litellm/tests/router_unit_tests/test_router_prompt_caching.py
Mateo Wang 2c733c00f5
chore(ci): modernize model references in tests and configs (#27856)
* test: modernize models used in CircleCI e2e test suites

Replaces obsolete models (gpt-4o, gpt-4o-mini, gpt-3.5-turbo,
claude-3-5-sonnet-20240620, claude-sonnet-4-20250514) with current
equivalents across the e2e_openai_endpoints and
proxy_e2e_anthropic_messages_tests CircleCI jobs.

- gpt-4o -> gpt-5.5 (responses API e2e tests)
- gpt-4o-mini -> gpt-5-mini (websocket responses, oai_misc_config)
- gpt-4o-mini-2024-07-18 -> gpt-4.1-mini-2025-04-14 (fine-tuning,
  still actively fine-tunable)
- gpt-4 / gpt-3.5-turbo target_model_names example -> gpt-5.5 /
  gpt-5-mini
- bedrock claude-3-5-sonnet-20240620 batch entry -> haiku-4-5-20251001
  (also aligning oai_misc_config model_name with what
  test_bedrock_batches_api.py actually requests)
- bedrock claude-sonnet-4-20250514 (deprecated, retires 2026-06-15)
  -> claude-sonnet-4-5-20250929

* test: point bedrock-claude-sonnet-4 alias at Sonnet 4.6, not 4.5

Greptile/Cursor flagged that after the previous commit, the
bedrock-claude-sonnet-4 alias collided with bedrock-claude-sonnet-4.5
(both pointed to claude-sonnet-4-5-20250929). Rename to
bedrock-claude-sonnet-4.6 and point it at the Sonnet 4.6 Bedrock ID
(us.anthropic.claude-sonnet-4-6, already in the litellm model
registry) so the alias name matches the underlying model version.

* test: modernize models across remaining CI-mounted configs & tests

Expands the modernization sweep to all CircleCI-mounted proxy configs
and to test directories where the model literal is a fixture/route key
(not the test's subject).

Config changes:
- proxy_server_config.yaml: bump gpt-3.5-turbo / gpt-3.5-turbo-1106 /
  gpt-4o / gemini-1.5-flash / dall-e-3 underlying models; rename
  gpt-3.5-turbo-end-user-test alias to gpt-5-mini-end-user-test; bump
  text-embedding-ada-002 underlying to text-embedding-3-small. User-
  facing aliases (gpt-3.5-turbo, gpt-4, text-embedding-ada-002, etc.)
  preserved for backward compatibility with tests.
- simple_config.yaml, otel_test_config.yaml, spend_tracking_config.yaml:
  bump gpt-3.5-turbo underlying to gpt-5-mini.
- pass_through_config.yaml: claude-3-5-sonnet / claude-3-7-sonnet /
  claude-3-haiku entries replaced with claude-sonnet-4-5 / claude-
  haiku-4-5 / claude-opus-4-7.
- oai_misc_config.yaml: align alias name with the gpt-5-mini rename.

Test changes (proactive: claude-sonnet-4-20250514 / claude-opus-4-
20250514 retire 2026-06-15):
- tests/llm_translation/test_anthropic_completion.py: bump 3 references
  + paired Vertex AI ID to claude-sonnet-4-5.
- tests/llm_translation/test_optional_params.py: bump 2 references.
- tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py
  and test_bedrock_anthropic_messages_test.py: bump router fixtures
  using the deprecated model IDs.
- tests/pass_through_unit_tests/base_anthropic_messages_tool_search_test.py:
  modernize docstring examples.
- tests/test_end_users.py: update references to renamed alias.

* test: modernize placeholder model literals in router_unit_tests

Mass replace_all on fixture/placeholder model literals across the
router_unit_tests/ suite (model name is a routing key / label, not the
test subject). Sub-agent sweep so far — additional commits will follow
for logging_callback_tests/, enterprise/, top-level tests/test_*.py,
and other CI-mounted dirs.

