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fix(passthrough): resolve costing model when body model is unknown (#30160)
(cherry picked from commit 1828a7c6f0)
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parent
7193461559
commit
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1 changed files with 49 additions and 0 deletions
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@ -100,6 +100,42 @@ class AnthropicPassthroughLoggingHandler:
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return get_end_user_id_from_request_body(request_body)
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return None
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@staticmethod
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def _resolve_costing_model(model: str, logging_obj: LiteLLMLoggingObj) -> str:
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if model and model != "unknown":
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return model
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litellm_params = (getattr(logging_obj, "model_call_details", {}) or {}).get(
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"litellm_params", {}
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) or {}
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deployment_model = litellm_params.get("model")
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if deployment_model and deployment_model != "unknown":
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return deployment_model
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model_group = (litellm_params.get("metadata", {}) or {}).get("model_group")
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if model_group:
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return model_group.removeprefix("passthrough/")
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return model
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@staticmethod
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def _extract_model_from_anthropic_chunks(
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all_chunks: Sequence[Union[str, bytes]],
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) -> Optional[str]:
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for raw in all_chunks:
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text = raw.decode("utf-8") if isinstance(raw, bytes) else raw
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for line in text.splitlines():
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if not line.startswith("data:"):
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continue
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try:
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data = json.loads(line[len("data:") :].strip())
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except (json.JSONDecodeError, ValueError):
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continue
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if not isinstance(data, dict):
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continue
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if data.get("type") == "message_start":
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model = (data.get("message") or {}).get("model")
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if model:
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return model
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return None
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@staticmethod
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def _create_anthropic_response_logging_payload(
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litellm_model_response: Union[ModelResponse, TextCompletionResponse],
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@ -127,6 +163,10 @@ class AnthropicPassthroughLoggingHandler:
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"custom_llm_provider"
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)
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model = AnthropicPassthroughLoggingHandler._resolve_costing_model(
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model, logging_obj
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)
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# Prepend custom_llm_provider to model if not already present
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model_for_cost = model
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if custom_llm_provider and not model.startswith(f"{custom_llm_provider}/"):
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@ -213,6 +253,15 @@ class AnthropicPassthroughLoggingHandler:
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):
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model = cast(str, litellm_logging_obj.model_call_details.get("model"))
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if not model or model == "unknown":
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chunk_model = (
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AnthropicPassthroughLoggingHandler._extract_model_from_anthropic_chunks(
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all_chunks
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)
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)
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if chunk_model:
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model = chunk_model
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complete_streaming_response = (
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AnthropicPassthroughLoggingHandler._build_complete_streaming_response(
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all_chunks=all_chunks,
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