mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-06 08:16:43 +00:00
fix linting errors
This commit is contained in:
mubashir1osmani
parent
410daf6e6d
commit
5b64539d12
1 changed files with 188 additions and 144 deletions
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@ -96,132 +96,147 @@ def _set_response_attributes(span: "Span", response_obj):
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SpanAttributes,
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AudioAttributes,
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EmbeddingAttributes,
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RerankerAttributes,
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)
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if not hasattr(response_obj, "get"):
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return
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# set chat completion attributes
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_set_choice_outputs(span, response_obj, MessageAttributes, SpanAttributes)
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_set_image_outputs(span, response_obj, ImageAttributes, SpanAttributes)
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_set_audio_outputs(span, response_obj, AudioAttributes, SpanAttributes)
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_set_embedding_outputs(span, response_obj, EmbeddingAttributes, SpanAttributes)
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_set_structured_outputs(span, response_obj, MessageAttributes, SpanAttributes)
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_set_usage_outputs(span, response_obj, SpanAttributes)
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def _set_choice_outputs(span: "Span", response_obj, msg_attrs, span_attrs):
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for idx, choice in enumerate(response_obj.get("choices", [])):
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response_message = choice.get("message", {})
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safe_set_attribute(
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span,
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SpanAttributes.OUTPUT_VALUE,
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span_attrs.OUTPUT_VALUE,
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response_message.get("content", ""),
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)
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prefix = f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.{idx}"
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prefix = f"{span_attrs.LLM_OUTPUT_MESSAGES}.{idx}"
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safe_set_attribute(
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span,
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f"{prefix}.{MessageAttributes.MESSAGE_ROLE}",
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f"{prefix}.{msg_attrs.MESSAGE_ROLE}",
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response_message.get("role"),
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)
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safe_set_attribute(
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span,
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f"{prefix}.{MessageAttributes.MESSAGE_CONTENT}",
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f"{prefix}.{msg_attrs.MESSAGE_CONTENT}",
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response_message.get("content", ""),
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)
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# set image generation attributes
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def _set_image_outputs(span: "Span", response_obj, image_attrs, span_attrs):
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images = response_obj.get("data", [])
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if images:
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for i, image in enumerate(images):
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img_url = image.get("url")
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if img_url is None and image.get("b64_json"):
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img_url = f"data:image/png;base64,{image.get('b64_json')}"
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for i, image in enumerate(images):
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img_url = image.get("url")
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if img_url is None and image.get("b64_json"):
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img_url = f"data:image/png;base64,{image.get('b64_json')}"
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if img_url:
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if i == 0:
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safe_set_attribute(span, SpanAttributes.OUTPUT_VALUE, img_url)
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if not img_url:
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continue
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# ste attribute per-image url
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safe_set_attribute(span, f"{ImageAttributes.IMAGE_URL}.{i}", img_url)
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if i == 0:
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safe_set_attribute(span, span_attrs.OUTPUT_VALUE, img_url)
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# set audio generation attr
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safe_set_attribute(span, f"{image_attrs.IMAGE_URL}.{i}", img_url)
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def _set_audio_outputs(span: "Span", response_obj, audio_attrs, span_attrs):
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audio = response_obj.get("audio", [])
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if audio:
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for i, audio_item in enumerate(audio):
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audio_url = audio_item.get("url")
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if audio_url is None and audio_item.get("b64_json"):
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audio_url = f"data:audio/wav;base64,{audio_item.get('b64_json')}"
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for i, audio_item in enumerate(audio):
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audio_url = audio_item.get("url")
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if audio_url is None and audio_item.get("b64_json"):
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audio_url = f"data:audio/wav;base64,{audio_item.get('b64_json')}"
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if audio_url:
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if i == 0:
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safe_set_attribute(span, SpanAttributes.OUTPUT_VALUE, audio_url)
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if audio_url:
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if i == 0:
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safe_set_attribute(span, span_attrs.OUTPUT_VALUE, audio_url)
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safe_set_attribute(span, f"{audio_attrs.AUDIO_URL}.{i}", audio_url)
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safe_set_attribute(span, f"{AudioAttributes.AUDIO_URL}.{i}", audio_url)
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audio_mime = audio_item.get("mime_type")
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if audio_mime:
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safe_set_attribute(span, f"{audio_attrs.AUDIO_MIME_TYPE}.{i}", audio_mime)
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audio_mime = audio_item.get("mime_type")
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if audio_mime:
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safe_set_attribute(span, f"{AudioAttributes.AUDIO_MIME_TYPE}.{i}", audio_mime)
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audio_transcript = audio_item.get("transcript")
