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Merge pull request #3449 from kingster/telemetry-fix-system-message

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fix: Record correct system_instruction in LLM spans for LLM services
This commit is contained in:
Mark Backman
2026-03-20 13:42:47 -04:00
committed by GitHub
parent a12ad27348 c89e366739
commit d8af4447ff
4 changed files with 51 additions and 22 deletions
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1
changelog/3449.changed.md Normal file
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@@ -0,0 +1 @@
- Renamed tracing span attributes to align with OpenTelemetry GenAI semantic conventions: `gen_ai.system` to `gen_ai.provider.name`, `system` to `gen_ai.system_instructions`, `gen_ai.usage.cache_read_input_tokens` to `gen_ai.usage.cache_read.input_tokens`, and `gen_ai.usage.cache_creation_input_tokens` to `gen_ai.usage.cache_creation.input_tokens`.

1
changelog/3449.fixed.md Normal file
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@@ -0,0 +1 @@
- Fixed stale `system_instruction` in LLM tracing spans by reading from `_settings.system_instruction` instead of the removed `_system_instruction` attribute.

28
src/pipecat/utils/tracing/service_attributes.py
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@@ -25,20 +25,20 @@ if is_tracing_available():
from opentelemetry.trace import Span from opentelemetry.trace import Span
def _get_gen_ai_system_from_service_name(service_name: str) -> str: def _get_provider_name_from_service_name(service_name: str) -> str:
"""Extract the standardized gen_ai.system value from a service class name. """Extract the standardized gen_ai.provider.name value from a service class name.
Source: Source:
https://opentelemetry.io/docs/specs/semconv/attributes-registry/gen-ai/#gen-ai-system https://opentelemetry.io/docs/specs/semconv/attributes-registry/gen-ai/
Uses standard OTel names where possible, with special case mappings for Uses standard OTel names where possible, with special case mappings for
service names that don't follow the pattern. service names that don't follow the pattern.
Args: Args:
service_name: The service class name to extract system name from. service_name: The service class name to extract provider name from.
Returns: Returns:
The standardized gen_ai.system value. The standardized gen_ai.provider.name value.
""" """
SPECIAL_CASE_MAPPINGS = { SPECIAL_CASE_MAPPINGS = {
# AWS # AWS
@@ -91,7 +91,7 @@ def add_tts_span_attributes(
**kwargs: Additional attributes to add. **kwargs: Additional attributes to add.
""" """
# Add standard attributes # Add standard attributes
span.set_attribute("gen_ai.system", service_name.replace("TTSService", "").lower()) span.set_attribute("gen_ai.provider.name", service_name.replace("TTSService", "").lower())
span.set_attribute("gen_ai.request.model", model) span.set_attribute("gen_ai.request.model", model)
span.set_attribute("gen_ai.operation.name", operation_name) span.set_attribute("gen_ai.operation.name", operation_name)
span.set_attribute("gen_ai.output.type", "speech") span.set_attribute("gen_ai.output.type", "speech")
@@ -150,7 +150,7 @@ def add_stt_span_attributes(
**kwargs: Additional attributes to add. **kwargs: Additional attributes to add.
""" """
# Add standard attributes # Add standard attributes
span.set_attribute("gen_ai.system", service_name.replace("STTService", "").lower()) span.set_attribute("gen_ai.provider.name", service_name.replace("STTService", "").lower())
span.set_attribute("gen_ai.request.model", model) span.set_attribute("gen_ai.request.model", model)
span.set_attribute("gen_ai.operation.name", operation_name) span.set_attribute("gen_ai.operation.name", operation_name)
span.set_attribute("vad_enabled", vad_enabled) span.set_attribute("vad_enabled", vad_enabled)
@@ -193,7 +193,7 @@ def add_llm_span_attributes(
tools: Optional[str] = None, tools: Optional[str] = None,
tool_count: Optional[int] = None, tool_count: Optional[int] = None,
tool_choice: Optional[str] = None, tool_choice: Optional[str] = None,
system: Optional[str] = None, system_instructions: Optional[str] = None,
parameters: Optional[Dict[str, Any]] = None, parameters: Optional[Dict[str, Any]] = None,
extra_parameters: Optional[Dict[str, Any]] = None, extra_parameters: Optional[Dict[str, Any]] = None,
ttfb: Optional[float] = None, ttfb: Optional[float] = None,
@@ -211,14 +211,14 @@ def add_llm_span_attributes(
tools: JSON-serialized tools configuration. tools: JSON-serialized tools configuration.
