mirror of
https://github.com/BerriAI/litellm.git
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114 lines
5.2 KiB
Python
114 lines
5.2 KiB
Python
class TraceloopLogger:
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def __init__(self):
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from traceloop.sdk.tracing.tracing import TracerWrapper
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from traceloop.sdk import Traceloop
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Traceloop.init(app_name="Litellm-Server", disable_batch=True)
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self.tracer_wrapper = TracerWrapper()
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def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
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from opentelemetry.trace import SpanKind
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from opentelemetry.semconv.ai import SpanAttributes
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try:
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tracer = self.tracer_wrapper.get_tracer()
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model = kwargs.get("model")
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# LiteLLM uses the standard OpenAI library, so it's already handled by Traceloop SDK
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if kwargs.get("litellm_params").get("custom_llm_provider") == "openai":
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return
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optional_params = kwargs.get("optional_params", {})
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with tracer.start_as_current_span(
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"litellm.completion",
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kind=SpanKind.CLIENT,
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) as span:
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if span.is_recording():
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model")
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)
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if "stop" in optional_params:
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span.set_attribute(
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SpanAttributes.LLM_CHAT_STOP_SEQUENCES,
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optional_params.get("stop"),
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)
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if "frequency_penalty" in optional_params:
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span.set_attribute(
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SpanAttributes.LLM_FREQUENCY_PENALTY,
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optional_params.get("frequency_penalty"),
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)
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if "presence_penalty" in optional_params:
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span.set_attribute(
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SpanAttributes.LLM_PRESENCE_PENALTY,
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optional_params.get("presence_penalty"),
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)
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if "top_p" in optional_params:
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span.set_attribute(
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SpanAttributes.LLM_TOP_P, optional_params.get("top_p")
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)
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if "tools" in optional_params or "functions" in optional_params:
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_FUNCTIONS,
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optional_params.get(
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"tools", optional_params.get("functions")
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),
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)
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if "user" in optional_params:
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span.set_attribute(
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SpanAttributes.LLM_USER, optional_params.get("user")
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)
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if "max_tokens" in optional_params:
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_MAX_TOKENS,
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kwargs.get("max_tokens"),
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)
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if "temperature" in optional_params:
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span.set_attribute(
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SpanAttributes.LLM_TEMPERATURE, kwargs.get("temperature")
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)
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for idx, prompt in enumerate(kwargs.get("messages")):
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span.set_attribute(
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f"{SpanAttributes.LLM_PROMPTS}.{idx}.role",
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prompt.get("role"),
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)
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span.set_attribute(
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f"{SpanAttributes.LLM_PROMPTS}.{idx}.content",
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prompt.get("content"),
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)
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span.set_attribute(
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SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model")
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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_USAGE_TOTAL_TOKENS,
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usage.get("total_tokens"),
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)
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span.set_attribute(
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SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
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usage.get("completion_tokens"),
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)
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span.set_attribute(
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SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
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usage.get("prompt_tokens"),
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)
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for idx, choice in enumerate(response_obj.get("choices")):
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span.set_attribute(
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f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.finish_reason",
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choice.get("finish_reason"),
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)
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span.set_attribute(
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f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.role",
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choice.get("message").get("role"),
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)
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span.set_attribute(
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f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content",
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choice.get("message").get("content"),
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)
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except Exception as e:
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print_verbose(f"Traceloop Layer Error - {e}")
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