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fix: correct tracing for workflows and chatflows for phoenix (#22547)

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kurokobo
2025-07-18 14:54:18 +09:00
committed by GitHub
parent f2389771cf
commit a93db6d797
1 changed files with 41 additions and 38 deletions
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79
api/core/ops/arize_phoenix_trace/arize_phoenix_trace.py
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@@ -3,7 +3,7 @@ import json
import logging
import os
from datetime import datetime, timedelta
from typing import Optional, Union, cast
from typing import Any, Optional, Union, cast
from openinference.semconv.trace import OpenInferenceSpanKindValues, SpanAttributes
from opentelemetry import trace
@@ -142,11 +142,8 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
raise
def workflow_trace(self, trace_info: WorkflowTraceInfo):
if trace_info.message_data is None:
return
workflow_metadata = {
"workflow_id": trace_info.workflow_run_id or "",
"workflow_run_id": trace_info.workflow_run_id or "",
"message_id": trace_info.message_id or "",
"workflow_app_log_id": trace_info.workflow_app_log_id or "",
"status": trace_info.workflow_run_status or "",
@@ -156,7 +153,7 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
}
workflow_metadata.update(trace_info.metadata)
trace_id = uuid_to_trace_id(trace_info.message_id)
trace_id = uuid_to_trace_id(trace_info.workflow_run_id)
span_id = RandomIdGenerator().generate_span_id()
context = SpanContext(
trace_id=trace_id,
@@ -213,7 +210,7 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
if model:
node_metadata["ls_model_name"] = model
outputs = json.loads(node_execution.outputs).get("usage", {})
outputs = json.loads(node_execution.outputs).get("usage", {}) if "outputs" in node_execution else {}
usage_data = process_data.get("usage", {}) if "usage" in process_data else outputs.get("usage", {})
if usage_data:
node_metadata["total_tokens"] = usage_data.get("total_tokens", 0)
@@ -236,31 +233,34 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
SpanAttributes.SESSION_ID: trace_info.conversation_id or "",
},
start_time=datetime_to_nanos(created_at),
context=trace.set_span_in_context(trace.NonRecordingSpan(context)),
)
try:
if node_execution.node_type == "llm":
llm_attributes: dict[str, Any] = {
SpanAttributes.INPUT_VALUE: json.dumps(process_data.get("prompts", []), ensure_ascii=False),
}
provider = process_data.get("model_provider")
model = process_data.get("model_name")
if provider:
node_span.set_attribute(SpanAttributes.LLM_PROVIDER, provider)
llm_attributes[SpanAttributes.LLM_PROVIDER] = provider
if model:
node_span.set_attribute(SpanAttributes.LLM_MODEL_NAME, model)
outputs = json.loads(node_execution.outputs).get("usage", {})
llm_attributes[SpanAttributes.LLM_MODEL_NAME] = model
outputs = (
json.loads(node_execution.outputs).get("usage", {}) if "outputs" in node_execution else {}
)
usage_data = (
process_data.get("usage", {}) if "usage" in process_data else outputs.get("usage", {})
)
if usage_data:
node_span.set_attribute(
SpanAttributes.LLM_TOKEN_COUNT_TOTAL, usage_data.get("total_tokens", 0)
)
node_span.set_attribute(
SpanAttributes.LLM_TOKEN_COUNT_PROMPT, usage_data.get("prompt_tokens", 0)
)
node_span.set_attribute(
SpanAttributes.LLM_TOKEN_COUNT_COMPLETION, usage_data.get("completion_tokens", 0)
llm_attributes[SpanAttributes.LLM_TOKEN_COUNT_TOTAL] = usage_data.get("total_tokens", 0)
llm_attributes[SpanAttributes.LLM_TOKEN_COUNT_PROMPT] = usage_data.get("prompt_tokens", 0)
llm_attributes[SpanAttributes.LLM_TOKEN_COUNT_COMPLETION] = usage_data.get(
"completion_tokens", 0
)
llm_attributes.update(self._construct_llm_attributes(process_data.get("prompts", [])))
node_span.set_attributes(llm_attributes)
finally:
node_span.end(end_time=datetime_to_nanos(finished_at))
finally:
@@ -352,25 +352,7 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
SpanAttributes.METADATA: json.dumps(message_metadata, ensure_ascii=False),
SpanAttributes.SESSION_ID: trace_info.message_data.conversation_id,
}
if isinstance(trace_info.inputs, list):
for i, msg in enumerate(trace_info.inputs):
if isinstance(msg, dict):
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.{i}.message.content"] = msg.get("text", "")
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.{i}.message.role"] = msg.get(
"role", "user"
)
# todo: handle assistant and tool role messages, as they don't always
# have a text field, but may have a tool_calls field instead
# e.g. 'tool_calls': [{'id': '98af3a29-b066-45a5-b4b1-46c74ddafc58',
# 'type': 'function', 'function': {'name': 'current_time', 'arguments': '{}'}}]}
elif isinstance(trace_info.inputs, dict):
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.content"] = json.dumps(trace_info.inputs)
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.role"] = "user"
elif isinstance(trace_info.inputs, str):
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.content"] = trace_info.inputs
llm_attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.role"] = "user"
llm_attributes.update(self._construct_llm_attributes(trace_info.inputs))
if trace_info.total_tokens is not None and trace_info.total_tokens > 0:
llm_attributes[SpanAttributes.LLM_TOKEN_COUNT_TOTAL] = trace_info.total_tokens
if trace_info.message_tokens is not None and trace_info.message_tokens > 0:
@@ -724,3 +706,24 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
.all()
)
return workflow_nodes
def _construct_llm_attributes(self, prompts: dict | list | str | None) -> dict[str, str]:
"""Helper method to construct LLM attributes with passed prompts."""
attributes = {}
if isinstance(prompts, list):
for i, msg in enumerate(prompts):
if isinstance(msg, dict):
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.{i}.message.content"] = msg.get("text", "")
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.{i}.message.role"] = msg.get("role", "user")
# todo: handle assistant and tool role messages, as they don't always
# have a text field, but may have a tool_calls field instead
# e.g. 'tool_calls': [{'id': '98af3a29-b066-45a5-b4b1-46c74ddafc58',
# 'type': 'function', 'function': {'name': 'current_time', 'arguments': '{}'}}]}
elif isinstance(prompts, dict):
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.content"] = json.dumps(prompts)
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.role"] = "user"
elif isinstance(prompts, str):
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.content"] = prompts
attributes[f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.message.role"] = "user"
return attributes