Files
litellm/tests/logging_callback_tests/test_generic_api_callback.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

567 lines
18 KiB
Python

import io
import os
import sys
sys.path.insert(0, os.path.abspath("../.."))
import asyncio
import litellm
import gzip
import httpx
import json
import logging
import time
from unittest.mock import AsyncMock, patch
import pytest
import litellm
from litellm import completion
from litellm._logging import verbose_logger
from litellm.integrations.gcs_pubsub.pub_sub import *
from datetime import datetime, timedelta
from litellm.types.utils import (
StandardLoggingPayload,
StandardLoggingModelInformation,
StandardLoggingMetadata,
StandardLoggingHiddenParams,
)
verbose_logger.setLevel(logging.DEBUG)
from litellm.integrations.generic_api.generic_api_callback import GenericAPILogger
@pytest.mark.asyncio
async def test_generic_api_callback():
"""
Test the GenericAPILogger callback with a standard logging payload.
This test mocks the HTTP client and validates that the logger properly
formats and sends the expected payload.
"""
# Create a mock for the async_httpx_client's post method
mock_post = AsyncMock()
mock_post.return_value.status_code = 200
mock_post.return_value.text = "OK"
# Set up an endpoint for testing
test_endpoint = "https://example.com/api/logs"
test_headers = {"Authorization": "Bearer test_token"}
os.environ["GENERIC_LOGGER_ENDPOINT"] = test_endpoint
# Initialize the GenericAPILogger and set the mock
generic_logger = GenericAPILogger(
endpoint=test_endpoint, headers=test_headers, flush_interval=1
)
generic_logger.async_httpx_client.post = mock_post
litellm.callbacks = [generic_logger]
# Make the completion call
response = await litellm.acompletion(
model="gpt-5.5",
messages=[{"role": "user", "content": "Hello, world!"}],
mock_response="hi",
user="test_user",
)
# Wait for async flush
await asyncio.sleep(3)
# Assert httpx post was called
mock_post.assert_called_once()
# Get the actual request body from the mock
actual_url = mock_post.call_args[1]["url"]
print("##########\n")
print(
"logs were flushed to URL",
actual_url,
"with the following headers",
mock_post.call_args[1]["headers"],
)
assert (
actual_url == test_endpoint
), f"Expected URL {test_endpoint}, got {actual_url}"
# Validate headers
assert (
mock_post.call_args[1]["headers"]["Content-Type"] == "application/json"
), "Content-Type should be application/json"
# For the GenericAPILogger, it sends the payload directly as JSON in the data field
json_data = mock_post.call_args[1]["data"]
# Parse the JSON string
print("##########\n")
print("json_data", json_data)
actual_request = json.loads(json_data)
# The payload is a list of StandardLoggingPayload objects in the log queue
assert isinstance(actual_request, list), "Request body should be a list"
assert len(actual_request) > 0, "Request body list should not be empty"
# Validate the first payload item
payload_item: StandardLoggingPayload = StandardLoggingPayload(**actual_request[0])
print("##########\n")
print(json.dumps(payload_item, indent=4))
print("##########\n")
# Basic assertions for standard logging payload
assert payload_item["response_cost"] > 0, "Response cost should be greater than 0"
assert payload_item["model"] == "gpt-5.5", "Model should be gpt-5.5"
assert (
payload_item["model_parameters"]["user"] == "test_user"
), "User should be test_user"
assert payload_item["model"] == "gpt-5.5", "Model should be gpt-5.5"
assert payload_item["messages"] == [
{"role": "user", "content": "Hello, world!"}
], "Messages should be the same"
assert (
payload_item["response"]["choices"][0]["message"]["content"] == "hi"
), "Response should be hi"
@pytest.mark.asyncio
async def test_generic_api_callback_multiple_logs():
"""
Test the GenericAPILogger callback with multiple chat completions
"""
# Create a mock for the async_httpx_client's post method
mock_post = AsyncMock()
mock_post.return_value.status_code = 200
mock_post.return_value.text = "OK"
# Set up an endpoint for testing
test_endpoint = "https://example.com/api/logs"
test_headers = {"Authorization": "Bearer test_token"}
os.environ["GENERIC_LOGGER_ENDPOINT"] = test_endpoint
# Initialize the GenericAPILogger and set the mock
generic_logger = GenericAPILogger(
endpoint=test_endpoint, headers=test_headers, flush_interval=5
)
generic_logger.async_httpx_client.post = mock_post
litellm.callbacks = [generic_logger]
# Make the completion call
for _ in range(10):
response = await litellm.acompletion(
model="gpt-5.5",
messages=[{"role": "user", "content": "Hello, world!"}],
mock_response="hi",
user="test_user",
)
# Wait for async flush
await asyncio.sleep(6)
# Assert httpx post was called
mock_post.assert_called_once()
# Get the actual request body from the mock
actual_url = mock_post.call_args[1]["url"]
print("##########\n")
print(
"logs were flushed to URL",
actual_url,
"with the following headers",
mock_post.call_args[1]["headers"],
)
assert (
actual_url == test_endpoint
), f"Expected URL {test_endpoint}, got {actual_url}"
# For the GenericAPILogger, it sends the payload directly as JSON in the data field
json_data = mock_post.call_args[1]["data"]
# Parse the JSON string
print("##########\n")
print("json_data", json_data)
actual_request = json.loads(json_data)
# The payload is a list of StandardLoggingPayload objects in the log queue
assert isinstance(actual_request, list), "Request body should be a list"
assert len(actual_request) > 0, "Request body list should not be empty"
assert (
len(actual_request) == 10
), "Request body list should be 10 items, since we made 10 calls"
# Validate all payload items
for payload_item in actual_request:
payload_item: StandardLoggingPayload = StandardLoggingPayload(**payload_item)
print("##########\n")
print(json.dumps(payload_item, indent=4))
print("##########\n")
assert (
payload_item["response_cost"] > 0
), "Response cost should be greater than 0"
assert payload_item["model"] == "gpt-5.5", "Model should be gpt-5.5"
assert (
payload_item["model_parameters"]["user"] == "test_user"
), "User should be test_user"
assert payload_item["model"] == "gpt-5.5", "Model should be gpt-5.5"
assert payload_item["messages"] == [
{"role": "user", "content": "Hello, world!"}
], "Messages should be the same"
assert (
payload_item["response"]["choices"][0]["message"]["content"] == "hi"
), "Response should be hi"
@pytest.mark.asyncio
async def test_generic_api_callback_ndjson_format():
"""
Test the GenericAPILogger callback with ndjson log format.
Validates that logs are sent as newline-delimited JSON.
"""
# Create a mock for the async_httpx_client's post method
mock_post = AsyncMock()
mock_post.return_value.status_code = 200
mock_post.return_value.text = "OK"
# Set up an endpoint for testing
test_endpoint = "https://example.com/api/logs"
test_headers = {"Authorization": "Bearer test_token"}
os.environ["GENERIC_LOGGER_ENDPOINT"] = test_endpoint
# Initialize the GenericAPILogger with ndjson format
generic_logger = GenericAPILogger(
endpoint=test_endpoint,
headers=test_headers,
flush_interval=1,
log_format="ndjson", # Set NDJSON format
)
generic_logger.async_httpx_client.post = mock_post
litellm.callbacks = [generic_logger]
# Make multiple completion calls to generate multiple logs
for i in range(3):
response = await litellm.acompletion(
model="gpt-5.5",
messages=[{"role": "user", "content": f"Hello, world! {i}"}],
mock_response="hi",
user="test_user",
)
# Wait for async flush
await asyncio.sleep(3)
# Assert httpx post was called
mock_post.assert_called_once()
# Get the actual request body from the mock
actual_url = mock_post.call_args[1]["url"]
assert (
actual_url == test_endpoint
), f"Expected URL {test_endpoint}, got {actual_url}"
# Get the data sent
ndjson_data = mock_post.call_args[1]["data"]
print("##########\n")
print("ndjson_data:", ndjson_data)
print("##########\n")
# Validate it's NDJSON format (newline-delimited)
assert isinstance(ndjson_data, str), "Data should be a string for NDJSON"
# Split by newlines and parse each line
lines = ndjson_data.strip().split("\n")
assert len(lines) == 3, f"Expected 3 lines of NDJSON, got {len(lines)}"
# Validate each line is valid JSON
for i, line in enumerate(lines):
payload_item = json.loads(line)
payload_item = StandardLoggingPayload(**payload_item)
# Basic assertions
assert (
payload_item["response_cost"] > 0
), "Response cost should be greater than 0"
assert payload_item["model"] == "gpt-5.5", "Model should be gpt-5.5"
assert (
payload_item["model_parameters"]["user"] == "test_user"
), "User should be test_user"
@pytest.mark.asyncio
async def test_generic_api_callback_single_format():
"""
Test the GenericAPILogger callback with single log format.
Validates that each log is sent as an individual request in parallel.
"""
# Create a mock for the async_httpx_client's post method
mock_post = AsyncMock()
mock_post.return_value.status_code = 200
mock_post.return_value.text = "OK"
# Set up an endpoint for testing
test_endpoint = "https://example.com/api/logs"
test_headers = {"Authorization": "Bearer test_token"}
os.environ["GENERIC_LOGGER_ENDPOINT"] = test_endpoint
# Initialize the GenericAPILogger with single format
generic_logger = GenericAPILogger(
endpoint=test_endpoint,
headers=test_headers,
flush_interval=1, # Quick flush to trigger batch send
log_format="single", # Set single format
)
generic_logger.async_httpx_client.post = mock_post
litellm.callbacks = [generic_logger]
# Make 3 completion calls
for i in range(3):
response = await litellm.acompletion(
model="gpt-5.5",
messages=[{"role": "user", "content": f"Hello, world! {i}"}],
mock_response="hi",
user="test_user",
)
# Wait for async flush
await asyncio.sleep(3)
# Assert httpx post was called 3 times (once per log in batch)
assert mock_post.call_count == 3, f"Expected 3 calls, got {mock_post.call_count}"
# Validate each call sent a single log object (not an array)
for call_idx in range(3):
call_args = mock_post.call_args_list[call_idx]
json_data = call_args[1]["data"]
print(f"########## Call {call_idx} ##########")
print("json_data:", json_data)
# Parse and validate - should be a single object, not an array
actual_request = json.loads(json_data)
assert isinstance(
actual_request, dict
), f"Call {call_idx}: Expected dict, got {type(actual_request)}"
# Validate it's a valid StandardLoggingPayload
payload_item = StandardLoggingPayload(**actual_request)
assert (
payload_item["response_cost"] > 0
), "Response cost should be greater than 0"
assert payload_item["model"] == "gpt-5.5", "Model should be gpt-5.5"
@pytest.mark.asyncio
async def test_generic_api_callback_json_array_format_explicit():
"""
Test the GenericAPILogger callback with explicit json_array format.
Validates backward compatibility when explicitly set to json_array.
"""
# Create a mock for the async_httpx_client's post method
mock_post = AsyncMock()
mock_post.return_value.status_code = 200
mock_post.return_value.text = "OK"
# Set up an endpoint for testing
test_endpoint = "https://example.com/api/logs"
test_headers = {"Authorization": "Bearer test_token"}
os.environ["GENERIC_LOGGER_ENDPOINT"] = test_endpoint
# Initialize the GenericAPILogger with explicit json_array format
generic_logger = GenericAPILogger(
endpoint=test_endpoint,
headers=test_headers,
flush_interval=1,
log_format="json_array", # Explicitly set json_array
)
generic_logger.async_httpx_client.post = mock_post
litellm.callbacks = [generic_logger]
# Make multiple completion calls
for i in range(5):
response = await litellm.acompletion(
model="gpt-5.5",
messages=[{"role": "user", "content": f"Hello, world! {i}"}],
mock_response="hi",
user="test_user",
)
# Wait for async flush
await asyncio.sleep(3)
# Assert httpx post was called once (batched)
mock_post.assert_called_once()
# Get the data and validate it's a JSON array
json_data = mock_post.call_args[1]["data"]
actual_request = json.loads(json_data)
assert isinstance(
actual_request, list
), "Request body should be a list (JSON array)"
assert len(actual_request) == 5, f"Expected 5 items, got {len(actual_request)}"
# Validate each item
for payload_item in actual_request:
payload_item = StandardLoggingPayload(**payload_item)
assert (
payload_item["response_cost"] > 0
), "Response cost should be greater than 0"
assert payload_item["model"] == "gpt-5.5", "Model should be gpt-5.5"
@pytest.mark.asyncio
async def test_generic_api_callback_sumologic_uses_ndjson():
"""
Test that the sumologic callback uses ndjson format by default
when loaded from generic_api_compatible_callbacks.json
"""
# Create a mock for the async_httpx_client's post method
mock_post = AsyncMock()
mock_post.return_value.status_code = 200
mock_post.return_value.text = "OK"
# Set environment variable for sumologic
os.environ["SUMOLOGIC_WEBHOOK_URL"] = (
"https://collectors.sumologic.com/receiver/v1/http/test123"
)
# Initialize using callback_name (loads from JSON config)
generic_logger = GenericAPILogger(callback_name="sumologic", flush_interval=1)
generic_logger.async_httpx_client.post = mock_post
litellm.callbacks = [generic_logger]
# Verify the logger has ndjson format
assert generic_logger.log_format == "ndjson", "Sumologic should use ndjson format"
# Make completion calls
for i in range(2):
await litellm.acompletion(
model="gpt-5.5",
messages=[{"role": "user", "content": f"Test {i}"}],
mock_response="response",
user="test_user",
)
# Wait for async flush
await asyncio.sleep(3)
# Assert httpx post was called
mock_post.assert_called_once()
# Verify NDJSON format
ndjson_data = mock_post.call_args[1]["data"]
assert isinstance(ndjson_data, str), "Data should be a string for NDJSON"
lines = ndjson_data.strip().split("\n")
assert len(lines) == 2, f"Expected 2 lines of NDJSON, got {len(lines)}"
# Each line should be valid JSON
for line in lines:
json.loads(line) # Will raise if invalid JSON
@pytest.mark.asyncio
async def test_generic_api_callback_invalid_log_format():
"""
Test that invalid log_format values raise a ValueError
"""
test_endpoint = "https://example.com/api/logs"
os.environ["GENERIC_LOGGER_ENDPOINT"] = test_endpoint
with pytest.raises(ValueError, match="Invalid log_format"):
GenericAPILogger(
endpoint=test_endpoint,
log_format="invalid_format", # type: ignore # Intentionally invalid for testing
)
@pytest.mark.asyncio
async def test_generic_api_callback_retries_timeout_then_succeeds():
"""
Test that GenericAPILogger retries LiteLLM timeout errors when configured.
"""
test_endpoint = "https://example.com/api/logs"
generic_logger = GenericAPILogger(
endpoint=test_endpoint,
max_retries=1,
retry_delay=0,
timeout=0.2,
)
mock_post = AsyncMock()
mock_post.side_effect = [
litellm.Timeout(
message="Connection timed out",
model="default-model-name",
llm_provider="litellm-httpx-handler",
),
type("Response", (), {"status_code": 200})(),
]
generic_logger.async_httpx_client.post = mock_post
generic_logger.log_queue = [{"event": "timeout-retry"}]
await generic_logger.async_send_batch()
assert mock_post.call_count == 2
first_call = mock_post.call_args_list[0][1]
assert first_call["url"] == test_endpoint
assert first_call["timeout"] == 0.2
assert json.loads(first_call["data"]) == [{"event": "timeout-retry"}]
@pytest.mark.asyncio
async def test_generic_api_callback_retries_5xx_then_succeeds():
"""
Test that GenericAPILogger retries transient HTTP 5xx errors when configured.
"""
test_endpoint = "https://example.com/api/logs"
generic_logger = GenericAPILogger(
endpoint=test_endpoint,
max_retries=1,
retry_delay=0,
)
request = httpx.Request("POST", test_endpoint)
response = httpx.Response(status_code=503, request=request)
mock_post = AsyncMock()
mock_post.side_effect = [
httpx.HTTPStatusError(
"Server error",
request=request,
response=response,
),
type("Response", (), {"status_code": 200})(),
]
generic_logger.async_httpx_client.post = mock_post
generic_logger.log_queue = [{"event": "5xx-retry"}]
await generic_logger.async_send_batch()
assert mock_post.call_count == 2
@pytest.mark.asyncio
async def test_generic_api_callback_does_not_retry_4xx():
"""
Test that GenericAPILogger does not retry non-transient HTTP 4xx errors.
"""
test_endpoint = "https://example.com/api/logs"
generic_logger = GenericAPILogger(
endpoint=test_endpoint,
max_retries=2,
retry_delay=0,
)
request = httpx.Request("POST", test_endpoint)
response = httpx.Response(status_code=401, request=request)
mock_post = AsyncMock()
mock_post.side_effect = httpx.HTTPStatusError(
"Unauthorized",
request=request,
response=response,
)
generic_logger.async_httpx_client.post = mock_post
generic_logger.log_queue = [{"event": "4xx-no-retry"}]
await generic_logger.async_send_batch()
mock_post.assert_called_once()