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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427
backend/services/studio_step_respond.py
Normal file
427
backend/services/studio_step_respond.py
Normal file
@@ -0,0 +1,427 @@
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"""
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Studio worldbook step LLM responder (R3/R4).
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Assembles R2 context blocks + short step dialogue, calls LLM for structured JSON,
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returns thinking, draft, questions, and evaluation.
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"""
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from __future__ import annotations
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import json
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import re
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import uuid
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from datetime import datetime
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from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple
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from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
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from models.studio_models import (
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LastToolResponse,
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PromptBlock,
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StudioNode,
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StepMessage,
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ToolQuestionOption,
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)
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from services.studio_context_service import assemble_prompt_blocks
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from utils.llm_client import LLMClient
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_llm_client = LLMClient()
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_JSON_FENCE = re.compile(r"```(?:json)?\s*([\s\S]*?)```", re.IGNORECASE)
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def resolve_api_config(
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profile_id: Optional[str],
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api_config: Optional[Dict[str, str]],
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) -> Dict[str, str]:
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"""Merge frontend apiConfig with stored profile mainLLM key (same as chat WS)."""
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resolved = dict(api_config or {})
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if profile_id:
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try:
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try:
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from api.routes.apiConfigRoute import load_profile
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except ImportError:
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from backend.api.routes.apiConfigRoute import load_profile
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profile = load_profile(profile_id)
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if profile:
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main_llm = profile.get("apis", {}).get("mainLLM", {})
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if main_llm.get("apiUrl") and not resolved.get("api_url"):
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resolved["api_url"] = main_llm.get("apiUrl", "")
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if main_llm.get("model") and not resolved.get("model"):
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resolved["model"] = main_llm.get("model", "")
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api_key = main_llm.get("apiKey", "")
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if api_key:
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resolved["api_key"] = api_key
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except Exception as exc:
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print(f"[StudioStepRespond] 加载 API 配置失败: {exc}")
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return resolved
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def _blocks_to_context_text(blocks: List[PromptBlock]) -> str:
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sections: List[str] = []
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for block in blocks:
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sections.append(f"## {block.label}\n{block.content}")
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return "\n\n".join(sections)
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def _build_system_prompt(node: StudioNode) -> str:
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insertion = (node.config or {}).get("insertion") or {}
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key = insertion.get("key") or "（未配置关键词）"
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comment = insertion.get("comment") or ""
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return f"""你是 Studio 创作助手，负责为当前流水线步骤生成或修订世界书条目草稿。
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当前步骤：{node.displayName}
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目标关键词：{key}
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备注：{comment or "（无）"}
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你必须只输出一个 JSON 对象（不要 markdown 代码块外的其他文字），字段如下：
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{{
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"thinking": "你的内部思考过程（逐步推理，中文）",
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"currentProduct": "世界书条目正文（纯文本或 Markdown，可直接写入条目 content）",
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"questions": [
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{{
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"question": "需要用户澄清的问题",
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"options": ["选项A", "选项B", "选项C"]
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}}
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],
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"evaluation": "对照评价维度的自检与优化建议（中文，面向用户）"
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}}
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规则：
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1. currentProduct 必须是完整、可注入世界书的条目正文。
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2. questions 为 0–3 条；每条至少 2 个 options；若无需澄清则 questions 为空数组。
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3. evaluation 需引用上下文中的评价标准，给出具体、可操作的反馈。
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4. 若用户要求修改，在 currentProduct 中输出修订后的完整条目，而非仅说明改了什么。
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5. 全部字段使用中文（专有名词除外）。"""
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def _dialogue_to_langchain(
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step_messages: List[StepMessage],
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) -> List[Any]:
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messages: List[Any] = []
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for msg in step_messages:
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if msg.role == "user":
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messages.append(HumanMessage(content=msg.content))
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elif msg.role == "assistant":
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messages.append(AIMessage(content=msg.content))
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return messages
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def _build_llm_messages(
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node: StudioNode,
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prompt_blocks: List[PromptBlock],
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step_messages: List[StepMessage],
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user_message: str,
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) -> List[Any]:
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context_text = _blocks_to_context_text(prompt_blocks)
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system_prompt = _build_system_prompt(node)
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messages: List[Any] = [SystemMessage(content=system_prompt)]
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messages.append(
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HumanMessage(
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content=f"以下为当前步骤上下文（不含完整聊天历史）：\n\n{context_text}"
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)
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)
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messages.extend(_dialogue_to_langchain(step_messages))
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messages.append(HumanMessage(content=user_message))
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return messages
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def _validate_api_config(api_config: Dict[str, str]) -> None:
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if not api_config.get("api_key"):
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raise ValueError("API Key 未配置，请先在 API 配置页面保存密钥")
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if not api_config.get("api_url"):
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raise ValueError("API 地址未配置，请先在 API 配置页面保存 mainLLM")
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def _extract_json(raw: str) -> Dict[str, Any]:
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text = (raw or "").strip()
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if not text:
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raise ValueError("模型返回为空")
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fence = _JSON_FENCE.search(text)
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if fence:
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text = fence.group(1).strip()
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try:
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return json.loads(text)
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except json.JSONDecodeError:
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start = text.find("{")
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end = text.rfind("}")
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if start >= 0 and end > start:
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return json.loads(text[start : end + 1])
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raise ValueError("无法解析模型返回的 JSON")
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def _decode_json_string_partial(raw: str) -> str:
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"""Decode a possibly incomplete JSON string body (no surrounding quotes)."""
|
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out: List[str] = []
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i = 0
|
||||
while i < len(raw):
|
||||
if raw[i] == "\\" and i + 1 < len(raw):
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nxt = raw[i + 1]
|
||||
if nxt == "n":
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out.append("\n")
|
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elif nxt == "t":
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out.append("\t")
|
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elif nxt == "r":
|
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out.append("\r")
|
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elif nxt == '"':
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out.append('"')
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elif nxt == "\\":
|
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out.append("\\")
|
||||
elif nxt == "/":
|
||||
out.append("/")
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||||
elif nxt == "u" and i + 5 < len(raw):
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try:
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out.append(chr(int(raw[i + 2 : i + 6], 16)))
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i += 6
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continue
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||||
except ValueError:
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out.append(nxt)
|
||||
else:
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out.append(nxt)
|
||||
i += 2
|
||||
else:
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out.append(raw[i])
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i += 1
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return "".join(out)
|
||||
|
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|
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def _extract_partial_thinking(raw: str) -> Optional[str]:
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"""Best-effort extraction of thinking field from incomplete JSON stream."""
|
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marker = '"thinking"'
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||||
idx = raw.find(marker)
|
||||
if idx < 0:
|
||||
return None
|
||||
|
||||
colon = raw.find(":", idx + len(marker))
|
||||
if colon < 0:
|
||||
return None
|
||||
|
||||
rest = raw[colon + 1 :].lstrip()
|
||||
if not rest.startswith('"'):
|
||||
return None
|
||||
|
||||
body_start = 1
|
||||
i = body_start
|
||||
while i < len(rest):
|
||||
ch = rest[i]
|
||||
if ch == '"':
|
||||
break
|
||||
if ch == "\\":
|
||||
i += 2
|
||||
continue
|
||||
i += 1
|
||||
|
||||
partial = rest[body_start:i]
|
||||
if not partial:
|
||||
return None
|
||||
return _decode_json_string_partial(partial)
|
||||
|
||||
|
||||
def _normalize_draft(
|
||||
current_product: Any,
|
||||
node: StudioNode,
|
||||
existing_draft: Optional[Dict[str, Any]],
|
||||
) -> Dict[str, Any]:
|
||||
draft: Dict[str, Any] = dict(existing_draft or {})
|
||||
insertion = (node.config or {}).get("insertion") or {}
|
||||
|
||||
if isinstance(current_product, str):
|
||||
draft["entryContent"] = current_product.strip()
|
||||
elif isinstance(current_product, dict):
|
||||
draft.update(current_product)
|
||||
if "entryContent" not in draft and "content" in draft:
|
||||
draft["entryContent"] = draft["content"]
|
||||
else:
|
||||
draft["entryContent"] = str(current_product)
|
||||
|
||||
if insertion.get("key"):
|
||||
draft["insertionKey"] = insertion["key"]
|
||||
if insertion.get("comment"):
|
||||
draft["insertionComment"] = insertion["comment"]
|
||||
draft["nodeId"] = node.id
|
||||
draft["displayName"] = node.displayName
|
||||
return draft
|
||||
|
||||
|
||||
def _normalize_questions(raw: Any) -> List[ToolQuestionOption]:
|
||||
if not isinstance(raw, list):
|
||||
return []
|
||||
result: List[ToolQuestionOption] = []
|
||||
for item in raw:
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
question = (item.get("question") or "").strip()
|
||||
if not question:
|
||||
continue
|
||||
options = [
|
||||
str(o).strip()
|
||||
for o in (item.get("options") or [])
|
||||
if str(o).strip()
|
||||
]
|
||||
if len(options) < 2:
|
||||
continue
|
||||
result.append(ToolQuestionOption(question=question, options=options))
|
||||
return result[:3]
|
||||
|
||||
|
||||
def _assistant_message_text(parsed: Dict[str, Any]) -> str:
|
||||
evaluation = (parsed.get("evaluation") or "").strip()
|
||||
if evaluation:
|
||||
return evaluation
|
||||
product = parsed.get("currentProduct")
|
||||
if isinstance(product, str) and product.strip():
|
||||
preview = product.strip()
|
||||
if len(preview) > 400:
|
||||
preview = preview[:400] + "…"
|
||||
return f"已更新条目草稿：\n\n{preview}"
|
||||
return "已处理您的消息，请查看左侧目前产物。"
|
||||
|
||||
|
||||
def _build_turn_result(
|
||||
parsed: Dict[str, Any],
|
||||
*,
|
||||
node: StudioNode,
|
||||
user_message: str,
|
||||
existing_draft: Optional[Dict[str, Any]],
|
||||
) -> Tuple[Dict[str, Any], LastToolResponse, StepMessage, StepMessage]:
|
||||
now = datetime.now().isoformat()
|
||||
|
||||
last_draft = _normalize_draft(
|
||||
parsed.get("currentProduct"),
|
||||
node,
|
||||
existing_draft,
|
||||
)
|
||||
last_tool_response = LastToolResponse(
|
||||
thinking=(parsed.get("thinking") or "").strip() or None,
|
||||
evaluation=(parsed.get("evaluation") or "").strip() or None,
|
||||
questions=_normalize_questions(parsed.get("questions")),
|
||||
generatedAt=now,
|
||||
)
|
||||
|
||||
user_step_msg = StepMessage(
|
||||
id=f"msg-{uuid.uuid4().hex[:12]}",
|
||||
role="user",
|
||||
content=user_message,
|
||||
createdAt=now,
|
||||
)
|
||||
assistant_step_msg = StepMessage(
|
||||
id=f"msg-{uuid.uuid4().hex[:12]}",
|
||||
role="assistant",
|
||||
content=_assistant_message_text(parsed),
|
||||
createdAt=now,
|
||||
)
|
||||
|
||||
return last_draft, last_tool_response, user_step_msg, assistant_step_msg
|
||||
|
||||
|
||||
async def studio_step_respond(
|
||||
*,
|
||||
node: StudioNode,
|
||||
prompt_blocks: List[PromptBlock],
|
||||
step_messages: List[StepMessage],
|
||||
user_message: str,
|
||||
existing_draft: Optional[Dict[str, Any]],
|
||||
api_config: Dict[str, str],
|
||||
stream: bool = False,
|
||||
) -> Tuple[Dict[str, Any], LastToolResponse, StepMessage, StepMessage]:
|
||||
"""
|
||||
Execute one worldbook step turn (non-streaming).
|
||||
|
||||
Returns (last_draft, last_tool_response, user_step_msg, assistant_step_msg).
|
||||
"""
|
||||
_validate_api_config(api_config)
|
||||
messages = _build_llm_messages(node, prompt_blocks, step_messages, user_message)
|
||||
model = api_config.get("model") or "gpt-4o-mini"
|
||||
|
||||
if stream:
|
||||
print("[StudioStepRespond] stream=True 应使用 studio_step_respond_stream")
|
||||
|
||||
response = await _llm_client.chat_completion(
|
||||
messages=messages,
|
||||
api_url=api_config.get("api_url", ""),
|
||||
api_key=api_config.get("api_key", ""),
|
||||
model=model,
|
||||
temperature=0.7,
|
||||
max_tokens=8000,
|
||||
request_timeout=120,
|
||||
stream=False,
|
||||
)
|
||||
|
||||
raw_content = ""
|
||||
if isinstance(response, dict):
|
||||
raw_content = (
|
||||
response.get("choices", [{}])[0]
|
||||
.get("message", {})
|
||||
.get("content", "")
|
||||
)
|
||||
else:
|
||||
raw_content = str(response)
|
||||
|
||||
parsed = _extract_json(raw_content)
|
||||
return _build_turn_result(
|
||||
parsed,
|
||||
node=node,
|
||||
user_message=user_message,
|
||||
existing_draft=existing_draft,
|
||||
)
|
||||
|
||||
|
||||
async def studio_step_respond_stream(
|
||||
*,
|
||||
node: StudioNode,
|
||||
prompt_blocks: List[PromptBlock],
|
||||
step_messages: List[StepMessage],
|
||||
user_message: str,
|
||||
existing_draft: Optional[Dict[str, Any]],
|
||||
api_config: Dict[str, str],
|
||||
) -> AsyncGenerator[Dict[str, Any], None]:
|
||||
"""
|
||||
Stream thinking field while LLM generates structured JSON (R4).
|
||||
|
||||
Yields:
|
||||
- {"type": "thinking_delta", "content": "..."}
|
||||
- {"type": "complete", "last_draft", "last_tool_response", "user_msg", "assistant_msg"}
|
||||
"""
|
||||
_validate_api_config(api_config)
|
||||
messages = _build_llm_messages(node, prompt_blocks, step_messages, user_message)
|
||||
model = api_config.get("model") or "gpt-4o-mini"
|
||||
|
||||
accumulated = ""
|
||||
last_thinking = ""
|
||||
|
||||
async for chunk in _llm_client.stream_chat(
|
||||
messages=messages,
|
||||
api_url=api_config.get("api_url", ""),
|
||||
api_key=api_config.get("api_key", ""),
|
||||
model=model,
|
||||
temperature=0.7,
|
||||
max_tokens=8000,
|
||||
request_timeout=120,
|
||||
):
|
||||
if chunk.get("type") != "chunk":
|
||||
continue
|
||||
accumulated += chunk.get("content") or ""
|
||||
partial = _extract_partial_thinking(accumulated)
|
||||
if partial and partial != last_thinking:
|
||||
last_thinking = partial
|
||||
yield {"type": "thinking_delta", "content": partial}
|
||||
|
||||
parsed = _extract_json(accumulated)
|
||||
last_draft, last_tool_response, user_msg, assistant_msg = _build_turn_result(
|
||||
parsed,
|
||||
node=node,
|
||||
user_message=user_message,
|
||||
existing_draft=existing_draft,
|
||||
)
|
||||
|
||||
yield {
|
||||
"type": "complete",
|
||||
"last_draft": last_draft,
|
||||
"last_tool_response": last_tool_response.model_dump(mode="json"),
|
||||
"user_msg": user_msg.model_dump(mode="json"),
|
||||
"assistant_msg": assistant_msg.model_dump(mode="json"),
|
||||
}
|
||||
Reference in New Issue
Block a user