litellm/tests/logging_callback_tests/test_amazing_s3_logs.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

419 lines
14 KiB
Python

import sys
import os
import io, asyncio
# import logging
# logging.basicConfig(level=logging.DEBUG)
sys.path.insert(0, os.path.abspath("../.."))
from litellm import completion
import litellm
litellm.num_retries = 3
import time, random
import pytest
import boto3
from litellm._logging import verbose_logger
import logging
@pytest.mark.asyncio
@pytest.mark.parametrize(
"sync_mode,streaming", [(True, True), (True, False), (False, True), (False, False)]
)
@pytest.mark.flaky(retries=3, delay=1)
async def test_basic_s3_logging(sync_mode, streaming):
verbose_logger.setLevel(level=logging.DEBUG)
litellm.success_callback = ["s3"]
litellm.s3_callback_params = {
"s3_bucket_name": "load-testing-oct",
"s3_aws_secret_access_key": "os.environ/AWS_SECRET_ACCESS_KEY",
"s3_aws_access_key_id": "os.environ/AWS_ACCESS_KEY_ID",
"s3_region_name": "us-west-2",
}
litellm.set_verbose = True
response_id = None
if sync_mode is True:
response = litellm.completion(
model="gpt-5-mini",
messages=[{"role": "user", "content": "This is a test"}],
mock_response="It's simple to use and easy to get started",
stream=streaming,
)
if streaming:
for chunk in response:
print()
response_id = chunk.id
else:
response_id = response.id
time.sleep(2)
else:
response = await litellm.acompletion(
model="gpt-5-mini",
messages=[{"role": "user", "content": "This is a test"}],
mock_response="It's simple to use and easy to get started",
stream=streaming,
)
if streaming:
async for chunk in response:
print(chunk)
response_id = chunk.id
else:
response_id = response.id
await asyncio.sleep(2)
print(f"response: {response}")
total_objects, all_s3_keys = list_all_s3_objects("load-testing-oct")
# assert that atlest one key has response.id in it
assert any(response_id in key for key in all_s3_keys)
s3 = boto3.client("s3")
# delete all objects
for key in all_s3_keys:
s3.delete_object(Bucket="load-testing-oct", Key=key)
@pytest.mark.asyncio
@pytest.mark.parametrize("streaming", [True])
@pytest.mark.flaky(retries=3, delay=1)
async def test_basic_s3_v2_logging(streaming):
from unittest.mock import AsyncMock, MagicMock, patch
from litellm.integrations.s3_v2 import S3Logger
litellm.s3_callback_params = {
"s3_bucket_name": "load-testing-oct",
"s3_aws_secret_access_key": "test-secret",
"s3_aws_access_key_id": "test-key",
"s3_region_name": "us-west-2",
}
s3_v2_logger = S3Logger(s3_flush_interval=1)
litellm.callbacks = [s3_v2_logger]
uploaded_keys: list = []
original_upload = s3_v2_logger.async_upload_data_to_s3
async def mock_upload(batch_logging_element):
uploaded_keys.append(batch_logging_element.s3_object_key)
s3_v2_logger.async_upload_data_to_s3 = mock_upload
litellm.set_verbose = True
response_id = None
response = await litellm.acompletion(
model="gpt-5-mini",
messages=[{"role": "user", "content": "This is a test"}],
mock_response="It's simple to use and easy to get started",
stream=streaming,
)
if streaming:
async for chunk in response:
response_id = chunk.id
else:
response_id = response.id
await asyncio.sleep(5)
assert len(uploaded_keys) > 0, "S3 upload was never called"
assert any(
response_id in key for key in uploaded_keys
), f"Expected response_id={response_id} in one of the uploaded S3 keys: {uploaded_keys}"
@pytest.mark.asyncio
@pytest.mark.flaky(retries=3, delay=1)
async def test_basic_s3_v2_logging_failure():
"""Test that S3 v2 logger makes httpx PUT request when logging failures"""
from unittest.mock import AsyncMock, MagicMock, patch
from litellm.integrations.s3_v2 import S3Logger
# Create S3 logger with short flush interval
s3_v2_logger = S3Logger(s3_flush_interval=1)
# Mock the httpx client to capture the PUT request
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.raise_for_status = MagicMock()
s3_v2_logger.async_httpx_client = AsyncMock()
