mirror of
https://github.com/langgenius/dify.git
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1092 lines
44 KiB
Python
1092 lines
44 KiB
Python
import json
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import logging
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import re
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import threading
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import time
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from collections.abc import Callable, Generator, Mapping, Sequence
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from datetime import UTC, datetime
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from typing import Any, Optional, cast
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from uuid import uuid4
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from flask_login import current_user
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from sqlalchemy import func, or_, select
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from sqlalchemy.orm import Session
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import contexts
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from configs import dify_config
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from core.app.entities.app_invoke_entities import InvokeFrom
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from core.datasource.entities.datasource_entities import (
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DatasourceMessage,
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DatasourceProviderType,
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GetOnlineDocumentPageContentRequest,
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OnlineDocumentPagesMessage,
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OnlineDriveBrowseFilesRequest,
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OnlineDriveBrowseFilesResponse,
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WebsiteCrawlMessage,
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)
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from core.datasource.online_document.online_document_plugin import OnlineDocumentDatasourcePlugin
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from core.datasource.online_drive.online_drive_plugin import OnlineDriveDatasourcePlugin
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from core.datasource.website_crawl.website_crawl_plugin import WebsiteCrawlDatasourcePlugin
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from core.rag.entities.event import (
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BaseDatasourceEvent,
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DatasourceCompletedEvent,
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DatasourceErrorEvent,
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DatasourceProcessingEvent,
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)
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from core.repositories.sqlalchemy_workflow_node_execution_repository import SQLAlchemyWorkflowNodeExecutionRepository
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from core.variables.variables import Variable
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from core.workflow.entities.node_entities import NodeRunResult
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from core.workflow.entities.workflow_node_execution import (
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WorkflowNodeExecution,
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WorkflowNodeExecutionStatus,
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)
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from core.workflow.enums import SystemVariableKey
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from core.workflow.errors import WorkflowNodeRunFailedError
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from core.workflow.graph_engine.entities.event import InNodeEvent
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from core.workflow.nodes.base.node import BaseNode
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from core.workflow.nodes.enums import ErrorStrategy, NodeType
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from core.workflow.nodes.event.event import RunCompletedEvent
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from core.workflow.nodes.event.types import NodeEvent
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from core.workflow.nodes.node_mapping import LATEST_VERSION, NODE_TYPE_CLASSES_MAPPING
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from core.workflow.repositories.workflow_node_execution_repository import OrderConfig
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from core.workflow.workflow_entry import WorkflowEntry
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from extensions.ext_database import db
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from libs.infinite_scroll_pagination import InfiniteScrollPagination
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from models.account import Account
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from models.dataset import Document, Pipeline, PipelineCustomizedTemplate # type: ignore
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from models.enums import WorkflowRunTriggeredFrom
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from models.model import EndUser
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from models.workflow import (
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Workflow,
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WorkflowNodeExecutionModel,
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WorkflowNodeExecutionTriggeredFrom,
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WorkflowRun,
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WorkflowType,
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)
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from services.dataset_service import DatasetService
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from services.datasource_provider_service import DatasourceProviderService
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from services.entities.knowledge_entities.rag_pipeline_entities import (
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KnowledgeConfiguration,
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PipelineTemplateInfoEntity,
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)
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from services.errors.app import WorkflowHashNotEqualError
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from services.rag_pipeline.pipeline_template.pipeline_template_factory import PipelineTemplateRetrievalFactory
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logger = logging.getLogger(__name__)
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class RagPipelineService:
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@classmethod
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def get_pipeline_templates(cls, type: str = "built-in", language: str = "en-US") -> dict:
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if type == "built-in":
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mode = dify_config.HOSTED_FETCH_PIPELINE_TEMPLATES_MODE
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retrieval_instance = PipelineTemplateRetrievalFactory.get_pipeline_template_factory(mode)()
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result = retrieval_instance.get_pipeline_templates(language)
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if not result.get("pipeline_templates") and language != "en-US":
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template_retrieval = PipelineTemplateRetrievalFactory.get_built_in_pipeline_template_retrieval()
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result = template_retrieval.fetch_pipeline_templates_from_builtin("en-US")
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return result
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else:
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mode = "customized"
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retrieval_instance = PipelineTemplateRetrievalFactory.get_pipeline_template_factory(mode)()
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result = retrieval_instance.get_pipeline_templates(language)
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return result
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@classmethod
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def get_pipeline_template_detail(cls, template_id: str, type: str = "built-in") -> Optional[dict]:
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"""
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Get pipeline template detail.
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:param template_id: template id
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:return:
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"""
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if type == "built-in":
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mode = dify_config.HOSTED_FETCH_PIPELINE_TEMPLATES_MODE
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retrieval_instance = PipelineTemplateRetrievalFactory.get_pipeline_template_factory(mode)()
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result: Optional[dict] = retrieval_instance.get_pipeline_template_detail(template_id)
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else:
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mode = "customized"
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retrieval_instance = PipelineTemplateRetrievalFactory.get_pipeline_template_factory(mode)()
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result: Optional[dict] = retrieval_instance.get_pipeline_template_detail(template_id)
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return result
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@classmethod
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def update_customized_pipeline_template(cls, template_id: str, template_info: PipelineTemplateInfoEntity):
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"""
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Update pipeline template.
