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refactor: preserve Anthropic usage type discipline

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This commit is contained in:
David Wu 2026-08-08 14:47:42 +08:00
parent 8a45cfa2ec
commit f476158f7a
1 changed files with 39 additions and 25 deletions
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64
litellm/llms/anthropic/experimental_pass_through/responses_adapters/streaming_iterator.py
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@ -20,6 +20,43 @@ def _get_field(obj: Any, key: str, default: Any = None) -> Any:
return getattr(obj, key, default)
def _translate_usage(raw_usage: Any) -> AnthropicUsage:
if raw_usage is None or isinstance(raw_usage, ResponseAPIUsage):
return LiteLLMAnthropicToResponsesAPIAdapter.translate_responses_api_usage_to_anthropic_usage(raw_usage)
input_tokens: Final = int(_get_field(raw_usage, "input_tokens", 0) or 0)
output_tokens: Final = int(_get_field(raw_usage, "output_tokens", 0) or 0)
input_tokens_details: Final = _get_field(raw_usage, "input_tokens_details")
cache_creation_tokens: Final = int(_get_field(raw_usage, "cache_creation_input_tokens", 0) or 0) or int(
_get_field(input_tokens_details, "cache_write_tokens", 0) or 0
)
cache_read_tokens: Final = int(_get_field(raw_usage, "cache_read_input_tokens", 0) or 0) or int(
_get_field(input_tokens_details, "cached_tokens", 0) or 0
)
uncached_input_tokens: Final = max(input_tokens - cache_read_tokens - cache_creation_tokens, 0)
if cache_creation_tokens and cache_read_tokens:
return AnthropicUsage(
input_tokens=uncached_input_tokens,
output_tokens=output_tokens,
cache_creation_input_tokens=cache_creation_tokens,
cache_read_input_tokens=cache_read_tokens,
)
if cache_creation_tokens:
return AnthropicUsage(
input_tokens=uncached_input_tokens,
output_tokens=output_tokens,
cache_creation_input_tokens=cache_creation_tokens,
)
if cache_read_tokens:
return AnthropicUsage(
input_tokens=uncached_input_tokens,
output_tokens=output_tokens,
cache_read_input_tokens=cache_read_tokens,
)
return AnthropicUsage(input_tokens=uncached_input_tokens, output_tokens=output_tokens)
class AnthropicResponsesStreamWrapper:
"""
Wraps a Responses API streaming iterator and re-emits events in Anthropic SSE format.
@ -237,36 +274,13 @@ class AnthropicResponsesStreamWrapper:
event.get("response") if isinstance(event, dict) else None
)
stop_reason = "end_turn"
anthropic_usage: AnthropicUsage = AnthropicUsage(input_tokens=0, output_tokens=0)
raw_usage: Final = _get_field(response_obj, "usage") if response_obj is not None else None
anthropic_usage: Final = _translate_usage(raw_usage)
if response_obj is not None:
status: Final = _get_field(response_obj, "status")
if status == "incomplete":
stop_reason = "max_tokens"
raw_usage: Final = _get_field(response_obj, "usage")
if raw_usage is not None and not isinstance(raw_usage, ResponseAPIUsage):
input_tokens = int(_get_field(raw_usage, "input_tokens", 0) or 0)
output_tokens = int(_get_field(raw_usage, "output_tokens", 0) or 0)
cache_creation_tokens = int(_get_field(raw_usage, "cache_creation_input_tokens", 0) or 0)
cache_read_tokens = int(_get_field(raw_usage, "cache_read_input_tokens", 0) or 0)
input_tokens_details = _get_field(raw_usage, "input_tokens_details")
if input_tokens_details is not None:
if cache_creation_tokens == 0:
cache_creation_tokens = int(_get_field(input_tokens_details, "cache_write_tokens", 0) or 0)
if cache_read_tokens == 0:
cache_read_tokens = int(_get_field(input_tokens_details, "cached_tokens", 0) or 0)
anthropic_usage = AnthropicUsage(
input_tokens=max(input_tokens - cache_read_tokens - cache_creation_tokens, 0),
output_tokens=output_tokens,
)
if cache_creation_tokens:
anthropic_usage["cache_creation_input_tokens"] = cache_creation_tokens
if cache_read_tokens:
anthropic_usage["cache_read_input_tokens"] = cache_read_tokens
else:
anthropic_usage = (
LiteLLMAnthropicToResponsesAPIAdapter.translate_responses_api_usage_to_anthropic_usage(raw_usage)
)
# Check if tool_use was in the output to override stop_reason
if response_obj is not None: