import json
from typing import TYPE_CHECKING, Any, Optional, Union
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
from litellm.types.utils import StandardLoggingPayload
if TYPE_CHECKING:
from opentelemetry.trace import Span as _Span
Span = Union[_Span, Any]
else:
Span = Any
def cast_as_primitive_value_type(value) -> Union[str, bool, int, float]:
"""
Converts a value to an OTEL-supported primitive for Arize/Phoenix observability.
"""
if value is None:
return ""
if isinstance(value, (str, bool, int, float)):
return value
try:
return str(value)
except Exception:
return ""
def safe_set_attribute(span: Span, key: str, value: Any):
"""
Sets a span attribute safely with OTEL-compliant primitive typing for Arize/Phoenix.
"""
primitive_value = cast_as_primitive_value_type(value)
span.set_attribute(key, primitive_value)
def set_attributes(span: Span, kwargs, response_obj): # noqa: PLR0915
"""
Populates span with OpenInference-compliant LLM attributes for Arize and Phoenix tracing.
"""
from litellm.integrations._types.open_inference import (
MessageAttributes,
OpenInferenceSpanKindValues,
SpanAttributes,
ToolCallAttributes,
)
try:
optional_params = kwargs.get("optional_params", {})
litellm_params = kwargs.get("litellm_params", {})
standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get(
"standard_logging_object"
)
if standard_logging_payload is None:
raise ValueError("standard_logging_object not found in kwargs")
#############################################
############ LLM CALL METADATA ##############
#############################################
# Set custom metadata for observability and trace enrichment.
metadata = (
standard_logging_payload.get("metadata")
if standard_logging_payload
else None
)
if metadata is not None:
safe_set_attribute(span, SpanAttributes.METADATA, safe_dumps(metadata))
#############################################
########## LLM Request Attributes ###########
#############################################
# The name of the LLM a request is being made to.
if kwargs.get("model"):
safe_set_attribute(
span,
SpanAttributes.LLM_MODEL_NAME,
kwargs.get("model"),
)
# The LLM request type.
safe_set_attribute(
span,
"llm.request.type",
standard_logging_payload["call_type"],
)
# The Generative AI Provider: Azure, OpenAI, etc.
safe_set_attribute(
span,
SpanAttributes.LLM_PROVIDER,
litellm_params.get("custom_llm_provider", "Unknown"),
)
# The maximum number of tokens the LLM generates for a request.
if optional_params.get("max_tokens"):
safe_set_attribute(
span,
"llm.request.max_tokens",
optional_params.get("max_tokens"),
)
# The temperature setting for the LLM request.
if optional_params.get("temperature"):
safe_set_attribute(
span,
"llm.request.temperature",
optional_params.get("temperature"),
)
# The top_p sampling setting for the LLM request.
if optional_params.get("top_p"):
safe_set_attribute(
span,
"llm.request.top_p",
optional_params.get("top_p"),
)
# Indicates whether response is streamed.
safe_set_attribute(
span,
"llm.is_streaming",
str(optional_params.get("stream", False)),
)
# Logs the user ID if present.
if optional_params.get("user"):
safe_set_attribute(
span,
"llm.user",
optional_params.get("user"),
)
# The unique identifier for the completion.
if response_obj and response_obj.get("id"):
safe_set_attribute(span, "llm.response.id", response_obj.get("id"))
# The model used to generate the response.
if response_obj and response_obj.get("model"):
safe_set_attribute(
span,
"llm.response.model",
response_obj.get("model"),
)
# Required by OpenInference to mark span as LLM kind.
safe_set_attribute(
span,
SpanAttributes.OPENINFERENCE_SPAN_KIND,
OpenInferenceSpanKindValues.LLM.value,
)
messages = kwargs.get("messages")
# for /chat/completions
# https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
if messages:
last_message = messages[-1]
safe_set_attribute(
span,
SpanAttributes.INPUT_VALUE,
last_message.get("content", ""),
)
# LLM_INPUT_MESSAGES shows up under `input_messages` tab on the span page.
for idx, msg in enumerate(messages):
prefix = f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}"
# Set the role per message.
safe_set_attribute(
span, f"{prefix}.{MessageAttributes.MESSAGE_ROLE}", msg.get("role")
)
# Set the content per message.
safe_set_attribute(
span,
f"{prefix}.{MessageAttributes.MESSAGE_CONTENT}",
msg.get("content", ""),
)
# Capture tools (function definitions) used in the LLM call.
tools = optional_params.get("tools")
if tools:
for idx, tool in enumerate(tools):
function = tool.get("function")
if not function:
continue
prefix = f"{SpanAttributes.LLM_TOOLS}.{idx}"
safe_set_attribute(
span, f"{prefix}.{SpanAttributes.TOOL_NAME}", function.get("name")
)
safe_set_attribute(
span,
f"{prefix}.{SpanAttributes.TOOL_DESCRIPTION}",
function.get("description"),
)
safe_set_attribute(
span,
f"{prefix}.{SpanAttributes.TOOL_PARAMETERS}",
json.dumps(function.get("parameters")),
)
# Capture tool calls made during function-calling LLM flows.
functions = optional_params.get("functions")
if functions:
for idx, function in enumerate(functions):
prefix = f"{MessageAttributes.MESSAGE_TOOL_CALLS}.{idx}"
safe_set_attribute(
span,
f"{prefix}.{ToolCallAttributes.TOOL_CALL_FUNCTION_NAME}",
function.get("name"),
)
# Capture invocation parameters and user ID if available.
model_params = (
standard_logging_payload.get("model_parameters")
if standard_logging_payload
else None
)
if model_params:
# The Generative AI Provider: Azure, OpenAI, etc.
safe_set_attribute(
span,
SpanAttributes.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, SpanAttributes.USER_ID, user_id)
#############################################
########## LLM Response Attributes ##########
#############################################
# Captures response tokens, message, and content.
if hasattr(response_obj, "get"):
for idx, choice in enumerate(response_obj.get("choices", [])):
response_message = choice.get("message", {})
safe_set_attribute(
span,
SpanAttributes.OUTPUT_VALUE,
response_message.get("content", ""),
)
# This shows up under `output_messages` tab on the span page.
prefix = f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.{idx}"
safe_set_attribute(
span,
f"{prefix}.{MessageAttributes.MESSAGE_ROLE}",
response_message.get("role"),
)
safe_set_attribute(
span,
f"{prefix}.{MessageAttributes.MESSAGE_CONTENT}",
response_message.get("content", ""),
)
# Token usage info.
usage = response_obj and response_obj.get("usage")
if usage:
safe_set_attribute(
span,
SpanAttributes.LLM_TOKEN_COUNT_TOTAL,
usage.get("total_tokens"),
)
# The number of tokens used in the LLM response (completion).
safe_set_attribute(
span,
SpanAttributes.LLM_TOKEN_COUNT_COMPLETION,
usage.get("completion_tokens"),
)
# The number of tokens used in the LLM prompt.
safe_set_attribute(
span,
SpanAttributes.LLM_TOKEN_COUNT_PROMPT,
usage.get("prompt_tokens"),
)
except Exception as e:
verbose_logger.error(
f"[Arize/Phoenix] Failed to set OpenInference span attributes: {e}"
)
if hasattr(span, "record_exception"):
span.record_exception(e)