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:param template_id: template id
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:param template_info: template info
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"""
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customized_template: PipelineCustomizedTemplate | None = (
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db.session.query(PipelineCustomizedTemplate)
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.filter(
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PipelineCustomizedTemplate.id == template_id,
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PipelineCustomizedTemplate.tenant_id == current_user.current_tenant_id,
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)
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.first()
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)
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if not customized_template:
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raise ValueError("Customized pipeline template not found.")
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# check template name is exist
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template_name = template_info.name
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if template_name:
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template = (
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db.session.query(PipelineCustomizedTemplate)
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.filter(
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PipelineCustomizedTemplate.name == template_name,
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PipelineCustomizedTemplate.tenant_id == current_user.current_tenant_id,
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PipelineCustomizedTemplate.id != template_id,
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)
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.first()
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)
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if template:
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raise ValueError("Template name is already exists")
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customized_template.name = template_info.name
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customized_template.description = template_info.description
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customized_template.icon = template_info.icon_info.model_dump()
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customized_template.updated_by = current_user.id
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db.session.commit()
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return customized_template
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@classmethod
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def delete_customized_pipeline_template(cls, template_id: str):
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"""
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Delete customized pipeline template.
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"""
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customized_template: PipelineCustomizedTemplate | None = (
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db.session.query(PipelineCustomizedTemplate)
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.filter(
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PipelineCustomizedTemplate.id == template_id,
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PipelineCustomizedTemplate.tenant_id == current_user.current_tenant_id,
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)
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.first()
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)
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if not customized_template:
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raise ValueError("Customized pipeline template not found.")
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db.session.delete(customized_template)
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db.session.commit()
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def get_draft_workflow(self, pipeline: Pipeline) -> Optional[Workflow]:
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"""
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Get draft workflow
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"""
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# fetch draft workflow by rag pipeline
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workflow = (
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db.session.query(Workflow)
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.filter(
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Workflow.tenant_id == pipeline.tenant_id,
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Workflow.app_id == pipeline.id,
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Workflow.version == "draft",
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)
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.first()
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)
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# return draft workflow
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return workflow
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def get_published_workflow(self, pipeline: Pipeline) -> Optional[Workflow]:
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"""
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Get published workflow
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"""
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if not pipeline.workflow_id:
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return None
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# fetch published workflow by workflow_id
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workflow = (
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db.session.query(Workflow)
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.filter(
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Workflow.tenant_id == pipeline.tenant_id,
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Workflow.app_id == pipeline.id,
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Workflow.id == pipeline.workflow_id,
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)
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.first()
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)
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return workflow
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def get_all_published_workflow(
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self,
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*,
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session: Session,
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pipeline: Pipeline,
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page: int,
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limit: int,
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user_id: str | None,
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named_only: bool = False,
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) -> tuple[Sequence[Workflow], bool]:
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"""
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Get published workflow with pagination
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"""
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if not pipeline.workflow_id:
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return [], False
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stmt = (
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select(Workflow)
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.where(Workflow.app_id == pipeline.id)
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.order_by(Workflow.version.desc())
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.limit(limit + 1)
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.offset((page - 1) * limit)
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)
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if user_id:
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stmt = stmt.where(Workflow.created_by == user_id)
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if named_only:
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stmt = stmt.where(Workflow.marked_name != "")
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workflows = session.scalars(stmt).all()
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has_more = len(workflows) > limit
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if has_more:
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workflows = workflows[:-1]
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return workflows, has_more
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def sync_draft_workflow(
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self,
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*,
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pipeline: Pipeline,
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graph: dict,
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unique_hash: Optional[str],
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account: Account,
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environment_variables: Sequence[Variable],
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conversation_variables: Sequence[Variable],
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rag_pipeline_variables: list,
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) -> Workflow:
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"""
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Sync draft workflow
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:raises WorkflowHashNotEqualError
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"""
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# fetch draft workflow by app_model
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workflow = self.get_draft_workflow(pipeline=pipeline)
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if workflow and workflow.unique_hash != unique_hash:
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raise WorkflowHashNotEqualError()
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# create draft workflow if not found
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if not workflow:
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workflow = Workflow(
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tenant_id=pipeline.tenant_id,
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app_id=pipeline.id,
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features="{}",
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type=WorkflowType.RAG_PIPELINE.value,
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version="draft",
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graph=json.dumps(graph),
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created_by=account.id,
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environment_variables=environment_variables,
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conversation_variables=conversation_variables,
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rag_pipeline_variables=rag_pipeline_variables,
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)
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db.session.add(workflow)
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db.session.flush()
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pipeline.workflow_id = workflow.id
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# update draft workflow if found
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else:
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workflow.graph = json.dumps(graph)
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workflow.updated_by = account.id
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workflow.updated_at = datetime.now(UTC).replace(tzinfo=None)
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workflow.environment_variables = environment_variables
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workflow.conversation_variables = conversation_variables
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workflow.rag_pipeline_variables = rag_pipeline_variables
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# commit db session changes
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db.session.commit()
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# trigger workflow events TODO
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# app_draft_workflow_was_synced.send(pipeline, synced_draft_workflow=workflow)
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# return draft workflow
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return workflow
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def publish_workflow(
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self,
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*,
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session: Session,
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pipeline: Pipeline,
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account: Account,
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) -> Workflow:
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draft_workflow_stmt = select(Workflow).where(
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Workflow.tenant_id == pipeline.tenant_id,
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Workflow.app_id == pipeline.id,
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Workflow.version == "draft",
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)
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draft_workflow = session.scalar(draft_workflow_stmt)
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if not draft_workflow:
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raise ValueError("No valid workflow found.")
