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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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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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