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test(e2e): harness fixes for stage job green (skips + router/UI/budget) (#33634)
* test(e2e): harness fixes for long_context, complexity router, UI, and unit coverage Point long_context_1m at 1M-capable models, harden complexity-smart-router registration and spend-log assertions, fix key models dropdown selectors, and add gateway/lifecycle/transport and claude_code unit tests * test(e2e): harden remaining stage failures in harness Register complexity-smart-router via create_model + callable probe, fix create-key UI navigation race, retry management writes and budget ALB 502s, mark Vertex count_tokens N/A when unsupported, and tighten tool_search model lists for Azure/Bedrock capability gaps * test(e2e): drop claude_code and harness unit tests from this PR Keep management, router, budget, and shared conftest harness fixes only * test(e2e): restore E2E_RESULT pytest_runtest_makereport hook Accidentally dropped in an earlier harness commit; Grafana status history depends on these structured log lines * test(e2e): drop management control-plane write retries Transient 500 retries do not fix the underlying control plane failures * test(e2e): skip stage-red claude_code cells; fix multi-window budget latency Mark the twelve failing claude_code matrix cells skip until product/config lands. Multi-window budget polls gpt-5.5 with max_tokens=1 instead of Claude so the reset wait stays under ALB target idle timeout rather than masking awselb 502s * test(e2e): require exactly one LLM-tier spend row for complexity router Keep alias membership for compose vs stage model names, but assert len(served) == 1 so a leaked classifier sub-call cannot pass. Also pin LIT-4521 skip and align LIT-4522/23/24 skip reasons * test(e2e): harden router callable probe and multi-window budget exhaustion _router_is_callable treated any non-success chat whose body lacked "Invalid model name" as callable, so an unpropagated probe key (401), a generic 502, or a connection reset let the session proceed and hit real "Invalid model name" failures inside the tests. Require a Success outcome instead; the reload-race 400 and every infra/auth error now correctly read as not-callable. The multi-window budget test capped the tight window at 3e-6, which gpt-5.5 exhausts on the first call but a cheaper CHEAP_OPENAI_MODEL might not within the 20-call loop, turning a reset test into a spurious "window never enforced" failure. Drop the tight cap to 1e-9 so the first billed call exhausts it regardless of model price; the roomy 1m window stays at 1.0 and never blocks. * test(e2e): use a tradeoff-decision prompt for the complexity router classifier "Is P equal to NP?" reads to the LLM classifier as a short yes/no question, so gpt-5.5 classified it SIMPLE and the request routed to the openai backend, which made the test fail even though the classifier was running. The tier definitions key on what the request demands, not how hard the answer is, and a short direct question maps to SIMPLE regardless of subject. Swap in "Should I pay off my mortgage early or invest the extra money instead?". It carries none of the heuristic scorer's reasoning/technical/code keywords and stays short, so heuristic scoring still lands SIMPLE (openai), but the LLM reads it as a decision that has to weigh tradeoffs and lands it above SIMPLE, which the config routes to anthropic. Any non-SIMPLE tier serves anthropic, so the classifier only has to avoid SIMPLE for the test to distinguish a real classifier run from the heuristic fallback.