tool_count: Number of tools available. tool_count: Number of tools available.
tool_choice: Tool selection configuration. tool_choice: Tool selection configuration.
system: System message. system_instructions: System instructions.
parameters: Service parameters. parameters: Service parameters.
extra_parameters: Additional parameters. extra_parameters: Additional parameters.
ttfb: Time to first byte in seconds. ttfb: Time to first byte in seconds.
**kwargs: Additional attributes to add. **kwargs: Additional attributes to add.
""" """
# Add standard attributes # Add standard attributes
span.set_attribute("gen_ai.system", _get_gen_ai_system_from_service_name(service_name)) span.set_attribute("gen_ai.provider.name", _get_provider_name_from_service_name(service_name))
span.set_attribute("gen_ai.request.model", model) span.set_attribute("gen_ai.request.model", model)
span.set_attribute("gen_ai.operation.name", "chat") span.set_attribute("gen_ai.operation.name", "chat")
span.set_attribute("gen_ai.output.type", "text") span.set_attribute("gen_ai.output.type", "text")
@@ -240,8 +240,8 @@ def add_llm_span_attributes(
if tool_choice: if tool_choice:
span.set_attribute("tool_choice", tool_choice) span.set_attribute("tool_choice", tool_choice)
if system: if system_instructions:
span.set_attribute("system", system) span.set_attribute("gen_ai.system_instructions", system_instructions)
if ttfb is not None: if ttfb is not None:
span.set_attribute("metrics.ttfb", ttfb) span.set_attribute("metrics.ttfb", ttfb)
@@ -313,7 +313,7 @@ def add_gemini_live_span_attributes(
**kwargs: Additional attributes to add. **kwargs: Additional attributes to add.
""" """
# Add standard attributes # Add standard attributes
span.set_attribute("gen_ai.system", "gcp.gemini") span.set_attribute("gen_ai.provider.name", "gcp.gemini")
span.set_attribute("gen_ai.request.model", model) span.set_attribute("gen_ai.request.model", model)
span.set_attribute("gen_ai.operation.name", operation_name) span.set_attribute("gen_ai.operation.name", operation_name)
span.set_attribute("service.operation", operation_name) span.set_attribute("service.operation", operation_name)
@@ -414,7 +414,7 @@ def add_openai_realtime_span_attributes(
**kwargs: Additional attributes to add. **kwargs: Additional attributes to add.
""" """
# Add standard attributes # Add standard attributes
span.set_attribute("gen_ai.system", "openai") span.set_attribute("gen_ai.provider.name", "openai")
span.set_attribute("gen_ai.request.model", model) span.set_attribute("gen_ai.request.model", model)
span.set_attribute("gen_ai.operation.name", operation_name) span.set_attribute("gen_ai.operation.name", operation_name)
span.set_attribute("service.operation", operation_name) span.set_attribute("service.operation", operation_name)

43
src/pipecat/utils/tracing/service_decorators.py
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@@ -137,14 +137,14 @@ def _add_token_usage_to_span(span, token_usage):
and token_usage["cache_read_input_tokens"] is not None and token_usage["cache_read_input_tokens"] is not None
): ):
span.set_attribute( span.set_attribute(
"gen_ai.usage.cache_read_input_tokens", token_usage["cache_read_input_tokens"] "gen_ai.usage.cache_read.input_tokens", token_usage["cache_read_input_tokens"]
) )
if ( if (
"cache_creation_input_tokens" in token_usage "cache_creation_input_tokens" in token_usage
and token_usage["cache_creation_input_tokens"] is not None and token_usage["cache_creation_input_tokens"] is not None
): ):
span.set_attribute( span.set_attribute(
"gen_ai.usage.cache_creation_input_tokens", "gen_ai.usage.cache_creation.input_tokens",
token_usage["cache_creation_input_tokens"], token_usage["cache_creation_input_tokens"],
) )
if "reasoning_tokens" in token_usage and token_usage["reasoning_tokens"] is not None: if "reasoning_tokens" in token_usage and token_usage["reasoning_tokens"] is not None:
@@ -159,11 +159,11 @@ def _add_token_usage_to_span(span, token_usage):
# Add cached token metrics for LLMTokenUsage object # Add cached token metrics for LLMTokenUsage object
cache_read_tokens = getattr(token_usage, "cache_read_input_tokens", None) cache_read_tokens = getattr(token_usage, "cache_read_input_tokens", None)
if cache_read_tokens is not None: if cache_read_tokens is not None:
span.set_attribute("gen_ai.usage.cache_read_input_tokens", cache_read_tokens) span.set_attribute("gen_ai.usage.cache_read.input_tokens", cache_read_tokens)
cache_creation_tokens = getattr(token_usage, "cache_creation_input_tokens", None) cache_creation_tokens = getattr(token_usage, "cache_creation_input_tokens", None)
if cache_creation_tokens is not None: if cache_creation_tokens is not None:
span.set_attribute("gen_ai.usage.cache_creation_input_tokens", cache_creation_tokens) span.set_attribute("gen_ai.usage.cache_creation.input_tokens", cache_creation_tokens)
reasoning_tokens = getattr(token_usage, "reasoning_tokens", None) reasoning_tokens = getattr(token_usage, "reasoning_tokens", None)
if reasoning_tokens is not None: if reasoning_tokens is not None:
@@ -502,18 +502,45 @@ def traced_llm(func: Optional[Callable] = None, *, name: Optional[str] = None) -
# Handle system message for different services # Handle system message for different services
system_message = None system_message = None
if hasattr(context, "system"): if isinstance(context, LLMContext):
# settings.system_instruction takes priority (matches service behavior)
if hasattr(self, "_settings") and getattr(
self._settings, "system_instruction", None
):
system_message = self._settings.system_instruction
else:
# Fall back to extracting from context messages
ctx_messages = context.get_messages()
if ctx_messages:
first = ctx_messages[0]
if (
isinstance(first, dict)
and first.get("role") == "system"
):
content = first.get("content")
if isinstance(content, str):
system_message = content
elif isinstance(content, list):
system_message = " ".join(
part.get("text", "")
for part in content
if isinstance(part, dict)
and part.get("type") == "text"
)
elif hasattr(context, "system"):
system_message = context.system system_message = context.system
elif hasattr(context, "system_message"): elif hasattr(context, "system_message"):
system_message = context.system_message system_message = context.system_message
elif hasattr(self, "_system_instruction"):
system_message = self._system_instruction
# Use given_fields() defensively in case a service doesn't # Use given_fields() defensively in case a service doesn't
# initialize all settings. # initialize all settings.
params = {} params = {}
if hasattr(self, "_settings"): if hasattr(self, "_settings"):
for key, value in self._settings.given_fields().items(): for key, value in self._settings.given_fields().items():
# system_instruction is already captured as the
# "system_instructions" span attribute above.
if key == "system_instruction":
continue
if isinstance(value, (int, float, bool, str)): if isinstance(value, (int, float, bool, str)):
params[key] = value params[key] = value
elif value is None: elif value is None:
@@ -534,7 +561,7 @@ def traced_llm(func: Optional[Callable] = None, *, name: Optional[str] = None) -
attribute_kwargs["tools"] = serialized_tools attribute_kwargs["tools"] = serialized_tools
attribute_kwargs["tool_count"] = tool_count attribute_kwargs["tool_count"] = tool_count
if system_message: if system_message:
attribute_kwargs["system"] = system_message attribute_kwargs["system_instructions"] = system_message
# Add all gathered attributes to the span # Add all gathered attributes to the span
add_llm_span_attributes(span=current_span, **attribute_kwargs) add_llm_span_attributes(span=current_span, **attribute_kwargs)