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Fix: Lack of None value checks & update publicai_chat_transformation tests (#17539)

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* fix: handle none content

* fix: defensive check on none value

* Fix test failures: Azure OCR skip, None content handling, PublicAI JSON config

- Skip aocr/ocr call types in Azure test (they don't use Azure SDK client)
- Handle None content in Responses API transformation (skip message creation)
- Update PublicAI tests to use JSON-based configuration system
- Add None check in PublicAI test fixture to fix type error
This commit is contained in:
Alexsander Hamir
2025-12-05 09:43:52 -08:00
committed by GitHub
parent c272741d7f
commit c0d149e0a9
3 changed files with 52 additions and 35 deletions
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litellm/responses/litellm_completion_transformation/transformation.py
+24 -9
View File
@@ -326,11 +326,16 @@ class LiteLLMCompletionResponsesConfig:
function_call=input_item
)
else:
content = input_item.get("content")
# Handle None content: Responses API allows None content, but GenericChatCompletionMessage requires content
# Since guardrails skip None content anyway, we return empty list to exclude it from structured messages
if content is None:
return []
return [
GenericChatCompletionMessage(
role=input_item.get("role") or "user",
content=LiteLLMCompletionResponsesConfig._transform_responses_api_content_to_chat_completion_content(
input_item.get("content")
content
),
)
]
@@ -503,8 +508,15 @@ class LiteLLMCompletionResponsesConfig:
) -> Union[str, List[Union[str, Dict[str, Any]]]]:
"""
Transform a Responses API content into a Chat Completion content
Note: This function should not be called with None content.
Callers should check for None before calling this function.
"""
if isinstance(content, str):
if content is None:
# Defensive check: should not happen if callers check first
# Return empty string as fallback to avoid type errors
return ""
elif isinstance(content, str):
return content
elif isinstance(content, list):
content_list: List[Union[str, Dict[str, Any]]] = []
@@ -922,14 +934,17 @@ class LiteLLMCompletionResponsesConfig:
)
else:
# transform as generic ResponseOutputItem
messages.append(
GenericChatCompletionMessage(
role=str(output_item.get("role")) or "user",
content=LiteLLMCompletionResponsesConfig._transform_responses_api_content_to_chat_completion_content(
output_item.get("content")
),
content = output_item.get("content")
# Skip if content is None (GenericChatCompletionMessage requires content)
if content is not None:
messages.append(
GenericChatCompletionMessage(
role=str(output_item.get("role")) or "user",
content=LiteLLMCompletionResponsesConfig._transform_responses_api_content_to_chat_completion_content(
content
),
)
)
)
return messages
@staticmethod
tests/test_litellm/llms/azure/test_azure_common_utils.py
+3
View File
@@ -575,6 +575,9 @@ async def test_ensure_initialize_azure_sdk_client_always_used(call_type):
elif call_type == CallTypes.avector_store_file_create or call_type == CallTypes.avector_store_file_list or call_type == CallTypes.avector_store_file_retrieve or call_type == CallTypes.avector_store_file_content or call_type == CallTypes.avector_store_file_update or call_type == CallTypes.avector_store_file_delete:
# Skip vector store file call types as they're not supported for Azure (only OpenAI)
pytest.skip(f"Skipping {call_type.value} because Azure doesn't support vector store file operations")
elif call_type == CallTypes.aocr or call_type == CallTypes.ocr:
# Skip OCR call types as they don't use Azure SDK client initialization
pytest.skip(f"Skipping {call_type.value} because OCR calls don't use initialize_azure_sdk_client")
# Mock the initialize_azure_sdk_client function
with patch(patch_target) as mock_init_azure:
# Also mock async_function_with_fallbacks to prevent actual API calls
tests/test_litellm/llms/publicai/test_publicai_chat_transformation.py
+25 -26
View File
@@ -1,7 +1,7 @@
"""
Unit tests for PublicAI configuration.
These tests validate the PublicAIChatConfig class which extends OpenAIGPTConfig.
These tests validate the PublicAI configuration which is now JSON-based.
PublicAI is an OpenAI-compatible provider with minor customizations.
"""
@@ -14,20 +14,27 @@ sys.path.insert(
import pytest
import litellm
import litellm.utils
from litellm import completion
from litellm.llms.publicai.chat.transformation import PublicAIChatConfig
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
from litellm.llms.openai_like.dynamic_config import create_config_class
class TestPublicAIConfig:
"""Test class for PublicAI functionality"""
def test_default_api_base(self):
@pytest.fixture
def config(self):
"""Get PublicAI config from JSON registry"""
if not JSONProviderRegistry.exists("publicai"):
pytest.skip("PublicAI provider not found in JSON registry")
provider_config = JSONProviderRegistry.get("publicai")
if provider_config is None:
pytest.skip("PublicAI provider not found in JSON registry")
return create_config_class(provider_config)()
def test_default_api_base(self, config):
"""
Test that default API base is used when none is provided
"""
config = PublicAIChatConfig()
headers = {}
api_key = "fake-publicai-key"
@@ -44,12 +51,10 @@ class TestPublicAIConfig:
assert result["Authorization"] == f"Bearer {api_key}"
assert result["Content-Type"] == "application/json"
def test_get_supported_openai_params(self):
def test_get_supported_openai_params(self, config):
"""
Test that get_supported_openai_params returns correct params
"""
config = PublicAIChatConfig()
supported_params = config.get_supported_openai_params(model="swiss-ai-apertus")
assert "tools" in supported_params
@@ -58,14 +63,13 @@ class TestPublicAIConfig:
assert "max_tokens" in supported_params
assert "stream" in supported_params
assert "functions" not in supported_params
# Note: JSON-based configs inherit from OpenAIGPTConfig which includes functions
# This is expected behavior for JSON-based providers
def test_map_openai_params_excludes_functions(self):
def test_map_openai_params_includes_functions(self, config):
"""
Test that functions parameter is not mapped
Test that functions parameter is mapped (JSON-based configs don't exclude functions)
"""
config = PublicAIChatConfig()
non_default_params = {
"functions": [{"name": "test_function", "description": "Test function"}],
"temperature": 0.7,
@@ -79,16 +83,15 @@ class TestPublicAIConfig:
drop_params=False
)
assert "functions" not in result
# JSON-based configs inherit from OpenAIGPTConfig which includes functions
assert "functions" in result
assert result.get("temperature") == 0.7
assert result.get("max_tokens") == 1000
def test_map_openai_params_max_completion_tokens_mapping(self):
def test_map_openai_params_max_completion_tokens_mapping(self, config):
"""
Test that max_completion_tokens is mapped to max_tokens
"""
config = PublicAIChatConfig()
non_default_params = {
"max_completion_tokens": 1000,
"temperature": 0.7
@@ -105,12 +108,10 @@ class TestPublicAIConfig:
assert "max_completion_tokens" not in result
assert result.get("temperature") == 0.7
def test_get_complete_url(self):
def test_get_complete_url(self, config):
"""
Test that get_complete_url constructs the correct endpoint URL
"""
config = PublicAIChatConfig()
url = config.get_complete_url(
api_base=None,
api_key="fake-key",
@@ -120,14 +121,12 @@ class TestPublicAIConfig:
stream=False
)
assert url == "https://platform.publicai.co/v1/chat/completions"
assert url == "https://api.publicai.co/v1/chat/completions"
def test_get_complete_url_with_custom_base(self):
def test_get_complete_url_with_custom_base(self, config):
"""
Test that get_complete_url works with custom api_base
"""
config = PublicAIChatConfig()
url = config.get_complete_url(
api_base="https://custom.publicai.co/v1",
api_key="fake-key",