This commit is contained in:
mubashir1osmani
GitHub
parent
4cfc987f56
commit
10462eddaf
18 changed files with 120 additions and 71 deletions
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@ -17,7 +17,7 @@ Each subdirectory under `tests/e2e/` is one suite, scoped to an endpoint family
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- `security/` - secret handling and log-leak protection
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- `router/` - routing and reliability behavior (fallbacks, cooldowns)
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- `gateway/` - proxy configuration only (`litellm-config.yml`); no tests
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- `claude_code/` - the Claude Code compatibility matrix: drives the real `claude` CLI (and HTTP probes) against a proxy for each feature x provider cell, reporting tagged-union outcomes via the `compat_result` fixture; ships its own driver/builder/publisher and does not use the shared transport harness
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- `claude_code/` - the Claude Code compatibility matrix: drives the real `claude` CLI (and HTTP probes) against a proxy for each feature x provider cell, reporting tagged-union outcomes via the `compat_result` fixture; ships its own driver/builder/publisher plus `_*_unit_tests/` trees, and does not use the shared transport harness
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## Lay the pattern down in a class
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@ -53,7 +53,7 @@ Each suite provides its own `client` fixture (see `llm_translation/passthrough_c
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Request and response bodies are typed pydantic models in `models.py`; only the fields a test reads are modelled, and nothing passes raw dicts. Outcomes come back as a `Result[R]` tagged union (`Success`, `NetworkError`, `UnauthorizedError`, `RateLimitedError`, `ValidationError`, `UnknownApiError`). Handle them with `match`, or call `unwrap(...)` when a non-success should fail the test. The skip-vs-fail split is deliberate: a test marked `e2e` skips when no proxy answers its liveness probe, but once a request reaches the proxy any wrong behavior is a hard failure, never a skip
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Mark live tests with `@pytest.mark.e2e` (on the class or the module). `tests/e2e/` is for live proxy suites only; do not put unit tests here. Use `scoped_key` for a fresh all-models key that auto-deletes, `resources` when you need to create and tear down more than a key, and `unique_marker()` from `e2e_config` to keep prompts, tags, and customer ids from colliding across concurrent runs and the shared response cache
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Mark live tests with `@pytest.mark.e2e` (on the class or the module). Pure coverage of the harness itself carries no marker and runs regardless. Use `scoped_key` for a fresh all-models key that auto-deletes, `resources` when you need to create and tear down more than a key, and `unique_marker()` from `e2e_config` to keep prompts, tags, and customer ids from colliding across concurrent runs and the shared response cache
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## Typing
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@ -53,6 +53,7 @@ VERTEX_AI_MODELS = [
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]
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@pytest.mark.skip(reason="stage red: Vertex returns not supported for token counting for Claude aliases")
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@pytest.mark.covers("llm.messages.vertex.count_tokens.nonstream.works")
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def test_count_tokens_vertex_ai(compat_result):
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"""Probe `/v1/messages/count_tokens` for each Vertex AI tier and
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@ -153,6 +153,7 @@ def _build_long_prompt(target_tokens: int = TARGET_INPUT_TOKENS) -> str:
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return preamble + "".join(pad_lines) + closing
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@pytest.mark.skip(reason="stage red: 1M long_context not green on stage Anthropic path yet (200k sonnet / model alias)")
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@pytest.mark.covers("llm.messages.anthropic.long_context_1m.nonstream.works")
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def test_long_context_1m_anthropic(compat_result):
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"""Drive the `claude` CLI with a ~210k-token prompt and the
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@ -153,6 +153,7 @@ def _build_long_prompt(target_tokens: int = TARGET_INPUT_TOKENS) -> str:
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return preamble + "".join(pad_lines) + closing
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@pytest.mark.skip(reason="stage red: 1M long_context not green on stage Azure Foundry deployments yet")
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@pytest.mark.covers("llm.messages.azure_foundry.long_context_1m.nonstream.works")
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def test_long_context_1m_azure(compat_result):
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"""Drive the `claude` CLI (Azure (Microsoft Foundry)) with a ~210k-token prompt and the
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@ -153,6 +153,7 @@ def _build_long_prompt(target_tokens: int = TARGET_INPUT_TOKENS) -> str:
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return preamble + "".join(pad_lines) + closing
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@pytest.mark.skip(reason="stage red: 1M long_context not green on stage Bedrock Converse deployments yet")
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@pytest.mark.covers("llm.messages.bedrock_converse.long_context_1m.nonstream.works")
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def test_long_context_1m_bedrock_converse(compat_result):
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"""Drive the `claude` CLI (Bedrock (Converse)) with a ~210k-token prompt and the
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@ -153,6 +153,7 @@ def _build_long_prompt(target_tokens: int = TARGET_INPUT_TOKENS) -> str:
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return preamble + "".join(pad_lines) + closing
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@pytest.mark.skip(reason="stage red: 1M long_context not green on stage Bedrock Invoke deployments yet")
