mirror of
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122 lines
4.4 KiB
Python
122 lines
4.4 KiB
Python
from collections.abc import Mapping
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from typing import Any
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from sqlalchemy.orm import Session
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from configs import dify_config
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from core.app.apps.pipeline.pipeline_generator import PipelineGenerator
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from core.app.entities.app_invoke_entities import InvokeFrom
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from models.dataset import Document, Pipeline
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from models.enums import IndexingStatus
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from models.model import Account, App, EndUser
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from models.workflow import Workflow
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from services.rag_pipeline.rag_pipeline import RagPipelineService
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class PipelineGenerateService:
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@classmethod
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def generate(
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cls,
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pipeline: Pipeline,
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user: Account | EndUser,
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args: Mapping[str, Any],
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invoke_from: InvokeFrom,
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streaming: bool = True,
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*,
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session: Session,
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):
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"""
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Pipeline Content Generate
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:param pipeline: pipeline
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:param user: user
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:param args: args
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:param invoke_from: invoke from
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:param streaming: streaming
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:return:
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"""
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try:
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workflow = cls._get_workflow(pipeline, invoke_from, session)
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if original_document_id := args.get("original_document_id"):
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# update document status to waiting
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cls.update_document_status(original_document_id, session=session)
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return PipelineGenerator.convert_to_event_stream(
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PipelineGenerator().generate(
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pipeline=pipeline,
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workflow=workflow,
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user=user,
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args=args,
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invoke_from=invoke_from,
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streaming=streaming,
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call_depth=0,
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workflow_thread_pool_id=None,
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),
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)
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except Exception:
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raise
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@staticmethod
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def _get_max_active_requests(app_model: App) -> int:
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app_limit = app_model.max_active_requests or dify_config.APP_DEFAULT_ACTIVE_REQUESTS
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config_limit = dify_config.APP_MAX_ACTIVE_REQUESTS
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# Filter out infinite (0) values and return the minimum, or 0 if both are infinite
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limits = [limit for limit in [app_limit, config_limit] if limit > 0]
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return min(limits) if limits else 0
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@classmethod
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def generate_single_iteration(
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cls, pipeline: Pipeline, user: Account, node_id: str, args: Any, session: Session, streaming: bool = True
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):
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workflow = cls._get_workflow(pipeline, InvokeFrom.DEBUGGER, session)
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return PipelineGenerator.convert_to_event_stream(
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PipelineGenerator().single_iteration_generate(
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pipeline=pipeline, workflow=workflow, node_id=node_id, user=user, args=args, streaming=streaming
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)
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)
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@classmethod
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def generate_single_loop(
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cls, pipeline: Pipeline, user: Account, node_id: str, args: Any, session: Session, streaming: bool = True
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):
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workflow = cls._get_workflow(pipeline, InvokeFrom.DEBUGGER, session)
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return PipelineGenerator.convert_to_event_stream(
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PipelineGenerator().single_loop_generate(
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pipeline=pipeline, workflow=workflow, node_id=node_id, user=user, args=args, streaming=streaming
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)
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)
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@classmethod
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def _get_workflow(cls, pipeline: Pipeline, invoke_from: InvokeFrom, session: Session) -> Workflow:
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"""
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Get workflow
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:param pipeline: pipeline
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:param invoke_from: invoke from
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:return:
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"""
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rag_pipeline_service = RagPipelineService(session)
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if invoke_from == InvokeFrom.DEBUGGER:
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# fetch draft workflow by app_model
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workflow = rag_pipeline_service.get_draft_workflow(pipeline=pipeline)
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if not workflow:
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raise ValueError("Workflow not initialized")
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else:
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# fetch published workflow by app_model
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workflow = rag_pipeline_service.get_published_workflow(pipeline=pipeline)
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if not workflow:
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raise ValueError("Workflow not published")
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return workflow
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@classmethod
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def update_document_status(cls, document_id: str, *, session: Session):
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"""
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Update document status to waiting
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:param document_id: document id
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"""
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document = session.get(Document, document_id)
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if document:
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document.indexing_status = IndexingStatus.WAITING
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session.add(document)
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