jandres/dify
1
0
Fork 0
mirror of https://github.com/langgenius/dify.git synced 2025-12-19 22:28:46 +00:00
Code Issues Actions 6 Packages Projects Releases Wiki Activity

fix(api): Populate Missing Attributes For Arize Phoenix Integration (#29526)

Browse Source
This commit is contained in:
Ali Saleh
2025-12-15 08:15:18 +05:00
committed by GitHub
parent 7ee7155fd5
commit 02122907e5
1 changed files with 260 additions and 142 deletions
Download Patch File Download Diff File Expand all files Collapse all files

402
api/core/ops/arize_phoenix_trace/arize_phoenix_trace.py
View File

@@ -6,7 +6,13 @@ from datetime import datetime, timedelta
from typing import Any, Union, cast
from urllib.parse import urlparse
from openinference.semconv.trace import OpenInferenceMimeTypeValues, OpenInferenceSpanKindValues, SpanAttributes
from openinference.semconv.trace import (
MessageAttributes,
OpenInferenceMimeTypeValues,
OpenInferenceSpanKindValues,
SpanAttributes,
ToolCallAttributes,
)
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter as GrpcOTLPSpanExporter
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter as HttpOTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
@@ -95,14 +101,14 @@ def setup_tracer(arize_phoenix_config: ArizeConfig | PhoenixConfig) -> tuple[tra
def datetime_to_nanos(dt: datetime | None) -> int:
"""Convert datetime to nanoseconds since epoch. If None, use current time."""
"""Convert datetime to nanoseconds since epoch for Arize/Phoenix."""
if dt is None:
dt = datetime.now()
return int(dt.timestamp() * 1_000_000_000)
def error_to_string(error: Exception | str | None) -> str:
"""Convert an error to a string with traceback information."""
"""Convert an error to a string with traceback information for Arize/Phoenix."""
error_message = "Empty Stack Trace"
if error:
if isinstance(error, Exception):
@@ -114,7 +120,7 @@ def error_to_string(error: Exception | str | None) -> str:
def set_span_status(current_span: Span, error: Exception | str | None = None):
"""Set the status of the current span based on the presence of an error."""
"""Set the status of the current span based on the presence of an error for Arize/Phoenix."""
if error:
error_string = error_to_string(error)
current_span.set_status(Status(StatusCode.ERROR, error_string))
@@ -138,10 +144,17 @@ def set_span_status(current_span: Span, error: Exception | str | None = None):
def safe_json_dumps(obj: Any) -> str:
"""A convenience wrapper around `json.dumps` that ensures that any object can be safely encoded."""
"""A convenience wrapper to ensure that any object can be safely encoded for Arize/Phoenix."""
return json.dumps(obj, default=str, ensure_ascii=False)
def wrap_span_metadata(metadata, **kwargs):
"""Add common metatada to all trace entity types for Arize/Phoenix."""
metadata["created_from"] = "Dify"
metadata.update(kwargs)
return metadata
class ArizePhoenixDataTrace(BaseTraceInstance):
def __init__(
self,
@@ -183,16 +196,27 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
raise
def workflow_trace(self, trace_info: WorkflowTraceInfo):
workflow_metadata = {
"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 "",
"status_message": trace_info.error or "",
"level": "ERROR" if trace_info.error else "DEFAULT",
"total_tokens": trace_info.total_tokens or 0,
}
workflow_metadata.update(trace_info.metadata)
file_list = trace_info.file_list if isinstance(trace_info.file_list, list) else []
metadata = wrap_span_metadata(
trace_info.metadata,
trace_id=trace_info.trace_id or "",
message_id=trace_info.message_id or "",
status=trace_info.workflow_run_status or "",
status_message=trace_info.error or "",
level="ERROR" if trace_info.error else "DEFAULT",
trace_entity_type="workflow",
conversation_id=trace_info.conversation_id or "",
workflow_app_log_id=trace_info.workflow_app_log_id or "",
workflow_id=trace_info.workflow_id or "",
tenant_id=trace_info.tenant_id or "",
workflow_run_id=trace_info.workflow_run_id or "",
workflow_run_elapsed_time=trace_info.workflow_run_elapsed_time or 0,
workflow_run_version=trace_info.workflow_run_version or "",
total_tokens=trace_info.total_tokens or 0,
file_list=safe_json_dumps(file_list),
query=trace_info.query or "",
)
dify_trace_id = trace_info.trace_id or trace_info.message_id or trace_info.workflow_run_id
self.ensure_root_span(dify_trace_id)
@@ -201,10 +225,12 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
workflow_span = self.tracer.start_span(
name=TraceTaskName.WORKFLOW_TRACE.value,
attributes={
SpanAttributes.INPUT_VALUE: json.dumps(trace_info.workflow_run_inputs, ensure_ascii=False),
SpanAttributes.OUTPUT_VALUE: json.dumps(trace_info.workflow_run_outputs, ensure_ascii=False),
SpanAttributes.OPENINFERENCE_SPAN_KIND: OpenInferenceSpanKindValues.CHAIN.value,
SpanAttributes.METADATA: json.dumps(workflow_metadata, ensure_ascii=False),
SpanAttributes.INPUT_VALUE: safe_json_dumps(trace_info.workflow_run_inputs),
SpanAttributes.INPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.OUTPUT_VALUE: safe_json_dumps(trace_info.workflow_run_outputs),
SpanAttributes.OUTPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.METADATA: safe_json_dumps(metadata),
SpanAttributes.SESSION_ID: trace_info.conversation_id or "",
},
start_time=datetime_to_nanos(trace_info.start_time),
@@ -257,6 +283,7 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
"app_id": app_id,
"app_name": node_execution.title,
"status": node_execution.status,
"status_message": node_execution.error or "",
"level": "ERROR" if node_execution.status == "failed" else "DEFAULT",
}
)
@@ -290,11 +317,11 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
node_span = self.tracer.start_span(
name=node_execution.node_type,
attributes={
SpanAttributes.OPENINFERENCE_SPAN_KIND: span_kind.value,
SpanAttributes.INPUT_VALUE: safe_json_dumps(inputs_value),
SpanAttributes.INPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.OUTPUT_VALUE: safe_json_dumps(outputs_value),
SpanAttributes.OUTPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.OPENINFERENCE_SPAN_KIND: span_kind.value,
SpanAttributes.METADATA: safe_json_dumps(node_metadata),
SpanAttributes.SESSION_ID: trace_info.conversation_id or "",
},
@@ -339,30 +366,37 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
def message_trace(self, trace_info: MessageTraceInfo):
if trace_info.message_data is None:
logger.warning("[Arize/Phoenix] Message data is None, skipping message trace.")
return
file_list = cast(list[str], trace_info.file_list) or []
file_list = trace_info.file_list if isinstance(trace_info.file_list, list) else []
message_file_data: MessageFile | None = trace_info.message_file_data
if message_file_data is not None:
file_url = f"{self.file_base_url}/{message_file_data.url}" if message_file_data else ""
file_list.append(file_url)
message_metadata = {
"message_id": trace_info.message_id or "",
"conversation_mode": str(trace_info.conversation_mode or ""),
"user_id": trace_info.message_data.from_account_id or "",
"file_list": json.dumps(file_list),
