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https://github.com/langgenius/dify.git
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6405a30278
Integrate enterprise per-node telemetry into workflow execution pipeline and complete parent trace context propagation through the trace system. Enterprise node execution tracing (GATED): - Add WorkflowNodeTraceInfo entity with full node execution metadata - Emit node trace on every node completion (succeeded/failed/exception/paused) - Include LLM tokens, tool info, iteration/loop context, and timing data - Hook into workflow persistence layer via _enqueue_node_trace_task() Parent trace context wiring (COMMUNITY): - Pass parent_trace_context through TraceTask to WorkflowTraceInfo.metadata - Enables child workflows to include parent attributes for all trace providers - Completes the distributed tracing feature started in first commit Dual processing architecture: - TraceQueueManager processes traces when enterprise OR per-app tracing enabled - Celery task calls both EnterpriseDataTrace AND per-app trace providers - Single queue, dual dispatch pattern Files changed: - core/ops/entities/trace_entity.py: Add WorkflowNodeTraceInfo + NODE_EXECUTION_TRACE - core/app/workflow/layers/persistence.py: Emit node traces + parent context - core/ops/ops_trace_manager.py: node_execution_trace() + dual dispatch - tasks/ops_trace_task.py: Call enterprise trace handler
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
import json
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import logging
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from celery import shared_task
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from flask import current_app
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from core.ops.entities.config_entity import OPS_FILE_PATH, OPS_TRACE_FAILED_KEY
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from core.ops.entities.trace_entity import trace_info_info_map
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from core.rag.models.document import Document
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from extensions.ext_redis import redis_client
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from extensions.ext_storage import storage
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from models.model import Message
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from models.workflow import WorkflowRun
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logger = logging.getLogger(__name__)
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@shared_task(queue="ops_trace")
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def process_trace_tasks(file_info):
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"""
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Async process trace tasks
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Usage: process_trace_tasks.delay(tasks_data)
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"""
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from core.ops.ops_trace_manager import OpsTraceManager
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app_id = file_info.get("app_id")
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file_id = file_info.get("file_id")
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file_path = f"{OPS_FILE_PATH}{app_id}/{file_id}.json"
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file_data = json.loads(storage.load(file_path))
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trace_info = file_data.get("trace_info")
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trace_info_type = file_data.get("trace_info_type")
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trace_instance = OpsTraceManager.get_ops_trace_instance(app_id)
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if trace_info.get("message_data"):
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trace_info["message_data"] = Message.from_dict(data=trace_info["message_data"])
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if trace_info.get("workflow_data"):
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trace_info["workflow_data"] = WorkflowRun.from_dict(data=trace_info["workflow_data"])
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if trace_info.get("documents"):
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trace_info["documents"] = [Document.model_validate(doc) for doc in trace_info["documents"]]
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try:
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trace_type = trace_info_info_map.get(trace_info_type)
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if trace_type:
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trace_info = trace_type(**trace_info)
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# process enterprise trace separately
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from enterprise.telemetry.exporter import is_enterprise_telemetry_enabled
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if is_enterprise_telemetry_enabled():
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from enterprise.telemetry.enterprise_trace import EnterpriseDataTrace
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try:
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EnterpriseDataTrace().trace(trace_info)
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except Exception:
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logger.warning("Enterprise trace failed for app_id: %s", app_id, exc_info=True)
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if trace_instance:
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with current_app.app_context():
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trace_instance.trace(trace_info)
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logger.info("Processing trace tasks success, app_id: %s", app_id)
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except Exception as e:
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logger.info("error:\n\n\n%s\n\n\n\n", e)
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failed_key = f"{OPS_TRACE_FAILED_KEY}_{app_id}"
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redis_client.incr(failed_key)
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logger.info("Processing trace tasks failed, app_id: %s", app_id)
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finally:
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storage.delete(file_path)
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