missing analysis models with 400s.
32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
"""Verify the campaign_analyses migration creates the table with a unique campaign_id."""
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import importlib
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import sqlalchemy as sa
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from alembic.migration import MigrationContext
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from alembic.operations import Operations
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def test_campaign_analyses_migration_creates_table(tmp_path, monkeypatch):
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engine = sa.create_engine(f"sqlite:///{tmp_path / 'analyses.db'}")
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with engine.begin() as connection:
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operations = Operations(MigrationContext.configure(connection))
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migration = importlib.import_module(
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"migrations.versions.c8f5e4b13d26_add_campaign_analyses"
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)
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monkeypatch.setattr(migration, "op", operations)
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migration.upgrade()
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inspector = sa.inspect(connection)
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assert "campaign_analyses" in inspector.get_table_names()
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columns = {c["name"] for c in inspector.get_columns("campaign_analyses")}
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assert {
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"id", "campaign_id", "status", "result",
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"model_config_id", "error", "triggered_by", "created_at", "updated_at",
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} <= columns
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uniques = inspector.get_unique_constraints("campaign_analyses")
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assert any(uc["column_names"] == ["campaign_id"] for uc in uniques)
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