Refactor Workflow and Chat Architecture (#68)
* 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>
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+61
-68
@@ -12,101 +12,95 @@
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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
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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():
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"""处理针对 get workflow list 相关的 HTTP API 请求。
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该接口负责解析前端传入的载荷数据，调用底层核心业务逻辑进行处理，并组装标准化的 JSON 响应。
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Returns: : 序列化后的标准网络响应模型（如包含业务状态码、成功标志及对应的数据载荷 Data）。"""
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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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events = await global_workflow_manager.list_events.remote()
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return events
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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(trace_id: str):
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"""处理针对 get workflow detail 相关的 HTTP API 请求。
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该接口负责解析前端传入的载荷数据，调用底层核心业务逻辑进行处理，并组装标准化的 JSON 响应。
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Args: trace_id (str): 目标对象的唯一全局标识符 (UUID/ULID)，用于在数据库表或缓存结构中精准匹配该 trace 实例。
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Returns: : 序列化后的标准网络响应模型（如包含业务状态码、成功标志及对应的数据载荷 Data）。"""
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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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event = await global_workflow_manager.get_event.remote(trace_id)
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if not event:
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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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workflow = event.workflow
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if not workflow:
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return {
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"event_id": trace_id,
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"workflow_title": None,
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"status": "waiting",
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"user_name": event.user_name,
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"message": event.message,
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"create_time": event.create_time,
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"steps": [],
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}
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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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steps = []
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for step in workflow.work_link:
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steps.append(
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{
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"step": step.step,
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"name": step.name,
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"node": step.node,
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"action": step.action,
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"desc": step.desc,
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"status": step.status,
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"agent_id": step.agent_id,
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}
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)
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return {
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"event_id": trace_id,
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"workflow_title": workflow.title,
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"status": workflow.status.status,
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"command": workflow.command,
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"current_step": workflow.status.step,
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"user_name": event.user_name,
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"message": event.message,
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"create_time": event.create_time,
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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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"""处理针对 get workflow sse 相关的 HTTP API 请求。
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该接口负责解析前端传入的载荷数据，调用底层核心业务逻辑进行处理，并组装标准化的 JSON 响应。
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Args: trace_id (str): 目标对象的唯一全局标识符 (UUID/ULID)，用于在数据库表或缓存结构中精准匹配该 trace 实例。 request (Request): FastAPI 框架注入的原生 HTTP 请求对象，包含了完整的 Header 标头、查询参数和正文流。
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Returns: : 序列化后的标准网络响应模型（如包含业务状态码、成功标志及对应的数据载荷 Data）。"""
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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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"""执行与 event generator 相关的核心业务流转操作。
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该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。"""
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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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# You might also want to send the workflow state periodically or when updated
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# Here we just wait for pending messages and send them
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message = await global_workflow_manager.get_pending.remote(trace_id)
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# Ensure the message is formatted as SSE
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yield f"data: {message}\n\n"
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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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@@ -115,10 +109,9 @@ async def get_workflow_sse(trace_id: str, request: Request):
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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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"""处理针对 post workflow reply 相关的 HTTP API 请求。
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该接口负责解析前端传入的载荷数据，调用底层核心业务逻辑进行处理，并组装标准化的 JSON 响应。
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Args: trace_id (str): 目标对象的唯一全局标识符 (UUID/ULID)，用于在数据库表或缓存结构中精准匹配该 trace 实例。 request (Request): FastAPI 框架注入的原生 HTTP 请求对象，包含了完整的 Header 标头、查询参数和正文流。
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Returns: : 序列化后的标准网络响应模型（如包含业务状态码、成功标志及对应的数据载荷 Data）。"""
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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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