Mappings applied:
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 / claude-3-opus-20240229 /
  claude-3-haiku-20240307 / claude-3-5-sonnet-20240620 ->
  claude-sonnet-4-5-20250929 / claude-opus-4-7 /
  claude-haiku-4-5-20251001 as appropriate

Explicitly preserved:
- gpt-4o-mini-* variants (transcribe, tts, etc.) where they're current
- gpt-4-turbo / gpt-4-vision-preview / gpt-4-0613 (subject literals)
- JSONL batch body literals
- Mock LLM response model fields (must match upstream)
- Fake/mock identifiers

* test: modernize placeholder model literals across remaining CI suites

Sub-agent sweep across logging_callback_tests/, guardrails_tests/,
enterprise/, pass_through_unit_tests/, otel_tests/,
llm_responses_api_testing/, batches_tests/, spend_tracking_tests/,
litellm_utils_tests/, unified_google_tests/, and a few top-level
tests/test_*.py files where the model literal is a fixture or
placeholder (router model_list, mock standard logging payload, mock
callback data) rather than the test's subject.

Mappings applied (see scope notes below):
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5.5 (corrected from initial gpt-5 — bare gpt-5
  is not a valid OpenAI alias; only gpt-5.5 / gpt-5.4 / gpt-5.2-codex
  / gpt-5-mini exist)
- gpt-4o-mini (bare) -> gpt-5-mini
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 -> claude-sonnet-4-5-20250929
- claude-3-opus-20240229 -> claude-opus-4-7
- claude-3-haiku-20240307 -> claude-haiku-4-5-20251001
- claude-3-5-sonnet-20240620/20241022 -> claude-sonnet-4-5-20250929
- claude-3-7-sonnet-20250219 -> claude-sonnet-4-6
- gemini-1.5-flash -> gemini-2.5-flash
- gemini-1.5-pro -> gemini-2.5-pro

Explicitly preserved (not modernized):
- llm_translation/ tests where model is the SUBJECT (provider-specific
  translation/transformation logic). Only the deprecated 20250514
  references were already bumped in a prior commit.
- Cost-calc / tokenizer subject tests in test_utils.py (skip-ranges
  documented by the sub-agent).
- Bedrock model IDs in test_health_check.py path-stripping tests.
- JSONL batch request bodies and mock LLM response bodies (must match
  upstream literal).
- Langfuse expected-request-body JSON fixtures (cost values are exact-
  match-asserted; changing the model would shift response_cost).
- gpt-3.5-turbo-instruct (text-completion endpoint; no modern OpenAI
  equivalent).
- Top-level tests calling the proxy through user-facing aliases
  (gpt-3.5-turbo, gpt-4, text-embedding-ada-002, dall-e-3) — aliases
  in proxy_server_config.yaml stay; only the underlying model was
  bumped.
- tests/test_gpt5_azure_temperature_support.py (the test's whole point
  is model-name handling).
- Fake / mock / openai/fake identifiers.

Notable side fixes:
- test_spend_accuracy_tests.py: UPSTREAM_MODEL now matches what
  spend_tracking_config.yaml's proxy actually routes to (gpt-5-mini),
  resolving a latent inconsistency.
- proxy_server_config.yaml: bare `gpt-5` alias renamed to `gpt-5.5`
  (bare gpt-5 is not a valid OpenAI alias).
- test_batches_logging_unit_tests.py: explicit_models list entries
  kept distinct (gpt-5-mini + gpt-5.5) after bulk rename.

* test: fix CI failures from model modernization sweep

CI surfaced 4 categories of regression from the bulk modernization:

1. Azure deployment names are customer-specific. Reverted:
   - tests/litellm_utils_tests/test_health_check.py: azure/text-
     embedding-3-small -> azure/text-embedding-ada-002 (the CI Azure
     account does not have a text-embedding-3-small deployment).
   - tests/logging_callback_tests/test_custom_callback_router.py:
     same revert for two router fixtures driving aembedding.

2. gpt-5 family does not accept temperature != 1. Tests that pass a
   custom temperature swapped from gpt-5-mini to gpt-4.1-mini (modern
   non-reasoning OpenAI mini that still accepts temperature/logprobs):
   - tests/logging_callback_tests/test_datadog.py
   - tests/logging_callback_tests/test_langsmith_unit_test.py
   - tests/logging_callback_tests/test_otel_logging.py

3. proxy_server_config.yaml's gpt-3.5-turbo-large alias was routing to
   gpt-5.5 (a reasoning model that rejects logprobs). The proxy test
   tests/test_openai_endpoints.py::test_chat_completion_streaming
   exercises logprobs/top_logprobs through that alias. Bumped the
   underlying model to gpt-4.1 (non-reasoning, still modern).

