Files
6krrt/tests/test_apply_tiering.py
adlee-was-taken c3484f05e5 feat: retarget router to NeuralWatt-only, add serving-class routing and an OpenAI-compatible dispatcher
Drops OpenRouter entirely. The provider column and (model_id, provider) key
stay so a second provider needs no migration.

Verified against the live API — the poller's field mappings were previously
unconfirmed guesses and turned out correct.

Routing correctness:

- Tier on metadata.reasoning.default_enabled, not capabilities.reasoning.
  The latter only means "the endpoint accepts a reasoning param" and is true
  for 17 of 19 rows, which put 17 models in tier 3 and left tier 1 empty.
  Cost is now checked before the reasoning rule so $0.28/1M models can reach
  tier 1. Distribution goes from 2/17 to 4/6/9.

- Capture serving class. NeuralWatt ships ~6 base models as 19 rows whose id
  suffixes are three orthogonal dimensions (hence glm-5.2-short-fast-flex):
  -flex is discounted async held during peak, -fast is reasoning disabled or
  capped, -short is a 200K pool. They carry identical catalog pricing, so
  without these columns all 7 GLM rows tie exactly and an interactive request
  could land on a preemptible row. Latency tolerance is a hard filter, not a
  weight. Suffixes match whole segments so deepseek-v4-flash is not read as
  a -fast row.

- Exclude access-gated models. 6 of 19 rows are grant-gated or canary, marked
  only in prose, and would 403 at dispatch.

- Log the provider's real billed cost and carbon rather than a
  tokens x list-price estimate, and score eco on carbon per design doc §4.

New dispatcher.py exposes /health, /route (dry run, no spend), /dispatch,
plus an OpenAI-compatible /v1/models and /v1/chat/completions so any normal
client can use it. Streaming is proxied chunk by chunk; NeuralWatt emits
energy and cost as SSE comment lines, which clients ignore and the router
reads on the way past — otherwise streamed calls would log no energy at all.

Classifier now gets the allowed category list injected from config (it was
returning invented labels that join against nothing) and runs at
temperature 0, because the same prompt was classifying tier 2 then tier 1 and
routing to different models.

Ships systemd user units. The poller timer is load-bearing, not
housekeeping: stale_after_days is 3 with exclude_stale true, so an unpolled
catalog eventually marks every row stale and the router returns no candidates
at all.

Documents the finding that most affects this project: NeuralWatt bills a flat
$8.00/kWh, not per token. List price ranks models backwards — on the same
prompt kimi-k2.7-code-fast ($4/1M) cost 10x more than kimi-k3-fast ($15/1M).
scoring.cost_score still reads list price; re-basing it is the open call.

Tests 28 -> 74.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018xTPER7K8fNyKiuqNvTCTa
2026-08-12 00:03:43 -04:00

