feat(studio): R3 step_respond、运行页 UX 与 R4 思考流式
新增 worldbook 步骤 LLM 回复（thinking/draft/questions/evaluation）， 运行页聊天区与产物/选项联动；评价区标签改为「评价与修改建议」； 流式开关开启时 NDJSON 推送思考内容，完成后拆分更新各字段。 Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
@@ -10,7 +10,7 @@ import shutil
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import uuid
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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from typing import Any, AsyncGenerator, Dict, List, Optional
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from core.config import settings
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from models.studio_models import (
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@@ -22,8 +22,13 @@ from models.studio_models import (
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StudioRunSummary,
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)
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from services.character_service import CharacterService
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from services.studio_context_service import store_context_on_run
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from services.studio_context_service import assemble_prompt_blocks, store_context_on_run
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from services.studio_project_service import studio_project_service
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from services.studio_step_respond import (
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resolve_api_config,
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studio_step_respond,
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studio_step_respond_stream,
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)
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from services.worldbook_service import worldbook_service
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logger = logging.getLogger(__name__)
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@@ -302,6 +307,181 @@ class StudioRunService:
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self._save_run(project_id, run_id, updated_run)
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return updated_run
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async def send_run_message(
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self,
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project_id: str,
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run_id: str,
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content: str,
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*,
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stream: bool = False,
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profile_id: Optional[str] = None,
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api_config: Optional[Dict[str, str]] = None,
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) -> StudioRun:
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"""Send a user message to the active worldbook step and invoke LLM (R3)."""
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trimmed = (content or "").strip()
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if not trimmed:
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raise ValueError("消息内容不能为空")
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run, current_node, current_state, current_node_id = self._prepare_run_message(
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project_id, run_id, trimmed
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)
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resolved_api = resolve_api_config(profile_id, api_config)
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prompt_blocks = assemble_prompt_blocks(run, current_node_id)
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step_messages = list(current_state.stepMessages or [])
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last_draft, last_tool_response, user_msg, assistant_msg = (
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await studio_step_respond(
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node=current_node,
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prompt_blocks=prompt_blocks,
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step_messages=step_messages,
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user_message=trimmed,
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existing_draft=current_state.lastDraft,
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api_config=resolved_api,
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stream=stream,
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)
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)
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return self._persist_run_message_turn(
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project_id,
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run_id,
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run,
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current_node_id,
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prompt_blocks,
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step_messages,
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last_draft,
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last_tool_response,
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user_msg,
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assistant_msg,
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)
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async def send_run_message_stream(
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self,
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project_id: str,
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run_id: str,
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content: str,
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*,
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profile_id: Optional[str] = None,
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api_config: Optional[Dict[str, str]] = None,
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) -> AsyncGenerator[Dict[str, Any], None]:
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"""Stream thinking deltas, then persist and emit complete run (R4)."""
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trimmed = (content or "").strip()
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if not trimmed:
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raise ValueError("消息内容不能为空")
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run, current_node, current_state, current_node_id = self._prepare_run_message(
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project_id, run_id, trimmed
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)
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resolved_api = resolve_api_config(profile_id, api_config)
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prompt_blocks = assemble_prompt_blocks(run, current_node_id)
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step_messages = list(current_state.stepMessages or [])
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async for event in studio_step_respond_stream(
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node=current_node,
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prompt_blocks=prompt_blocks,
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step_messages=step_messages,
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user_message=trimmed,
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existing_draft=current_state.lastDraft,
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api_config=resolved_api,
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):
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if event.get("type") == "thinking_delta":
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yield event
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continue
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if event.get("type") == "complete":
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from models.studio_models import LastToolResponse, StepMessage
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last_draft = event["last_draft"]
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last_tool_response = LastToolResponse(**event["last_tool_response"])
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user_msg = StepMessage(**event["user_msg"])
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assistant_msg = StepMessage(**event["assistant_msg"])
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updated_run = self._persist_run_message_turn(
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project_id,
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run_id,
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run,
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current_node_id,
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prompt_blocks,
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step_messages,
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last_draft,
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last_tool_response,
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user_msg,
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assistant_msg,
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)
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yield {
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"type": "complete",
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"run": updated_run.model_dump(mode="json"),
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}
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def _prepare_run_message(
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self,
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project_id: str,
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run_id: str,
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trimmed: str,
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) -> tuple[StudioRun, StudioNode, StudioNodeRunState, str]:
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run = self.get_run(project_id, run_id)
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if run.status != StudioRunStatus.RUNNING:
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raise ValueError("运行未处于进行中，无法发送消息")
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current_node_id = run.currentNodeId
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if not current_node_id:
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raise ValueError("当前运行无活动节点")
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current_node = _find_node(run.pipelineSnapshot, current_node_id)
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if not current_node:
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raise ValueError(f"节点不存在：{current_node_id}")
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if current_node.skillId != "studio.worldbook_entry":
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raise ValueError(
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f"当前步骤「{current_node.displayName}」不支持对话消息"
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)
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current_state = next(
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(s for s in run.nodeStates if s.nodeId == current_node_id), None
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)
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if not current_state or current_state.status != "active":
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raise ValueError("当前节点不可执行")
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return run, current_node, current_state, current_node_id
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def _persist_run_message_turn(
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self,
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project_id: str,
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run_id: str,
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run: StudioRun,
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current_node_id: str,
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prompt_blocks: list,
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step_messages: list,
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last_draft: Dict[str, Any],
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last_tool_response,
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user_msg,
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assistant_msg,
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) -> StudioRun:
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step_messages = list(step_messages)
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step_messages.extend([user_msg, assistant_msg])
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now = datetime.now().isoformat()
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new_node_states: list[StudioNodeRunState] = []
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for state in run.nodeStates:
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updated = state.model_copy()
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if state.nodeId == current_node_id:
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updated.lastDraft = last_draft
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updated.lastToolResponse = last_tool_response
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updated.stepMessages = step_messages
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new_node_states.append(updated)
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updated_run = run.model_copy(
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update={
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"nodeStates": new_node_states,
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"lastPromptBlocks": prompt_blocks,
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"updatedAt": now,
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}
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)
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updated_run = store_context_on_run(updated_run, current_node_id)
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self._save_run(project_id, run_id, updated_run)
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return updated_run
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def delete_run(self, project_id: str, run_id: str) -> None:
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run_dir = self._run_dir(project_id, run_id)
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if not run_dir.exists():
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