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https://github.com/BerriAI/litellm.git
synced 2025-04-26 19:24:27 +00:00
feat - set span attributes OTEL with raw request / response
This commit is contained in:
Ishaan Jaff
parent
9da1b27793
commit
7f86dc859e
1 changed files with 83 additions and 153 deletions
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@ -1,6 +1,7 @@
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import os
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from dataclasses import dataclass
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from datetime import datetime
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import litellm
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from litellm.integrations.custom_logger import CustomLogger
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from litellm._logging import verbose_logger
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@ -193,6 +194,7 @@ class OpenTelemetry(CustomLogger):
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def _handle_sucess(self, kwargs, response_obj, start_time, end_time):
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from opentelemetry.trace import Status, StatusCode
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from opentelemetry import trace
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verbose_logger.debug(
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"OpenTelemetry Logger: Logging kwargs: %s, OTEL config settings=%s",
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@ -209,18 +211,18 @@ class OpenTelemetry(CustomLogger):
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)
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span.set_status(Status(StatusCode.OK))
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self.set_attributes(span, kwargs, response_obj)
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span.end(end_time=self._to_ns(end_time))
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# Span 2: Raw Request / Response to LLM
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raw_request_span = self.tracer.start_span(
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name=RAW_REQUEST_SPAN_NAME,
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start_time=self._to_ns(start_time),
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context=_parent_context,
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context=trace.set_span_in_context(span),
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)
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raw_request_span.set_status(Status(StatusCode.OK))
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self.set_raw_request_attributes(raw_request_span, kwargs, response_obj)
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raw_request_span.end(end_time=self._to_ns(end_time))
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span.end(end_time=self._to_ns(end_time))
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if parent_otel_span is not None:
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parent_otel_span.end(end_time=self._to_ns(datetime.now()))
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@ -251,7 +253,8 @@ class OpenTelemetry(CustomLogger):
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#############################################
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# The name of the LLM a request is being made to
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span.set_attribute(SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model"))
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if kwargs.get("model"):
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span.set_attribute(SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model"))
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# The Generative AI Provider: Azure, OpenAI, etc.
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span.set_attribute(
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@ -260,64 +263,87 @@ class OpenTelemetry(CustomLogger):
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)
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# The maximum number of tokens the LLM generates for a request.
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_MAX_TOKENS, optional_params.get("max_tokens")
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)
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if optional_params.get("max_tokens"):
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_MAX_TOKENS, optional_params.get("max_tokens")
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)
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# The temperature setting for the LLM request.
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_TEMPERATURE, optional_params.get("temperature")
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)
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if optional_params.get("temperature"):
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_TEMPERATURE,
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optional_params.get("temperature"),
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)
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# The top_p sampling setting for the LLM request.
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_TOP_P, optional_params.get("top_p")
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)
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span.set_attribute(
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SpanAttributes.LLM_IS_STREAMING, optional_params.get("stream")
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)
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_FUNCTIONS,
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optional_params.get("tools"),
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)
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span.set_attribute(SpanAttributes.LLM_USER, optional_params.get("user"))
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for idx, prompt in enumerate(kwargs.get("messages")):
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if optional_params.get("top_p"):
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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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SpanAttributes.LLM_REQUEST_TOP_P, optional_params.get("top_p")
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)
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span.set_attribute(
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SpanAttributes.LLM_IS_STREAMING, optional_params.get("stream", False)
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)
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if optional_params.get("tools"):
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# cast to str - since OTEL only accepts string values
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_tools = str(optional_params.get("tools"))
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span.set_attribute(SpanAttributes.LLM_REQUEST_FUNCTIONS, _tools)
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if optional_params.get("user"):
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span.set_attribute(SpanAttributes.LLM_USER, optional_params.get("user"))
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if kwargs.get("messages"):
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for idx, prompt in enumerate(kwargs.get("messages")):
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if prompt.get("role"):
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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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if prompt.get("content"):
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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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#############################################
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########## LLM Response Attributes ##########
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#############################################
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if response_obj.get("choices"):
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for idx, choice in enumerate(response_obj.get("choices")):
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if choice.get("finish_reason"):
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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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if choice.get("message"):
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if choice.get("message").get("role"):
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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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if choice.get("message").get("content"):
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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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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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if choice.get("message").get("tool_calls"):
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_tool_calls = choice.get("message").get("tool_calls")
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span.set_attribute(
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f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.tool_calls",
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_tool_calls,
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)
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# The unique identifier for the completion.