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if audio_transcript:
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safe_set_attribute(span, f"{audio_attrs.AUDIO_TRANSCRIPT}.{i}", audio_transcript)
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audio_transcript = audio_item.get("transcript")
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if audio_transcript:
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safe_set_attribute(span, f"{AudioAttributes.AUDIO_TRANSCRIPT}.{i}", audio_transcript)
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def _set_embedding_outputs(span: "Span", response_obj, embedding_attrs, span_attrs):
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embeddings = response_obj.get("data", [])
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if embeddings:
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for i, embedding_item in enumerate(embeddings):
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embedding_vector = embedding_item.get("embedding")
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if embedding_vector:
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if i == 0:
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safe_set_attribute(
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span,
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SpanAttributes.OUTPUT_VALUE,
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str(embedding_vector),
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)
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for i, embedding_item in enumerate(embeddings):
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embedding_vector = embedding_item.get("embedding")
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if embedding_vector:
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if i == 0:
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safe_set_attribute(
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span,
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f"{EmbeddingAttributes.EMBEDDING_VECTOR}.{i}",
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span_attrs.OUTPUT_VALUE,
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str(embedding_vector),
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)
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embedding_text = embedding_item.get("text")
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if embedding_text:
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safe_set_attribute(
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span,
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f"{EmbeddingAttributes.EMBEDDING_TEXT}.{i}",
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str(embedding_text),
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)
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safe_set_attribute(
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span,
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f"{embedding_attrs.EMBEDDING_VECTOR}.{i}",
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str(embedding_vector),
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)
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embedding_text = embedding_item.get("text")
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if embedding_text:
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safe_set_attribute(
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span,
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f"{embedding_attrs.EMBEDDING_TEXT}.{i}",
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str(embedding_text),
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)
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def _set_structured_outputs(span: "Span", response_obj, msg_attrs, span_attrs):
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output_items = response_obj.get("output", [])
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if output_items:
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for i, item in enumerate(output_items):
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prefix = f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.{i}"
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if hasattr(item, "type"):
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item_type = item.type
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if item_type == "reasoning" and hasattr(item, "summary"):
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for summary in item.summary:
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if hasattr(summary, "text"):
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safe_set_attribute(
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span,
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f"{prefix}.{MessageAttributes.MESSAGE_REASONING_SUMMARY}",
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summary.text,
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)
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elif item_type == "message" and hasattr(item, "content"):
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message_content = ""
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content_list = item.content
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if content_list and len(content_list) > 0:
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first_content = content_list[0]
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message_content = getattr(first_content, "text", "")
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message_role = getattr(item, "role", "assistant")
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safe_set_attribute(span, SpanAttributes.OUTPUT_VALUE, message_content)
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safe_set_attribute(span, f"{prefix}.{MessageAttributes.MESSAGE_CONTENT}", message_content)
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safe_set_attribute(span, f"{prefix}.{MessageAttributes.MESSAGE_ROLE}", message_role)
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for i, item in enumerate(output_items):
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prefix = f"{span_attrs.LLM_OUTPUT_MESSAGES}.{i}"
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if not hasattr(item, "type"):
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continue
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item_type = item.type
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if item_type == "reasoning" and hasattr(item, "summary"):
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for summary in item.summary:
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if hasattr(summary, "text"):
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safe_set_attribute(
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span,
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f"{prefix}.{msg_attrs.MESSAGE_REASONING_SUMMARY}",
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summary.text,
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)
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elif item_type == "message" and hasattr(item, "content"):
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message_content = ""
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content_list = item.content
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if content_list and len(content_list) > 0:
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first_content = content_list[0]
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message_content = getattr(first_content, "text", "")
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message_role = getattr(item, "role", "assistant")
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safe_set_attribute(span, span_attrs.OUTPUT_VALUE, message_content)
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safe_set_attribute(span, f"{prefix}.{msg_attrs.MESSAGE_CONTENT}", message_content)
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safe_set_attribute(span, f"{prefix}.{msg_attrs.MESSAGE_ROLE}", message_role)
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def _set_usage_outputs(span: "Span", response_obj, span_attrs):