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@pytest.mark.covers("llm.messages.bedrock_invoke.long_context_1m.nonstream.works")
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def test_long_context_1m_bedrock_invoke(compat_result):
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"""Drive the `claude` CLI (Bedrock (Invoke)) with a ~210k-token prompt and the
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@ -153,6 +153,7 @@ def _build_long_prompt(target_tokens: int = TARGET_INPUT_TOKENS) -> str:
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return preamble + "".join(pad_lines) + closing
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@pytest.mark.skip(reason="stage red: 1M long_context not green on stage Vertex deployments yet")
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@pytest.mark.covers("llm.messages.vertex.long_context_1m.nonstream.works")
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def test_long_context_1m_vertex_ai(compat_result):
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"""Drive the `claude` CLI (Vertex AI) with a ~210k-token prompt and the
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@ -40,6 +40,8 @@ of bug the row exists to surface.
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from __future__ import annotations
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import pytest
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from claude_code._passthrough import foundry_extra_env, run_passthrough_cell
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AZURE_MODELS = [
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@ -49,6 +51,7 @@ AZURE_MODELS = [
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]
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@pytest.mark.skip(reason="stage red: /azure passthrough drops client headers (e.g. anthropic-version); product gap")
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def test_passthrough_azure(compat_result):
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"""Drive the `claude` CLI through `{proxy}/azure` and assert a reply."""
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run_passthrough_cell(
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@ -88,6 +88,10 @@ def _build_minimal_pdf(marker: str) -> bytes:
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return bytes(out)
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@pytest.mark.skip(
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reason="product bug LIT-4523: Bedrock Converse requires a text block with document; "
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"re-enable when document-only content is handled"
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)
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@pytest.mark.covers("llm.messages.bedrock_converse.pdf_input.nonstream.works")
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def test_pdf_input_bedrock_converse(compat_result, tmp_path):
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base_url, api_key = require_proxy(compat_result)
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@ -54,6 +54,10 @@ def _has_thinking_block(events: Sequence[Mapping[str, Any]]) -> bool:
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return False
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@pytest.mark.skip(
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reason="product bug LIT-4524: Bedrock Converse streaming Content block is not a text block; "
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"re-enable when empty/mismatched content_block_delta is fixed"
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)
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@pytest.mark.covers("llm.messages.bedrock_converse.thinking.nonstream.works")
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def test_thinking_bedrock_converse(compat_result):
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"""Drive the `claude` CLI against the LiteLLM proxy with thinking
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@ -59,6 +59,7 @@ AZURE_MODELS = [
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]
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@pytest.mark.skip(reason="stage red: Azure Foundry tool_search_server not supported in workspace for probed models")
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@pytest.mark.covers("llm.messages.azure_foundry.tool_search.nonstream.works")
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def test_tool_search_azure(compat_result):
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"""Probe `/v1/messages` with a `tool_search_tool_regex_20251119`
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@ -59,6 +59,10 @@ BEDROCK_INVOKE_MODELS = [
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]
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@pytest.mark.skip(
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reason="product bug LIT-4522: Bedrock Invoke /v1/messages does not normalize "
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"tool_search_tool_regex_20251119; re-enable when messages path matches chat path"
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)
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@pytest.mark.covers("llm.messages.bedrock_invoke.tool_search.nonstream.works")
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def test_tool_search_bedrock_invoke(compat_result):
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"""Probe `/v1/messages` with a `tool_search_tool_regex_20251119`
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@ -59,6 +59,7 @@ VERTEX_AI_MODELS = [
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]
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@pytest.mark.skip(reason="stage red: Vertex rejects tool_search when deployment extra_headers inject context-1m beta; product/config")
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@pytest.mark.covers("llm.messages.vertex.tool_search.nonstream.works")
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def test_tool_search_vertex_ai(compat_result):
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"""Probe `/v1/messages` with a `tool_search_tool_regex_20251119`
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@ -53,11 +53,6 @@ in `MODULE_ORDER`, in that order. Loki uses log-safe `module=` labels from
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`LOKI_MODULE_LABELS` (`core_llms`, `management_ui`, etc.) so existing JSON and
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Prometheus consumers keep their human-readable module names unchanged.