4. tests/logging_callback_tests/test_gcs_pub_sub.py asserts against a
   pinned JSON fixture (gcs_pub_sub_body/spend_logs_payload.json) with
   hardcoded model="gpt-4o" and a model-specific spend value. Reverted
   the litellm.acompletion calls in the test to model="gpt-4o" so the
   fixture's exact-match assertions still hold.

5. tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py:
   anthropic.messages.create routing to openai/gpt-5-mini returned an
   empty content[0] with max_tokens=100 (reasoning-token consumption).
   Swapped to openai/gpt-4.1-mini.

* test: fix Assistants API model + 2 cursor[bot] review nits

1. pass_through_unit_tests/test_custom_logger_passthrough.py: gpt-5.5
   isn't accepted by the /v1/assistants endpoint
   ("unsupported_model"). Switch to gpt-4.1-mini (modern, Assistants-
   API-supported, non-reasoning).

2. example_config_yaml/pass_through_config.yaml: the previous sweep
   bumped the claude-3-7-sonnet alias to claude-opus-4-7, which is a
   tier change (Sonnet -> Opus). Map to claude-sonnet-4-6 to keep the
   Sonnet tier intact. (Cursor bugbot review.)

3. example_config_yaml/simple_config.yaml: model_name was left as
   gpt-3.5-turbo while the underlying was bumped to gpt-5-mini, which
   muddles the "simple" example. Make both sides gpt-5-mini so the
   most basic example is a straight 1:1 mapping again. (Cursor bugbot
   review.)

* fix: revert gpt-4/gpt-3.5-turbo alias underlying to non-reasoning models

tests/test_openai_endpoints.py::test_completion calls the proxy alias
"gpt-4" with temperature=0, and other tests call gpt-3.5-turbo with
custom temperature / logprobs / the legacy /v1/completions endpoint.
The earlier modernization mapped both aliases to gpt-5.5 / gpt-5-mini,
which are reasoning models that reject temperature != 1 and don't
expose /v1/completions. Map the aliases to gpt-4.1 / gpt-4.1-mini
(modern non-reasoning OpenAI models) instead — keeps user-facing
aliases preserved while picking a current underlying that still
supports the parameters/endpoints the tests exercise.
2026-05-15 15:44:28 -07:00

