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test(caching): drop formatting-only churn from the redis semantic cache tests

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Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
kerry 2026-10-06 23:31:08 +00:00
parent 394abff55a
commit 050e9c0418
1 changed files with 38 additions and 12 deletions
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50
tests/unit/caching/test_redis_semantic_cache.py
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@ -203,7 +203,10 @@ def test_redis_semantic_cache_uses_isolated_index_for_old_schema(monkeypatch):
assert redis_semantic_cache.llmcache is fallback_cache_mock
assert semantic_cache_mock.call_args_list[0].kwargs["name"] == "existing_index"
assert semantic_cache_mock.call_args_list[1].kwargs["name"] == "existing_index_isolated"
assert (
semantic_cache_mock.call_args_list[1].kwargs["name"]
== "existing_index_isolated"
)
assert semantic_cache_mock.call_args_list[1].kwargs["filterable_fields"] == [
RedisSemanticCache._cache_key_filterable_field()
]
@ -233,7 +236,10 @@ def test_redis_semantic_cache_overwrites_stale_isolated_index(monkeypatch):
)
assert redis_semantic_cache.llmcache is fallback_cache_mock
assert semantic_cache_mock.call_args_list[2].kwargs["name"] == "existing_index_isolated"
assert (
semantic_cache_mock.call_args_list[2].kwargs["name"]
== "existing_index_isolated"
)
assert semantic_cache_mock.call_args_list[2].kwargs["overwrite"] is True
assert semantic_cache_mock.call_args_list[2].kwargs["filterable_fields"] == [
RedisSemanticCache._cache_key_filterable_field()
@ -357,7 +363,9 @@ async def test_redis_semantic_cache_async_get_cache(monkeypatch):
]
redis_semantic_cache.llmcache.acheck = AsyncMock(return_value=mock_result)
redis_semantic_cache._get_async_embedding = AsyncMock(return_value=[0.1, 0.2, 0.3])
redis_semantic_cache._get_async_embedding = AsyncMock(
return_value=[0.1, 0.2, 0.3]
)
with patch.object(
redis_semantic_cache,
@ -405,7 +413,9 @@ async def test_redis_semantic_cache_async_get_cache_rejects_unscoped_hit(monkeyp
}
]
)
redis_semantic_cache._get_async_embedding = AsyncMock(return_value=[0.1, 0.2, 0.3])
redis_semantic_cache._get_async_embedding = AsyncMock(
return_value=[0.1, 0.2, 0.3]
)
with patch.object(
redis_semantic_cache,
@ -437,7 +447,9 @@ async def test_redis_semantic_cache_async_set_cache_stores_cache_key_filter(
redis_semantic_cache = RedisSemanticCache(similarity_threshold=0.8)
redis_semantic_cache.llmcache.astore = AsyncMock()
redis_semantic_cache._get_async_embedding = AsyncMock(return_value=[0.1, 0.2, 0.3])
redis_semantic_cache._get_async_embedding = AsyncMock(
return_value=[0.1, 0.2, 0.3]
)
await redis_semantic_cache.async_set_cache(
key="test_key",
@ -597,7 +609,9 @@ def test_redis_semantic_cache_prompt_extraction_returns_none_without_text():
assert RedisSemanticCache._get_prompt_from_kwargs(input=None) is None
assert RedisSemanticCache._get_prompt_from_kwargs(input=" ") is None
assert (
RedisSemanticCache._get_prompt_from_kwargs(input=[{"type": "input_image", "image_url": "https://example.com"}])
RedisSemanticCache._get_prompt_from_kwargs(
input=[{"type": "input_image", "image_url": "https://example.com"}]
)
is None
)
@ -605,7 +619,9 @@ def test_redis_semantic_cache_prompt_extraction_returns_none_without_text():
def test_redis_semantic_cache_prompt_extraction_skips_blank_dict_text_keys():
from litellm.caching.redis_semantic_cache import RedisSemanticCache
prompt = RedisSemanticCache._get_prompt_from_kwargs(input={"text": " ", "input_text": "fallback prompt"})
prompt = RedisSemanticCache._get_prompt_from_kwargs(
input={"text": " ", "input_text": "fallback prompt"}
)
assert prompt == "fallback prompt"
@ -851,7 +867,9 @@ def test_redis_get_embedding_falls_back_to_direct(monkeypatch):
fake_proxy.llm_model_list = None
monkeypatch.setitem(sys.modules, "litellm.proxy.proxy_server", fake_proxy)
with patch("litellm.embedding", return_value={"data": [{"embedding": [0.1, 0.2]}]}) as direct_embed:
with patch(
"litellm.embedding", return_value={"data": [{"embedding": [0.1, 0.2]}]}
) as direct_embed:
vec = cache._get_embedding("hello")
assert vec == [0.1, 0.2]
@ -987,7 +1005,9 @@ def test_redis_sync_set_cache_passes_precomputed_vector():
cache = RedisSemanticCache.__new__(RedisSemanticCache)
cache.llmcache = MagicMock()
cache._get_cache_filters = MagicMock(return_value={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"})
cache._get_cache_filters = MagicMock(
return_value={RedisSemanticCache.CACHE_KEY_FIELD_NAME: "test_key"}
)
cache._get_ttl = MagicMock(return_value=None)
cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])
@ -1024,7 +1044,9 @@ def test_redis_sync_get_cache_passes_precomputed_vector():
)
cache._get_embedding = MagicMock(return_value=[0.1, 0.2, 0.3])
with patch.object(cache, "_get_cache_key_filter_expression", return_value="cache-key-filter"):
with patch.object(
cache, "_get_cache_key_filter_expression", return_value="cache-key-filter"
):
result = cache.get_cache(
key="test_key",
messages=[{"content": "What is the capital of France?"}],
@ -1138,7 +1160,9 @@ def test_redis_get_embedding_truncates_direct_path_with_explicit_limit(monkeypat
fake_proxy.llm_model_list = None
monkeypatch.setitem(sys.modules, "litellm.proxy.proxy_server", fake_proxy)
with patch("litellm.embedding", return_value={"data": [{"embedding": [0.1, 0.2]}]}) as direct_embed:
with patch(
"litellm.embedding", return_value={"data": [{"embedding": [0.1, 0.2]}]}
) as direct_embed:
cache._get_embedding(LONG_PROMPT)
sent_input = direct_embed.call_args.kwargs["input"]
@ -1185,7 +1209,9 @@ def test_redis_init_defers_redisvl_construction(monkeypatch):
def test_redis_failed_llmcache_build_is_not_memoized(monkeypatch):
built_cache = MagicMock()
semantic_cache_mock = MagicMock(side_effect=[ConnectionError("redis down"), built_cache])
semantic_cache_mock = MagicMock(
side_effect=[ConnectionError("redis down"), built_cache]
)
custom_vectorizer_mock = MagicMock()
with _fake_redisvl_modules(semantic_cache_mock, custom_vectorizer_mock):