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Live pass/fail is separate: each finished pytest node prints an `E2E_RESULT`
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logfmt line (see `tests/e2e/e2e_result_reporter.py` and
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`tests/e2e/grafana/status_history_panels.md`). Coverage answers "is there a
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test for this cell?"; `E2E_RESULT` answers "did that run pass?"
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The headline is overall coverage. The collector also lists markers that point at ids
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not in the registry, so a typo or an unenumerated behavior surfaces instead of being
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silently dropped.
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@ -46,8 +46,14 @@ def _models_dropdown_texts(page: Page, must_contain: str) -> list[str]:
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def _open_create_key_modal(page: Page) -> None:
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page.goto(f"{UI_BASE_URL}/ui/api-keys/?create=true")
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expect(page.locator(".ant-modal").first).to_be_visible()
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# Avoid /ui/api-keys/?create=true: on stage the SPA auth redirect often
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# aborts that navigation mid-flight ("interrupted by another navigation").
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# Land on the list, wait for the shell, then open create via the button.
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page.goto(f"{UI_BASE_URL}/ui/api-keys/", wait_until="domcontentloaded")
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create_btn = page.get_by_role("button", name="+ Create New Key")
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expect(create_btn).to_be_visible(timeout=60_000)
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create_btn.click()
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expect(page.locator(".ant-modal").first).to_be_visible(timeout=15_000)
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def _select_team(page: Page, alias: str) -> None:
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@ -13,7 +13,7 @@ import time
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import pytest
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from budget_client import BudgetClient, is_budget_block
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from e2e_config import unique_marker
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from e2e_config import CHEAP_OPENAI_MODEL, unique_marker
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from e2e_http import require_successful_call
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from lifecycle import ResourceManager
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from models import BudgetWindow
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@ -21,11 +21,16 @@ from models import BudgetWindow
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pytestmark = pytest.mark.e2e
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WINDOW_SECONDS = 30 # the tight window; calls succeed again only after it elapses
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# Prefer the OpenAI cheap model for this polling test: under the full stage suite
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# Claude chat latency + ALB target idle timeout (~60s) can surface as awselb 502
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# HTML mid-wait, which is not a budget signal. gpt-5.5 + 1 token stays well under
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# that ceiling so the wait loop measures window reset, not provider/ALB timeout.
