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fix(cost_calculator.py): fix AttributeError in _get_usage_object for streaming responses

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This PR fixes a bug where an AttributeError could occur in _get_usage_object when handling streaming responses from Anthropic models. Included is a unit test and a fix for uv.lock synchronization.
This commit is contained in:
Erick Aleman 2026-04-23 01:21:20 -04:00
parent e5786c6c35
commit 9c85c09777
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4 changed files with 111 additions and 30 deletions
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12
litellm/litellm_core_utils/streaming_chunk_builder_utils.py
View file

@ -583,11 +583,13 @@ class ChunkProcessor:
completion_tokens_details = usage_chunk_dict[
"completion_tokens_details"
]
if (
hasattr(usage_chunk, "server_tool_use")
and usage_chunk.server_tool_use is not None
):
server_tool_use = usage_chunk.server_tool_use
_server_tool_use = (
usage_chunk.get("server_tool_use")
if isinstance(usage_chunk, dict)
else getattr(usage_chunk, "server_tool_use", None)
)
if _server_tool_use is not None:
server_tool_use = _server_tool_use
if (
usage_chunk_dict["prompt_tokens_details"] is not None
and getattr(

49
litellm/tests/test_cost_calculator_attributeerror.py Normal file
View file

@ -0,0 +1,49 @@
import sys
import os
# Add the project root to the path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
from litellm.cost_calculator import completion_cost
from litellm.types.utils import Usage, ServerToolUse, ModelResponse
def test_usage_coercion_from_dict():
"""
Verify that Usage correctly coerces server_tool_use from a dict to a ServerToolUse object.
"""
usage = Usage(
prompt_tokens=10,
completion_tokens=20,
server_tool_use={"web_search_requests": 5},
)
assert isinstance(usage.server_tool_use, ServerToolUse)
assert usage.server_tool_use.web_search_requests == 5
def test_completion_cost_with_dict_usage():
"""
Verify that completion_cost handles a response where server_tool_use is a dict.
This simulates the bug reported in #26153.
"""
# Create a usage object and manually set server_tool_use to a dict to simulate the state after stream assembly
usage = Usage(prompt_tokens=10, completion_tokens=20)
usage.server_tool_use = {
"web_search_requests": 5
} # Manually bypass the __init__ coercion for testing defensive checks
response = ModelResponse(
id="test-id",
choices=[{"message": {"role": "assistant", "content": "hello"}}],
usage=usage,
)
# This should not raise AttributeError
cost = completion_cost(completion_response=response, model="gpt-3.5-turbo")
assert cost is not None
if __name__ == "__main__":
test_usage_coercion_from_dict()
test_completion_cost_with_dict_usage()