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# create new workflow
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workflow = Workflow.new(
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tenant_id=pipeline.tenant_id,
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app_id=pipeline.id,
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type=draft_workflow.type,
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version=str(datetime.now(UTC).replace(tzinfo=None)),
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graph=draft_workflow.graph,
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features=draft_workflow.features,
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created_by=account.id,
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environment_variables=draft_workflow.environment_variables,
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conversation_variables=draft_workflow.conversation_variables,
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rag_pipeline_variables=draft_workflow.rag_pipeline_variables,
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marked_name="",
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marked_comment="",
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)
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# commit db session changes
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session.add(workflow)
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graph = workflow.graph_dict
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nodes = graph.get("nodes", [])
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for node in nodes:
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if node.get("data", {}).get("type") == "knowledge-index":
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knowledge_configuration = node.get("data", {})
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knowledge_configuration = KnowledgeConfiguration(**knowledge_configuration)
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# update dataset
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dataset = pipeline.dataset
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if not dataset:
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raise ValueError("Dataset not found")
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DatasetService.update_rag_pipeline_dataset_settings(
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session=session,
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dataset=dataset,
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knowledge_configuration=knowledge_configuration,
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has_published=pipeline.is_published,
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)
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# return new workflow
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return workflow
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def get_default_block_configs(self) -> list[dict]:
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"""
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Get default block configs
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"""
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# return default block config
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default_block_configs = []
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for node_class_mapping in NODE_TYPE_CLASSES_MAPPING.values():
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node_class = node_class_mapping[LATEST_VERSION]
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default_config = node_class.get_default_config()
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if default_config:
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default_block_configs.append(default_config)
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return default_block_configs
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|
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def get_default_block_config(self, node_type: str, filters: Optional[dict] = None) -> Optional[dict]:
|
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"""
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Get default config of node.
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:param node_type: node type
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:param filters: filter by node config parameters.
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:return:
|
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"""
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node_type_enum = NodeType(node_type)
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# return default block config
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if node_type_enum not in NODE_TYPE_CLASSES_MAPPING:
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return None
|
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node_class = NODE_TYPE_CLASSES_MAPPING[node_type_enum][LATEST_VERSION]
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default_config = node_class.get_default_config(filters=filters)
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if not default_config:
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return None
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return default_config
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|
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def run_draft_workflow_node(
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self, pipeline: Pipeline, node_id: str, user_inputs: dict, account: Account
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) -> WorkflowNodeExecution:
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"""
|
|
Run draft workflow node
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"""
|
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# fetch draft workflow by app_model
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draft_workflow = self.get_draft_workflow(pipeline=pipeline)
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if not draft_workflow:
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raise ValueError("Workflow not initialized")
|
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|
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# run draft workflow node
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start_at = time.perf_counter()
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workflow_node_execution = self._handle_node_run_result(
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getter=lambda: WorkflowEntry.single_step_run(
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workflow=draft_workflow,
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node_id=node_id,
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user_inputs=user_inputs,
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user_id=account.id,
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),