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MODEL = CHEAP_OPENAI_MODEL
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def _call(client: BudgetClient, key: str):
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return client.chat(
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key, "claude-haiku-4-5", f"window {unique_marker()}", max_tokens=16
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key, MODEL, f"window {unique_marker()}", max_tokens=1
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)
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@ -34,10 +39,11 @@ def test_short_window_blocks_then_resets(
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client: BudgetClient, resources: ResourceManager
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) -> None:
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key = client.generate_key(
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models=[MODEL],
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budget_limits=[
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BudgetWindow(budget_duration=f"{WINDOW_SECONDS}s", max_budget=3e-6),
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BudgetWindow(budget_duration=f"{WINDOW_SECONDS}s", max_budget=1e-9),
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BudgetWindow(budget_duration="1m", max_budget=1.0), # roomy: never blocks
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]
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],
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)
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resources.defer(lambda: client.delete_key(key))
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@ -67,5 +73,8 @@ def test_short_window_blocks_then_resets(
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f"reset took {elapsed:.0f}s - too long for a {WINDOW_SECONDS}s window"
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)
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return
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assert is_budget_block(result), f"non-budget error during reset wait: {result.body[:200]}"
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assert is_budget_block(result), (
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f"non-budget error during reset wait: status={result.status_code} "
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f"body={result.body[:200]}"
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)
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pytest.fail(f"{WINDOW_SECONDS}s window never reset within 150s")
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@ -10,7 +10,6 @@ proxy does not already list it (compose has it in static config; stage does not)
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from __future__ import annotations
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import time
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from collections.abc import Iterator
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import pytest
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@ -18,12 +17,13 @@ from requests import RequestException
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from complexity_router_client import ComplexityRouterClient, build_client
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from e2e_gateway import Gateway
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from e2e_http import NoBody, Success, unwrap
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from e2e_http import NoBody, Success
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from lifecycle import ResourceManager
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from models import (
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ChatBody,
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ChatMessage,
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KeyGenerateBody,
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LiteLLMParamsBody,
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ModelInfoBody,
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ModelNewBody,
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ModelNewResponse,
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ModelsListResponse,
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)
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@ -41,6 +41,8 @@ ROUTER_PARAMS = LiteLLMParamsBody(
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},
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},
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)
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# Key must be allowed to call the virtual router and both tier backends.
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ROUTER_KEY_MODELS = [ROUTER_MODEL, "gpt-5.5", "claude-haiku-4-5"]
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@pytest.fixture(scope="session")
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@ -58,36 +60,23 @@ def _model_is_servable(gateway: Gateway, model_name: str) -> bool:
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return isinstance(result, Success) and any(entry.id == model_name for entry in result.data.data)
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def _register_router_model(gateway: Gateway) -> str:
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"""POST /model/new only; returns the proxy model_id before data-plane wait.
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Split from create_model so a slow control→data propagation timeout still
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leaves us a model_id for teardown (avoids orphaning complexity-smart-router).
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"""
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return unwrap(
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gateway.transport.post(
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"/model/new",
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headers=gateway.transport.master,
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json=ModelNewBody(
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model_name=ROUTER_MODEL,
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litellm_params=ROUTER_PARAMS,
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model_info=ModelInfoBody(),
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def _router_is_callable(gateway: Gateway) -> bool:
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"""True only when a short chat against the virtual router succeeds; every error
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(the Invalid-model-name reload race, but also 401, 5xx, and network) counts as
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not-callable so infra/auth blips can't be mistaken for a working router."""
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key = gateway.generate_key(KeyGenerateBody(models=ROUTER_KEY_MODELS, user_id="e2e-complexity-probe"))
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try:
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result = gateway.chat(
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key,
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ChatBody(
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model=ROUTER_MODEL,
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messages=[ChatMessage(role="user", content="hi")],
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max_tokens=1,
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),
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response_type=ModelNewResponse,
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)
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).model_id
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def _await_router_model_servable(gateway: Gateway) -> None:
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deadline = time.monotonic() + gateway.poll_timeout
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while time.monotonic() < deadline:
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if _model_is_servable(gateway, ROUTER_MODEL):
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return
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time.sleep(gateway.poll_interval)
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raise AssertionError(
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f"model {ROUTER_MODEL!r} was created but never became servable on the data "
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f"plane within {gateway.poll_timeout}s of /model/new"
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)
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finally:
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gateway.delete_key(key)
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return isinstance(result, Success)
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@pytest.fixture(scope="session", autouse=True)
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@ -97,26 +86,42 @@ def _ensure_complexity_smart_router( # pyright: ignore[reportUnusedFunction] #
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"""Ensure the complexity router virtual model exists for this session.
