ff1ede47a0
* refactor: overhaul workflow and chat architecture - Separate Chat and Workflow API endpoints and database models - Use JSONB to store workflow execution context in Postgres - Convert workflow engine to use pydantic-ai execution graphs inside a Ray task - Update frontend React components to support standalone workflow creation - Remove obsolete and broken workflow runner tests Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com> * refactor: overhaul workflow and chat architecture - Separate Chat and Workflow API endpoints and database models - Use JSONB to store workflow execution context in Postgres - Convert workflow engine to use pydantic-ai execution graphs inside a Ray task - Update frontend React components to support standalone workflow creation - Remove obsolete and broken workflow runner tests Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com> * refactor: overhaul workflow and chat architecture - Separate Chat and Workflow API endpoints and database models - Use JSONB to store workflow execution context in Postgres - Convert workflow engine to use pydantic-ai execution graphs inside a Ray task - Update frontend React components to support standalone workflow creation - Move workflow_engine inside workflow package to keep core root clean - Remove obsolete and broken workflow runner tests Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com> --------- Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com> Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com>
122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from kilostar.utils.ray_hook import ray_actor_hook
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from fastapi import APIRouter, Request, HTTPException, Depends
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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from ulid import ULID
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import asyncio
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from kilostar.utils.access import Accessor, TokenData
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workflow_router = APIRouter(prefix="/api/v1/workflow", tags=["workflow"])
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class CreateWorkflowRequest(BaseModel):
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title: str
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command: str
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@workflow_router.post("")
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async def create_workflow(
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request: CreateWorkflowRequest,
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token_data: TokenData = Depends(Accessor.get_current_user),
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):
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postgres_database = ray_actor_hook("postgres_database").postgres_database
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trace_id = str(ULID())
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await postgres_database.create_workflow.remote(
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trace_id=trace_id,
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user_id=token_data.user_id,
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title=request.title,
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command=request.command,
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)
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# 将需求发送给意识节点去处理构建
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consciousness_node = ray_actor_hook("consciousness_node").consciousness_node
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# 可以异步通知意识节点开始与用户在特定 Trace ID 下对话
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consciousness_node.start_workflow_design.remote(trace_id, request.command)
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return {"trace_id": trace_id, "status": "creating"}
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@workflow_router.get("/list")
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async def get_workflow_list(token_data: TokenData = Depends(Accessor.get_current_user)):
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postgres_database = ray_actor_hook("postgres_database").postgres_database
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workflows = await postgres_database.list_workflows.remote(
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user_id=token_data.user_id
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)
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return {"workflows": workflows}
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@workflow_router.get("/{trace_id}")
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async def get_workflow_detail(
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trace_id: str, token_data: TokenData = Depends(Accessor.get_current_user)
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):
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postgres_database = ray_actor_hook("postgres_database").postgres_database
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wf = await postgres_database.get_workflow.remote(trace_id)
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if not wf:
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raise HTTPException(status_code=404, detail="Workflow not found")
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context = await postgres_database.get_workflow_context.remote(trace_id)
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steps = context.work_link if context and hasattr(context, "work_link") else []
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return {
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"trace_id": trace_id,
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"title": wf.title,
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"status": wf.status,
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"command": wf.command,
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"steps": steps,
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"context_blackboard": context.blackboard if context else {},
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}
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@workflow_router.get("/sse/{trace_id}")
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async def get_workflow_sse(trace_id: str, request: Request):
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"""
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用于与意识节点交互,获取工作流状态或设计阶段的问答消息
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"""
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global_workflow_manager = ray_actor_hook(
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"global_workflow_manager"
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).global_workflow_manager
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async def event_generator():
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try:
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while True:
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if await request.is_disconnected():
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break
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message = await global_workflow_manager.get_pending.remote(trace_id)
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if message:
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yield f"data: {message}\n\n"
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else:
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await asyncio.sleep(0.5)
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except asyncio.CancelledError:
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pass
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return StreamingResponse(event_generator(), media_type="text/event-stream")
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@workflow_router.post("/reply/{trace_id}")
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async def post_workflow_reply(trace_id: str, request: Request):
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"""
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用于用户回复意识节点的提问(设计阶段或运行中的中断确认)
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"""
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data = await request.json()
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reply_msg = data.get("message", "")
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global_workflow_manager = ray_actor_hook(
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"global_workflow_manager"
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).global_workflow_manager
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await global_workflow_manager.put_received.remote(trace_id, reply_msg)
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return {"status": "ok"}
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