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span.set_attribute("gen_ai.response.id", response_obj.get("id"))
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if response_obj.get("id"):
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span.set_attribute("gen_ai.response.id", response_obj.get("id"))
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# The model used to generate the response.
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span.set_attribute(SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model"))
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if response_obj.get("model"):
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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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@ -338,7 +364,7 @@ class OpenTelemetry(CustomLogger):
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usage.get("prompt_tokens"),
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)
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def set_anthropic_raw_request_attributes(self, span: Span, kwargs, response_obj):
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def set_raw_request_attributes(self, span: Span, kwargs, response_obj):
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from opentelemetry.semconv.ai import SpanAttributes
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optional_params = kwargs.get("optional_params", {})
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@ -354,52 +380,12 @@ class OpenTelemetry(CustomLogger):
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# OTEL Attributes for the RAW Request to https://docs.anthropic.com/en/api/messages
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if complete_input_dict:
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if complete_input_dict.get("model"):
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for param, val in complete_input_dict.items():
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if not isinstance(val, str):
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val = str(val)
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_MODEL, complete_input_dict.get("model")
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)
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if complete_input_dict.get("messages"):
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for idx, prompt in enumerate(complete_input_dict.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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if complete_input_dict.get("max_tokens"):
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_MAX_TOKENS,
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complete_input_dict.get("max_tokens"),
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)
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if complete_input_dict.get("temperature"):
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_TEMPERATURE,
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complete_input_dict.get("temperature"),
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)
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if complete_input_dict.get("top_p"):
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_TOP_P, complete_input_dict.get("top_p")
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)
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if complete_input_dict.get("stream"):
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span.set_attribute(
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SpanAttributes.LLM_IS_STREAMING, complete_input_dict.get("stream")
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)
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if complete_input_dict.get("tools"):
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span.set_attribute(
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SpanAttributes.LLM_REQUEST_FUNCTIONS,
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complete_input_dict.get("tools"),
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)
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if complete_input_dict.get("user"):
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span.set_attribute(
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SpanAttributes.LLM_USER, complete_input_dict.get("user")
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f"gen_ai.request.{param}",
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val,
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)
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#############################################
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@ -410,72 +396,16 @@ class OpenTelemetry(CustomLogger):
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import json
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_raw_response = json.loads(_raw_response)
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# The unique identifier for the completion.
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if _raw_response.get("id"):
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span.set_attribute("gen_ai.response.id", _raw_response.get("id"))
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# The model used to generate the response.
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if _raw_response.get("model"):
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for param, val in _raw_response.items():
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if not isinstance(val, str):
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val = str(val)
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span.set_attribute(
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SpanAttributes.LLM_RESPONSE_MODEL, _raw_response.get("model")
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f"gen_ai.response.{param}",
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val,
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)
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if _raw_response.get("content"):
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for idx, choice in enumerate(_raw_response.get("content")):
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if choice.get("type"):
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span.set_attribute(
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f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.type",
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choice.get("type"),
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)
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if choice.get("text"):
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span.set_attribute(
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f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content",
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choice.get("text"),
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)
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if choice.get("id"):
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# https://docs.anthropic.com/en/docs/tool-use
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span.set_attribute(
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f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.id",
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choice.get("id"),
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)
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if choice.get("name"):
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span.set_attribute(
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f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.name",
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choice.get("name"),
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)
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if choice.get("input"):
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span.set_attribute(
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f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.input",
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choice.get("input"),
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)
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pass
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def set_openai_raw_request_attributes(self, span: Span, kwargs, response_obj):
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from opentelemetry.semconv.ai import SpanAttributes
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pass
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def set_default_raw_request_attributes(self, span: Span, kwargs, response_obj):
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from opentelemetry.semconv.ai import SpanAttributes
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pass
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def set_raw_request_attributes(self, span: Span, kwargs, response_obj):
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litellm_params = kwargs.get("litellm_params", {}) or {}
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custom_llm_provider = litellm_params.get("custom_llm_provider", "Unknown")
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if custom_llm_provider == "anthropic":
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self.set_anthropic_raw_request_attributes(span, kwargs, response_obj)
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elif custom_llm_provider == "openai":
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self.set_openai_raw_request_attributes(span, kwargs, response_obj)
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else:
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self.set_default_raw_request_attributes(span, kwargs, response_obj)
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def _to_ns(self, dt):
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return int(dt.timestamp() * 1e9)
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