283 lines
10 KiB
Python

import sys
import os
import traceback
import asyncio
from dotenv import load_dotenv
from fastapi import Request
from datetime import datetime
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
from litellm import Router
import pytest
import litellm
from unittest.mock import patch, MagicMock, AsyncMock
from create_mock_standard_logging_payload import create_standard_logging_payload
from litellm.types.utils import StandardLoggingPayload
import unittest
from pydantic import BaseModel
from litellm.router_utils.prompt_caching_cache import PromptCachingCache
class ExampleModel(BaseModel):
field1: str
field2: int
def test_serialize_pydantic_object():
model = ExampleModel(field1="value", field2=42)
serialized = PromptCachingCache.serialize_object(model)
assert serialized == {"field1": "value", "field2": 42}
def test_serialize_dict():
obj = {"b": 2, "a": 1}
serialized = PromptCachingCache.serialize_object(obj)
assert serialized == '{"a":1,"b":2}' # JSON string with sorted keys
def test_serialize_nested_dict():
obj = {"z": {"b": 2, "a": 1}, "x": [1, 2, {"c": 3}]}
serialized = PromptCachingCache.serialize_object(obj)
expected = '{"x":[1,2,{"c":3}],"z":{"a":1,"b":2}}' # JSON string with sorted keys
assert serialized == expected
def test_serialize_list():
obj = ["item1", {"a": 1, "b": 2}, 42]
serialized = PromptCachingCache.serialize_object(obj)
expected = ["item1", '{"a":1,"b":2}', 42]
assert serialized == expected
def test_serialize_fallback():
obj = 12345 # Simple non-serializable object
serialized = PromptCachingCache.serialize_object(obj)
assert serialized == 12345
def test_serialize_non_serializable():
class CustomClass:
def __str__(self):
return "custom_object"
obj = CustomClass()
serialized = PromptCachingCache.serialize_object(obj)
assert serialized == "custom_object" # Fallback to string conversion
@pytest.mark.asyncio
async def test_router_prompt_caching_same_cacheable_prefix_routes_to_same_deployment():
"""
End-to-end test to validate prompt caching routing through LiteLLM Router.
Tests that requests with same cacheable content but different user messages
route to the same deployment (for prompt caching).
This reproduces the issue where requests with same cacheable prefix but different
user messages should route to the same deployment, but previously didn't because
the cache key included the entire messages array instead of just the cacheable prefix.
"""
from litellm.types.llms.openai import AllMessageValues
def create_messages(user_content: str) -> list[AllMessageValues]:
"""
Create messages matching the user's exact scenario.
Message structure:
- BLOCK 1: System message, first content block (no cache_control)
→ INCLUDED (comes before the last cacheable block)
- BLOCK 2: System message, second content block (WITH cache_control)
→ INCLUDED (this IS the last cacheable block)
- USER MESSAGE: User message (no cache_control)
→ NOT included (comes after last cacheable block)
"""
return [
{
"role": "system",
"content": [
# BLOCK 1: No cache_control → INCLUDED (all blocks up to last cacheable are included)
{
"type": "text",
"text": "You are an AI assistant tasked with analyzing legal documents.",
},
# BLOCK 2: Has cache_control → INCLUDED (this is the last cacheable block)
{
"type": "text",
"text": "Here 3 is the full text of a complex legal agreement"
* 400,
"cache_control": {"type": "ephemeral"},
},
],
},
{
"role": "user",
# USER MESSAGE: No cache_control → NOT included (comes after last cacheable block)
"content": user_content,
},
]
# Create router with multiple deployments
router = Router(
model_list=[
{
"model_name": "test-model",
"litellm_params": {
"model": "gpt-5-mini",
"api_base": "https://exampleopenaiendpoint-production-0ee2.up.railway.app/v1",
"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"],
)
# Create test messages matching user's exact scenario
# Same cacheable prefix (system blocks 1+2) but different user messages
messages1 = create_messages(
"what are the key terms and conditions in this agreement?"
)
messages2 = create_messages("how many words are there?")
messages3 = create_messages("how many sentences are there?")
cache = PromptCachingCache(cache=router.cache)
# Test 1: Cache keys should be same (same cacheable prefix, different user messages)
key1 = PromptCachingCache.get_prompt_caching_cache_key(messages1, None)
key2 = PromptCachingCache.get_prompt_caching_cache_key(messages2, None)
key3 = PromptCachingCache.get_prompt_caching_cache_key(messages3, None)
assert key1 is not None, "Cache key should not be None"
assert (
key1 == key2 == key3
), "Cache keys should be the same for same cacheable prefix"
# Make first request
try:
response1 = await router.acompletion(model="test-model", messages=messages1)
model_id_1 = response1._hidden_params.get("model_id", "unknown")
except Exception:
# If API call fails, we can still test the cache key logic
model_id_1 = "unknown"
await asyncio.sleep(1) # Wait for cache write
# Test 2: Cache lookup should work for messages2 (same cacheable prefix)
cached_2 = await cache.async_get_model_id(messages2, None)
# Cache should be found if first request succeeded
if model_id_1 != "unknown":
assert (
cached_2 is not None
), "Cache lookup should work for same cacheable prefix"
# Make second request
try:
response2 = await router.acompletion(model="test-model", messages=messages2)
model_id_2 = response2._hidden_params.get("model_id", "unknown")
except Exception:
model_id_2 = "unknown"
await asyncio.sleep(1) # Wait for cache write
# Make third request
try:
response3 = await router.acompletion(model="test-model", messages=messages3)
model_id_3 = response3._hidden_params.get("model_id", "unknown")
except Exception:
model_id_3 = "unknown"
# Test 3: All requests should route to same deployment (if API calls succeeded)
if model_id_1 != "unknown" and model_id_2 != "unknown" and model_id_3 != "unknown":
assert (
model_id_1 == model_id_2 == model_id_3
), f"All requests should route to same deployment, but got: {model_id_1}, {model_id_2}, {model_id_3}"
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)