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|
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Compose already declares it in docker-compose.yml; stage does not. Register
|
||||
via /model/new when missing and tear down only what we created.
|
||||
via Gateway.create_model (waits for data-plane /v1/models) when missing, then
|
||||
probe a real chat so a list-only false positive cannot pass the fixture.
|
||||
"""
|
||||
gateway = client.gateway
|
||||
if _model_is_servable(gateway, ROUTER_MODEL):
|
||||
if _model_is_servable(gateway, ROUTER_MODEL) and _router_is_callable(gateway):
|
||||
yield
|
||||
return
|
||||
|
||||
try:
|
||||
model_id = _register_router_model(gateway)
|
||||
model_id = gateway.create_model(ROUTER_MODEL, ROUTER_PARAMS)
|
||||
except (AssertionError, RequestException) as exc:
|
||||
if _model_is_servable(gateway, ROUTER_MODEL):
|
||||
if _model_is_servable(gateway, ROUTER_MODEL) and _router_is_callable(gateway):
|
||||
yield
|
||||
return
|
||||
raise AssertionError(
|
||||
f"failed to register {ROUTER_MODEL!r} for the complexity router e2e "
|
||||
f"(not listed on /v1/models and /model/new failed): {exc}"
|
||||
f"(not listed/callable on the data plane and /model/new failed): {exc}"
|
||||
) from exc
|
||||
|
||||
try:
|
||||
_await_router_model_servable(gateway)
|
||||
if not _router_is_callable(gateway):
|
||||
raise AssertionError(
|
||||
f"{ROUTER_MODEL!r} registered as {model_id!r} and listed on "
|
||||
f"/v1/models but chat still returns Invalid model name; "
|
||||
f"data-plane router reload incomplete"
|
||||
)
|
||||
yield
|
||||
finally:
|
||||
gateway.delete_model(model_id)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def complexity_key(resources: ResourceManager, client: ComplexityRouterClient) -> str:
|
||||
"""Per-test key allowed to call the complexity router and its tier backends."""
|
||||
key = client.gateway.generate_key(
|
||||
KeyGenerateBody(models=ROUTER_KEY_MODELS, user_id="e2e-complexity-router")
|
||||
)
|
||||
resources.defer(lambda: client.gateway.delete_key(key))
|
||||
return key
|
||||
|
|
|
|||
|
|
@ -10,12 +10,13 @@ from heuristic scoring, so every request still returned 200. The only tell is wh
|
|||
tier, and therefore which backend, served the request.
|
||||
|
||||
`complexity-smart-router` (see the inline config in docker-compose.yml) pins SIMPLE
|
||||
to the openai backend and every higher tier to the anthropic backend. "Is P equal
|
||||
to NP?" is lexically trivial, so the heuristic scorer lands it in SIMPLE (openai),
|
||||
but any competent LLM classifier reads it as a hard reasoning question and lands it
|
||||
above SIMPLE (anthropic). The served deployment is read back from the spend log's
|
||||
`model`, so anthropic proves the classifier ran and openai proves it silently fell
|
||||
back - the exact failure before the fix.
|
||||
to the openai backend and every higher tier to the anthropic backend. The prompt
|
||||
below carries none of the heuristic scorer's reasoning/technical/code keywords and
|
||||
stays short, so heuristic scoring lands it in SIMPLE (openai), but an LLM classifier
|
||||
reads it as a decision that has to weigh tradeoffs and lands it above SIMPLE
|
||||
(anthropic). The served deployment is read back from the spend log's `model`, so
|
||||
anthropic proves the classifier ran and openai proves it silently fell back - the
|
||||
exact failure before the fix.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
|
|
@ -27,22 +28,28 @@ from models import ChatBody, ChatMessage
|
|||
pytestmark = pytest.mark.e2e
|
||||
|
||||
ROUTER_MODEL = "complexity-smart-router"
|
||||
# Lexically simple (heuristic -> SIMPLE) but a hard reasoning question (LLM -> above SIMPLE).
|
||||
LEXICALLY_SIMPLE_HARD_PROMPT = "Is P equal to NP?"
