Files
litellm/tests/logging_callback_tests/test_posthog.py
T
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

572 lines
21 KiB
Python

import os
import sys
sys.path.insert(0, os.path.abspath("../.."))
import pytest
from litellm.integrations.posthog import PostHogLogger
from litellm.types.utils import StandardLoggingPayload
from typing import cast
# Set env vars for tests
os.environ["POSTHOG_API_KEY"] = "test_key"
os.environ["POSTHOG_API_URL"] = "https://app.posthog.com"
def create_standard_logging_payload() -> StandardLoggingPayload:
# Use cast to bypass strict TypedDict requirements for tests
return cast(
StandardLoggingPayload,
{
"id": "test_id",
"trace_id": "test_trace_id",
"call_type": "completion",
"stream": False,
"response_cost": 0.1,
"status": "success",
"custom_llm_provider": "openai",
"total_tokens": 30,
"prompt_tokens": 20,
"completion_tokens": 10,
"startTime": 1234567890.0,
"endTime": 1234567891.0,
"completionStartTime": 1234567890.5,
"response_time": 1.0,
"model": "gpt-5-mini",
"model_id": "model-123",
"api_base": "https://api.openai.com",
"cache_hit": False,
"saved_cache_cost": 0.0,
"request_tags": [],
"end_user": None,
"messages": [{"role": "user", "content": "Hello, world!"}],
"response": {"choices": [{"message": {"content": "Hi there!"}}]},
"error_str": None,
"model_parameters": {"stream": True},
},
)
@pytest.mark.asyncio
async def test_create_posthog_event_payload():
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
kwargs = {"standard_logging_object": standard_payload}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
assert event_payload["event"] == "$ai_generation"
assert event_payload["properties"]["$ai_model"] == "gpt-5-mini"
assert event_payload["properties"]["$ai_input_tokens"] == 20
assert event_payload["properties"]["$ai_output_tokens"] == 10
@pytest.mark.asyncio
async def test_posthog_failure_logging():
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
standard_payload["status"] = "failure"
standard_payload["error_str"] = "Test error"
kwargs = {"standard_logging_object": standard_payload}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
assert event_payload["properties"]["$ai_is_error"] is True
assert event_payload["properties"]["$ai_error"] == "Test error"
@pytest.mark.asyncio
async def test_posthog_embedding_event():
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
standard_payload["call_type"] = "embedding"
kwargs = {"standard_logging_object": standard_payload}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
assert event_payload["event"] == "$ai_embedding"
assert "$ai_output_tokens" not in event_payload["properties"]
@pytest.mark.asyncio
async def test_trace_id_fallback_from_standard_logging_object():
"""Test that trace_id is properly extracted from standard_logging_object"""
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
standard_payload["trace_id"] = "test-trace-123"
kwargs = {"standard_logging_object": standard_payload}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
assert event_payload["properties"]["$ai_trace_id"] == "test-trace-123"
assert (
event_payload["properties"]["$ai_span_id"] == "test_id"
) # from standard_payload["id"]
@pytest.mark.asyncio
async def test_trace_id_uuid_fallback():
"""Test that UUID is generated when no trace_id is available"""
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
# Remove trace_id to test fallback
del standard_payload["trace_id"]
del standard_payload["id"]
kwargs = {"standard_logging_object": standard_payload}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
# Should have generated UUIDs
assert len(event_payload["properties"]["$ai_trace_id"]) == 36 # UUID length
assert len(event_payload["properties"]["$ai_span_id"]) == 36 # UUID length
assert "-" in event_payload["properties"]["$ai_trace_id"] # UUID format
@pytest.mark.asyncio
async def test_distinct_id_fallback_chain():
"""Test the distinct_id fallback priority chain"""
posthog_logger = PostHogLogger()
# Test 1: user_id from metadata (highest priority)
standard_payload = create_standard_logging_payload()
kwargs = {
"standard_logging_object": standard_payload,
"litellm_params": {"metadata": {"user_id": "metadata-user-123"}},
}
distinct_id = posthog_logger._get_distinct_id(standard_payload, kwargs)
assert distinct_id == "metadata-user-123"
# Test 2: trace_id from standard_logging_object (second priority)
kwargs = {"standard_logging_object": standard_payload} # no metadata
distinct_id = posthog_logger._get_distinct_id(standard_payload, kwargs)
assert distinct_id == "test_trace_id"
# Test 3: end_user from standard_logging_object (third priority)
standard_payload_no_trace = create_standard_logging_payload()
del standard_payload_no_trace["trace_id"]
standard_payload_no_trace["end_user"] = "end-user-456"
distinct_id = posthog_logger._get_distinct_id(standard_payload_no_trace, {})
assert distinct_id == "end-user-456"
# Test 4: UUID fallback (lowest priority)
standard_payload_empty = create_standard_logging_payload()
del standard_payload_empty["trace_id"]
del standard_payload_empty["end_user"]
distinct_id = posthog_logger._get_distinct_id(standard_payload_empty, {})
assert len(distinct_id) == 36 # UUID length
assert "-" in distinct_id # UUID format
@pytest.mark.asyncio
async def test_missing_standard_logging_object():
"""Test error handling when standard_logging_object is missing"""
posthog_logger = PostHogLogger()
kwargs = {} # Missing standard_logging_object
with pytest.raises(ValueError, match="standard_logging_object not found in kwargs"):
posthog_logger.create_posthog_event_payload(kwargs)
@pytest.mark.asyncio
async def test_custom_metadata_support():
"""Test that custom metadata fields are added directly to properties"""
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
kwargs = {
"standard_logging_object": standard_payload,
"litellm_params": {
"metadata": {
"user_id": "user-123", # should be used for distinct_id, not custom property
"project_name": "test_project", # should appear as project_name
"environment": "staging", # should appear as environment
"custom_field": "custom_value", # should appear as custom_field
}
},
}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
# Check that custom fields are added directly
assert event_payload["properties"]["project_name"] == "test_project"
assert event_payload["properties"]["environment"] == "staging"
assert event_payload["properties"]["custom_field"] == "custom_value"
# Check that user_id is used for distinct_id, not as custom property
assert event_payload["distinct_id"] == "user-123"
assert "user_id" not in event_payload["properties"]
@pytest.mark.asyncio
async def test_custom_metadata_filters_internal_fields():
"""Test that LiteLLM internal fields are filtered out from custom metadata"""
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
kwargs = {
"standard_logging_object": standard_payload,
"litellm_params": {
"metadata": {
"custom_field": "should_appear",
"endpoint": "/chat/completions", # internal field - should be filtered
"user_api_key_hash": "hash123", # internal field - should be filtered
"headers": {
"content-type": "application/json"
}, # internal field - should be filtered
"model_info": {"id": "123"}, # internal field - should be filtered
}
},
}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
# Check that custom field appears
assert event_payload["properties"]["custom_field"] == "should_appear"
# Check that internal fields are filtered out
assert "endpoint" not in event_payload["properties"]
assert "user_api_key_hash" not in event_payload["properties"]
assert "headers" not in event_payload["properties"]
assert "model_info" not in event_payload["properties"]
@pytest.mark.asyncio
async def test_custom_metadata_with_no_metadata():
"""Test that logger handles cases with no metadata gracefully"""
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
# Test with no litellm_params
kwargs = {"standard_logging_object": standard_payload}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
# Should not error and should have standard properties
assert event_payload["event"] == "$ai_generation"
assert event_payload["properties"]["$ai_model"] == "gpt-5-mini"
# Test with empty metadata
kwargs = {
"standard_logging_object": standard_payload,
"litellm_params": {"metadata": {}},
}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
# Should not error and should have standard properties
assert event_payload["event"] == "$ai_generation"
assert event_payload["properties"]["$ai_model"] == "gpt-5-mini"
@pytest.mark.asyncio
async def test_dynamic_credentials():
"""Test that per-request credentials override environment variables"""
from litellm.types.utils import StandardCallbackDynamicParams
posthog_logger = PostHogLogger()
# Test with no dynamic params - should use env vars
kwargs = {}
api_key, api_url = posthog_logger._get_credentials_for_request(kwargs)
assert api_key == "test_key" # from env var
assert api_url == "https://app.posthog.com" # from env var
# Test with dynamic params - should override env vars
standard_callback_dynamic_params = StandardCallbackDynamicParams(
posthog_api_key="dynamic_key", posthog_api_url="https://custom.posthog.com"
)
kwargs = {"standard_callback_dynamic_params": standard_callback_dynamic_params}
api_key, api_url = posthog_logger._get_credentials_for_request(kwargs)
assert api_key == "dynamic_key"
assert api_url == "https://custom.posthog.com"
# Test partial override - only api_key
standard_callback_dynamic_params = StandardCallbackDynamicParams(
posthog_api_key="another_key"
)
kwargs = {"standard_callback_dynamic_params": standard_callback_dynamic_params}
api_key, api_url = posthog_logger._get_credentials_for_request(kwargs)
assert api_key == "another_key"
assert api_url == "https://app.posthog.com" # falls back to env var
# Test partial override - only api_url
standard_callback_dynamic_params = StandardCallbackDynamicParams(
posthog_api_url="https://another.posthog.com"
)
kwargs = {"standard_callback_dynamic_params": standard_callback_dynamic_params}
api_key, api_url = posthog_logger._get_credentials_for_request(kwargs)
assert api_key == "test_key" # falls back to env var
assert api_url == "https://another.posthog.com"
def test_async_callback_atexit_handler_exists():
"""
Test that atexit handlers are properly registered.
This test verifies that both GLOBAL_LOGGING_WORKER and PostHogLogger
register atexit handlers for flushing pending events.
The actual functionality is validated by end-to-end tests (test_async_only.py)
since unit testing atexit behavior across event loop boundaries is complex.
"""
import atexit
from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER
# Verify GLOBAL_LOGGING_WORKER has _flush_on_exit method
assert hasattr(
GLOBAL_LOGGING_WORKER, "_flush_on_exit"
), "GLOBAL_LOGGING_WORKER should have _flush_on_exit method"
# Verify PostHogLogger has _flush_on_exit method
posthog_logger = PostHogLogger()
assert hasattr(
posthog_logger, "_flush_on_exit"
), "PostHogLogger should have _flush_on_exit method"
# Verify method can be called without crashing (with empty queue)
# This tests the early return paths
GLOBAL_LOGGING_WORKER._flush_on_exit()
posthog_logger._flush_on_exit()
@pytest.mark.asyncio
async def test_posthog_atexit_flushes_internal_queue():
"""
Test that PostHog's atexit handler flushes its internal log_queue.
This works in conjunction with GLOBAL_LOGGING_WORKER:
1. GLOBAL_LOGGING_WORKER atexit invokes pending callbacks
2. Callbacks add events to PostHog's internal log_queue
3. PostHog's atexit flushes log_queue via HTTP POST
"""
from unittest.mock import Mock, patch
import httpx
posthog_logger = PostHogLogger()
# Add mock events to internal queue (simulating what callbacks do)
standard_payload = create_standard_logging_payload()
kwargs = {"standard_logging_object": standard_payload}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
posthog_logger.log_queue.append(
{
"event": event_payload,
"api_key": "test_key",
"api_url": "https://app.posthog.com",
}
)
assert len(posthog_logger.log_queue) == 1, "Queue should have 1 event"
# Mock the sync HTTP client to avoid real API calls
with patch.object(posthog_logger.sync_client, "post") as mock_post:
mock_response = Mock()
mock_response.status_code = 200
mock_response.raise_for_status = Mock()
mock_post.return_value = mock_response
# Trigger atexit flush
posthog_logger._flush_on_exit()
# Verify HTTP POST was called
assert mock_post.called, "HTTP POST should be called during flush"
assert len(posthog_logger.log_queue) == 0, "Queue should be empty after flush"
# Verify correct endpoint was called
call_args = mock_post.call_args
assert "/batch/" in call_args.kwargs["url"], "Should POST to /batch/ endpoint"
@pytest.mark.asyncio
async def test_safe_dumps_serialization_in_sync_log():
"""
Regression test: sync log_success_event should not raise when the payload
contains objects that are not natively JSON-serializable (e.g. Pydantic
models like UserAPIKeyAuth).
Before the fix httpx's json= kwarg called stdlib json.dumps which would
raise ``TypeError: Object of type UserAPIKeyAuth is not JSON serializable``.
After the fix the body is pre-serialized via safe_dumps() and sent with
content= so non-primitive values are coerced to their str() representation.
"""
from unittest.mock import Mock, patch
from pydantic import BaseModel
class FakeNonSerializable(BaseModel):
"""Stand-in for UserAPIKeyAuth or any Pydantic object in metadata."""
token: str = "sk-secret"
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
kwargs = {
"standard_logging_object": standard_payload,
"litellm_params": {
"metadata": {
# This custom key would leak a non-serializable object into
# the PostHog properties dict:
"custom_auth_obj": FakeNonSerializable(),
}
},
"standard_callback_dynamic_params": None,
}
with patch.object(posthog_logger.sync_client, "post") as mock_post:
mock_response = Mock()
mock_response.status_code = 200
mock_response.raise_for_status = Mock()
mock_post.return_value = mock_response
# Should NOT raise TypeError
posthog_logger.log_success_event(kwargs, None, 0.0, 0.0)
assert mock_post.called, "sync_client.post should have been called"
call_kwargs = mock_post.call_args.kwargs
# Must use content= (pre-serialized), NOT json=
assert "content" in call_kwargs, "Should send pre-serialized body via content="
assert "json" not in call_kwargs, "Should NOT use json= kwarg"
@pytest.mark.asyncio
async def test_safe_dumps_serialization_in_async_send_batch():
"""
Regression test: async_send_batch should not raise when the event payload
contains non-JSON-serializable objects.
"""
from unittest.mock import Mock, AsyncMock, patch
from pydantic import BaseModel
class FakeNonSerializable(BaseModel):
token: str = "sk-secret"
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
kwargs = {
"standard_logging_object": standard_payload,
"litellm_params": {
"metadata": {
"custom_auth_obj": FakeNonSerializable(),
}
},
}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
posthog_logger.log_queue.append(
{
"event": event_payload,
"api_key": "test_key",
"api_url": "https://app.posthog.com",
}
)
with patch.object(posthog_logger.async_client, "post") as mock_post:
mock_response = Mock()
mock_response.status_code = 200
mock_response.raise_for_status = Mock()
mock_post.return_value = mock_response
# Should NOT raise TypeError
await posthog_logger.async_send_batch()
assert mock_post.called, "async_client.post should have been called"
call_kwargs = mock_post.call_args.kwargs
assert "content" in call_kwargs, "Should send pre-serialized body via content="
assert "json" not in call_kwargs, "Should NOT use json= kwarg"
@pytest.mark.asyncio
async def test_safe_dumps_serialization_in_flush_on_exit():
"""
Regression test: _flush_on_exit (atexit path) should not raise when the
event payload contains non-JSON-serializable objects.
"""
from unittest.mock import Mock, patch
from pydantic import BaseModel
class FakeNonSerializable(BaseModel):
token: str = "sk-secret"
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
kwargs = {
"standard_logging_object": standard_payload,
"litellm_params": {
"metadata": {
"custom_auth_obj": FakeNonSerializable(),
}
},
}
event_payload = posthog_logger.create_posthog_event_payload(kwargs)
posthog_logger.log_queue.append(
{
"event": event_payload,
"api_key": "test_key",
"api_url": "https://app.posthog.com",
}
)
with patch.object(posthog_logger.sync_client, "post") as mock_post:
mock_response = Mock()
mock_response.status_code = 200
mock_response.raise_for_status = Mock()
mock_post.return_value = mock_response
# Should NOT raise TypeError
posthog_logger._flush_on_exit()
assert mock_post.called, "sync_client.post should have been called"
call_kwargs = mock_post.call_args.kwargs
assert "content" in call_kwargs, "Should send pre-serialized body via content="
assert "json" not in call_kwargs, "Should NOT use json= kwarg"
assert len(posthog_logger.log_queue) == 0, "Queue should be empty after flush"
@pytest.mark.asyncio
async def test_sync_callback_not_affected_by_atexit():
"""
Regression test: ensure sync completions still work immediately.
Sync callbacks should be invoked immediately during completion(),
not deferred to atexit. This test verifies atexit handlers don't
interfere with the sync path.
"""
from unittest.mock import Mock, patch
# Track when callback is invoked
callback_invoked_immediately = False
def mock_log_success(self, kwargs, response_obj, start_time, end_time):
nonlocal callback_invoked_immediately
callback_invoked_immediately = True
with patch.object(PostHogLogger, "log_success_event", mock_log_success):
with patch("httpx.Client.post") as mock_post:
mock_response = Mock()
mock_response.status_code = 200
mock_response.raise_for_status = Mock()
mock_post.return_value = mock_response
posthog_logger = PostHogLogger()
standard_payload = create_standard_logging_payload()
kwargs = {"standard_logging_object": standard_payload}
# Call sync method directly (simulates what completion() does)
posthog_logger.log_success_event(kwargs, None, 0.0, 0.0)
# Callback should be invoked immediately, not queued for atexit
assert (
callback_invoked_immediately
), "Sync callback should be invoked immediately"