"status": trace_info.message_data.status or "",
"status_message": trace_info.error or "",
"level": "ERROR" if trace_info.error else "DEFAULT",
"total_tokens": trace_info.total_tokens or 0,
"prompt_tokens": trace_info.message_tokens or 0,
"completion_tokens": trace_info.answer_tokens or 0,
"ls_provider": trace_info.message_data.model_provider or "",
"ls_model_name": trace_info.message_data.model_id or "",
}
message_metadata.update(trace_info.metadata)
metadata = wrap_span_metadata(
trace_info.metadata,
trace_id=trace_info.trace_id or "",
message_id=trace_info.message_id or "",
status=trace_info.message_data.status or "",
status_message=trace_info.error or "",
level="ERROR" if trace_info.error else "DEFAULT",
trace_entity_type="message",
conversation_model=trace_info.conversation_model or "",
message_tokens=trace_info.message_tokens or 0,
answer_tokens=trace_info.answer_tokens or 0,
total_tokens=trace_info.total_tokens or 0,
conversation_mode=trace_info.conversation_mode or "",
gen_ai_server_time_to_first_token=trace_info.gen_ai_server_time_to_first_token or 0,
llm_streaming_time_to_generate=trace_info.llm_streaming_time_to_generate or 0,
is_streaming_request=trace_info.is_streaming_request or False,
user_id=trace_info.message_data.from_account_id or "",
file_list=safe_json_dumps(file_list),
model_provider=trace_info.message_data.model_provider or "",
model_id=trace_info.message_data.model_id or "",
)
# Add end user data if available
if trace_info.message_data.from_end_user_id:
@@ -370,14 +404,16 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
db.session.query(EndUser).where(EndUser.id == trace_info.message_data.from_end_user_id).first()
)
if end_user_data is not None:
message_metadata["end_user_id"] = end_user_data.session_id
metadata["end_user_id"] = end_user_data.session_id
attributes = {
SpanAttributes.INPUT_VALUE: trace_info.message_data.query,
SpanAttributes.OUTPUT_VALUE: trace_info.message_data.answer,
SpanAttributes.OPENINFERENCE_SPAN_KIND: OpenInferenceSpanKindValues.CHAIN.value,
SpanAttributes.METADATA: json.dumps(message_metadata, ensure_ascii=False),
SpanAttributes.SESSION_ID: trace_info.message_data.conversation_id,
SpanAttributes.INPUT_VALUE: trace_info.message_data.query,
SpanAttributes.INPUT_MIME_TYPE: OpenInferenceMimeTypeValues.TEXT.value,
SpanAttributes.OUTPUT_VALUE: trace_info.message_data.answer,
SpanAttributes.OUTPUT_MIME_TYPE: OpenInferenceMimeTypeValues.TEXT.value,
SpanAttributes.METADATA: safe_json_dumps(metadata),
SpanAttributes.SESSION_ID: trace_info.message_data.conversation_id or "",
}
dify_trace_id = trace_info.trace_id or trace_info.message_id
@@ -393,8 +429,10 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
try:
# Convert outputs to string based on type
outputs_mime_type = OpenInferenceMimeTypeValues.TEXT.value
if isinstance(trace_info.outputs, dict | list):
outputs_str = json.dumps(trace_info.outputs, ensure_ascii=False)
outputs_str = safe_json_dumps(trace_info.outputs)
outputs_mime_type = OpenInferenceMimeTypeValues.JSON.value
elif isinstance(trace_info.outputs, str):
outputs_str = trace_info.outputs
else:
@@ -402,10 +440,12 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
llm_attributes = {
SpanAttributes.OPENINFERENCE_SPAN_KIND: OpenInferenceSpanKindValues.LLM.value,
SpanAttributes.INPUT_VALUE: json.dumps(trace_info.inputs, ensure_ascii=False),
SpanAttributes.INPUT_VALUE: safe_json_dumps(trace_info.inputs),
SpanAttributes.INPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.OUTPUT_VALUE: outputs_str,
SpanAttributes.METADATA: json.dumps(message_metadata, ensure_ascii=False),
SpanAttributes.SESSION_ID: trace_info.message_data.conversation_id,
SpanAttributes.OUTPUT_MIME_TYPE: outputs_mime_type,
SpanAttributes.METADATA: safe_json_dumps(metadata),
SpanAttributes.SESSION_ID: trace_info.message_data.conversation_id or "",
}
llm_attributes.update(self._construct_llm_attributes(trace_info.inputs))
if trace_info.total_tokens is not None and trace_info.total_tokens > 0:
@@ -449,16 +489,20 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
def moderation_trace(self, trace_info: ModerationTraceInfo):
if trace_info.message_data is None:
logger.warning("[Arize/Phoenix] Message data is None, skipping moderation trace.")
