litellm/tests/unit/test_claude_fable_5_config.py
yuneng-jiang f6882246d4
test: move tests/test_litellm root and small trees into tests/unit (#43186)
* ci: run the unit_selection.sh shard files on every event instead of only fork pull requests

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* ci: rename fork-flag to unit-flag now that it applies on every event

* test: move tests/test_litellm root and small trees into tests/unit

Pure renames, no content changes. Follow-up commits in this PR fix
references, merge the three files that already existed in tests/unit,
keep live-provider tests in tests/test_litellm and wire CI.

* test: carry tests/test_litellm conftest isolation into tests/unit

Callback lists, routing fallbacks, cached HTTP clients, logger state, AWS,
proxy-URL and keychain env, and session-end client cleanup now reset for
unit tests too. The environment isolation owns its MonkeyPatch so a test's
own monkeypatch is undone before the model-cost teardown runs.

* test: merge, split and prune the moved root and small-tree tests

Merge batches/test_batch_utils.py and the chat_completions and messages
dispatch tests into the files that already existed in tests/unit. Keep
the live Gemini interactions tests, the async image-fetch format test and
the OpenAI embedding scorer test in tests/test_litellm since they need
real network or keys. Put test_router.py under tests/unit/test_router so
the existing package no longer shadows it. Delete eight tests the audit
found superseded by stronger ones kept in this move.

* ci: run the moved root and small-tree tests under their legacy flags

Add the misc and responses-caching-types flags to unit_selection.sh and
CircleCI, extend enterprise-routing and mcp-integration, and point the
legacy GHA shards, Makefile, redis-compat workflow, merge smoke manifest
and change classifier at the new paths.

* test: make the new tests/unit directories packages

tests/unit/test_package_layout.py requires every directory to carry an
__init__.py, and without one the moved and retained
test_litellm_responses_bridge.py modules collide on import.

* test: scope the unit socket block to tests/unit in shared sessions

The GHA shards collect the legacy test-path and the unit selection in one
pytest session. The unit conftest's loopback-only block leaked into legacy
modules that reach the network at import. The legacy conftest now lifts the
restriction at collect and setup time, and the unit conftest re-applies it
when collecting its own modules.

* test: give the shard-script tests their own GITHUB_OUTPUT

They only passed where the runner set it. The CircleCI unit job's env
allowlist drops it, so the script's redirect failed there.

* test: point the router and module-deletion checks at tests/unit

router_code_coverage and code_qa_check_tests only searched tests/test_litellm,
so the moved router tests no longer counted. The two silent-experiment tests
the audit deleted were the only direct callers of those methods; they are
replaced with tests that assert the forwarded shadow request and the
recursion guard.

---------

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
2026-09-25 11:30:43 -07:00

96 lines
3.1 KiB
Python

"""
Validate Claude Fable 5 and Claude Fable 5.1 model configuration entries.
Fable 5 is a new tier above Opus ($10/$50 per MTok) with the same adaptive-only
API surface as Opus 4.7/4.8. The cost-map entries below are what make the model
resolvable across Anthropic, Bedrock, Vertex AI, and Azure AI (Microsoft
Foundry), and the ``supports_adaptive_thinking`` flag is what makes LiteLLM send
``thinking.type='adaptive'`` instead of the legacy ``enabled``/``budget_tokens``
shape, which Fable 5 rejects with a 400.
"""
import json
import os
import pytest
from litellm.constants import BEDROCK_CONVERSE_MODELS
from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap
REPO_ROOT = os.path.join(os.path.dirname(__file__), "../..")
def _load_root_cost_map() -> dict:
json_path = os.path.join(REPO_ROOT, "model_prices_and_context_window.json")
with open(json_path) as f:
return json.load(f)
def test_fable_5_registered_for_bedrock_converse():
assert "anthropic.claude-fable-5" in BEDROCK_CONVERSE_MODELS
@pytest.mark.parametrize(
"model",
[
"claude-fable-5",
"anthropic/claude-fable-5",
"anthropic.claude-fable-5",
"bedrock/us.anthropic.claude-fable-5",
"bedrock/invoke/eu.anthropic.claude-fable-5",
"bedrock/global.anthropic.claude-fable-5",
"vertex_ai/claude-fable-5",
"azure_ai/claude-fable-5",
],
)
def test_adaptive_thinking_detected_for_fable_5(local_model_cost_map, model):
"""Provider-routed ids must resolve to a flagged entry so ``reasoning_effort``
maps to ``thinking.type='adaptive'`` + ``output_config.effort``."""
from litellm.llms.anthropic.common_utils import AnthropicModelInfo
assert AnthropicModelInfo._is_adaptive_thinking_model(model, "anthropic") is True
FABLE_5_1_VARIANTS = (
"claude-fable-5-1",
"anthropic.claude-fable-5-1",
"global.anthropic.claude-fable-5-1",
"us.anthropic.claude-fable-5-1",
"eu.anthropic.claude-fable-5-1",
"vertex_ai/claude-fable-5-1",
"vertex_ai/claude-fable-5-1@default",
"azure_ai/claude-fable-5-1",
)
def test_fable_5_1_present_in_bundled_backup():
backup = GetModelCostMap.load_local_model_cost_map()
root = _load_root_cost_map()
for model_name in FABLE_5_1_VARIANTS:
assert model_name in backup, f"Missing from backup cost map: {model_name}"
assert backup[model_name] == root[model_name], model_name
def test_fable_5_1_registered_for_bedrock_converse():
assert "anthropic.claude-fable-5-1" in BEDROCK_CONVERSE_MODELS
@pytest.mark.parametrize(
"model",
[
"claude-fable-5-1",
"anthropic/claude-fable-5-1",
"anthropic.claude-fable-5-1",
"bedrock/us.anthropic.claude-fable-5-1",
"bedrock/invoke/eu.anthropic.claude-fable-5-1",
"bedrock/global.anthropic.claude-fable-5-1",
"vertex_ai/claude-fable-5-1",
"azure_ai/claude-fable-5-1",
],
)
def test_adaptive_thinking_detected_for_fable_5_1(local_model_cost_map, model):
from litellm.llms.anthropic.common_utils import AnthropicModelInfo
assert AnthropicModelInfo._is_adaptive_thinking_model(model, "anthropic") is True