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usage = response_obj and response_obj.get("usage")
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if usage:
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safe_set_attribute(span, SpanAttributes.LLM_TOKEN_COUNT_TOTAL, usage.get("total_tokens"))
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completion_tokens = usage.get("completion_tokens") or usage.get("output_tokens")
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if completion_tokens:
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safe_set_attribute(span, SpanAttributes.LLM_TOKEN_COUNT_COMPLETION, completion_tokens)
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prompt_tokens = usage.get("prompt_tokens") or usage.get("input_tokens")
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if prompt_tokens:
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safe_set_attribute(span, SpanAttributes.LLM_TOKEN_COUNT_PROMPT, prompt_tokens)
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reasoning_tokens = usage.get("output_tokens_details", {}).get("reasoning_tokens")
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if reasoning_tokens:
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safe_set_attribute(span, SpanAttributes.LLM_TOKEN_COUNT_COMPLETION_DETAILS_REASONING, reasoning_tokens)
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if not usage:
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return
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safe_set_attribute(span, span_attrs.LLM_TOKEN_COUNT_TOTAL, usage.get("total_tokens"))
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completion_tokens = usage.get("completion_tokens") or usage.get("output_tokens")
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if completion_tokens:
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safe_set_attribute(span, span_attrs.LLM_TOKEN_COUNT_COMPLETION, completion_tokens)
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prompt_tokens = usage.get("prompt_tokens") or usage.get("input_tokens")
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if prompt_tokens:
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safe_set_attribute(span, span_attrs.LLM_TOKEN_COUNT_PROMPT, prompt_tokens)
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reasoning_tokens = usage.get("output_tokens_details", {}).get("reasoning_tokens")
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if reasoning_tokens:
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safe_set_attribute(span, span_attrs.LLM_TOKEN_COUNT_COMPLETION_DETAILS_REASONING, reasoning_tokens)
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def _infer_open_inference_span_kind(call_type: Optional[str]) -> str:
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@ -250,7 +265,7 @@ def _infer_open_inference_span_kind(call_type: Optional[str]) -> str:
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if lowered == "call_mcp_tool" or lowered == "mcp" or lowered.endswith("tool"):
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return OpenInferenceSpanKindValues.TOOL.value
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if "assistant" in lowered:
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if "asend_message" in lowered or "a2a" in lowered or "assistant" in lowered:
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return OpenInferenceSpanKindValues.AGENT.value
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if any(
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@ -336,81 +351,41 @@ def set_attributes(
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)
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try:
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# Remove secret_fields to prevent leaking sensitive data (e.g., authorization headers)
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optional_params = kwargs.get("optional_params", {})
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if isinstance(optional_params, dict):
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optional_params.pop("secret_fields", None)
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litellm_params = kwargs.get("litellm_params", {})
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optional_params = _sanitize_optional_params(kwargs.get("optional_params"))
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litellm_params = kwargs.get("litellm_params", {}) or {}
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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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if standard_logging_payload is None:
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raise ValueError("standard_logging_object not found in kwargs")
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metadata = (
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standard_logging_payload.get("metadata")
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if standard_logging_payload
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else None
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)
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if metadata is not None:
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safe_set_attribute(span, SpanAttributes.METADATA, safe_dumps(metadata))
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metadata = standard_logging_payload.get("metadata") if standard_logging_payload else None
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_set_metadata_attributes(span, metadata, SpanAttributes)
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metadata_tools: Optional[list] = None
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if isinstance(metadata, dict):
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llm_obj = metadata.get("llm")
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if isinstance(llm_obj, dict):
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metadata_tools = llm_obj.get("tools")
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optional_tools = None
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if isinstance(optional_params, dict):
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optional_tools = optional_params.get("tools")
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metadata_tools = _extract_metadata_tools(metadata)
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optional_tools = _extract_optional_tools(optional_params)
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call_type = standard_logging_payload.get("call_type")
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if kwargs.get("model"):
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safe_set_attribute(span, SpanAttributes.LLM_MODEL_NAME, kwargs.get("model"))
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safe_set_attribute(span, "llm.request.type", standard_logging_payload["call_type"])
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safe_set_attribute(span, SpanAttributes.LLM_PROVIDER, litellm_params.get("custom_llm_provider", "Unknown"))
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if optional_params.get("max_tokens"):
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safe_set_attribute(span, "llm.request.max_tokens", optional_params.get("max_tokens"))
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if optional_params.get("temperature"):
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safe_set_attribute(span, "llm.request.temperature", optional_params.get("temperature"))
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if optional_params.get("top_p"):
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safe_set_attribute(span, "llm.request.top_p", optional_params.get("top_p"))
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safe_set_attribute(span, "llm.is_streaming", str(optional_params.get("stream", False)))
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if optional_params.get("user"):
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safe_set_attribute(span, "llm.user", optional_params.get("user"))