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start_at=start_at,
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tenant_id=pipeline.tenant_id,
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node_id=node_id,
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)
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workflow_node_execution.workflow_id = draft_workflow.id
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|
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db.session.add(workflow_node_execution)
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db.session.commit()
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return workflow_node_execution
|
|
|
|
def run_published_workflow_node(
|
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self, pipeline: Pipeline, node_id: str, user_inputs: dict, account: Account
|
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) -> WorkflowNodeExecution:
|
|
"""
|
|
Run published workflow node
|
|
"""
|
|
# fetch published workflow by app_model
|
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published_workflow = self.get_published_workflow(pipeline=pipeline)
|
|
if not published_workflow:
|
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raise ValueError("Workflow not initialized")
|
|
|
|
# run draft workflow node
|
|
start_at = time.perf_counter()
|
|
|
|
workflow_node_execution = self._handle_node_run_result(
|
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getter=lambda: WorkflowEntry.single_step_run(
|
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workflow=published_workflow,
|
|
node_id=node_id,
|
|
user_inputs=user_inputs,
|
|
user_id=account.id,
|
|
),
|
|
start_at=start_at,
|
|
tenant_id=pipeline.tenant_id,
|
|
node_id=node_id,
|
|
)
|
|
|
|
workflow_node_execution.workflow_id = published_workflow.id
|
|
|
|
db.session.add(workflow_node_execution)
|
|
db.session.commit()
|
|
|
|
return workflow_node_execution
|
|
|
|
def run_datasource_workflow_node(
|
|
self,
|
|
pipeline: Pipeline,
|
|
node_id: str,
|
|
user_inputs: dict,
|
|
account: Account,
|
|
datasource_type: str,
|
|
is_published: bool,
|
|
) -> Generator[BaseDatasourceEvent, None, None]:
|
|
"""
|
|
Run published workflow datasource
|
|
"""
|
|
try:
|
|
if is_published:
|
|
# fetch published workflow by app_model
|
|
workflow = self.get_published_workflow(pipeline=pipeline)
|
|
else:
|
|
workflow = self.get_draft_workflow(pipeline=pipeline)
|
|
if not workflow:
|
|
raise ValueError("Workflow not initialized")
|
|
|
|
# run draft workflow node
|
|
datasource_node_data = None
|
|
datasource_nodes = workflow.graph_dict.get("nodes", [])
|
|
for datasource_node in datasource_nodes:
|
|
if datasource_node.get("id") == node_id:
|
|
datasource_node_data = datasource_node.get("data", {})
|
|
break
|
|
if not datasource_node_data:
|
|
raise ValueError("Datasource node data not found")
|
|
|
|
datasource_parameters = datasource_node_data.get("datasource_parameters", {})
|
|
for key, value in datasource_parameters.items():
|
|
if not user_inputs.get(key):
|
|
user_inputs[key] = value["value"]
|
|
|
|
from core.datasource.datasource_manager import DatasourceManager
|
|
|
|
datasource_runtime = DatasourceManager.get_datasource_runtime(
|
|
provider_id=f"{datasource_node_data.get('plugin_id')}/{datasource_node_data.get('provider_name')}",
|
|
datasource_name=datasource_node_data.get("datasource_name"),
|
|
tenant_id=pipeline.tenant_id,
|
|
datasource_type=DatasourceProviderType(datasource_type),
|
|
)
|
|
datasource_provider_service = DatasourceProviderService()
|
|
credentials = datasource_provider_service.get_real_datasource_credentials(
|
|
tenant_id=pipeline.tenant_id,
|
|
provider=datasource_node_data.get("provider_name"),
|
|
plugin_id=datasource_node_data.get("plugin_id"),
|
|
)
|
|
if credentials:
|
|
datasource_runtime.runtime.credentials = credentials[0].get("credentials")
|
|
match datasource_type:
|
|
case DatasourceProviderType.ONLINE_DOCUMENT:
|
|
datasource_runtime = cast(OnlineDocumentDatasourcePlugin, datasource_runtime)
|
|
online_document_result: Generator[OnlineDocumentPagesMessage, None, None] = (
|
|
datasource_runtime.get_online_document_pages(
|
|
user_id=account.id,
|
|
datasource_parameters=user_inputs,
|
|
provider_type=datasource_runtime.datasource_provider_type(),
|
|
)
|
|
)
|
|
start_time = time.time()
|
|
start_event = DatasourceProcessingEvent(
|
|
total=0,
|
|
completed=0,
|
|
)
|
|
yield start_event.model_dump()
|
|
try:
|
|
for message in online_document_result:
|
|
end_time = time.time()
|
|
online_document_event = DatasourceCompletedEvent(
|
|
data=message.result, time_consuming=round(end_time - start_time, 2)
|
|
)
|
|
yield online_document_event.model_dump()
|
|
except Exception as e:
|
|
logger.exception("Error during online document.")
|
|
yield DatasourceErrorEvent(error=str(e)).model_dump()
|
|
case DatasourceProviderType.ONLINE_DRIVE:
|
|
datasource_runtime = cast(OnlineDriveDatasourcePlugin, datasource_runtime)
|
|
online_drive_result: Generator[OnlineDriveBrowseFilesResponse, None, None] = (
|
|
datasource_runtime.online_drive_browse_files(
|
|
user_id=account.id,
|
|
request=OnlineDriveBrowseFilesRequest(
|
|
bucket=user_inputs.get("bucket"),
|
|
prefix=user_inputs.get("prefix"),
|
|
max_keys=user_inputs.get("max_keys", 20),
|
|
start_after=user_inputs.get("start_after"),
|
|
),
|
|
provider_type=datasource_runtime.datasource_provider_type(),
|
|
)
|
|
)
|
|
start_time = time.time()
|
|
start_event = DatasourceProcessingEvent(
|
|
total=0,
|
|
completed=0,
|
|
)
|
|
yield start_event.model_dump()
|
|
for message in online_drive_result:
|
|
end_time = time.time()
|
|
online_drive_event = DatasourceCompletedEvent(
|
|
data=message.result,
|
|
time_consuming=round(end_time - start_time, 2),
|
|
total=None,
|
|
completed=None,
|
|
)
|
|
yield online_drive_event.model_dump()
|
|
case DatasourceProviderType.WEBSITE_CRAWL:
|
|
datasource_runtime = cast(WebsiteCrawlDatasourcePlugin, datasource_runtime)
|
|
website_crawl_result: Generator[WebsiteCrawlMessage, None, None] = (
|
|
datasource_runtime.get_website_crawl(
|
|
user_id=account.id,
|
|
datasource_parameters=user_inputs,
|
|
provider_type=datasource_runtime.datasource_provider_type(),
|
|
)
|
|
)
|
|
start_time = time.time()
|
|
try:
|
|
for message in website_crawl_result:
|
|
end_time = time.time()
|
|
if message.result.status == "completed":
|
|
crawl_event = DatasourceCompletedEvent(
|
|
data=message.result.web_info_list,
|
|
total=message.result.total,
|
|
completed=message.result.completed,
|
|
time_consuming=round(end_time - start_time, 2),
|
|
)
|
|
else:
|
|
crawl_event = DatasourceProcessingEvent(
|
|
total=message.result.total,
|
|
completed=message.result.completed,
|
|
)
|
|
yield crawl_event.model_dump()
|
|
except Exception as e:
|
|
logger.exception("Error during website crawl.")
|
|
yield DatasourceErrorEvent(error=str(e)).model_dump()
|
|
case _:
|
|
raise ValueError(f"Unsupported datasource provider: {datasource_runtime.datasource_provider_type}")
|
|
except Exception as e:
|
|
logger.exception("Error in run_datasource_workflow_node.")
|
|
yield DatasourceErrorEvent(error=str(e)).model_dump()
|
|
|
|
def run_datasource_node_preview(
|
|
self,
|
|
pipeline: Pipeline,
|
|
node_id: str,
|
|
user_inputs: dict,
|
|
account: Account,
|
|
datasource_type: str,
|
|
is_published: bool,
|
|
) -> Mapping[str, Any]:
|
|
"""
|
|
Run published workflow datasource
|
|
"""
|
|
try:
|
|
if is_published:
|
|
# fetch published workflow by app_model
|
|
workflow = self.get_published_workflow(pipeline=pipeline)
|
|
else:
|
|
workflow = self.get_draft_workflow(pipeline=pipeline)
|
|
if not workflow:
|
|
raise ValueError("Workflow not initialized")
|
|
|
|
# run draft workflow node
|
|
datasource_node_data = None
|
|
datasource_nodes = workflow.graph_dict.get("nodes", [])
|
|
for datasource_node in datasource_nodes:
|
|
if datasource_node.get("id") == node_id:
|
|
datasource_node_data = datasource_node.get("data", {})
|
|
break
|
|
if not datasource_node_data:
|
|
raise ValueError("Datasource node data not found")
|
|
|
|
datasource_parameters = datasource_node_data.get("datasource_parameters", {})
|
|
for key, value in datasource_parameters.items():
|
|
if not user_inputs.get(key):
|
|
user_inputs[key] = value["value"]
|
|
|
|
from core.datasource.datasource_manager import DatasourceManager
|
|
|
|
datasource_runtime = DatasourceManager.get_datasource_runtime(
|
|
provider_id=f"{datasource_node_data.get('plugin_id')}/{datasource_node_data.get('provider_name')}",
|
|
datasource_name=datasource_node_data.get("datasource_name"),
|
|
tenant_id=pipeline.tenant_id,
|
|
datasource_type=DatasourceProviderType(datasource_type),
|
|
)
|
|
datasource_provider_service = DatasourceProviderService()
|
|
credentials = datasource_provider_service.get_real_datasource_credentials(
|
|
tenant_id=pipeline.tenant_id,
|
|
provider=datasource_node_data.get("provider_name"),
|
|
plugin_id=datasource_node_data.get("plugin_id"),
|
|
)
|
|
if credentials:
|
|
datasource_runtime.runtime.credentials = credentials[0].get("credentials")
|
|
match datasource_type:
|
|
case DatasourceProviderType.ONLINE_DOCUMENT:
|
|
datasource_runtime = cast(OnlineDocumentDatasourcePlugin, datasource_runtime)
|
|
online_document_result: Generator[DatasourceMessage, None, None] = (
|
|
datasource_runtime.get_online_document_page_content(
|
|
user_id=account.id,
|
|
datasource_parameters=GetOnlineDocumentPageContentRequest(
|
|
workspace_id=user_inputs.get("workspace_id"),
|
|
page_id=user_inputs.get("page_id"),
|
|
type=user_inputs.get("type"),
|
|
),
|
|
provider_type=datasource_type,
|
|
)
|
|
)
|
|
try:
|
|
variables: dict[str, Any] = {}
|
|
for message in online_document_result:
|
|
if message.type == DatasourceMessage.MessageType.VARIABLE:
|
|
assert isinstance(message.message, DatasourceMessage.VariableMessage)
|
|
variable_name = message.message.variable_name
|
|
variable_value = message.message.variable_value
|
|
if message.message.stream:
|
|
if not isinstance(variable_value, str):
|
|
raise ValueError("When 'stream' is True, 'variable_value' must be a string.")
|
|
if variable_name not in variables:
|
|
variables[variable_name] = ""
|
|
variables[variable_name] += variable_value
|
|
else:
|
|
variables[variable_name] = variable_value
|
|
return variables
|
|
except Exception as e:
|
|
logger.exception("Error during get online document content.")
|
|
raise RuntimeError(str(e))
|
|
# TODO Online Drive
|
|
case _:
|
|
raise ValueError(f"Unsupported datasource provider: {datasource_runtime.datasource_provider_type}")
|
|
except Exception as e:
|
|
logger.exception("Error in run_datasource_node_preview.")
|
|
raise RuntimeError(str(e))
|
|
|
|
def run_free_workflow_node(
|
|
self, node_data: dict, tenant_id: str, user_id: str, node_id: str, user_inputs: dict[str, Any]
|
|
) -> WorkflowNodeExecution:
|
|
"""
|
|
Run draft workflow node
|
|
"""
|
|
# run draft workflow node
|
|
start_at = time.perf_counter()
|
|
|
|
workflow_node_execution = self._handle_node_run_result(
|
|
getter=lambda: WorkflowEntry.run_free_node(
|
|
node_id=node_id,
|
|
node_data=node_data,
|
|
tenant_id=tenant_id,
|
|
user_id=user_id,
|
|
user_inputs=user_inputs,
|
|
),
|
|
start_at=start_at,
|
|
tenant_id=tenant_id,
|
|
node_id=node_id,
|
|
)
|
|
|
|
return workflow_node_execution
|
|
|
|
def _handle_node_run_result(
|
|
self,
|
|
getter: Callable[[], tuple[BaseNode, Generator[NodeEvent | InNodeEvent, None, None]]],
|
|
start_at: float,
|
|
tenant_id: str,
|
|
node_id: str,
|
|
) -> WorkflowNodeExecution:
|
|
"""
|
|
Handle node run result
|
|
|
|
:param getter: Callable[[], tuple[BaseNode, Generator[RunEvent | InNodeEvent, None, None]]]
|
|
:param start_at: float
|
|
:param tenant_id: str
|
|
:param node_id: str
|
|
"""
|
|
try:
|
|
node_instance, generator = getter()
|
|
|
|
node_run_result: NodeRunResult | None = None
|
|
for event in generator:
|
|
if isinstance(event, RunCompletedEvent):
|
|
node_run_result = event.run_result
|
|
|
|
# sign output files
|
|
node_run_result.outputs = WorkflowEntry.handle_special_values(node_run_result.outputs)
|
|
break
|
|
|
|
if not node_run_result:
|
|
raise ValueError("Node run failed with no run result")
|
|
# single step debug mode error handling return
|
|
if node_run_result.status == WorkflowNodeExecutionStatus.FAILED and node_instance.should_continue_on_error:
|
|
node_error_args: dict[str, Any] = {
|
|
"status": WorkflowNodeExecutionStatus.EXCEPTION,
|
|
"error": node_run_result.error,
|
|
"inputs": node_run_result.inputs,
|
|
"metadata": {"error_strategy": node_instance.node_data.error_strategy},
|
|
}
|
|
if node_instance.node_data.error_strategy is ErrorStrategy.DEFAULT_VALUE:
|
|
node_run_result = NodeRunResult(
|
|
**node_error_args,
|
|
outputs={
|
|
**node_instance.node_data.default_value_dict,
|
|
"error_message": node_run_result.error,
|
|
"error_type": node_run_result.error_type,
|
|
},
|
|
)
|
|
else:
|
|
node_run_result = NodeRunResult(
|
|
**node_error_args,
|
|
outputs={
|
|
"error_message": node_run_result.error,
|
|
"error_type": node_run_result.error_type,
|
|
},
|
|
)
|
|
run_succeeded = node_run_result.status in (
|
|
WorkflowNodeExecutionStatus.SUCCEEDED,
|
|
WorkflowNodeExecutionStatus.EXCEPTION,
|
|
)
|
|
error = node_run_result.error if not run_succeeded else None
|
|
except WorkflowNodeRunFailedError as e:
|
|
node_instance = e.node_instance
|
|
run_succeeded = False
|
|
node_run_result = None
|
|
error = e.error
|
|
|
|
workflow_node_execution = WorkflowNodeExecution(
|
|
id=str(uuid4()),
|
|
workflow_id=node_instance.workflow_id,
|
|
index=1,
|
|
node_id=node_id,
|
|
node_type=node_instance.node_type,
|
|
title=node_instance.node_data.title,
|
|
elapsed_time=time.perf_counter() - start_at,
|
|
finished_at=datetime.now(UTC).replace(tzinfo=None),
|
|
created_at=datetime.now(UTC).replace(tzinfo=None),
|
|
)
|
|
if run_succeeded and node_run_result:
|
|
# create workflow node execution
|
|
inputs = WorkflowEntry.handle_special_values(node_run_result.inputs) if node_run_result.inputs else None
|
|
process_data = (
|
|
WorkflowEntry.handle_special_values(node_run_result.process_data)
|
|
if node_run_result.process_data
|
|
else None
|
|
)
|
|
outputs = WorkflowEntry.handle_special_values(node_run_result.outputs) if node_run_result.outputs else None
|
|
|
|
workflow_node_execution.inputs = inputs
|
|
workflow_node_execution.process_data = process_data
|
|
workflow_node_execution.outputs = outputs
|
|
workflow_node_execution.metadata = node_run_result.metadata
|
|
if node_run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED:
|
|
workflow_node_execution.status = WorkflowNodeExecutionStatus.SUCCEEDED
|
|
elif node_run_result.status == WorkflowNodeExecutionStatus.EXCEPTION:
|
|
workflow_node_execution.status = WorkflowNodeExecutionStatus.EXCEPTION
|
|
workflow_node_execution.error = node_run_result.error
|
|
else:
|
|
# create workflow node execution
|
|
workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED
|
|
workflow_node_execution.error = error
|
|
# update document status
|
|
variable_pool = node_instance.graph_runtime_state.variable_pool
|
|
invoke_from = variable_pool.get(["sys", SystemVariableKey.INVOKE_FROM])
|
|
if invoke_from:
|
|
if invoke_from.value == InvokeFrom.PUBLISHED.value:
|
|
document_id = variable_pool.get(["sys", SystemVariableKey.DOCUMENT_ID])