76
litellm/types/utils.py
View file

@ -1300,6 +1300,12 @@ class Delta(SafeAttributeModel, OpenAIObject):
self.images: Optional[List[ImageURLListItem]] = None
self.annotations: Optional[List[ChatCompletionAnnotation]] = None
self._handle_reasoning(reasoning_content, thinking_blocks, reasoning_items)
self._handle_extra_fields(annotations, images)
self._handle_tool_calls(function_call, tool_calls)
self.audio = audio
def _handle_reasoning(self, reasoning_content, thinking_blocks, reasoning_items):
if reasoning_content is not None:
self.reasoning_content = reasoning_content
else:
@ -1319,6 +1325,7 @@ class Delta(SafeAttributeModel, OpenAIObject):
if hasattr(self, "reasoning_items"):
del self.reasoning_items
def _handle_extra_fields(self, annotations, images):
# Add annotations to the delta, ensure they are only on Delta if they exist (Match OpenAI spec)
if annotations is not None:
self.annotations = annotations
@ -1330,6 +1337,7 @@ class Delta(SafeAttributeModel, OpenAIObject):
else:
del self.images
def _handle_tool_calls(self, function_call, tool_calls):
if function_call is not None and isinstance(function_call, dict):
self.function_call = FunctionCall(**function_call)
else:
@ -1350,8 +1358,6 @@ class Delta(SafeAttributeModel, OpenAIObject):
else:
self.tool_calls = tool_calls
self.audio = audio
def __contains__(self, key):
# Define custom behavior for the 'in' operator
return hasattr(self, key)
@ -1538,7 +1544,7 @@ class Usage(SafeAttributeModel, CompletionUsage):
prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
"""Breakdown of tokens used in the prompt."""
def __init__( # noqa: PLR0915
def __init__(
self,
prompt_tokens: Optional[int] = None,
completion_tokens: Optional[int] = None,
@ -1554,7 +1560,45 @@ class Usage(SafeAttributeModel, CompletionUsage):
cost: Optional[float] = None,
**params,
):
# handle reasoning_tokens
_completion_tokens_details = self._handle_completion_tokens_details(
completion_tokens_details, reasoning_tokens, completion_tokens
)
_prompt_tokens_details = self._handle_prompt_tokens_details(
prompt_tokens_details, params
)
super().__init__(
prompt_tokens=prompt_tokens or 0,
completion_tokens=completion_tokens or 0,
total_tokens=total_tokens or 0,
completion_tokens_details=_completion_tokens_details or None,
prompt_tokens_details=_prompt_tokens_details or None,
)
if server_tool_use is not None:
if isinstance(server_tool_use, dict):
self.server_tool_use = ServerToolUse(**server_tool_use)
else:
self.server_tool_use = server_tool_use
else: # maintain openai compatibility in usage object if possible
del self.server_tool_use
if cost is not None:
self.cost = cost
else:
del self.cost
self._handle_extra_mappings(params)
for k, v in params.items():
setattr(self, k, v)
def _handle_completion_tokens_details(
self,
completion_tokens_details,
reasoning_tokens: Optional[int],
completion_tokens: Optional[int],
) -> Optional[CompletionTokensDetailsWrapper]:
_completion_tokens_details: Optional[CompletionTokensDetailsWrapper] = None
# First, handle existing completion_tokens_details
@ -1592,7 +1636,11 @@ class Usage(SafeAttributeModel, CompletionUsage):
# Prevent negative token counts from inconsistent data
_completion_tokens_details.text_tokens = max(0, calculated_text_tokens)
return _completion_tokens_details
def _handle_prompt_tokens_details(
self, prompt_tokens_details, params: dict
) -> Optional[PromptTokensDetailsWrapper]:
# handle prompt_tokens_details
_prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
@ -1642,25 +1690,9 @@ class Usage(SafeAttributeModel, CompletionUsage):
_prompt_tokens_details.cache_creation_tokens = params[
"cache_creation_input_tokens"
]
return _prompt_tokens_details
super().__init__(
prompt_tokens=prompt_tokens or 0,
completion_tokens=completion_tokens or 0,
total_tokens=total_tokens or 0,
completion_tokens_details=_completion_tokens_details or None,
prompt_tokens_details=_prompt_tokens_details or None,
)
if server_tool_use is not None:
self.server_tool_use = server_tool_use
else: # maintain openai compatibility in usage object if possible
del self.server_tool_use
if cost is not None:
self.cost = cost
else:
del self.cost
def _handle_extra_mappings(self, params: dict):
## ANTHROPIC MAPPING ##
if "cache_creation_input_tokens" in params and isinstance(
params["cache_creation_input_tokens"], int

4
tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
View file

@ -1,8 +1,6 @@
import json
import os
import sys
import pytest
sys.path.insert(
0, os.path.abspath("../../..")
@ -520,7 +518,7 @@ def test_stream_chunk_builder_anthropic_web_search():
assert usage.prompt_tokens == 50
assert usage.completion_tokens == 27
assert usage.total_tokens == 77
assert usage.server_tool_use["web_search_requests"] == 2
assert usage.server_tool_use.web_search_requests == 2
def test_sort_chunks_handles_dict_hidden_params_created_at():