s3_v2_logger.async_httpx_client.put.return_value = mock_response
# Track the upload method calls
original_upload = s3_v2_logger.async_upload_data_to_s3
upload_called = False
async def mock_upload(batch_logging_element):
nonlocal upload_called
upload_called = True
# Mock the upload process but still make the httpx call
url = f"https://test-bucket.s3.us-west-2.amazonaws.com/{batch_logging_element.s3_object_key}"
headers = {"Content-Type": "application/json"}
data = '{"model": "gpt-5-mini"}'
# Make the actual httpx call we want to test
await s3_v2_logger.async_httpx_client.put(url=url, headers=headers, data=data)
s3_v2_logger.async_upload_data_to_s3 = mock_upload
# Configure S3 callback params
litellm.callbacks = [s3_v2_logger]
litellm.s3_callback_params = {
"s3_bucket_name": "test-bucket",
"s3_aws_secret_access_key": "test-secret",
"s3_aws_access_key_id": "test-key",
"s3_region_name": "us-west-2",
}
litellm.set_verbose = True
# Trigger a failure by using invalid API key
try:
response = await litellm.acompletion(
model="gpt-5-mini",
api_key="invalid-api-key",
messages=[{"role": "user", "content": "This is a test"}],
)
except Exception as e:
print(f"Expected error: {e}")
# Wait for logger to process the failure
await asyncio.sleep(5)
# Verify that our mock upload was called
assert upload_called, "S3 upload method was not called"
print("✓ S3 upload method was called")
# Verify that httpx PUT was called
s3_v2_logger.async_httpx_client.put.assert_called()
# Get the call arguments to verify the S3 URL
call_args = s3_v2_logger.async_httpx_client.put.call_args
assert call_args is not None
url = call_args[1]["url"] if "url" in call_args[1] else call_args[0][0]
# Verify the URL contains expected S3 endpoint
assert "test-bucket.s3.us-west-2.amazonaws.com" in url
print(f"✓ S3 PUT request made to: {url}")
# Verify headers include expected content type
headers = call_args[1]["headers"]
assert headers["Content-Type"] == "application/json"
print("✓ S3 request headers are correct")
# Verify JSON data was included
data = call_args[1]["data"]
assert data is not None
assert '"model": "gpt-5-mini"' in data
print("✓ S3 request data contains expected log payload")
def list_all_s3_objects(bucket_name):
s3 = boto3.client("s3")
all_s3_keys = []
paginator = s3.get_paginator("list_objects_v2")
total_objects = 0
for page in paginator.paginate(Bucket=bucket_name):
if "Contents" in page:
total_objects += len(page["Contents"])
all_s3_keys.extend([obj["Key"] for obj in page["Contents"]])
print(f"Total number of objects in {bucket_name}: {total_objects}")
print(all_s3_keys)
return total_objects, all_s3_keys
@pytest.mark.skip(reason="AWS Suspended Account")
def test_s3_logging():
# all s3 requests need to be in one test function
# since we are modifying stdout, and pytests runs tests in parallel
# on circle ci - we only test litellm.acompletion()
try:
# redirect stdout to log_file
litellm.cache = litellm.Cache(
type="s3",
s3_bucket_name="litellm-my-test-bucket-2",
s3_region_name="us-east-1",
)
litellm.success_callback = ["s3"]
litellm.s3_callback_params = {
"s3_bucket_name": "litellm-logs-2",
"s3_aws_secret_access_key": "os.environ/AWS_SECRET_ACCESS_KEY",
"s3_aws_access_key_id": "os.environ/AWS_ACCESS_KEY_ID",
}
litellm.set_verbose = True
print("Testing async s3 logging")
expected_keys = []
import time
curr_time = str(time.time())
async def _test():
return await litellm.acompletion(
model="gpt-5-mini",
messages=[{"role": "user", "content": f"This is a test {curr_time}"}],
max_tokens=10,
temperature=0.7,
user="ishaan-2",
)
response = asyncio.run(_test())
print(f"response: {response}")
expected_keys.append(response.id)
async def _test():
return await litellm.acompletion(
model="gpt-5-mini",
messages=[{"role": "user", "content": f"This is a test {curr_time}"}],
max_tokens=10,
temperature=0.7,
user="ishaan-2",
)
response = asyncio.run(_test())
expected_keys.append(response.id)
print(f"response: {response}")
time.sleep(5) # wait 5s for logs to land
import boto3
s3 = boto3.client("s3")
bucket_name = "litellm-logs-2"
# List objects in the bucket
response = s3.list_objects(Bucket=bucket_name)
# Sort the objects based on the LastModified timestamp
objects = sorted(
response["Contents"], key=lambda x: x["LastModified"], reverse=True
)
# Get the keys of the most recent objects
most_recent_keys = [obj["Key"] for obj in objects]
print(most_recent_keys)
# for each key, get the part before "-" as the key. Do it safely
cleaned_keys = []
for key in most_recent_keys:
split_key = key.split("_")
if len(split_key) < 2:
continue
cleaned_keys.append(split_key[1])
print("\n most recent keys", most_recent_keys)
print("\n cleaned keys", cleaned_keys)
print("\n Expected keys: ", expected_keys)
matches = 0
for key in expected_keys:
key += ".json"
assert key in cleaned_keys
if key in cleaned_keys:
matches += 1
# remove the match key
cleaned_keys.remove(key)
# this asserts we log, the first request + the 2nd cached request
print("we had two matches ! passed ", matches)
assert matches == 2
try:
# cleanup s3 bucket in test
for key in most_recent_keys:
s3.delete_object(Bucket=bucket_name, Key=key)
except Exception:
# don't let cleanup fail a test
pass
except Exception as e:
pytest.fail(f"An exception occurred - {e}")
finally:
# post, close log file and verify
# Reset stdout to the original value
print("Passed! Testing async s3 logging")
# test_s3_logging()
@pytest.mark.skip(reason="AWS Suspended Account")
def test_s3_logging_async():
# this tests time added to make s3 logging calls, vs just acompletion calls
try:
litellm.set_verbose = True
# Make 5 calls with an empty success_callback
litellm.success_callback = []
start_time_empty_callback = asyncio.run(make_async_calls())
print("done with no callback test")
print("starting s3 logging load test")
# Make 5 calls with success_callback set to "langfuse"
litellm.success_callback = ["s3"]
litellm.s3_callback_params = {
"s3_bucket_name": "litellm-logs-2",
"s3_aws_secret_access_key": "os.environ/AWS_SECRET_ACCESS_KEY",
"s3_aws_access_key_id": "os.environ/AWS_ACCESS_KEY_ID",
}
start_time_s3 = asyncio.run(make_async_calls())
print("done with s3 test")
# Compare the time for both scenarios
print(f"Time taken with success_callback='s3': {start_time_s3}")
print(f"Time taken with empty success_callback: {start_time_empty_callback}")
# assert the diff is not more than 1 second
assert abs(start_time_s3 - start_time_empty_callback) < 1
except litellm.Timeout as e:
pass
except Exception as e:
pytest.fail(f"An exception occurred - {e}")
async def make_async_calls():
tasks = []
for _ in range(5):
task = asyncio.create_task(
litellm.acompletion(
model="azure/gpt-4.1-mini",
messages=[{"role": "user", "content": "This is a test"}],
max_tokens=5,
temperature=0.7,
timeout=5,
user="langfuse_latency_test_user",
mock_response="It's simple to use and easy to get started",
)
)
tasks.append(task)
# Measure the start time before running the tasks
start_time = asyncio.get_event_loop().time()
# Wait for all tasks to complete
responses = await asyncio.gather(*tasks)
# Print the responses when tasks return
for idx, response in enumerate(responses):
print(f"Response from Task {idx + 1}: {response}")
# Calculate the total time taken
total_time = asyncio.get_event_loop().time() - start_time
return total_time
from litellm.integrations.s3_v2 import S3Logger
class TestS3Logger(S3Logger):
def __init__(self, *args, **kwargs):
self.recorded_requests = {}
self.logged_standard_logging_payload: Optional[StandardLoggingPayload] = None
super().__init__(*args, **kwargs)
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
self.recorded_requests[response_obj["id"]] = start_time
print("recorded request", self.recorded_requests)
self.logged_standard_logging_payload = kwargs["standard_logging_object"]
return await super().async_log_success_event(
kwargs, response_obj, start_time, end_time
)