opensource
/
dify
mirror of https://github.com/langgenius/dify.git
2
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

update gen_ai semconv for aliyun trace (#26288) 

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
heyszt 2025-09-27 09:51:23 +08:00 committed by GitHub
parent 319ecdd312
commit 4da93ba579
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 123 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

40
api/core/ops/aliyun_trace/aliyun_trace.py
View File

@ -14,12 +14,12 @@ from core.ops.aliyun_trace.data_exporter.traceclient import (
from core.ops.aliyun_trace.entities.aliyun_trace_entity import SpanData, TraceMetadata
from core.ops.aliyun_trace.entities.semconv import (
GEN_AI_COMPLETION,
GEN_AI_MODEL_NAME,
GEN_AI_INPUT_MESSAGE,
GEN_AI_OUTPUT_MESSAGE,
GEN_AI_PROMPT,
GEN_AI_PROMPT_TEMPLATE_TEMPLATE,
GEN_AI_PROMPT_TEMPLATE_VARIABLE,
GEN_AI_PROVIDER_NAME,
GEN_AI_REQUEST_MODEL,
GEN_AI_RESPONSE_FINISH_REASON,
GEN_AI_SYSTEM,
GEN_AI_USAGE_INPUT_TOKENS,
GEN_AI_USAGE_OUTPUT_TOKENS,
GEN_AI_USAGE_TOTAL_TOKENS,
@ -35,6 +35,9 @@ from core.ops.aliyun_trace.utils import (
create_links_from_trace_id,
create_status_from_error,
extract_retrieval_documents,
format_input_messages,
format_output_messages,
format_retrieval_documents,
get_user_id_from_message_data,
get_workflow_node_status,
serialize_json_data,
@ -151,10 +154,6 @@ class AliyunDataTrace(BaseTraceInstance):
)
self.trace_client.add_span(message_span)
app_model_config = getattr(message_data, "app_model_config", {})
pre_prompt = getattr(app_model_config, "pre_prompt", "")
inputs_data = getattr(message_data, "inputs", {})
llm_span = SpanData(
trace_id=trace_metadata.trace_id,
parent_span_id=message_span_id,
@ -170,13 +169,11 @@ class AliyunDataTrace(BaseTraceInstance):
inputs=inputs_json,
outputs=outputs_str,
),
GEN_AI_MODEL_NAME: trace_info.metadata.get("ls_model_name") or "",
GEN_AI_SYSTEM: trace_info.metadata.get("ls_provider") or "",
GEN_AI_REQUEST_MODEL: trace_info.metadata.get("ls_model_name") or "",
GEN_AI_PROVIDER_NAME: trace_info.metadata.get("ls_provider") or "",
GEN_AI_USAGE_INPUT_TOKENS: str(trace_info.message_tokens),
GEN_AI_USAGE_OUTPUT_TOKENS: str(trace_info.answer_tokens),
GEN_AI_USAGE_TOTAL_TOKENS: str(trace_info.total_tokens),
GEN_AI_PROMPT_TEMPLATE_VARIABLE: serialize_json_data(inputs_data),
GEN_AI_PROMPT_TEMPLATE_TEMPLATE: pre_prompt,
GEN_AI_PROMPT: inputs_json,
GEN_AI_COMPLETION: outputs_str,
},
@ -364,6 +361,10 @@ class AliyunDataTrace(BaseTraceInstance):
input_value = str(node_execution.inputs.get("query", "")) if node_execution.inputs else ""
output_value = serialize_json_data(node_execution.outputs.get("result", [])) if node_execution.outputs else ""
retrieval_documents = node_execution.outputs.get("result", []) if node_execution.outputs else []
semantic_retrieval_documents = format_retrieval_documents(retrieval_documents)
semantic_retrieval_documents_json = serialize_json_data(semantic_retrieval_documents)
return SpanData(
trace_id=trace_metadata.trace_id,
parent_span_id=trace_metadata.workflow_span_id,
@ -380,7 +381,7 @@ class AliyunDataTrace(BaseTraceInstance):
outputs=output_value,
),
RETRIEVAL_QUERY: input_value,
RETRIEVAL_DOCUMENT: output_value,
RETRIEVAL_DOCUMENT: semantic_retrieval_documents_json,
},
status=get_workflow_node_status(node_execution),
links=trace_metadata.links,
@ -396,6 +397,9 @@ class AliyunDataTrace(BaseTraceInstance):
prompts_json = serialize_json_data(process_data.get("prompts", []))
text_output = str(outputs.get("text", ""))
gen_ai_input_message = format_input_messages(process_data)
gen_ai_output_message = format_output_messages(outputs)
return SpanData(
trace_id=trace_metadata.trace_id,
parent_span_id=trace_metadata.workflow_span_id,
@ -411,14 +415,16 @@ class AliyunDataTrace(BaseTraceInstance):
inputs=prompts_json,
outputs=text_output,
),
GEN_AI_MODEL_NAME: process_data.get("model_name") or "",
GEN_AI_SYSTEM: process_data.get("model_provider") or "",
GEN_AI_REQUEST_MODEL: process_data.get("model_name") or "",
GEN_AI_PROVIDER_NAME: process_data.get("model_provider") or "",
GEN_AI_USAGE_INPUT_TOKENS: str(usage_data.get("prompt_tokens", 0)),
GEN_AI_USAGE_OUTPUT_TOKENS: str(usage_data.get("completion_tokens", 0)),
GEN_AI_USAGE_TOTAL_TOKENS: str(usage_data.get("total_tokens", 0)),
GEN_AI_PROMPT: prompts_json,
GEN_AI_COMPLETION: text_output,
GEN_AI_RESPONSE_FINISH_REASON: outputs.get("finish_reason") or "",
GEN_AI_INPUT_MESSAGE: gen_ai_input_message,
GEN_AI_OUTPUT_MESSAGE: gen_ai_output_message,
},
status=get_workflow_node_status(node_execution),
links=trace_metadata.links,
@ -502,8 +508,8 @@ class AliyunDataTrace(BaseTraceInstance):
inputs=inputs_json,
outputs=suggested_question_json,
),
GEN_AI_MODEL_NAME: trace_info.metadata.get("ls_model_name") or "",
GEN_AI_SYSTEM: trace_info.metadata.get("ls_provider") or "",
GEN_AI_REQUEST_MODEL: trace_info.metadata.get("ls_model_name") or "",
GEN_AI_PROVIDER_NAME: trace_info.metadata.get("ls_provider") or "",
GEN_AI_PROMPT: inputs_json,
GEN_AI_COMPLETION: suggested_question_json,
},

9
api/core/ops/aliyun_trace/entities/semconv.py
View File

@ -17,17 +17,18 @@ RETRIEVAL_QUERY: Final[str] = "retrieval.query"
RETRIEVAL_DOCUMENT: Final[str] = "retrieval.document"
# LLM attributes
GEN_AI_MODEL_NAME: Final[str] = "gen_ai.model_name"
GEN_AI_SYSTEM: Final[str] = "gen_ai.system"
GEN_AI_REQUEST_MODEL: Final[str] = "gen_ai.request.model"
GEN_AI_PROVIDER_NAME: Final[str] = "gen_ai.provider.name"
GEN_AI_USAGE_INPUT_TOKENS: Final[str] = "gen_ai.usage.input_tokens"
GEN_AI_USAGE_OUTPUT_TOKENS: Final[str] = "gen_ai.usage.output_tokens"
GEN_AI_USAGE_TOTAL_TOKENS: Final[str] = "gen_ai.usage.total_tokens"
GEN_AI_PROMPT_TEMPLATE_TEMPLATE: Final[str] = "gen_ai.prompt_template.template"
GEN_AI_PROMPT_TEMPLATE_VARIABLE: Final[str] = "gen_ai.prompt_template.variable"
GEN_AI_PROMPT: Final[str] = "gen_ai.prompt"
GEN_AI_COMPLETION: Final[str] = "gen_ai.completion"
GEN_AI_RESPONSE_FINISH_REASON: Final[str] = "gen_ai.response.finish_reason"
GEN_AI_INPUT_MESSAGE: Final[str] = "gen_ai.input.messages"
GEN_AI_OUTPUT_MESSAGE: Final[str] = "gen_ai.output.messages"
# Tool attributes
TOOL_NAME: Final[str] = "tool.name"
TOOL_DESCRIPTION: Final[str] = "tool.description"

95
api/core/ops/aliyun_trace/utils.py
View File

@ -1,4 +1,5 @@
import json
from collections.abc import Mapping
from typing import Any
from opentelemetry.trace import Link, Status, StatusCode
@ -93,3 +94,97 @@ def create_common_span_attributes(
INPUT_VALUE: inputs,
OUTPUT_VALUE: outputs,
}
def format_retrieval_documents(retrieval_documents: list) -> list:
try:
if not isinstance(retrieval_documents, list):
return []
semantic_documents = []
for doc in retrieval_documents:
if not isinstance(doc, dict):
continue
metadata = doc.get("metadata", {})
content = doc.get("content", "")
title = doc.get("title", "")
score = metadata.get("score", 0.0)
document_id = metadata.get("document_id", "")
semantic_metadata = {}
if title:
semantic_metadata["title"] = title
if metadata.get("source"):
semantic_metadata["source"] = metadata["source"]
elif metadata.get("_source"):
semantic_metadata["source"] = metadata["_source"]
if metadata.get("doc_metadata"):
doc_metadata = metadata["doc_metadata"]
if isinstance(doc_metadata, dict):
semantic_metadata.update(doc_metadata)
semantic_doc = {
"document": {"content": content, "metadata": semantic_metadata, "score": score, "id": document_id}
}
semantic_documents.append(semantic_doc)
return semantic_documents
except Exception:
return []
def format_input_messages(process_data: Mapping[str, Any]) -> str:
try:
if not isinstance(process_data, dict):
return serialize_json_data([])
prompts = process_data.get("prompts", [])
if not prompts:
return serialize_json_data([])
valid_roles = {"system", "user", "assistant", "tool"}
input_messages = []
for prompt in prompts:
if not isinstance(prompt, dict):
continue
role = prompt.get("role", "")
text = prompt.get("text", "")
if not role or role not in valid_roles:
continue
if text:
message = {"role": role, "parts": [{"type": "text", "content": text}]}
input_messages.append(message)
return serialize_json_data(input_messages)
except Exception:
return serialize_json_data([])
def format_output_messages(outputs: Mapping[str, Any]) -> str:
try:
if not isinstance(outputs, dict):
return serialize_json_data([])
text = outputs.get("text", "")
finish_reason = outputs.get("finish_reason", "")
if not text:
return serialize_json_data([])
valid_finish_reasons = {"stop", "length", "content_filter", "tool_call", "error"}
if finish_reason not in valid_finish_reasons:
finish_reason = "stop"
output_message = {
"role": "assistant",
"parts": [{"type": "text", "content": text}],
"finish_reason": finish_reason,
}
return serialize_json_data([output_message])
except Exception:
return serialize_json_data([])