return
metadata = {
"message_id": trace_info.message_id,
"tool_name": "moderation",
"status": trace_info.message_data.status,
"status_message": trace_info.message_data.error or "",
"level": "ERROR" if trace_info.message_data.error else "DEFAULT",
}
metadata.update(trace_info.metadata)
metadata = wrap_span_metadata(
trace_info.metadata,
trace_id=trace_info.trace_id or "",
message_id=trace_info.message_id or "",
status=trace_info.message_data.status or "",
status_message=trace_info.message_data.error or "",
level="ERROR" if trace_info.message_data.error else "DEFAULT",
trace_entity_type="moderation",
model_provider=trace_info.message_data.model_provider or "",
model_id=trace_info.message_data.model_id or "",
)
dify_trace_id = trace_info.trace_id or trace_info.message_id
self.ensure_root_span(dify_trace_id)
@@ -467,18 +511,19 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
span = self.tracer.start_span(
name=TraceTaskName.MODERATION_TRACE.value,
attributes={
SpanAttributes.INPUT_VALUE: json.dumps(trace_info.inputs, ensure_ascii=False),
SpanAttributes.OUTPUT_VALUE: json.dumps(
SpanAttributes.OPENINFERENCE_SPAN_KIND: OpenInferenceSpanKindValues.TOOL.value,
SpanAttributes.INPUT_VALUE: safe_json_dumps(trace_info.inputs),
SpanAttributes.INPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.OUTPUT_VALUE: safe_json_dumps(
{
"action": trace_info.action,
"flagged": trace_info.flagged,
"action": trace_info.action,
"preset_response": trace_info.preset_response,
"inputs": trace_info.inputs,
},
ensure_ascii=False,
"query": trace_info.query,
}
),
SpanAttributes.OPENINFERENCE_SPAN_KIND: OpenInferenceSpanKindValues.CHAIN.value,
SpanAttributes.METADATA: json.dumps(metadata, ensure_ascii=False),
SpanAttributes.OUTPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.METADATA: safe_json_dumps(metadata),
},
start_time=datetime_to_nanos(trace_info.start_time),
context=root_span_context,
@@ -494,22 +539,28 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
def suggested_question_trace(self, trace_info: SuggestedQuestionTraceInfo):
if trace_info.message_data is None:
logger.warning("[Arize/Phoenix] Message data is None, skipping suggested question trace.")
return
start_time = trace_info.start_time or trace_info.message_data.created_at
end_time = trace_info.end_time or trace_info.message_data.updated_at
metadata = {
"message_id": trace_info.message_id,
"tool_name": "suggested_question",
"status": trace_info.status,
"status_message": trace_info.error or "",
"level": "ERROR" if trace_info.error else "DEFAULT",
"total_tokens": trace_info.total_tokens,
"ls_provider": trace_info.model_provider or "",
"ls_model_name": trace_info.model_id or "",
}
metadata.update(trace_info.metadata)
metadata = wrap_span_metadata(
trace_info.metadata,
trace_id=trace_info.trace_id or "",
message_id=trace_info.message_id or "",
status=trace_info.status or "",
status_message=trace_info.status_message or "",
level=trace_info.level or "",
trace_entity_type="suggested_question",
total_tokens=trace_info.total_tokens or 0,
from_account_id=trace_info.from_account_id or "",
agent_based=trace_info.agent_based or False,
from_source=trace_info.from_source or "",
model_provider=trace_info.model_provider or "",
model_id=trace_info.model_id or "",
workflow_run_id=trace_info.workflow_run_id or "",
)
dify_trace_id = trace_info.trace_id or trace_info.message_id
self.ensure_root_span(dify_trace_id)
@@ -518,10 +569,12 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
span = self.tracer.start_span(
name=TraceTaskName.SUGGESTED_QUESTION_TRACE.value,
attributes={
SpanAttributes.INPUT_VALUE: json.dumps(trace_info.inputs, ensure_ascii=False),
SpanAttributes.OUTPUT_VALUE: json.dumps(trace_info.suggested_question, ensure_ascii=False),
SpanAttributes.OPENINFERENCE_SPAN_KIND: OpenInferenceSpanKindValues.CHAIN.value,
SpanAttributes.METADATA: json.dumps(metadata, ensure_ascii=False),
SpanAttributes.OPENINFERENCE_SPAN_KIND: OpenInferenceSpanKindValues.TOOL.value,
SpanAttributes.INPUT_VALUE: safe_json_dumps(trace_info.inputs),
SpanAttributes.INPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.OUTPUT_VALUE: safe_json_dumps(trace_info.suggested_question),
SpanAttributes.OUTPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.METADATA: safe_json_dumps(metadata),
},
start_time=datetime_to_nanos(start_time),
context=root_span_context,
@@ -537,21 +590,23 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
def dataset_retrieval_trace(self, trace_info: DatasetRetrievalTraceInfo):
if trace_info.message_data is None:
logger.warning("[Arize/Phoenix] Message data is None, skipping dataset retrieval trace.")
return
start_time = trace_info.start_time or trace_info.message_data.created_at
end_time = trace_info.end_time or trace_info.message_data.updated_at
metadata = {
"message_id": trace_info.message_id,
"tool_name": "dataset_retrieval",
"status": trace_info.message_data.status,
"status_message": trace_info.message_data.error or "",
"level": "ERROR" if trace_info.message_data.error else "DEFAULT",
"ls_provider": trace_info.message_data.model_provider or "",
"ls_model_name": trace_info.message_data.model_id or "",
}
metadata.update(trace_info.metadata)
metadata = wrap_span_metadata(
trace_info.metadata,
trace_id=trace_info.trace_id or "",
message_id=trace_info.message_id or "",
status=trace_info.message_data.status or "",
status_message=trace_info.error or "",
level="ERROR" if trace_info.error else "DEFAULT",
trace_entity_type="dataset_retrieval",
model_provider=trace_info.message_data.model_provider or "",
model_id=trace_info.message_data.model_id or "",
)
dify_trace_id = trace_info.trace_id or trace_info.message_id
self.ensure_root_span(dify_trace_id)
@@ -560,20 +615,20 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
span = self.tracer.start_span(
name=TraceTaskName.DATASET_RETRIEVAL_TRACE.value,
attributes={
SpanAttributes.INPUT_VALUE: json.dumps(trace_info.inputs, ensure_ascii=False),
SpanAttributes.OUTPUT_VALUE: json.dumps({"documents": trace_info.documents}, ensure_ascii=False),
SpanAttributes.OPENINFERENCE_SPAN_KIND: OpenInferenceSpanKindValues.RETRIEVER.value,
SpanAttributes.METADATA: json.dumps(metadata, ensure_ascii=False),
"start_time": start_time.isoformat() if start_time else "",
"end_time": end_time.isoformat() if end_time else "",
SpanAttributes.INPUT_VALUE: safe_json_dumps(trace_info.inputs),
SpanAttributes.INPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.OUTPUT_VALUE: safe_json_dumps({"documents": trace_info.documents}),
SpanAttributes.OUTPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.METADATA: safe_json_dumps(metadata),
},
start_time=datetime_to_nanos(start_time),
context=root_span_context,
)
try:
if trace_info.message_data.error:
set_span_status(span, trace_info.message_data.error)
if trace_info.error:
set_span_status(span, trace_info.error)
else:
set_span_status(span)
finally:
@@ -584,30 +639,34 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
logger.warning("[Arize/Phoenix] Message data is None, skipping tool trace.")
return
metadata = {
"message_id": trace_info.message_id,
"tool_config": json.dumps(trace_info.tool_config, ensure_ascii=False),
}
metadata = wrap_span_metadata(
trace_info.metadata,
trace_id=trace_info.trace_id or "",
message_id=trace_info.message_id or "",
status=trace_info.message_data.status or "",
status_message=trace_info.error or "",
level="ERROR" if trace_info.error else "DEFAULT",
trace_entity_type="tool",
tool_config=safe_json_dumps(trace_info.tool_config),
time_cost=trace_info.time_cost or 0,
file_url=trace_info.file_url or "",
)
dify_trace_id = trace_info.trace_id or trace_info.message_id
self.ensure_root_span(dify_trace_id)
root_span_context = self.propagator.extract(carrier=self.carrier)
tool_params_str = (
json.dumps(trace_info.tool_parameters, ensure_ascii=False)
if isinstance(trace_info.tool_parameters, dict)
else str(trace_info.tool_parameters)
)
span = self.tracer.start_span(
name=trace_info.tool_name,
attributes={
SpanAttributes.INPUT_VALUE: json.dumps(trace_info.tool_inputs, ensure_ascii=False),
SpanAttributes.OUTPUT_VALUE: trace_info.tool_outputs,
SpanAttributes.OPENINFERENCE_SPAN_KIND: OpenInferenceSpanKindValues.TOOL.value,
SpanAttributes.METADATA: json.dumps(metadata, ensure_ascii=False),
SpanAttributes.INPUT_VALUE: safe_json_dumps(trace_info.tool_inputs),
SpanAttributes.INPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.OUTPUT_VALUE: trace_info.tool_outputs,
SpanAttributes.OUTPUT_MIME_TYPE: OpenInferenceMimeTypeValues.TEXT.value,
SpanAttributes.METADATA: safe_json_dumps(metadata),
SpanAttributes.TOOL_NAME: trace_info.tool_name,
SpanAttributes.TOOL_PARAMETERS: tool_params_str,
SpanAttributes.TOOL_PARAMETERS: safe_json_dumps(trace_info.tool_parameters),
},
start_time=datetime_to_nanos(trace_info.start_time),
context=root_span_context,
@@ -623,16 +682,22 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
def generate_name_trace(self, trace_info: GenerateNameTraceInfo):
if trace_info.message_data is None:
logger.warning("[Arize/Phoenix] Message data is None, skipping generate name trace.")
return
metadata = {
"project_name": self.project,
"message_id": trace_info.message_id,
"status": trace_info.message_data.status,
"status_message": trace_info.message_data.error or "",
"level": "ERROR" if trace_info.message_data.error else "DEFAULT",
}
metadata.update(trace_info.metadata)
metadata = wrap_span_metadata(
trace_info.metadata,
trace_id=trace_info.trace_id or "",
message_id=trace_info.message_id or "",
status=trace_info.message_data.status or "",
status_message=trace_info.message_data.error or "",
level="ERROR" if trace_info.message_data.error else "DEFAULT",
trace_entity_type="generate_name",
model_provider=trace_info.message_data.model_provider or "",
model_id=trace_info.message_data.model_id or "",
conversation_id=trace_info.conversation_id or "",
tenant_id=trace_info.tenant_id,
)
dify_trace_id = trace_info.trace_id or trace_info.message_id or trace_info.conversation_id
self.ensure_root_span(dify_trace_id)
@@ -641,13 +706,13 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
span = self.tracer.start_span(
name=TraceTaskName.GENERATE_NAME_TRACE.value,
attributes={
SpanAttributes.INPUT_VALUE: json.dumps(trace_info.inputs, ensure_ascii=False),
SpanAttributes.OUTPUT_VALUE: json.dumps(trace_info.outputs, ensure_ascii=False),
SpanAttributes.OPENINFERENCE_SPAN_KIND: OpenInferenceSpanKindValues.CHAIN.value,
SpanAttributes.METADATA: json.dumps(metadata, ensure_ascii=False),
SpanAttributes.SESSION_ID: trace_info.message_data.conversation_id,
"start_time": trace_info.start_time.isoformat() if trace_info.start_time else "",
"end_time": trace_info.end_time.isoformat() if trace_info.end_time else "",
SpanAttributes.INPUT_VALUE: safe_json_dumps(trace_info.inputs),
SpanAttributes.INPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.OUTPUT_VALUE: safe_json_dumps(trace_info.outputs),
SpanAttributes.OUTPUT_MIME_TYPE: OpenInferenceMimeTypeValues.JSON.value,
SpanAttributes.METADATA: safe_json_dumps(metadata),
SpanAttributes.SESSION_ID: trace_info.conversation_id or "",
},
start_time=datetime_to_nanos(trace_info.start_time),
context=root_span_context,
@@ -688,32 +753,85 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
raise ValueError(f"[Arize/Phoenix] API check failed: {str(e)}")
def get_project_url(self):
"""Build a redirect URL that forwards the user to the correct project for Arize/Phoenix."""
try:
if self.arize_phoenix_config.endpoint == "https://otlp.arize.com":
return "https://app.arize.com/"
else:
return f"{self.arize_phoenix_config.endpoint}/projects/"
project_name = self.arize_phoenix_config.project
endpoint = self.arize_phoenix_config.endpoint.rstrip("/")
# Arize
if isinstance(self.arize_phoenix_config, ArizeConfig):
return f"https://app.arize.com/?redirect_project_name={project_name}"
# Phoenix
return f"{endpoint}/projects/?redirect_project_name={project_name}"
except Exception as e:
logger.info("[Arize/Phoenix] Get run url failed: %s", str(e), exc_info=True)
raise ValueError(f"[Arize/Phoenix] Get run url failed: {str(e)}")
logger.info("[Arize/Phoenix] Failed to construct project URL: %s", str(e), exc_info=True)
raise ValueError(f"[Arize/Phoenix] Failed to construct project URL: {str(e)}")
def _construct_llm_attributes(self, prompts: dict | list | str | None) -> dict[str, str]:
"""Helper method to construct LLM attributes with passed prompts."""
attributes = {}
"""Construct LLM attributes with passed prompts for Arize/Phoenix."""
attributes: dict[str, str] = {}
def set_attribute(path: str, value: object) -> None:
"""Store an attribute safely as a string."""
if value is None:
return
try:
if isinstance(value, (dict, list)):
value = safe_json_dumps(value)
attributes[path] = str(value)
except Exception:
attributes[path] = str(value)
def set_message_attribute(message_index: int, key: str, value: object) -> None:
path = f"{SpanAttributes.LLM_INPUT_MESSAGES}.{message_index}.{key}"
set_attribute(path, value)
def set_tool_call_attributes(message_index: int, tool_index: int, tool_call: dict | object | None) -> None:
"""Extract and assign tool call details safely."""
if not tool_call:
return
def safe_get(obj, key, default=None):
if isinstance(obj, dict):
return obj.get(key, default)
return getattr(obj, key, default)
function_obj = safe_get(tool_call, "function", {})
function_name = safe_get(function_obj, "name", "")
function_args = safe_get(function_obj, "arguments", {})
call_id = safe_get(tool_call, "id", "")
base_path = (
f"{SpanAttributes.LLM_INPUT_MESSAGES}."
f"{message_index}.{MessageAttributes.MESSAGE_TOOL_CALLS}.{tool_index}"
)
set_attribute(f"{base_path}.{ToolCallAttributes.TOOL_CALL_FUNCTION_NAME}", function_name)
set_attribute(f"{base_path}.{ToolCallAttributes.TOOL_CALL_FUNCTION_ARGUMENTS_JSON}", function_args)
set_attribute(f"{base_path}.{ToolCallAttributes.TOOL_CALL_ID}", call_id)
# Handle list of messages
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"
for message_index, message in enumerate(prompts):
if not isinstance(message, dict):
continue
role = message.get("role", "user")
content = message.get("text") or message.get("content") or ""
set_message_attribute(message_index, MessageAttributes.MESSAGE_ROLE, role)
set_message_attribute(message_index, MessageAttributes.MESSAGE_CONTENT, content)
tool_calls = message.get("tool_calls") or []
if isinstance(tool_calls, list):
for tool_index, tool_call in enumerate(tool_calls):
set_tool_call_attributes(message_index, tool_index, tool_call)
# Handle single dict or plain string prompt
elif isinstance(prompts, (dict, str)):
set_message_attribute(0, MessageAttributes.MESSAGE_CONTENT, prompts)
set_message_attribute(0, MessageAttributes.MESSAGE_ROLE, "user")
return attributes