litellm/tests/unit/router_utils/test_prompt_caching_cache.py
devin-ai-integration[bot] a7a3a6b375
test: migrate legacy unit dirs and unrun folders into tests/unit, tests/integration and tests/e2e (#45940)
* test: migrate legacy unit dirs and unrun folders into tests/unit, tests/integration and tests/e2e

* test: fix disk cache collection, ungate saturation check, drop RSS memory tests

* test: assert one delivered log per completion in Datadog and LangSmith checks

* test: drop SDK-direct e2e copies and align cost-map path filter cases

---------

Co-authored-by: yuneng <yuneng@berri.ai>
2026-10-11 06:24:57 -07:00

186 lines
6.6 KiB
Python

import asyncio
from typing import Final
import pytest
from litellm.router_utils.prompt_caching_cache import PromptCachingCache
async def _first_non_none(read):
while True:
value = await read()
if value is not None:
return value
await asyncio.sleep(0)
def test_extract_cacheable_prefix_with_string_content_and_message_level_cache_control():
"""
Test that extract_cacheable_prefix correctly handles messages where:
- content is a string (not a list of content blocks)
- cache_control is a sibling key at the message level
This is a valid message format per LiteLLM's ChatCompletionUserMessage type:
{"role": "user", "content": "...", "cache_control": {"type": "ephemeral"}}
Regression test for issue #19228.
"""
# Test case 1: Single message with string content and message-level cache_control
messages_string_content = [
{"role": "system", "content": "You are a helpful assistant"},
{
"role": "user",
"content": "This is a large message that should be cached",
"cache_control": {"type": "ephemeral", "ttl": "5m"},
},
]
result = PromptCachingCache.extract_cacheable_prefix(messages_string_content)
# Should return both messages (system + user with cache_control)
assert len(result) == 2, f"Expected 2 messages, got {len(result)}"
assert result[0]["role"] == "system"
assert result[1]["role"] == "user"
assert result[1]["content"] == "This is a large message that should be cached"
assert result[1].get("cache_control") == {"type": "ephemeral", "ttl": "5m"}
def test_extract_cacheable_prefix_with_string_content_no_cache_control():
"""
Test that extract_cacheable_prefix returns empty list when:
- content is a string
- no cache_control is present
"""
messages_no_cache = [
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Hello"},
]
result = PromptCachingCache.extract_cacheable_prefix(messages_no_cache)
# Should return empty list (no cacheable content)
assert len(result) == 0, f"Expected 0 messages, got {len(result)}"
def test_extract_cacheable_prefix_mixed_string_and_list_content():
"""
Test that extract_cacheable_prefix handles messages with a mix of:
- String content with message-level cache_control
- List content with block-level cache_control
The last cache_control (regardless of format) should determine the cacheable prefix.
"""
# Message with string content + cache_control, followed by message with list content + cache_control
messages_mixed = [
{"role": "system", "content": "You are a helpful assistant"},
{
"role": "user",
"content": "First cached message",
"cache_control": {"type": "ephemeral"},
},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Second cached message in list format",
"cache_control": {"type": "ephemeral"},
}
],
},
{"role": "user", "content": "This should not be in the prefix"},
]
result = PromptCachingCache.extract_cacheable_prefix(messages_mixed)
# Should include first 3 messages (up to and including the last cache_control)
assert len(result) == 3, f"Expected 3 messages, got {len(result)}"
assert result[0]["role"] == "system"
assert result[1]["content"] == "First cached message"
assert isinstance(result[2]["content"], list)
@pytest.mark.asyncio
async def test_same_cacheable_prefix_routes_to_same_deployment() -> None:
from litellm import Router
def _messages(user_content: str):
return [
{
"role": "system",
"content": [
{
"type": "text",
"text": "You are an AI assistant tasked with analyzing legal documents.",
},
{
"type": "text",
"text": "Here is the full text of a complex legal agreement" * 400,
"cache_control": {"type": "ephemeral"},
},
],
},
{"role": "user", "content": user_content},
]
router: Final = Router(
model_list=[
{
"model_name": "test-model",
"litellm_params": {
"model": "gpt-5-mini",
"api_key": f"test-key-{i}",
},
"model_info": {"id": f"deployment-{i}"},
}
for i in range(1, 4)
],
routing_strategy="simple-shuffle",
optional_pre_call_checks=["prompt_caching"],
)
messages1: Final = _messages("what are the key terms and conditions in this agreement?")
messages2: Final = _messages("how many words are there?")
messages3: Final = _messages("how many sentences are there?")
key1: Final = PromptCachingCache.get_prompt_caching_cache_key(messages1, None)
key2: Final = PromptCachingCache.get_prompt_caching_cache_key(messages2, None)
key3: Final = PromptCachingCache.get_prompt_caching_cache_key(messages3, None)
assert key1 is not None
assert key1 == key2 == key3
response1: Final = await router.acompletion(
model="test-model", messages=messages1, mock_response="ok"
)
model_id_1: Final = response1._hidden_params["model_id"]
assert model_id_1 in {"deployment-1", "deployment-2", "deployment-3"}
from litellm.integrations.anthropic_cache_control_hook import (
AnthropicCacheControlHook,
)
affinity_messages: Final = AnthropicCacheControlHook.messages_with_default_injections(
messages=messages2,
models=("gpt-5-mini",),
tools=None,
enable_prompt_caching=None,
request_kwargs={},
)
cache: Final = PromptCachingCache(cache=router.cache)
cached_2: Final = await asyncio.wait_for(
_first_non_none(lambda: cache.async_get_model_id(affinity_messages, None)),
timeout=10,
)
assert cached_2 == {"model_id": model_id_1}
response2: Final = await router.acompletion(
model="test-model", messages=messages2, mock_response="ok"
)
response3: Final = await router.acompletion(
model="test-model", messages=messages3, mock_response="ok"
)
model_id_2: Final = response2._hidden_params["model_id"]
model_id_3: Final = response3._hidden_params["model_id"]
assert model_id_1 == model_id_2 == model_id_3