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if response_obj and response_obj.get("id"):
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safe_set_attribute(span, "llm.response.id", response_obj.get("id"))
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if response_obj and response_obj.get("model"):
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safe_set_attribute(span, "llm.response.model", response_obj.get("model"))
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_set_request_attributes(
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span=span,
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kwargs=kwargs,
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standard_logging_payload=standard_logging_payload,
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optional_params=optional_params,
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litellm_params=litellm_params,
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response_obj=response_obj,
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span_attrs=SpanAttributes,
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)
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span_kind = _infer_open_inference_span_kind(call_type=call_type)
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_set_tool_attributes(span, optional_tools, metadata_tools)
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if (optional_tools or metadata_tools) and span_kind != OpenInferenceSpanKindValues.TOOL.value:
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span_kind = OpenInferenceSpanKindValues.TOOL.value
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safe_set_attribute(span, SpanAttributes.OPENINFERENCE_SPAN_KIND, span_kind)
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attributes.set_messages(span, kwargs)
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model_params = (
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standard_logging_payload.get("model_parameters")
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if standard_logging_payload
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else None
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)
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if model_params:
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safe_set_attribute(span, SpanAttributes.LLM_INVOCATION_PARAMETERS, safe_dumps(model_params))
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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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safe_set_attribute(span, SpanAttributes.USER_ID, user_id)
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model_params = standard_logging_payload.get("model_parameters") if standard_logging_payload else None
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_set_model_params(span, model_params, SpanAttributes)
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_set_response_attributes(span=span, response_obj=response_obj)
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@ -420,3 +395,72 @@ def set_attributes(
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)
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if hasattr(span, "record_exception"):
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span.record_exception(e)
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def _sanitize_optional_params(optional_params: Optional[dict]) -> dict:
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if not isinstance(optional_params, dict):
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return {}
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optional_params.pop("secret_fields", None)
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return optional_params
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def _set_metadata_attributes(span: "Span", metadata: Optional[dict], span_attrs) -> None:
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if metadata is not None:
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safe_set_attribute(span, span_attrs.METADATA, safe_dumps(metadata))
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def _extract_metadata_tools(metadata: Optional[dict]) -> Optional[list]:
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if not isinstance(metadata, dict):
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return None
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llm_obj = metadata.get("llm")
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if isinstance(llm_obj, dict):
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return llm_obj.get("tools")
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return None
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def _extract_optional_tools(optional_params: dict) -> Optional[list]:
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return optional_params.get("tools") if isinstance(optional_params, dict) else None
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def _set_request_attributes(
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span: "Span",
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kwargs,
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standard_logging_payload: StandardLoggingPayload,
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optional_params: dict,
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litellm_params: dict,
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response_obj,
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span_attrs,
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):
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if kwargs.get("model"):
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safe_set_attribute(span, span_attrs.LLM_MODEL_NAME, kwargs.get("model"))
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safe_set_attribute(span, "llm.request.type", standard_logging_payload.get("call_type"))
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safe_set_attribute(span, span_attrs.LLM_PROVIDER, litellm_params.get("custom_llm_provider", "Unknown"))
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if optional_params.get("max_tokens"):
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safe_set_attribute(span, "llm.request.max_tokens", optional_params.get("max_tokens"))
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if optional_params.get("temperature"):
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safe_set_attribute(span, "llm.request.temperature", optional_params.get("temperature"))
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if optional_params.get("top_p"):
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safe_set_attribute(span, "llm.request.top_p", optional_params.get("top_p"))
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safe_set_attribute(span, "llm.is_streaming", str(optional_params.get("stream", False)))
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if optional_params.get("user"):
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safe_set_attribute(span, "llm.user", optional_params.get("user"))
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if response_obj and response_obj.get("id"):
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safe_set_attribute(span, "llm.response.id", response_obj.get("id"))
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if response_obj and response_obj.get("model"):
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safe_set_attribute(span, "llm.response.model", response_obj.get("model"))
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def _set_model_params(span: "Span", model_params: Optional[dict], span_attrs) -> None:
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if not model_params:
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return
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|
||||
safe_set_attribute(span, span_attrs.LLM_INVOCATION_PARAMETERS, safe_dumps(model_params))
|
||||
if model_params.get("user"):
|
||||
user_id = model_params.get("user")
|
||||
if user_id is not None:
|
||||
safe_set_attribute(span, span_attrs.USER_ID, user_id)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue