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feat(workflow): domain model for workflow node execution (#19430)

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Signed-off-by: -LAN- <laipz8200@outlook.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
-LAN-
2025-05-17 00:56:16 +08:00
committed by GitHub
parent aeceb200ec
commit 4977bb21ec
31 changed files with 1108 additions and 483 deletions
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13
api/core/app/apps/advanced_chat/app_generator.py
View File

@@ -29,9 +29,7 @@ from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
from core.workflow.repository.workflow_node_execution_repository import WorkflowNodeExecutionRepository
from extensions.ext_database import db
from factories import file_factory
from models.account import Account
from models.model import App, Conversation, EndUser, Message
from models.workflow import Workflow
from models import Account, App, Conversation, EndUser, Message, Workflow, WorkflowNodeExecutionTriggeredFrom
from services.conversation_service import ConversationService
from services.errors.message import MessageNotExistsError
@@ -165,8 +163,9 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
session_factory=session_factory,
tenant_id=application_generate_entity.app_config.tenant_id,
user=user,
app_id=application_generate_entity.app_config.app_id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
)
return self._generate(
@@ -231,8 +230,9 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
session_factory=session_factory,
tenant_id=application_generate_entity.app_config.tenant_id,
user=user,
app_id=application_generate_entity.app_config.app_id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP,
)
return self._generate(
@@ -295,8 +295,9 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
session_factory=session_factory,
tenant_id=application_generate_entity.app_config.tenant_id,
user=user,
app_id=application_generate_entity.app_config.app_id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP,
)
return self._generate(

10
api/core/app/apps/advanced_chat/generate_task_pipeline.py
View File

@@ -70,7 +70,7 @@ from events.message_event import message_was_created
from extensions.ext_database import db
from models import Conversation, EndUser, Message, MessageFile
from models.account import Account
from models.enums import CreatedByRole
from models.enums import CreatorUserRole
from models.workflow import (
Workflow,
WorkflowRunStatus,
@@ -105,11 +105,11 @@ class AdvancedChatAppGenerateTaskPipeline:
if isinstance(user, EndUser):
self._user_id = user.id
user_session_id = user.session_id
self._created_by_role = CreatedByRole.END_USER
self._created_by_role = CreatorUserRole.END_USER
elif isinstance(user, Account):
self._user_id = user.id
user_session_id = user.id
self._created_by_role = CreatedByRole.ACCOUNT
self._created_by_role = CreatorUserRole.ACCOUNT
else:
raise NotImplementedError(f"User type not supported: {type(user)}")
@@ -739,9 +739,9 @@ class AdvancedChatAppGenerateTaskPipeline:
url=file["remote_url"],
belongs_to="assistant",
upload_file_id=file["related_id"],
created_by_role=CreatedByRole.ACCOUNT
created_by_role=CreatorUserRole.ACCOUNT
if message.invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER}
else CreatedByRole.END_USER,
else CreatorUserRole.END_USER,
created_by=message.from_account_id or message.from_end_user_id or "",
)
for file in self._recorded_files

4
api/core/app/apps/message_based_app_generator.py
View File

@@ -25,7 +25,7 @@ from core.app.task_pipeline.easy_ui_based_generate_task_pipeline import EasyUIBa
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
from extensions.ext_database import db
from models import Account
from models.enums import CreatedByRole
from models.enums import CreatorUserRole
from models.model import App, AppMode, AppModelConfig, Conversation, EndUser, Message, MessageFile
from services.errors.app_model_config import AppModelConfigBrokenError
from services.errors.conversation import ConversationNotExistsError
@@ -223,7 +223,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
belongs_to="user",
url=file.remote_url,
upload_file_id=file.related_id,
created_by_role=(CreatedByRole.ACCOUNT if account_id else CreatedByRole.END_USER),
created_by_role=(CreatorUserRole.ACCOUNT if account_id else CreatorUserRole.END_USER),
created_by=account_id or end_user_id or "",
)
db.session.add(message_file)

14
api/core/app/apps/workflow/app_generator.py
View File

@@ -27,7 +27,7 @@ from core.workflow.repository.workflow_node_execution_repository import Workflow
from core.workflow.workflow_app_generate_task_pipeline import WorkflowAppGenerateTaskPipeline
from extensions.ext_database import db
from factories import file_factory
from models import Account, App, EndUser, Workflow
from models import Account, App, EndUser, Workflow, WorkflowNodeExecutionTriggeredFrom
logger = logging.getLogger(__name__)
@@ -138,10 +138,12 @@ class WorkflowAppGenerator(BaseAppGenerator):
# Create workflow node execution repository
session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
session_factory=session_factory,
tenant_id=application_generate_entity.app_config.tenant_id,
user=user,
app_id=application_generate_entity.app_config.app_id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
)
return self._generate(
@@ -262,10 +264,12 @@ class WorkflowAppGenerator(BaseAppGenerator):
# Create workflow node execution repository
session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
session_factory=session_factory,
tenant_id=application_generate_entity.app_config.tenant_id,
user=user,
app_id=application_generate_entity.app_config.app_id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP,
)
return self._generate(
@@ -325,10 +329,12 @@ class WorkflowAppGenerator(BaseAppGenerator):
# Create workflow node execution repository
session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
session_factory=session_factory,
tenant_id=application_generate_entity.app_config.tenant_id,
user=user,
app_id=application_generate_entity.app_config.app_id,
triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP,
)
return self._generate(

20
api/core/app/entities/task_entities.py
View File

@@ -6,7 +6,7 @@ from pydantic import BaseModel, ConfigDict
from core.model_runtime.entities.llm_entities import LLMResult
from core.model_runtime.utils.encoders import jsonable_encoder
from core.workflow.entities.node_entities import AgentNodeStrategyInit
from core.workflow.entities.node_entities import AgentNodeStrategyInit, NodeRunMetadataKey
from models.workflow import WorkflowNodeExecutionStatus
@@ -244,7 +244,7 @@ class NodeStartStreamResponse(StreamResponse):
title: str
index: int
predecessor_node_id: Optional[str] = None
inputs: Optional[dict] = None
inputs: Optional[Mapping[str, Any]] = None
created_at: int
extras: dict = {}
parallel_id: Optional[str] = None
@@ -301,13 +301,13 @@ class NodeFinishStreamResponse(StreamResponse):
title: str
index: int
predecessor_node_id: Optional[str] = None
inputs: Optional[dict] = None
process_data: Optional[dict] = None
outputs: Optional[dict] = None
inputs: Optional[Mapping[str, Any]] = None
process_data: Optional[Mapping[str, Any]] = None
outputs: Optional[Mapping[str, Any]] = None
status: str
error: Optional[str] = None
elapsed_time: float
execution_metadata: Optional[dict] = None
execution_metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None
created_at: int
finished_at: int
files: Optional[Sequence[Mapping[str, Any]]] = []
@@ -370,13 +370,13 @@ class NodeRetryStreamResponse(StreamResponse):
title: str
index: int
predecessor_node_id: Optional[str] = None
inputs: Optional[dict] = None
process_data: Optional[dict] = None
outputs: Optional[dict] = None
inputs: Optional[Mapping[str, Any]] = None
process_data: Optional[Mapping[str, Any]] = None
outputs: Optional[Mapping[str, Any]] = None
status: str
error: Optional[str] = None
elapsed_time: float
execution_metadata: Optional[dict] = None
execution_metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None
created_at: int
finished_at: int
files: Optional[Sequence[Mapping[str, Any]]] = []

5
api/core/ops/langfuse_trace/entities/langfuse_trace_entity.py
View File

@@ -1,3 +1,4 @@
from collections.abc import Mapping
from datetime import datetime
from enum import StrEnum
from typing import Any, Optional, Union
@@ -155,10 +156,10 @@ class LangfuseSpan(BaseModel):
description="The status message of the span. Additional field for context of the event. E.g. the error "
"message of an error event.",
)
input: Optional[Union[str, dict[str, Any], list, None]] = Field(
input: Optional[Union[str, Mapping[str, Any], list, None]] = Field(
default=None, description="The input of the span. Can be any JSON object."
)
output: Optional[Union[str, dict[str, Any], list, None]] = Field(
output: Optional[Union[str, Mapping[str, Any], list, None]] = Field(
default=None, description="The output of the span. Can be any JSON object."
)
version: Optional[str] = Field(

48
api/core/ops/langfuse_trace/langfuse_trace.py
View File

@@ -1,11 +1,10 @@
import json
import logging
import os
from datetime import datetime, timedelta
from typing import Optional
from langfuse import Langfuse # type: ignore
from sqlalchemy.orm import sessionmaker
from sqlalchemy.orm import Session, sessionmaker
from core.ops.base_trace_instance import BaseTraceInstance
from core.ops.entities.config_entity import LangfuseConfig
@@ -30,8 +29,9 @@ from core.ops.langfuse_trace.entities.langfuse_trace_entity import (
)
from core.ops.utils import filter_none_values
from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
from core.workflow.nodes.enums import NodeType
from extensions.ext_database import db
from models.model import EndUser
from models import Account, App, EndUser, WorkflowNodeExecutionTriggeredFrom
logger = logging.getLogger(__name__)
@@ -113,8 +113,29 @@ class LangFuseDataTrace(BaseTraceInstance):
# through workflow_run_id get all_nodes_execution using repository
session_factory = sessionmaker(bind=db.engine)
# Find the app's creator account
with Session(db.engine, expire_on_commit=False) as session:
# Get the app to find its creator
app_id = trace_info.metadata.get("app_id")
if not app_id:
raise ValueError("No app_id found in trace_info metadata")
app = session.query(App).filter(App.id == app_id).first()
if not app:
raise ValueError(f"App with id {app_id} not found")
if not app.created_by:
raise ValueError(f"App with id {app_id} has no creator (created_by is None)")
service_account = session.query(Account).filter(Account.id == app.created_by).first()
if not service_account:
raise ValueError(f"Creator account with id {app.created_by} not found for app {app_id}")
workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
session_factory=session_factory, tenant_id=trace_info.tenant_id
session_factory=session_factory,
user=service_account,
app_id=trace_info.metadata.get("app_id"),
triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
)
# Get all executions for this workflow run
@@ -124,23 +145,22 @@ class LangFuseDataTrace(BaseTraceInstance):
for node_execution in workflow_node_executions:
node_execution_id = node_execution.id
tenant_id = node_execution.tenant_id
app_id = node_execution.app_id
tenant_id = trace_info.tenant_id # Use from trace_info instead
app_id = trace_info.metadata.get("app_id") # Use from trace_info instead
node_name = node_execution.title
node_type = node_execution.node_type
status = node_execution.status
if node_type == "llm":
inputs = (
json.loads(node_execution.process_data).get("prompts", {}) if node_execution.process_data else {}
)
if node_type == NodeType.LLM:
inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
else:
inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
inputs = node_execution.inputs if node_execution.inputs else {}
outputs = node_execution.outputs if node_execution.outputs else {}
created_at = node_execution.created_at or datetime.now()
elapsed_time = node_execution.elapsed_time
finished_at = created_at + timedelta(seconds=elapsed_time)
metadata = json.loads(node_execution.execution_metadata) if node_execution.execution_metadata else {}
execution_metadata = node_execution.metadata if node_execution.metadata else {}
metadata = {str(k): v for k, v in execution_metadata.items()}
metadata.update(
{
"workflow_run_id": trace_info.workflow_run_id,
@@ -152,7 +172,7 @@ class LangFuseDataTrace(BaseTraceInstance):
"status": status,
}
)
process_data = json.loads(node_execution.process_data) if node_execution.process_data else {}
process_data = node_execution.process_data if node_execution.process_data else {}
model_provider = process_data.get("model_provider", None)
model_name = process_data.get("model_name", None)
if model_provider is not None and model_name is not None:

5
api/core/ops/langsmith_trace/entities/langsmith_trace_entity.py
View File

@@ -1,3 +1,4 @@
from collections.abc import Mapping
from datetime import datetime
from enum import StrEnum
from typing import Any, Optional, Union
@@ -30,8 +31,8 @@ class LangSmithMultiModel(BaseModel):
class LangSmithRunModel(LangSmithTokenUsage, LangSmithMultiModel):
name: Optional[str] = Field(..., description="Name of the run")
inputs: Optional[Union[str, dict[str, Any], list, None]] = Field(None, description="Inputs of the run")
outputs: Optional[Union[str, dict[str, Any], list, None]] = Field(None, description="Outputs of the run")
inputs: Optional[Union[str, Mapping[str, Any], list, None]] = Field(None, description="Inputs of the run")
outputs: Optional[Union[str, Mapping[str, Any], list, None]] = Field(None, description="Outputs of the run")
run_type: LangSmithRunType = Field(..., description="Type of the run")
start_time: Optional[datetime | str] = Field(None, description="Start time of the run")
end_time: Optional[datetime | str] = Field(None, description="End time of the run")

56
api/core/ops/langsmith_trace/langsmith_trace.py
View File

@@ -1,4 +1,3 @@
import json
import logging
import os
import uuid
@@ -7,7 +6,7 @@ from typing import Optional, cast
from langsmith import Client
from langsmith.schemas import RunBase
from sqlalchemy.orm import sessionmaker
from sqlalchemy.orm import Session, sessionmaker
from core.ops.base_trace_instance import BaseTraceInstance
from core.ops.entities.config_entity import LangSmithConfig
@@ -29,8 +28,10 @@ from core.ops.langsmith_trace.entities.langsmith_trace_entity import (
)
from core.ops.utils import filter_none_values, generate_dotted_order
from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
from core.workflow.entities.node_entities import NodeRunMetadataKey
from core.workflow.nodes.enums import NodeType
from extensions.ext_database import db
from models.model import EndUser, MessageFile
from models import Account, App, EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
logger = logging.getLogger(__name__)
@@ -137,8 +138,29 @@ class LangSmithDataTrace(BaseTraceInstance):
# through workflow_run_id get all_nodes_execution using repository
session_factory = sessionmaker(bind=db.engine)
# Find the app's creator account
with Session(db.engine, expire_on_commit=False) as session:
# Get the app to find its creator
app_id = trace_info.metadata.get("app_id")
if not app_id:
raise ValueError("No app_id found in trace_info metadata")
app = session.query(App).filter(App.id == app_id).first()
if not app:
raise ValueError(f"App with id {app_id} not found")
if not app.created_by:
raise ValueError(f"App with id {app_id} has no creator (created_by is None)")
service_account = session.query(Account).filter(Account.id == app.created_by).first()
if not service_account:
raise ValueError(f"Creator account with id {app.created_by} not found for app {app_id}")
workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
session_factory=session_factory, tenant_id=trace_info.tenant_id, app_id=trace_info.metadata.get("app_id")
session_factory=session_factory,
user=service_account,
app_id=trace_info.metadata.get("app_id"),
triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
)
# Get all executions for this workflow run
@@ -148,27 +170,23 @@ class LangSmithDataTrace(BaseTraceInstance):
for node_execution in workflow_node_executions:
node_execution_id = node_execution.id
tenant_id = node_execution.tenant_id
app_id = node_execution.app_id
tenant_id = trace_info.tenant_id # Use from trace_info instead
app_id = trace_info.metadata.get("app_id") # Use from trace_info instead
node_name = node_execution.title
node_type = node_execution.node_type
status = node_execution.status
if node_type == "llm":
inputs = (
json.loads(node_execution.process_data).get("prompts", {}) if node_execution.process_data else {}
)
if node_type == NodeType.LLM:
inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
else:
inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
inputs = node_execution.inputs if node_execution.inputs else {}
outputs = node_execution.outputs if node_execution.outputs else {}
created_at = node_execution.created_at or datetime.now()
elapsed_time = node_execution.elapsed_time
finished_at = created_at + timedelta(seconds=elapsed_time)
execution_metadata = (
json.loads(node_execution.execution_metadata) if node_execution.execution_metadata else {}
)
node_total_tokens = execution_metadata.get("total_tokens", 0)
metadata = execution_metadata.copy()
execution_metadata = node_execution.metadata if node_execution.metadata else {}
node_total_tokens = execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS) or 0
metadata = {str(key): value for key, value in execution_metadata.items()}
metadata.update(
{
"workflow_run_id": trace_info.workflow_run_id,
@@ -181,7 +199,7 @@ class LangSmithDataTrace(BaseTraceInstance):
}
)
process_data = json.loads(node_execution.process_data) if node_execution.process_data else {}
process_data = node_execution.process_data if node_execution.process_data else {}
if process_data and process_data.get("model_mode") == "chat":
run_type = LangSmithRunType.llm
@@ -191,7 +209,7 @@ class LangSmithDataTrace(BaseTraceInstance):
"ls_model_name": process_data.get("model_name", ""),
}
)
elif node_type == "knowledge-retrieval":
elif node_type == NodeType.KNOWLEDGE_RETRIEVAL:
run_type = LangSmithRunType.retriever
else:
run_type = LangSmithRunType.tool

54
api/core/ops/opik_trace/opik_trace.py
View File

@@ -1,4 +1,3 @@
import json
import logging
import os
import uuid
@@ -7,7 +6,7 @@ from typing import Optional, cast
from opik import Opik, Trace
from opik.id_helpers import uuid4_to_uuid7
from sqlalchemy.orm import sessionmaker
from sqlalchemy.orm import Session, sessionmaker
from core.ops.base_trace_instance import BaseTraceInstance
from core.ops.entities.config_entity import OpikConfig
@@ -23,8 +22,10 @@ from core.ops.entities.trace_entity import (
WorkflowTraceInfo,
)
from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
from core.workflow.entities.node_entities import NodeRunMetadataKey
from core.workflow.nodes.enums import NodeType
from extensions.ext_database import db
from models.model import EndUser, MessageFile
from models import Account, App, EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
logger = logging.getLogger(__name__)
@@ -150,8 +151,29 @@ class OpikDataTrace(BaseTraceInstance):
# through workflow_run_id get all_nodes_execution using repository
session_factory = sessionmaker(bind=db.engine)
# Find the app's creator account
with Session(db.engine, expire_on_commit=False) as session:
# Get the app to find its creator
app_id = trace_info.metadata.get("app_id")
if not app_id:
raise ValueError("No app_id found in trace_info metadata")
app = session.query(App).filter(App.id == app_id).first()
if not app:
raise ValueError(f"App with id {app_id} not found")
if not app.created_by:
raise ValueError(f"App with id {app_id} has no creator (created_by is None)")
service_account = session.query(Account).filter(Account.id == app.created_by).first()
if not service_account:
raise ValueError(f"Creator account with id {app.created_by} not found for app {app_id}")
workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
session_factory=session_factory, tenant_id=trace_info.tenant_id, app_id=trace_info.metadata.get("app_id")
session_factory=session_factory,
user=service_account,
app_id=trace_info.metadata.get("app_id"),
triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
)
# Get all executions for this workflow run
@@ -161,26 +183,22 @@ class OpikDataTrace(BaseTraceInstance):
for node_execution in workflow_node_executions:
node_execution_id = node_execution.id
tenant_id = node_execution.tenant_id
app_id = node_execution.app_id
tenant_id = trace_info.tenant_id # Use from trace_info instead
app_id = trace_info.metadata.get("app_id") # Use from trace_info instead
node_name = node_execution.title
node_type = node_execution.node_type
status = node_execution.status
if node_type == "llm":
inputs = (
json.loads(node_execution.process_data).get("prompts", {}) if node_execution.process_data else {}
)
if node_type == NodeType.LLM:
inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
else:
inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
inputs = node_execution.inputs if node_execution.inputs else {}
outputs = node_execution.outputs if node_execution.outputs else {}
created_at = node_execution.created_at or datetime.now()
elapsed_time = node_execution.elapsed_time
finished_at = created_at + timedelta(seconds=elapsed_time)
execution_metadata = (
json.loads(node_execution.execution_metadata) if node_execution.execution_metadata else {}
)
metadata = execution_metadata.copy()
execution_metadata = node_execution.metadata if node_execution.metadata else {}
metadata = {str(k): v for k, v in execution_metadata.items()}
metadata.update(
{
"workflow_run_id": trace_info.workflow_run_id,
@@ -193,7 +211,7 @@ class OpikDataTrace(BaseTraceInstance):
}
)
process_data = json.loads(node_execution.process_data) if node_execution.process_data else {}
process_data = node_execution.process_data if node_execution.process_data else {}
provider = None
model = None
@@ -226,7 +244,7 @@ class OpikDataTrace(BaseTraceInstance):
parent_span_id = trace_info.workflow_app_log_id or trace_info.workflow_run_id
if not total_tokens:
total_tokens = execution_metadata.get("total_tokens", 0)
total_tokens = execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS) or 0
span_data = {
"trace_id": opik_trace_id,

5
api/core/ops/weave_trace/entities/weave_trace_entity.py
View File

@@ -1,3 +1,4 @@
from collections.abc import Mapping
from typing import Any, Optional, Union
from pydantic import BaseModel, Field, field_validator
@@ -19,8 +20,8 @@ class WeaveMultiModel(BaseModel):
class WeaveTraceModel(WeaveTokenUsage, WeaveMultiModel):
id: str = Field(..., description="ID of the trace")
op: str = Field(..., description="Name of the operation")
inputs: Optional[Union[str, dict[str, Any], list, None]] = Field(None, description="Inputs of the trace")
outputs: Optional[Union[str, dict[str, Any], list, None]] = Field(None, description="Outputs of the trace")
inputs: Optional[Union[str, Mapping[str, Any], list, None]] = Field(None, description="Inputs of the trace")
outputs: Optional[Union[str, Mapping[str, Any], list, None]] = Field(None, description="Outputs of the trace")
attributes: Optional[Union[str, dict[str, Any], list, None]] = Field(
None, description="Metadata and attributes associated with trace"
)

89
api/core/ops/weave_trace/weave_trace.py
View File

@@ -1,4 +1,3 @@
import json
import logging
import os
import uuid
@@ -7,6 +6,7 @@ from typing import Any, Optional, cast
import wandb
import weave
from sqlalchemy.orm import Session, sessionmaker
from core.ops.base_trace_instance import BaseTraceInstance
from core.ops.entities.config_entity import WeaveConfig
@@ -22,9 +22,11 @@ from core.ops.entities.trace_entity import (
WorkflowTraceInfo,
)
from core.ops.weave_trace.entities.weave_trace_entity import WeaveTraceModel
from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
from core.workflow.entities.node_entities import NodeRunMetadataKey
from core.workflow.nodes.enums import NodeType
from extensions.ext_database import db
from models.model import EndUser, MessageFile
from models.workflow import WorkflowNodeExecution
from models import Account, App, EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
logger = logging.getLogger(__name__)
@@ -128,58 +130,57 @@ class WeaveDataTrace(BaseTraceInstance):
self.start_call(workflow_run, parent_run_id=trace_info.message_id)
# through workflow_run_id get all_nodes_execution
workflow_nodes_execution_id_records = (
db.session.query(WorkflowNodeExecution.id)
.filter(WorkflowNodeExecution.workflow_run_id == trace_info.workflow_run_id)
.all()
# through workflow_run_id get all_nodes_execution using repository
session_factory = sessionmaker(bind=db.engine)
# Find the app's creator account
with Session(db.engine, expire_on_commit=False) as session:
# Get the app to find its creator
app_id = trace_info.metadata.get("app_id")
if not app_id:
raise ValueError("No app_id found in trace_info metadata")
app = session.query(App).filter(App.id == app_id).first()
if not app:
raise ValueError(f"App with id {app_id} not found")
if not app.created_by:
raise ValueError(f"App with id {app_id} has no creator (created_by is None)")
service_account = session.query(Account).filter(Account.id == app.created_by).first()
if not service_account:
raise ValueError(f"Creator account with id {app.created_by} not found for app {app_id}")
workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
session_factory=session_factory,
user=service_account,
app_id=trace_info.metadata.get("app_id"),
triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
)
for node_execution_id_record in workflow_nodes_execution_id_records:
node_execution = (
db.session.query(
WorkflowNodeExecution.id,
WorkflowNodeExecution.tenant_id,
WorkflowNodeExecution.app_id,
WorkflowNodeExecution.title,
WorkflowNodeExecution.node_type,
WorkflowNodeExecution.status,
WorkflowNodeExecution.inputs,
WorkflowNodeExecution.outputs,
WorkflowNodeExecution.created_at,
WorkflowNodeExecution.elapsed_time,
WorkflowNodeExecution.process_data,
WorkflowNodeExecution.execution_metadata,
)
.filter(WorkflowNodeExecution.id == node_execution_id_record.id)
.first()
)
if not node_execution:
continue
# Get all executions for this workflow run
workflow_node_executions = workflow_node_execution_repository.get_by_workflow_run(
workflow_run_id=trace_info.workflow_run_id
)
for node_execution in workflow_node_executions:
node_execution_id = node_execution.id
tenant_id = node_execution.tenant_id
app_id = node_execution.app_id
tenant_id = trace_info.tenant_id # Use from trace_info instead
app_id = trace_info.metadata.get("app_id") # Use from trace_info instead
node_name = node_execution.title
node_type = node_execution.node_type
status = node_execution.status
if node_type == "llm":
inputs = (
json.loads(node_execution.process_data).get("prompts", {}) if node_execution.process_data else {}
)
if node_type == NodeType.LLM:
inputs = node_execution.process_data.get("prompts", {}) if node_execution.process_data else {}
else:
inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
inputs = node_execution.inputs if node_execution.inputs else {}
outputs = node_execution.outputs if node_execution.outputs else {}
created_at = node_execution.created_at or datetime.now()
elapsed_time = node_execution.elapsed_time
finished_at = created_at + timedelta(seconds=elapsed_time)
execution_metadata = (
json.loads(node_execution.execution_metadata) if node_execution.execution_metadata else {}
)
node_total_tokens = execution_metadata.get("total_tokens", 0)
attributes = execution_metadata.copy()
execution_metadata = node_execution.metadata if node_execution.metadata else {}
node_total_tokens = execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS) or 0
attributes = {str(k): v for k, v in execution_metadata.items()}
attributes.update(
{
"workflow_run_id": trace_info.workflow_run_id,
@@ -192,7 +193,7 @@ class WeaveDataTrace(BaseTraceInstance):
}
)
process_data = json.loads(node_execution.process_data) if node_execution.process_data else {}
process_data = node_execution.process_data if node_execution.process_data else {}
if process_data and process_data.get("model_mode") == "chat":
attributes.update(
{

4
api/core/rag/extractor/word_extractor.py
View File

@@ -19,7 +19,7 @@ from core.rag.extractor.extractor_base import BaseExtractor
from core.rag.models.document import Document
from extensions.ext_database import db
from extensions.ext_storage import storage
from models.enums import CreatedByRole
from models.enums import CreatorUserRole
from models.model import UploadFile
logger = logging.getLogger(__name__)
@@ -116,7 +116,7 @@ class WordExtractor(BaseExtractor):
extension=str(image_ext),
mime_type=mime_type or "",
created_by=self.user_id,
created_by_role=CreatedByRole.ACCOUNT,
created_by_role=CreatorUserRole.ACCOUNT,
created_at=datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
used=True,
used_by=self.user_id,

284
api/core/repositories/sqlalchemy_workflow_node_execution_repository.py
View File

@@ -2,16 +2,29 @@
SQLAlchemy implementation of the WorkflowNodeExecutionRepository.
"""
import json
import logging
from collections.abc import Sequence
from typing import Optional
from typing import Optional, Union
from sqlalchemy import UnaryExpression, asc, delete, desc, select
from sqlalchemy.engine import Engine
from sqlalchemy.orm import sessionmaker
from core.workflow.entities.node_execution_entities import (
NodeExecution,
NodeExecutionStatus,
)
from core.workflow.nodes.enums import NodeType
from core.workflow.repository.workflow_node_execution_repository import OrderConfig, WorkflowNodeExecutionRepository
from models.workflow import WorkflowNodeExecution, WorkflowNodeExecutionStatus, WorkflowNodeExecutionTriggeredFrom
from models import (
Account,
CreatorUserRole,
EndUser,
WorkflowNodeExecution,
WorkflowNodeExecutionStatus,
WorkflowNodeExecutionTriggeredFrom,
)
logger = logging.getLogger(__name__)
@@ -23,16 +36,26 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
This implementation supports multi-tenancy by filtering operations based on tenant_id.
Each method creates its own session, handles the transaction, and commits changes
to the database. This prevents long-running connections in the workflow core.
This implementation also includes an in-memory cache for node executions to improve
performance by reducing database queries.
"""
def __init__(self, session_factory: sessionmaker | Engine, tenant_id: str, app_id: Optional[str] = None):
def __init__(
self,
session_factory: sessionmaker | Engine,
user: Union[Account, EndUser],
app_id: Optional[str],
triggered_from: Optional[WorkflowNodeExecutionTriggeredFrom],
):
"""
Initialize the repository with a SQLAlchemy sessionmaker or engine and tenant context.
Initialize the repository with a SQLAlchemy sessionmaker or engine and context information.
Args:
session_factory: SQLAlchemy sessionmaker or engine for creating sessions
tenant_id: Tenant ID for multi-tenancy
app_id: Optional app ID for filtering by application
user: Account or EndUser object containing tenant_id, user ID, and role information
app_id: App ID for filtering by application (can be None)
triggered_from: Source of the execution trigger (SINGLE_STEP or WORKFLOW_RUN)
"""
# If an engine is provided, create a sessionmaker from it
if isinstance(session_factory, Engine):
@@ -44,38 +67,155 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
f"Invalid session_factory type {type(session_factory).__name__}; expected sessionmaker or Engine"
)
# Extract tenant_id from user
tenant_id: str | None = user.tenant_id if isinstance(user, EndUser) else user.current_tenant_id
if not tenant_id:
raise ValueError("User must have a tenant_id or current_tenant_id")
self._tenant_id = tenant_id
# Store app context
self._app_id = app_id
def save(self, execution: WorkflowNodeExecution) -> None:
# Extract user context
self._triggered_from = triggered_from
self._creator_user_id = user.id
# Determine user role based on user type
self._creator_user_role = CreatorUserRole.ACCOUNT if isinstance(user, Account) else CreatorUserRole.END_USER
# Initialize in-memory cache for node executions
# Key: node_execution_id, Value: NodeExecution
self._node_execution_cache: dict[str, NodeExecution] = {}
def _to_domain_model(self, db_model: WorkflowNodeExecution) -> NodeExecution:
"""
Save a WorkflowNodeExecution instance and commit changes to the database.
Convert a database model to a domain model.
Args:
execution: The WorkflowNodeExecution instance to save
db_model: The database model to convert
Returns:
The domain model
"""
# Parse JSON fields
inputs = db_model.inputs_dict
process_data = db_model.process_data_dict
outputs = db_model.outputs_dict
metadata = db_model.execution_metadata_dict
# Convert status to domain enum
status = NodeExecutionStatus(db_model.status)
return NodeExecution(
id=db_model.id,
node_execution_id=db_model.node_execution_id,
workflow_id=db_model.workflow_id,
workflow_run_id=db_model.workflow_run_id,
index=db_model.index,
predecessor_node_id=db_model.predecessor_node_id,
node_id=db_model.node_id,
node_type=NodeType(db_model.node_type),
title=db_model.title,
inputs=inputs,
process_data=process_data,
outputs=outputs,
status=status,
error=db_model.error,
elapsed_time=db_model.elapsed_time,
metadata=metadata,
created_at=db_model.created_at,
finished_at=db_model.finished_at,
)
def _to_db_model(self, domain_model: NodeExecution) -> WorkflowNodeExecution:
"""
Convert a domain model to a database model.
Args:
domain_model: The domain model to convert
Returns:
The database model
"""
# Use values from constructor if provided
if not self._triggered_from:
raise ValueError("triggered_from is required in repository constructor")
if not self._creator_user_id:
raise ValueError("created_by is required in repository constructor")
if not self._creator_user_role:
raise ValueError("created_by_role is required in repository constructor")
db_model = WorkflowNodeExecution()
db_model.id = domain_model.id
db_model.tenant_id = self._tenant_id
if self._app_id is not None:
db_model.app_id = self._app_id
db_model.workflow_id = domain_model.workflow_id
db_model.triggered_from = self._triggered_from
db_model.workflow_run_id = domain_model.workflow_run_id
db_model.index = domain_model.index
db_model.predecessor_node_id = domain_model.predecessor_node_id
db_model.node_execution_id = domain_model.node_execution_id
db_model.node_id = domain_model.node_id
db_model.node_type = domain_model.node_type
db_model.title = domain_model.title
db_model.inputs = json.dumps(domain_model.inputs) if domain_model.inputs else None
db_model.process_data = json.dumps(domain_model.process_data) if domain_model.process_data else None
db_model.outputs = json.dumps(domain_model.outputs) if domain_model.outputs else None
db_model.status = domain_model.status
db_model.error = domain_model.error
db_model.elapsed_time = domain_model.elapsed_time
db_model.execution_metadata = json.dumps(domain_model.metadata) if domain_model.metadata else None
db_model.created_at = domain_model.created_at
db_model.created_by_role = self._creator_user_role
db_model.created_by = self._creator_user_id
db_model.finished_at = domain_model.finished_at
return db_model
def save(self, execution: NodeExecution) -> None:
"""
Save or update a NodeExecution instance and commit changes to the database.
This method handles both creating new records and updating existing ones.
It determines whether to create or update based on whether the record
already exists in the database. It also updates the in-memory cache.
Args:
execution: The NodeExecution instance to save or update
"""
with self._session_factory() as session:
# Ensure tenant_id is set
if not execution.tenant_id:
execution.tenant_id = self._tenant_id
# Convert domain model to database model using instance attributes
db_model = self._to_db_model(execution)
# Set app_id if provided and not already set
if self._app_id and not execution.app_id:
execution.app_id = self._app_id
session.add(execution)
# Use merge which will handle both insert and update
session.merge(db_model)
session.commit()
def get_by_node_execution_id(self, node_execution_id: str) -> Optional[WorkflowNodeExecution]:
# Update the cache if node_execution_id is present
if execution.node_execution_id:
logger.debug(f"Updating cache for node_execution_id: {execution.node_execution_id}")
self._node_execution_cache[execution.node_execution_id] = execution
def get_by_node_execution_id(self, node_execution_id: str) -> Optional[NodeExecution]:
"""
Retrieve a WorkflowNodeExecution by its node_execution_id.
Retrieve a NodeExecution by its node_execution_id.
First checks the in-memory cache, and if not found, queries the database.
If found in the database, adds it to the cache for future lookups.
Args:
node_execution_id: The node execution ID
Returns:
The WorkflowNodeExecution instance if found, None otherwise
The NodeExecution instance if found, None otherwise
"""
# First check the cache
if node_execution_id in self._node_execution_cache:
logger.debug(f"Cache hit for node_execution_id: {node_execution_id}")
return self._node_execution_cache[node_execution_id]
# If not in cache, query the database
logger.debug(f"Cache miss for node_execution_id: {node_execution_id}, querying database")
with self._session_factory() as session:
stmt = select(WorkflowNodeExecution).where(
WorkflowNodeExecution.node_execution_id == node_execution_id,
@@ -85,15 +225,28 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
if self._app_id:
stmt = stmt.where(WorkflowNodeExecution.app_id == self._app_id)
return session.scalar(stmt)
db_model = session.scalar(stmt)
if db_model:
# Convert to domain model
domain_model = self._to_domain_model(db_model)
# Add to cache
self._node_execution_cache[node_execution_id] = domain_model
return domain_model
return None
def get_by_workflow_run(
self,
workflow_run_id: str,
order_config: Optional[OrderConfig] = None,
) -> Sequence[WorkflowNodeExecution]:
) -> Sequence[NodeExecution]:
"""
Retrieve all WorkflowNodeExecution instances for a specific workflow run.
Retrieve all NodeExecution instances for a specific workflow run.
This method always queries the database to ensure complete and ordered results,
but updates the cache with any retrieved executions.
Args:
workflow_run_id: The workflow run ID
@@ -102,7 +255,42 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
order_config.order_direction: Direction to order ("asc" or "desc")
Returns:
A list of WorkflowNodeExecution instances
A list of NodeExecution instances
"""
# Get the raw database models using the new method
db_models = self.get_db_models_by_workflow_run(workflow_run_id, order_config)
# Convert database models to domain models and update cache
domain_models = []
for model in db_models:
domain_model = self._to_domain_model(model)
# Update cache if node_execution_id is present
if domain_model.node_execution_id:
self._node_execution_cache[domain_model.node_execution_id] = domain_model
domain_models.append(domain_model)
return domain_models
def get_db_models_by_workflow_run(
self,
workflow_run_id: str,
order_config: Optional[OrderConfig] = None,
) -> Sequence[WorkflowNodeExecution]:
"""
Retrieve all WorkflowNodeExecution database models for a specific workflow run.
This method is similar to get_by_workflow_run but returns the raw database models
instead of converting them to domain models. This can be useful when direct access
to database model properties is needed.
Args:
workflow_run_id: The workflow run ID
order_config: Optional configuration for ordering results
order_config.order_by: List of fields to order by (e.g., ["index", "created_at"])
order_config.order_direction: Direction to order ("asc" or "desc")
Returns:
A list of WorkflowNodeExecution database models
"""
with self._session_factory() as session:
stmt = select(WorkflowNodeExecution).where(
@@ -129,17 +317,25 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
if order_columns:
stmt = stmt.order_by(*order_columns)
return session.scalars(stmt).all()
db_models = session.scalars(stmt).all()
def get_running_executions(self, workflow_run_id: str) -> Sequence[WorkflowNodeExecution]:
# Note: We don't update the cache here since we're returning raw DB models
# and not converting to domain models
return db_models
def get_running_executions(self, workflow_run_id: str) -> Sequence[NodeExecution]:
"""
Retrieve all running WorkflowNodeExecution instances for a specific workflow run.
Retrieve all running NodeExecution instances for a specific workflow run.
This method queries the database directly and updates the cache with any
retrieved executions that have a node_execution_id.
Args:
workflow_run_id: The workflow run ID
Returns:
A list of running WorkflowNodeExecution instances
A list of running NodeExecution instances
"""
with self._session_factory() as session:
stmt = select(WorkflowNodeExecution).where(
@@ -152,26 +348,17 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
if self._app_id:
stmt = stmt.where(WorkflowNodeExecution.app_id == self._app_id)
return session.scalars(stmt).all()
db_models = session.scalars(stmt).all()
domain_models = []
def update(self, execution: WorkflowNodeExecution) -> None:
"""
Update an existing WorkflowNodeExecution instance and commit changes to the database.
for model in db_models:
domain_model = self._to_domain_model(model)
# Update cache if node_execution_id is present
if domain_model.node_execution_id:
self._node_execution_cache[domain_model.node_execution_id] = domain_model
domain_models.append(domain_model)
Args:
execution: The WorkflowNodeExecution instance to update
"""
with self._session_factory() as session:
# Ensure tenant_id is set
if not execution.tenant_id:
execution.tenant_id = self._tenant_id
# Set app_id if provided and not already set
if self._app_id and not execution.app_id:
execution.app_id = self._app_id
session.merge(execution)
session.commit()
return domain_models
def clear(self) -> None:
"""
@@ -179,6 +366,7 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
This method deletes all WorkflowNodeExecution records that match the tenant_id
and app_id (if provided) associated with this repository instance.
It also clears the in-memory cache.
"""
with self._session_factory() as session:
stmt = delete(WorkflowNodeExecution).where(WorkflowNodeExecution.tenant_id == self._tenant_id)
@@ -194,3 +382,7 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
f"Cleared {deleted_count} workflow node execution records for tenant {self._tenant_id}"
+ (f" and app {self._app_id}" if self._app_id else "")
)
# Clear the in-memory cache
self._node_execution_cache.clear()
logger.info("Cleared in-memory node execution cache")

6
api/core/tools/tool_engine.py
View File

@@ -32,7 +32,7 @@ from core.tools.errors import (
from core.tools.utils.message_transformer import ToolFileMessageTransformer
from core.tools.workflow_as_tool.tool import WorkflowTool
from extensions.ext_database import db
from models.enums import CreatedByRole
from models.enums import CreatorUserRole
from models.model import Message, MessageFile
@@ -339,9 +339,9 @@ class ToolEngine:
url=message.url,
upload_file_id=tool_file_id,
created_by_role=(
CreatedByRole.ACCOUNT
CreatorUserRole.ACCOUNT
if invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER}
else CreatedByRole.END_USER
else CreatorUserRole.END_USER
),
created_by=user_id,
)

98
api/core/workflow/entities/node_execution_entities.py Normal file
View File

@@ -0,0 +1,98 @@
"""
Domain entities for workflow node execution.
This module contains the domain model for workflow node execution, which is used
by the core workflow module. These models are independent of the storage mechanism
and don't contain implementation details like tenant_id, app_id, etc.
"""
from collections.abc import Mapping
from datetime import datetime
from enum import StrEnum
from typing import Any, Optional
from pydantic import BaseModel, Field
from core.workflow.entities.node_entities import NodeRunMetadataKey
from core.workflow.nodes.enums import NodeType
class NodeExecutionStatus(StrEnum):
"""
Node Execution Status Enum.
"""
RUNNING = "running"
SUCCEEDED = "succeeded"
FAILED = "failed"
EXCEPTION = "exception"
RETRY = "retry"
class NodeExecution(BaseModel):
"""
Domain model for workflow node execution.
This model represents the core business entity of a node execution,
without implementation details like tenant_id, app_id, etc.
Note: User/context-specific fields (triggered_from, created_by, created_by_role)
have been moved to the repository implementation to keep the domain model clean.
These fields are still accepted in the constructor for backward compatibility,
but they are not stored in the model.
"""
# Core identification fields
id: str # Unique identifier for this execution record
node_execution_id: Optional[str] = None # Optional secondary ID for cross-referencing
workflow_id: str # ID of the workflow this node belongs to
workflow_run_id: Optional[str] = None # ID of the specific workflow run (null for single-step debugging)
# Execution positioning and flow
index: int # Sequence number for ordering in trace visualization
predecessor_node_id: Optional[str] = None # ID of the node that executed before this one
node_id: str # ID of the node being executed
node_type: NodeType # Type of node (e.g., start, llm, knowledge)
title: str # Display title of the node
# Execution data
inputs: Optional[Mapping[str, Any]] = None # Input variables used by this node
process_data: Optional[Mapping[str, Any]] = None # Intermediate processing data
outputs: Optional[Mapping[str, Any]] = None # Output variables produced by this node
# Execution state
status: NodeExecutionStatus = NodeExecutionStatus.RUNNING # Current execution status
error: Optional[str] = None # Error message if execution failed
elapsed_time: float = Field(default=0.0) # Time taken for execution in seconds
# Additional metadata
metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None # Execution metadata (tokens, cost, etc.)
# Timing information
created_at: datetime # When execution started
finished_at: Optional[datetime] = None # When execution completed
def update_from_mapping(
self,
inputs: Optional[Mapping[str, Any]] = None,
process_data: Optional[Mapping[str, Any]] = None,
outputs: Optional[Mapping[str, Any]] = None,
metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None,
) -> None:
"""
Update the model from mappings.
Args:
inputs: The inputs to update
process_data: The process data to update
outputs: The outputs to update
metadata: The metadata to update
"""
if inputs is not None:
self.inputs = dict(inputs)
if process_data is not None:
self.process_data = dict(process_data)
if outputs is not None:
self.outputs = dict(outputs)
if metadata is not None:
self.metadata = dict(metadata)

47
api/core/workflow/repository/workflow_node_execution_repository.py
View File

@@ -2,12 +2,12 @@ from collections.abc import Sequence
from dataclasses import dataclass
from typing import Literal, Optional, Protocol
from models.workflow import WorkflowNodeExecution
from core.workflow.entities.node_execution_entities import NodeExecution
@dataclass
class OrderConfig:
"""Configuration for ordering WorkflowNodeExecution instances."""
"""Configuration for ordering NodeExecution instances."""
order_by: list[str]
order_direction: Optional[Literal["asc", "desc"]] = None
@@ -15,10 +15,10 @@ class OrderConfig:
class WorkflowNodeExecutionRepository(Protocol):
"""
Repository interface for WorkflowNodeExecution.
Repository interface for NodeExecution.
This interface defines the contract for accessing and manipulating
WorkflowNodeExecution data, regardless of the underlying storage mechanism.
NodeExecution data, regardless of the underlying storage mechanism.
Note: Domain-specific concepts like multi-tenancy (tenant_id), application context (app_id),
and trigger sources (triggered_from) should be handled at the implementation level, not in
@@ -26,24 +26,28 @@ class WorkflowNodeExecutionRepository(Protocol):
application domains or deployment scenarios.
"""
def save(self, execution: WorkflowNodeExecution) -> None:
def save(self, execution: NodeExecution) -> None:
"""
Save a WorkflowNodeExecution instance.
Save or update a NodeExecution instance.
This method handles both creating new records and updating existing ones.
The implementation should determine whether to create or update based on
the execution's ID or other identifying fields.
Args:
execution: The WorkflowNodeExecution instance to save
execution: The NodeExecution instance to save or update
"""
...
def get_by_node_execution_id(self, node_execution_id: str) -> Optional[WorkflowNodeExecution]:
def get_by_node_execution_id(self, node_execution_id: str) -> Optional[NodeExecution]:
"""
Retrieve a WorkflowNodeExecution by its node_execution_id.
Retrieve a NodeExecution by its node_execution_id.
Args:
node_execution_id: The node execution ID
Returns:
The WorkflowNodeExecution instance if found, None otherwise
The NodeExecution instance if found, None otherwise
"""
...
@@ -51,9 +55,9 @@ class WorkflowNodeExecutionRepository(Protocol):
self,
workflow_run_id: str,
order_config: Optional[OrderConfig] = None,
) -> Sequence[WorkflowNodeExecution]:
) -> Sequence[NodeExecution]:
"""
Retrieve all WorkflowNodeExecution instances for a specific workflow run.
Retrieve all NodeExecution instances for a specific workflow run.
Args:
workflow_run_id: The workflow run ID
@@ -62,34 +66,25 @@ class WorkflowNodeExecutionRepository(Protocol):
order_config.order_direction: Direction to order ("asc" or "desc")
Returns:
A list of WorkflowNodeExecution instances
A list of NodeExecution instances
"""
...
def get_running_executions(self, workflow_run_id: str) -> Sequence[WorkflowNodeExecution]:
def get_running_executions(self, workflow_run_id: str) -> Sequence[NodeExecution]:
"""
Retrieve all running WorkflowNodeExecution instances for a specific workflow run.
Retrieve all running NodeExecution instances for a specific workflow run.
Args:
workflow_run_id: The workflow run ID
Returns:
A list of running WorkflowNodeExecution instances
"""
...
def update(self, execution: WorkflowNodeExecution) -> None:
"""
Update an existing WorkflowNodeExecution instance.
Args:
execution: The WorkflowNodeExecution instance to update
A list of running NodeExecution instances
"""
...
def clear(self) -> None:
"""
Clear all WorkflowNodeExecution records based on implementation-specific criteria.
Clear all NodeExecution records based on implementation-specific criteria.
This method is intended to be used for bulk deletion operations, such as removing
all records associated with a specific app_id and tenant_id in multi-tenant implementations.

6
api/core/workflow/workflow_app_generate_task_pipeline.py
View File

@@ -58,7 +58,7 @@ from core.workflow.repository.workflow_node_execution_repository import Workflow
from core.workflow.workflow_cycle_manager import WorkflowCycleManager
from extensions.ext_database import db
from models.account import Account
from models.enums import CreatedByRole
from models.enums import CreatorUserRole
from models.model import EndUser
from models.workflow import (
Workflow,
@@ -94,11 +94,11 @@ class WorkflowAppGenerateTaskPipeline:
if isinstance(user, EndUser):
self._user_id = user.id
user_session_id = user.session_id
self._created_by_role = CreatedByRole.END_USER
self._created_by_role = CreatorUserRole.END_USER
elif isinstance(user, Account):
self._user_id = user.id
user_session_id = user.id
self._created_by_role = CreatedByRole.ACCOUNT
self._created_by_role = CreatorUserRole.ACCOUNT
else:
raise ValueError(f"Invalid user type: {type(user)}")

302
api/core/workflow/workflow_cycle_manager.py
View File

@@ -46,26 +46,28 @@ from core.app.entities.task_entities import (
)
from core.app.task_pipeline.exc import WorkflowRunNotFoundError
from core.file import FILE_MODEL_IDENTITY, File
from core.model_runtime.utils.encoders import jsonable_encoder
from core.ops.entities.trace_entity import TraceTaskName
from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
from core.tools.tool_manager import ToolManager
from core.workflow.entities.node_entities import NodeRunMetadataKey
from core.workflow.entities.node_execution_entities import (
NodeExecution,
NodeExecutionStatus,
)
from core.workflow.enums import SystemVariableKey
from core.workflow.nodes import NodeType
from core.workflow.nodes.tool.entities import ToolNodeData
from core.workflow.repository.workflow_node_execution_repository import WorkflowNodeExecutionRepository
from core.workflow.workflow_entry import WorkflowEntry
from models.account import Account
from models.enums import CreatedByRole, WorkflowRunTriggeredFrom
from models.model import EndUser
from models.workflow import (
from models import (
Account,
CreatorUserRole,
EndUser,
Workflow,
WorkflowNodeExecution,
WorkflowNodeExecutionStatus,
WorkflowNodeExecutionTriggeredFrom,
WorkflowRun,
WorkflowRunStatus,
WorkflowRunTriggeredFrom,
)
@@ -78,7 +80,6 @@ class WorkflowCycleManager:
workflow_node_execution_repository: WorkflowNodeExecutionRepository,
) -> None:
self._workflow_run: WorkflowRun | None = None
self._workflow_node_executions: dict[str, WorkflowNodeExecution] = {}
self._application_generate_entity = application_generate_entity
self._workflow_system_variables = workflow_system_variables
self._workflow_node_execution_repository = workflow_node_execution_repository
@@ -89,7 +90,7 @@ class WorkflowCycleManager:
session: Session,
workflow_id: str,
user_id: str,
created_by_role: CreatedByRole,
created_by_role: CreatorUserRole,
) -> WorkflowRun:
workflow_stmt = select(Workflow).where(Workflow.id == workflow_id)
workflow = session.scalar(workflow_stmt)
@@ -258,21 +259,22 @@ class WorkflowCycleManager:
workflow_run.exceptions_count = exceptions_count
# Use the instance repository to find running executions for a workflow run
running_workflow_node_executions = self._workflow_node_execution_repository.get_running_executions(
running_domain_executions = self._workflow_node_execution_repository.get_running_executions(
workflow_run_id=workflow_run.id
)
# Update the cache with the retrieved executions
for execution in running_workflow_node_executions:
if execution.node_execution_id:
self._workflow_node_executions[execution.node_execution_id] = execution
# Update the domain models
now = datetime.now(UTC).replace(tzinfo=None)
for domain_execution in running_domain_executions:
if domain_execution.node_execution_id:
# Update the domain model
domain_execution.status = NodeExecutionStatus.FAILED
domain_execution.error = error
domain_execution.finished_at = now
domain_execution.elapsed_time = (now - domain_execution.created_at).total_seconds()
for workflow_node_execution in running_workflow_node_executions:
now = datetime.now(UTC).replace(tzinfo=None)
workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
workflow_node_execution.error = error
workflow_node_execution.finished_at = now
workflow_node_execution.elapsed_time = (now - workflow_node_execution.created_at).total_seconds()
# Update the repository with the domain model
self._workflow_node_execution_repository.save(domain_execution)
if trace_manager:
trace_manager.add_trace_task(
@@ -286,63 +288,67 @@ class WorkflowCycleManager:
return workflow_run
def _handle_node_execution_start(
self, *, workflow_run: WorkflowRun, event: QueueNodeStartedEvent
) -> WorkflowNodeExecution:
workflow_node_execution = WorkflowNodeExecution()
workflow_node_execution.id = str(uuid4())
workflow_node_execution.tenant_id = workflow_run.tenant_id
workflow_node_execution.app_id = workflow_run.app_id
workflow_node_execution.workflow_id = workflow_run.workflow_id
workflow_node_execution.triggered_from = WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value
workflow_node_execution.workflow_run_id = workflow_run.id
workflow_node_execution.predecessor_node_id = event.predecessor_node_id
workflow_node_execution.index = event.node_run_index
workflow_node_execution.node_execution_id = event.node_execution_id
workflow_node_execution.node_id = event.node_id
workflow_node_execution.node_type = event.node_type.value
workflow_node_execution.title = event.node_data.title
workflow_node_execution.status = WorkflowNodeExecutionStatus.RUNNING.value
workflow_node_execution.created_by_role = workflow_run.created_by_role
workflow_node_execution.created_by = workflow_run.created_by
workflow_node_execution.execution_metadata = json.dumps(
{
NodeRunMetadataKey.PARALLEL_MODE_RUN_ID: event.parallel_mode_run_id,
NodeRunMetadataKey.ITERATION_ID: event.in_iteration_id,
NodeRunMetadataKey.LOOP_ID: event.in_loop_id,
}
def _handle_node_execution_start(self, *, workflow_run: WorkflowRun, event: QueueNodeStartedEvent) -> NodeExecution:
# Create a domain model
created_at = datetime.now(UTC).replace(tzinfo=None)
metadata = {
NodeRunMetadataKey.PARALLEL_MODE_RUN_ID: event.parallel_mode_run_id,
NodeRunMetadataKey.ITERATION_ID: event.in_iteration_id,
NodeRunMetadataKey.LOOP_ID: event.in_loop_id,
}
domain_execution = NodeExecution(
id=str(uuid4()),
workflow_id=workflow_run.workflow_id,
workflow_run_id=workflow_run.id,
predecessor_node_id=event.predecessor_node_id,
index=event.node_run_index,
node_execution_id=event.node_execution_id,
node_id=event.node_id,
node_type=event.node_type,
title=event.node_data.title,
status=NodeExecutionStatus.RUNNING,
metadata=metadata,
created_at=created_at,
)
workflow_node_execution.created_at = datetime.now(UTC).replace(tzinfo=None)
# Use the instance repository to save the workflow node execution
self._workflow_node_execution_repository.save(workflow_node_execution)
# Use the instance repository to save the domain model
self._workflow_node_execution_repository.save(domain_execution)
self._workflow_node_executions[event.node_execution_id] = workflow_node_execution
return workflow_node_execution
return domain_execution
def _handle_workflow_node_execution_success(self, *, event: QueueNodeSucceededEvent) -> WorkflowNodeExecution:
workflow_node_execution = self._get_workflow_node_execution(node_execution_id=event.node_execution_id)
def _handle_workflow_node_execution_success(self, *, event: QueueNodeSucceededEvent) -> NodeExecution:
# Get the domain model from repository
domain_execution = self._workflow_node_execution_repository.get_by_node_execution_id(event.node_execution_id)
if not domain_execution:
raise ValueError(f"Domain node execution not found: {event.node_execution_id}")
# Process data
inputs = WorkflowEntry.handle_special_values(event.inputs)
process_data = WorkflowEntry.handle_special_values(event.process_data)
outputs = WorkflowEntry.handle_special_values(event.outputs)
execution_metadata_dict = dict(event.execution_metadata or {})
execution_metadata = json.dumps(jsonable_encoder(execution_metadata_dict)) if execution_metadata_dict else None
# Convert metadata keys to strings
execution_metadata_dict = {}
if event.execution_metadata:
for key, value in event.execution_metadata.items():
execution_metadata_dict[key] = value
finished_at = datetime.now(UTC).replace(tzinfo=None)
elapsed_time = (finished_at - event.start_at).total_seconds()
process_data = WorkflowEntry.handle_special_values(event.process_data)
# Update domain model
domain_execution.status = NodeExecutionStatus.SUCCEEDED
domain_execution.update_from_mapping(
inputs=inputs, process_data=process_data, outputs=outputs, metadata=execution_metadata_dict
)
domain_execution.finished_at = finished_at
domain_execution.elapsed_time = elapsed_time
workflow_node_execution.status = WorkflowNodeExecutionStatus.SUCCEEDED.value
workflow_node_execution.inputs = json.dumps(inputs) if inputs else None
workflow_node_execution.process_data = json.dumps(process_data) if process_data else None
workflow_node_execution.outputs = json.dumps(outputs) if outputs else None
workflow_node_execution.execution_metadata = execution_metadata
workflow_node_execution.finished_at = finished_at
workflow_node_execution.elapsed_time = elapsed_time
# Update the repository with the domain model
self._workflow_node_execution_repository.save(domain_execution)
# Use the instance repository to update the workflow node execution
self._workflow_node_execution_repository.update(workflow_node_execution)
return workflow_node_execution
return domain_execution
def _handle_workflow_node_execution_failed(
self,
@@ -351,43 +357,52 @@ class WorkflowCycleManager:
| QueueNodeInIterationFailedEvent
| QueueNodeInLoopFailedEvent
| QueueNodeExceptionEvent,
) -> WorkflowNodeExecution:
) -> NodeExecution:
"""
Workflow node execution failed
:param event: queue node failed event
:return:
"""
workflow_node_execution = self._get_workflow_node_execution(node_execution_id=event.node_execution_id)
# Get the domain model from repository
domain_execution = self._workflow_node_execution_repository.get_by_node_execution_id(event.node_execution_id)
if not domain_execution:
raise ValueError(f"Domain node execution not found: {event.node_execution_id}")
# Process data
inputs = WorkflowEntry.handle_special_values(event.inputs)
process_data = WorkflowEntry.handle_special_values(event.process_data)
outputs = WorkflowEntry.handle_special_values(event.outputs)
# Convert metadata keys to strings
execution_metadata_dict = {}
if event.execution_metadata:
for key, value in event.execution_metadata.items():
execution_metadata_dict[key] = value
finished_at = datetime.now(UTC).replace(tzinfo=None)
elapsed_time = (finished_at - event.start_at).total_seconds()
execution_metadata = (
json.dumps(jsonable_encoder(event.execution_metadata)) if event.execution_metadata else None
)
process_data = WorkflowEntry.handle_special_values(event.process_data)
workflow_node_execution.status = (
WorkflowNodeExecutionStatus.FAILED.value
# Update domain model
domain_execution.status = (
NodeExecutionStatus.FAILED
if not isinstance(event, QueueNodeExceptionEvent)
else WorkflowNodeExecutionStatus.EXCEPTION.value
else NodeExecutionStatus.EXCEPTION
)
workflow_node_execution.error = event.error
workflow_node_execution.inputs = json.dumps(inputs) if inputs else None
workflow_node_execution.process_data = json.dumps(process_data) if process_data else None
workflow_node_execution.outputs = json.dumps(outputs) if outputs else None
workflow_node_execution.finished_at = finished_at
workflow_node_execution.elapsed_time = elapsed_time
workflow_node_execution.execution_metadata = execution_metadata
domain_execution.error = event.error
domain_execution.update_from_mapping(
inputs=inputs, process_data=process_data, outputs=outputs, metadata=execution_metadata_dict
)
domain_execution.finished_at = finished_at
domain_execution.elapsed_time = elapsed_time
self._workflow_node_execution_repository.update(workflow_node_execution)
# Update the repository with the domain model
self._workflow_node_execution_repository.save(domain_execution)
return workflow_node_execution
return domain_execution
def _handle_workflow_node_execution_retried(
self, *, workflow_run: WorkflowRun, event: QueueNodeRetryEvent
) -> WorkflowNodeExecution:
) -> NodeExecution:
"""
Workflow node execution failed
:param workflow_run: workflow run
@@ -399,47 +414,47 @@ class WorkflowCycleManager:
elapsed_time = (finished_at - created_at).total_seconds()
inputs = WorkflowEntry.handle_special_values(event.inputs)
outputs = WorkflowEntry.handle_special_values(event.outputs)
# Convert metadata keys to strings
origin_metadata = {
NodeRunMetadataKey.ITERATION_ID: event.in_iteration_id,
NodeRunMetadataKey.PARALLEL_MODE_RUN_ID: event.parallel_mode_run_id,
NodeRunMetadataKey.LOOP_ID: event.in_loop_id,
}
merged_metadata = (
{**jsonable_encoder(event.execution_metadata), **origin_metadata}
if event.execution_metadata is not None
else origin_metadata
# Convert execution metadata keys to strings
execution_metadata_dict: dict[NodeRunMetadataKey, str | None] = {}
if event.execution_metadata:
for key, value in event.execution_metadata.items():
execution_metadata_dict[key] = value
merged_metadata = {**execution_metadata_dict, **origin_metadata} if execution_metadata_dict else origin_metadata
# Create a domain model
domain_execution = NodeExecution(
id=str(uuid4()),
workflow_id=workflow_run.workflow_id,
workflow_run_id=workflow_run.id,
predecessor_node_id=event.predecessor_node_id,
node_execution_id=event.node_execution_id,
node_id=event.node_id,
node_type=event.node_type,
title=event.node_data.title,
status=NodeExecutionStatus.RETRY,
created_at=created_at,
finished_at=finished_at,
elapsed_time=elapsed_time,
error=event.error,
index=event.node_run_index,
)
execution_metadata = json.dumps(merged_metadata)
workflow_node_execution = WorkflowNodeExecution()
workflow_node_execution.id = str(uuid4())
workflow_node_execution.tenant_id = workflow_run.tenant_id
workflow_node_execution.app_id = workflow_run.app_id
workflow_node_execution.workflow_id = workflow_run.workflow_id
workflow_node_execution.triggered_from = WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value
workflow_node_execution.workflow_run_id = workflow_run.id
workflow_node_execution.predecessor_node_id = event.predecessor_node_id
workflow_node_execution.node_execution_id = event.node_execution_id
workflow_node_execution.node_id = event.node_id
workflow_node_execution.node_type = event.node_type.value
workflow_node_execution.title = event.node_data.title
workflow_node_execution.status = WorkflowNodeExecutionStatus.RETRY.value
workflow_node_execution.created_by_role = workflow_run.created_by_role
workflow_node_execution.created_by = workflow_run.created_by
workflow_node_execution.created_at = created_at
workflow_node_execution.finished_at = finished_at
workflow_node_execution.elapsed_time = elapsed_time
workflow_node_execution.error = event.error
workflow_node_execution.inputs = json.dumps(inputs) if inputs else None
workflow_node_execution.outputs = json.dumps(outputs) if outputs else None
workflow_node_execution.execution_metadata = execution_metadata
workflow_node_execution.index = event.node_run_index
# Update with mappings
domain_execution.update_from_mapping(inputs=inputs, outputs=outputs, metadata=merged_metadata)
# Use the instance repository to save the workflow node execution
self._workflow_node_execution_repository.save(workflow_node_execution)
# Use the instance repository to save the domain model
self._workflow_node_execution_repository.save(domain_execution)
self._workflow_node_executions[event.node_execution_id] = workflow_node_execution
return workflow_node_execution
return domain_execution
def _workflow_start_to_stream_response(
self,
@@ -469,7 +484,7 @@ class WorkflowCycleManager:
workflow_run: WorkflowRun,
) -> WorkflowFinishStreamResponse:
created_by = None
if workflow_run.created_by_role == CreatedByRole.ACCOUNT:
if workflow_run.created_by_role == CreatorUserRole.ACCOUNT:
stmt = select(Account).where(Account.id == workflow_run.created_by)
account = session.scalar(stmt)
if account:
@@ -478,7 +493,7 @@ class WorkflowCycleManager:
"name": account.name,
"email": account.email,
}
elif workflow_run.created_by_role == CreatedByRole.END_USER:
elif workflow_run.created_by_role == CreatorUserRole.END_USER:
stmt = select(EndUser).where(EndUser.id == workflow_run.created_by)
end_user = session.scalar(stmt)
if end_user:
@@ -515,9 +530,9 @@ class WorkflowCycleManager:
*,
event: QueueNodeStartedEvent,
task_id: str,
workflow_node_execution: WorkflowNodeExecution,
workflow_node_execution: NodeExecution,
) -> Optional[NodeStartStreamResponse]:
if workflow_node_execution.node_type in {NodeType.ITERATION.value, NodeType.LOOP.value}:
if workflow_node_execution.node_type in {NodeType.ITERATION, NodeType.LOOP}:
return None
if not workflow_node_execution.workflow_run_id:
return None
@@ -532,7 +547,7 @@ class WorkflowCycleManager:
title=workflow_node_execution.title,
index=workflow_node_execution.index,
predecessor_node_id=workflow_node_execution.predecessor_node_id,
inputs=workflow_node_execution.inputs_dict,
inputs=workflow_node_execution.inputs,
created_at=int(workflow_node_execution.created_at.timestamp()),
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
@@ -565,9 +580,9 @@ class WorkflowCycleManager:
| QueueNodeInLoopFailedEvent
| QueueNodeExceptionEvent,
task_id: str,
workflow_node_execution: WorkflowNodeExecution,
workflow_node_execution: NodeExecution,
) -> Optional[NodeFinishStreamResponse]:
if workflow_node_execution.node_type in {NodeType.ITERATION.value, NodeType.LOOP.value}:
if workflow_node_execution.node_type in {NodeType.ITERATION, NodeType.LOOP}:
return None
if not workflow_node_execution.workflow_run_id:
return None
@@ -584,16 +599,16 @@ class WorkflowCycleManager:
index=workflow_node_execution.index,
title=workflow_node_execution.title,
predecessor_node_id=workflow_node_execution.predecessor_node_id,
inputs=workflow_node_execution.inputs_dict,
process_data=workflow_node_execution.process_data_dict,
outputs=workflow_node_execution.outputs_dict,
inputs=workflow_node_execution.inputs,
process_data=workflow_node_execution.process_data,
outputs=workflow_node_execution.outputs,
status=workflow_node_execution.status,
error=workflow_node_execution.error,
elapsed_time=workflow_node_execution.elapsed_time,
execution_metadata=workflow_node_execution.execution_metadata_dict,
execution_metadata=workflow_node_execution.metadata,
created_at=int(workflow_node_execution.created_at.timestamp()),
finished_at=int(workflow_node_execution.finished_at.timestamp()),
files=self._fetch_files_from_node_outputs(workflow_node_execution.outputs_dict or {}),
files=self._fetch_files_from_node_outputs(workflow_node_execution.outputs or {}),
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
parent_parallel_id=event.parent_parallel_id,
@@ -608,9 +623,9 @@ class WorkflowCycleManager:
*,
event: QueueNodeRetryEvent,
task_id: str,
workflow_node_execution: WorkflowNodeExecution,
workflow_node_execution: NodeExecution,
) -> Optional[Union[NodeRetryStreamResponse, NodeFinishStreamResponse]]:
if workflow_node_execution.node_type in {NodeType.ITERATION.value, NodeType.LOOP.value}:
if workflow_node_execution.node_type in {NodeType.ITERATION, NodeType.LOOP}:
return None
if not workflow_node_execution.workflow_run_id:
return None
@@ -627,16 +642,16 @@ class WorkflowCycleManager:
index=workflow_node_execution.index,
title=workflow_node_execution.title,
predecessor_node_id=workflow_node_execution.predecessor_node_id,
inputs=workflow_node_execution.inputs_dict,
process_data=workflow_node_execution.process_data_dict,
outputs=workflow_node_execution.outputs_dict,
inputs=workflow_node_execution.inputs,
process_data=workflow_node_execution.process_data,
outputs=workflow_node_execution.outputs,
status=workflow_node_execution.status,
error=workflow_node_execution.error,
elapsed_time=workflow_node_execution.elapsed_time,
execution_metadata=workflow_node_execution.execution_metadata_dict,
execution_metadata=workflow_node_execution.metadata,
created_at=int(workflow_node_execution.created_at.timestamp()),
finished_at=int(workflow_node_execution.finished_at.timestamp()),
files=self._fetch_files_from_node_outputs(workflow_node_execution.outputs_dict or {}),
files=self._fetch_files_from_node_outputs(workflow_node_execution.outputs or {}),
parallel_id=event.parallel_id,
parallel_start_node_id=event.parallel_start_node_id,
parent_parallel_id=event.parent_parallel_id,
@@ -908,23 +923,6 @@ class WorkflowCycleManager:
return workflow_run
def _get_workflow_node_execution(self, node_execution_id: str) -> WorkflowNodeExecution:
# First check the cache for performance
if node_execution_id in self._workflow_node_executions:
cached_execution = self._workflow_node_executions[node_execution_id]
# No need to merge with session since expire_on_commit=False
return cached_execution
# If not in cache, use the instance repository to get by node_execution_id
execution = self._workflow_node_execution_repository.get_by_node_execution_id(node_execution_id)
if not execution:
raise ValueError(f"Workflow node execution not found: {node_execution_id}")
# Update cache
self._workflow_node_executions[node_execution_id] = execution
return execution
def _handle_agent_log(self, task_id: str, event: QueueAgentLogEvent) -> AgentLogStreamResponse:
"""
Handle agent log