177 lines
6.8 KiB
Python

"""Integration tests for tier.py — apply_tiering DB upsert.
Seeds a temp sqlite DB from schema.sql, inserts synthetic model rows with
known expected tiers, and asserts the tiering pass writes the right tier
for every row (no NULLs), honors the override map, matches the heuristic,
is idempotent, and warns when reasoning_default_enabled is uniformly False.
"""
import sqlite3
from pathlib import Path
import pytest
from config import RouterConfig, TieringConfig, load_config
from tier import apply_tiering
ROOT = Path(__file__).resolve().parent.parent
SCHEMA_SQL = (ROOT / "schema.sql").read_text()
def _make_config(model_tiers: dict[str, int] | None = None) -> RouterConfig:
"""Real config.yaml with a synthetic tiering block (override map)."""
cfg = load_config(ROOT / "config.yaml")
return cfg.model_copy(
update={
"tiering": TieringConfig(
cheap_completion_max=1.00,
model_tiers=model_tiers or {},
)
}
)
@pytest.fixture
def db(tmp_path):
"""Empty temp sqlite DB created from schema.sql."""
conn = sqlite3.connect(tmp_path / "test.db")
conn.executescript(SCHEMA_SQL)
yield conn
conn.close()
def _insert(conn: sqlite3.Connection, rows: list[dict]) -> None:
"""Insert synthetic model rows (only the columns the tiering pass reads).
``supports_reasoning`` defaults to mirroring ``reasoning_default_enabled``
so callers only specify the signal under test; the fallback path is
exercised by setting them independently.
"""
for r in rows:
default_enabled = r["reasoning_default_enabled"]
conn.execute(
"""
INSERT INTO models (
model_id, provider, supports_reasoning, reasoning_default_enabled,
reasoning_mode, cost_per_1m_completion, pricing_tbd, last_updated
) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
""",
(
r["model_id"],
r["provider"],
int(r.get("supports_reasoning", default_enabled)),
int(default_enabled),
r.get("reasoning_mode", "default"),
r["cost_per_1m_completion"],
int(r["pricing_tbd"]),
"2026-08-07T00:00:00+00:00",
),
)
conn.commit()
def _tiers(conn: sqlite3.Connection) -> dict[tuple[str, str], int | None]:
return {
(model_id, provider): tier
for model_id, provider, tier in conn.execute(
"SELECT model_id, provider, tier FROM models"
)
}
# --- required test 1: no NULLs, every tier in {1, 2, 3} -------------------
def test_every_row_gets_a_tier_in_1_2_3(db):
# Given: a mixed catalog covering every heuristic branch
_insert(db, [
{"model_id": "reasoner", "provider": "p", "reasoning_default_enabled": True,
"cost_per_1m_completion": 5.0, "pricing_tbd": False},
{"model_id": "cheap", "provider": "p", "reasoning_default_enabled": False,
"cost_per_1m_completion": 0.25, "pricing_tbd": False},
{"model_id": "mid", "provider": "p", "reasoning_default_enabled": False,
"cost_per_1m_completion": 5.0, "pricing_tbd": False},
{"model_id": "null-cost", "provider": "p", "reasoning_default_enabled": False,
"cost_per_1m_completion": None, "pricing_tbd": False},
{"model_id": "tbd", "provider": "p", "reasoning_default_enabled": False,
"cost_per_1m_completion": 0.10, "pricing_tbd": True},
])
# When: applying the tiering pass
apply_tiering(db, _make_config())
# Then: every row has a tier in {1, 2, 3} and none are NULL
tiers = _tiers(db)
assert len(tiers) == 5
assert all(tier in (1, 2, 3) for tier in tiers.values())
# --- required test 2: override-map rows match the override ----------------
def test_override_map_row_matches_override(db):
# Given: a reasoning model (heuristic would say 3) overridden to tier 1
_insert(db, [
{"model_id": "deep-reasoner", "provider": "p", "reasoning_default_enabled": True,
"cost_per_1m_completion": 5.0, "pricing_tbd": False},
])
# When: applying tiering with an override map
apply_tiering(db, _make_config(model_tiers={"deep-reasoner": 1}))
# Then: the override wins over the heuristic
assert _tiers(db)[("deep-reasoner", "p")] == 1
# --- required test 3: heuristic rows match expected -----------------------
def test_heuristic_rows_match_expected_tiers(db):
# Given: reasoning -> 3, cheap non-reasoning -> 1, everything else -> 2
_insert(db, [
{"model_id": "reasoner", "provider": "p", "reasoning_default_enabled": True,
"cost_per_1m_completion": 5.0, "pricing_tbd": False},
{"model_id": "cheap", "provider": "p", "reasoning_default_enabled": False,
"cost_per_1m_completion": 0.25, "pricing_tbd": False},
{"model_id": "mid", "provider": "p", "reasoning_default_enabled": False,
"cost_per_1m_completion": 5.0, "pricing_tbd": False},
])
# When: applying the tiering pass
apply_tiering(db, _make_config())
# Then: each row matches its expected heuristic tier
tiers = _tiers(db)
assert tiers[("reasoner", "p")] == 3
assert tiers[("cheap", "p")] == 1
assert tiers[("mid", "p")] == 2
# --- required test 4: re-running is idempotent ----------------------------
def test_rerun_is_idempotent(db):
# Given: a mixed catalog
_insert(db, [
{"model_id": "reasoner", "provider": "p", "reasoning_default_enabled": True,
"cost_per_1m_completion": 5.0, "pricing_tbd": False},
{"model_id": "cheap", "provider": "p", "reasoning_default_enabled": False,
"cost_per_1m_completion": 0.25, "pricing_tbd": False},
{"model_id": "mid", "provider": "p", "reasoning_default_enabled": False,
"cost_per_1m_completion": 5.0, "pricing_tbd": False},
])
# When: applying the tiering pass twice
apply_tiering(db, _make_config())
first = _tiers(db)
apply_tiering(db, _make_config())
second = _tiers(db)
# Then: tiers are identical across runs
assert first == second
# --- required test 5: sanity-guard warning path ---------------------------
def test_warns_when_reasoning_default_enabled_uniformly_false(db, capsys):
# Given: a catalog where no row reasons by default
_insert(db, [
{"model_id": "cheap", "provider": "p", "reasoning_default_enabled": False,
"cost_per_1m_completion": 0.25, "pricing_tbd": False},
{"model_id": "mid", "provider": "p", "reasoning_default_enabled": False,
"cost_per_1m_completion": 5.0, "pricing_tbd": False},
])
# When: applying the tiering pass
apply_tiering(db, _make_config())
# Then: a clear warning is emitted (not a hard failure)
captured = capsys.readouterr()
assert "reasoning_default_enabled is uniformly False" in captured.err