|
|
if document_id:
|
|
document = db.session.query(Document).filter(Document.id == document_id.value).first()
|
|
if document:
|
|
document.indexing_status = "error"
|
|
document.error = error
|
|
db.session.add(document)
|
|
db.session.commit()
|
|
|
|
return workflow_node_execution
|
|
|
|
def update_workflow(
|
|
self, *, session: Session, workflow_id: str, tenant_id: str, account_id: str, data: dict
|
|
) -> Optional[Workflow]:
|
|
"""
|
|
Update workflow attributes
|
|
|
|
:param session: SQLAlchemy database session
|
|
:param workflow_id: Workflow ID
|
|
:param tenant_id: Tenant ID
|
|
:param account_id: Account ID (for permission check)
|
|
:param data: Dictionary containing fields to update
|
|
:return: Updated workflow or None if not found
|
|
"""
|
|
stmt = select(Workflow).where(Workflow.id == workflow_id, Workflow.tenant_id == tenant_id)
|
|
workflow = session.scalar(stmt)
|
|
|
|
if not workflow:
|
|
return None
|
|
|
|
allowed_fields = ["marked_name", "marked_comment"]
|
|
|
|
for field, value in data.items():
|
|
if field in allowed_fields:
|
|
setattr(workflow, field, value)
|
|
|
|
workflow.updated_by = account_id
|
|
workflow.updated_at = datetime.now(UTC).replace(tzinfo=None)
|
|
|
|
return workflow
|
|
|
|
def get_first_step_parameters(self, pipeline: Pipeline, node_id: str, is_draft: bool = False) -> list[dict]:
|
|
"""
|
|
Get first step parameters of rag pipeline
|
|
"""
|
|
|
|
workflow = (
|
|
self.get_draft_workflow(pipeline=pipeline) if is_draft else self.get_published_workflow(pipeline=pipeline)
|
|
)
|
|
if not workflow:
|
|
raise ValueError("Workflow not initialized")
|
|
|
|
datasource_node_data = None
|
|
datasource_nodes = workflow.graph_dict.get("nodes", [])
|
|
for datasource_node in datasource_nodes:
|
|
if datasource_node.get("id") == node_id:
|
|
datasource_node_data = datasource_node.get("data", {})
|
|
break
|
|
if not datasource_node_data:
|
|
raise ValueError("Datasource node data not found")
|
|
variables = workflow.rag_pipeline_variables
|
|
if variables:
|
|
variables_map = {item["variable"]: item for item in variables}
|
|
else:
|
|
return []
|
|
datasource_parameters = datasource_node_data.get("datasource_parameters", {})
|
|
|
|
user_input_variables = []
|
|
for key, value in datasource_parameters.items():
|
|
if value.get("value") and isinstance(value.get("value"), str):
|
|
pattern = r"\{\{#([a-zA-Z0-9_]{1,50}(?:\.[a-zA-Z0-9_][a-zA-Z0-9_]{0,29}){1,10})#\}\}"
|
|
match = re.match(pattern, value["value"])
|
|
if match:
|
|
full_path = match.group(1)
|
|
last_part = full_path.split(".")[-1]
|
|
user_input_variables.append(variables_map.get(last_part, {}))
|
|
return user_input_variables
|
|
|
|
def get_second_step_parameters(self, pipeline: Pipeline, node_id: str, is_draft: bool = False) -> list[dict]:
|
|
"""
|
|
Get second step parameters of rag pipeline
|
|
"""
|
|
|
|
workflow = (
|
|
self.get_draft_workflow(pipeline=pipeline) if is_draft else self.get_published_workflow(pipeline=pipeline)
|
|
)
|
|
if not workflow:
|
|
raise ValueError("Workflow not initialized")
|
|
|
|
# get second step node
|
|
rag_pipeline_variables = workflow.rag_pipeline_variables
|
|
if not rag_pipeline_variables:
|
|
return []
|
|
variables_map = {item["variable"]: item for item in rag_pipeline_variables}
|
|
|
|
# get datasource node data
|
|
datasource_node_data = None
|
|
datasource_nodes = workflow.graph_dict.get("nodes", [])
|
|
for datasource_node in datasource_nodes:
|
|
if datasource_node.get("id") == node_id:
|
|
datasource_node_data = datasource_node.get("data", {})
|
|
break
|
|
if datasource_node_data:
|
|
datasource_parameters = datasource_node_data.get("datasource_parameters", {})
|
|
|
|
for key, value in datasource_parameters.items():
|
|
if value.get("value") and isinstance(value.get("value"), str):
|
|
pattern = r"\{\{#([a-zA-Z0-9_]{1,50}(?:\.[a-zA-Z0-9_][a-zA-Z0-9_]{0,29}){1,10})#\}\}"
|
|
match = re.match(pattern, value["value"])
|
|
if match:
|
|
full_path = match.group(1)
|
|
last_part = full_path.split(".")[-1]
|
|
variables_map.pop(last_part)
|
|
all_second_step_variables = list(variables_map.values())
|
|
datasource_provider_variables = [
|
|
item
|
|
for item in all_second_step_variables
|
|
if item.get("belong_to_node_id") == node_id or item.get("belong_to_node_id") == "shared"
|
|
]
|
|
return datasource_provider_variables
|
|
|
|
def get_rag_pipeline_paginate_workflow_runs(self, pipeline: Pipeline, args: dict) -> InfiniteScrollPagination:
|
|
"""
|
|
Get debug workflow run list
|
|
Only return triggered_from == debugging
|
|
|
|
:param app_model: app model
|
|
:param args: request args
|
|
"""
|
|
limit = int(args.get("limit", 20))
|
|
|
|
base_query = db.session.query(WorkflowRun).filter(
|
|
WorkflowRun.tenant_id == pipeline.tenant_id,
|
|
WorkflowRun.app_id == pipeline.id,
|
|
or_(
|
|
WorkflowRun.triggered_from == WorkflowRunTriggeredFrom.RAG_PIPELINE_RUN.value,
|
|
WorkflowRun.triggered_from == WorkflowRunTriggeredFrom.RAG_PIPELINE_DEBUGGING.value,
|
|
),
|
|
)
|
|
|
|
if args.get("last_id"):
|
|
last_workflow_run = base_query.filter(
|
|
WorkflowRun.id == args.get("last_id"),
|
|
).first()
|
|
|
|
if not last_workflow_run:
|
|
raise ValueError("Last workflow run not exists")
|
|
|
|
workflow_runs = (
|
|
base_query.filter(
|
|
WorkflowRun.created_at < last_workflow_run.created_at, WorkflowRun.id != last_workflow_run.id
|
|
)
|
|
.order_by(WorkflowRun.created_at.desc())
|
|
.limit(limit)
|
|
.all()
|
|
)
|
|
else:
|
|
workflow_runs = base_query.order_by(WorkflowRun.created_at.desc()).limit(limit).all()
|
|
|
|
has_more = False
|
|
if len(workflow_runs) == limit:
|
|
current_page_first_workflow_run = workflow_runs[-1]
|
|
rest_count = base_query.filter(
|
|
WorkflowRun.created_at < current_page_first_workflow_run.created_at,
|
|
WorkflowRun.id != current_page_first_workflow_run.id,
|
|
).count()
|
|
|
|
if rest_count > 0:
|
|
has_more = True
|
|
|
|
return InfiniteScrollPagination(data=workflow_runs, limit=limit, has_more=has_more)
|
|
|
|
def get_rag_pipeline_workflow_run(self, pipeline: Pipeline, run_id: str) -> Optional[WorkflowRun]:
|
|
"""
|
|
Get workflow run detail
|
|
|
|
:param app_model: app model
|
|
:param run_id: workflow run id
|
|
"""
|
|
workflow_run = (
|
|
db.session.query(WorkflowRun)
|
|
.filter(
|
|
WorkflowRun.tenant_id == pipeline.tenant_id,
|
|
WorkflowRun.app_id == pipeline.id,
|
|
WorkflowRun.id == run_id,
|
|
)
|
|
.first()
|
|
)
|
|
|
|
return workflow_run
|
|
|
|
def get_rag_pipeline_workflow_run_node_executions(
|
|
self,
|
|
pipeline: Pipeline,
|
|
run_id: str,
|
|
user: Account | EndUser,
|
|
) -> list[WorkflowNodeExecutionModel]:
|
|
"""
|
|
Get workflow run node execution list
|
|
"""
|
|
workflow_run = self.get_rag_pipeline_workflow_run(pipeline, run_id)
|
|
|
|
contexts.plugin_tool_providers.set({})
|
|
contexts.plugin_tool_providers_lock.set(threading.Lock())
|
|
|
|
if not workflow_run:
|
|
return []
|
|
|
|
# Use the repository to get the node execution
|
|
repository = SQLAlchemyWorkflowNodeExecutionRepository(
|
|
session_factory=db.engine, app_id=pipeline.id, user=user, triggered_from=None
|
|
)
|
|
|
|
# Use the repository to get the node executions with ordering
|
|
order_config = OrderConfig(order_by=["index"], order_direction="desc")
|
|
node_executions = repository.get_db_models_by_workflow_run(
|
|
workflow_run_id=run_id,
|
|
order_config=order_config,
|
|
triggered_from=WorkflowNodeExecutionTriggeredFrom.RAG_PIPELINE_RUN,
|
|
)
|
|
|
|
return list(node_executions)
|
|
|
|
@classmethod
|
|
def publish_customized_pipeline_template(cls, pipeline_id: str, args: dict):
|
|
"""
|
|
Publish customized pipeline template
|
|
"""
|
|
pipeline = db.session.query(Pipeline).filter(Pipeline.id == pipeline_id).first()
|
|
if not pipeline:
|
|
raise ValueError("Pipeline not found")
|
|
if not pipeline.workflow_id:
|
|
raise ValueError("Pipeline workflow not found")
|
|
workflow = db.session.query(Workflow).filter(Workflow.id == pipeline.workflow_id).first()
|
|
if not workflow:
|
|
raise ValueError("Workflow not found")
|
|
dataset = pipeline.dataset
|
|
if not dataset:
|
|
raise ValueError("Dataset not found")
|
|
|
|
# check template name is exist
|
|
template_name = args.get("name")
|
|
if template_name:
|
|
template = (
|
|
db.session.query(PipelineCustomizedTemplate)
|
|
.filter(
|
|
PipelineCustomizedTemplate.name == template_name,
|
|
PipelineCustomizedTemplate.tenant_id == pipeline.tenant_id,
|
|
)
|
|
.first()
|
|
)
|
|
if template:
|
|
raise ValueError("Template name is already exists")
|
|
|
|
max_position = (
|
|
db.session.query(func.max(PipelineCustomizedTemplate.position))
|
|
.filter(PipelineCustomizedTemplate.tenant_id == pipeline.tenant_id)
|
|
.scalar()
|
|
)
|
|
|
|
from services.rag_pipeline.rag_pipeline_dsl_service import RagPipelineDslService
|
|
|
|
dsl = RagPipelineDslService.export_rag_pipeline_dsl(pipeline=pipeline, include_secret=True)
|
|
|
|
pipeline_customized_template = PipelineCustomizedTemplate(
|
|
name=args.get("name"),
|
|
description=args.get("description"),
|
|
icon=args.get("icon_info"),
|
|
tenant_id=pipeline.tenant_id,
|
|
yaml_content=dsl,
|
|
position=max_position + 1 if max_position else 1,
|
|
chunk_structure=dataset.chunk_structure,
|
|
language="en-US",
|
|
created_by=current_user.id,
|
|
)
|
|
db.session.add(pipeline_customized_template)
|
|
db.session.commit()
|