mirror of
https://github.com/BerriAI/litellm.git
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126 lines
4.8 KiB
Python
126 lines
4.8 KiB
Python
from typing import TYPE_CHECKING, Any, Optional
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from litellm._logging import verbose_logger
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from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
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from litellm.types.utils import StandardLoggingPayload
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if TYPE_CHECKING:
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from opentelemetry.trace import Span as _Span
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Span = _Span
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else:
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Span = Any
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def set_attributes(span: Span, kwargs, response_obj):
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from litellm.integrations._types.open_inference import (
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MessageAttributes,
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OpenInferenceSpanKindValues,
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SpanAttributes,
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)
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try:
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standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get(
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"standard_logging_object"
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)
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#############################################
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############ LLM CALL METADATA ##############
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#############################################
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if standard_logging_payload and (
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metadata := standard_logging_payload["metadata"]
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):
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span.set_attribute(SpanAttributes.METADATA, safe_dumps(metadata))
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#############################################
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########## LLM Request Attributes ###########
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#############################################
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# The name of the LLM a request is being made to
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if kwargs.get("model"):
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span.set_attribute(SpanAttributes.LLM_MODEL_NAME, kwargs.get("model"))
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span.set_attribute(
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SpanAttributes.OPENINFERENCE_SPAN_KIND,
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OpenInferenceSpanKindValues.LLM.value,
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)
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messages = kwargs.get("messages")
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# for /chat/completions
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# https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
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if messages:
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span.set_attribute(
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SpanAttributes.INPUT_VALUE,
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messages[-1].get("content", ""), # get the last message for input
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)
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# LLM_INPUT_MESSAGES shows up under `input_messages` tab on the span page
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for idx, msg in enumerate(messages):
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# Set the role per message
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span.set_attribute(
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f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_ROLE}",
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msg["role"],
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)
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# Set the content per message
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span.set_attribute(
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f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_CONTENT}",
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msg.get("content", ""),
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)
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if standard_logging_payload and (
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model_params := standard_logging_payload["model_parameters"]
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):
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# The Generative AI Provider: Azure, OpenAI, etc.
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span.set_attribute(
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SpanAttributes.LLM_INVOCATION_PARAMETERS, safe_dumps(model_params)
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)
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if model_params.get("user"):
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user_id = model_params.get("user")
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if user_id is not None:
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span.set_attribute(SpanAttributes.USER_ID, user_id)
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#############################################
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########## LLM Response Attributes ##########
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# https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
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#############################################
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if hasattr(response_obj, "get"):
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for choice in response_obj.get("choices", []):
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response_message = choice.get("message", {})
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span.set_attribute(
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SpanAttributes.OUTPUT_VALUE, response_message.get("content", "")
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)
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# This shows up under `output_messages` tab on the span page
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# This code assumes a single response
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span.set_attribute(
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f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_ROLE}",
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response_message.get("role"),
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)
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span.set_attribute(
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f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_CONTENT}",
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response_message.get("content", ""),
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)
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usage = response_obj.get("usage")
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if usage:
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span.set_attribute(
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SpanAttributes.LLM_TOKEN_COUNT_TOTAL,
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usage.get("total_tokens"),
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)
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# The number of tokens used in the LLM response (completion).
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span.set_attribute(
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SpanAttributes.LLM_TOKEN_COUNT_COMPLETION,
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usage.get("completion_tokens"),
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)
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# The number of tokens used in the LLM prompt.
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span.set_attribute(
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SpanAttributes.LLM_TOKEN_COUNT_PROMPT,
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usage.get("prompt_tokens"),
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)
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pass
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except Exception as e:
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verbose_logger.error(f"Error setting arize attributes: {e}")
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