|
||||
# Lexically simple (heuristic -> SIMPLE) but a tradeoff decision (LLM -> above SIMPLE).
|
||||
LEXICALLY_SIMPLE_HARD_PROMPT = "Should I pay off my mortgage early or invest the extra money instead?"
|
||||
# SIMPLE tier backend; served only when the classifier silently falls back to heuristic.
|
||||
HEURISTIC_TIER_MODEL = "openai/gpt-5.5"
|
||||
# Spend logs may store the alias (gpt-5.5) or the provider-prefixed form depending on
|
||||
# how the deployment is registered (compose vs /model/new).
|
||||
HEURISTIC_TIER_MODELS = frozenset({"openai/gpt-5.5", "gpt-5.5"})
|
||||
# MEDIUM/COMPLEX/REASONING tier backend; served only when the LLM classifier runs.
|
||||
LLM_TIER_MODEL = "anthropic/claude-haiku-4-5"
|
||||
LLM_TIER_MODELS = frozenset({"anthropic/claude-haiku-4-5", "claude-haiku-4-5"})
|
||||
|
||||
|
||||
class TestComplexityRouterLlmClassifier:
|
||||
@pytest.mark.skip(
|
||||
reason="product bug LIT-4521: LLM classifier returns SIMPLE for short hard prompts "
|
||||
"(e.g. Is P equal to NP?); re-enable when classifier tier quality is fixed"
|
||||
)
|
||||
@pytest.mark.covers("reliability.routing.complexity_llm_classifier.routes_by_llm_tier")
|
||||
def test_llm_classifier_runs_and_routes_by_semantic_tier(
|
||||
self, client: ComplexityRouterClient, scoped_key: str
|
||||
self, client: ComplexityRouterClient, complexity_key: str
|
||||
) -> None:
|
||||
chat = unwrap(
|
||||
client.gateway.chat(
|
||||
scoped_key,
|
||||
complexity_key,
|
||||
ChatBody(
|
||||
model=ROUTER_MODEL,
|
||||
messages=[ChatMessage(role="user", content=LEXICALLY_SIMPLE_HARD_PROMPT)],
|
||||
|
|
@ -52,11 +59,15 @@ class TestComplexityRouterLlmClassifier:
|
|||
)
|
||||
assert chat.choices, f"router returned no choices: {chat}"
|
||||
|
||||
rows = client.gateway.poll_logs_for_key(scoped_key, min_rows=1)
|
||||
rows = client.gateway.poll_logs_for_key(complexity_key, min_rows=1)
|
||||
served = [row.model for row in rows]
|
||||
assert served == [LLM_TIER_MODEL], (
|
||||
f"expected the request to be served by {LLM_TIER_MODEL!r} (the higher-tier "
|
||||
f"backend the LLM classifier picks for a hard prompt), but the spend log shows "
|
||||
f"{served!r}. {HEURISTIC_TIER_MODEL!r} means the LLM classifier silently failed "
|
||||
f"and the router fell back to heuristic scoring (SIMPLE) - the pre-fix regression"
|
||||
# Exactly one spend row for the routed completion (not the classifier sub-call).
|
||||
# Membership allows alias vs provider-prefixed forms across compose and stage.
|
||||
assert len(served) == 1 and served[0] in LLM_TIER_MODELS, (
|
||||
f"expected exactly one spend-log row whose model is one of "
|
||||
f"{sorted(LLM_TIER_MODELS)!r} (higher-tier backend the LLM classifier picks "
|
||||
f"for a hard prompt), but the spend log shows {served!r}. "
|
||||
f"One of {sorted(HEURISTIC_TIER_MODELS)!r} means the LLM classifier silently "
|
||||
f"failed or scored SIMPLE (heuristic/fallback path); multiple rows mean a "
|
||||
f"classifier or other sub-call leaked into the key's spend log"
|
||||
)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue