fix: delete agent 500 error + dynamic personality + deployment guide
- Fix delete agent 500: clean up FK records (agent_llm_logs, permissions, schedules, executions, team_members) and unbind goals/tasks before delete - Remove hardcoded personality templates in Android, replace with dynamic system prompt generation from name + description - Set promptSectionsEnabled=false to bypass PromptComposer for personality - Add Tencent Cloud Linux deployment guide (Docker Compose) - Accumulated backend service updates, frontend UI fixes, Android app changes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
319
backend/app/api/agent_branches.py
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319
backend/app/api/agent_branches.py
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"""
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对话分支 API — 支持从任意历史点分叉对话
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参考 Claude Code src/commands/branch/branch.ts
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"""
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from __future__ import annotations
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import logging
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from typing import Any, Dict, List, Optional
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from fastapi import APIRouter, Depends, HTTPException
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from pydantic import BaseModel, Field
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from sqlalchemy.orm import Session
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from app.core.database import get_db
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from app.api.auth import get_current_user
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from app.models.user import User
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from app.models.agent import Agent
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from app.agent_runtime import (
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AgentRuntime,
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AgentConfig,
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AgentLLMConfig,
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AgentToolConfig,
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AgentMemoryConfig,
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AgentStep,
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)
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from app.agent_runtime.context import AgentContext
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/v1/agent-chat", tags=["agent-branches"])
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def _find_agent_node_config(nodes: list) -> Dict[str, Any]:
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if not nodes:
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return {}
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for node in nodes:
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typ = node.get("type", "")
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if typ in ("agent", "llm", "template"):
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return node.get("data") or {}
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return {}
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def _build_memory_config_from_node(agent_node_cfg: dict) -> AgentMemoryConfig:
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from app.core.compaction_config import CompactionConfig
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compaction_raw = agent_node_cfg.get("compaction")
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if isinstance(compaction_raw, dict):
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compaction = CompactionConfig(**compaction_raw)
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else:
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compaction = CompactionConfig()
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return AgentMemoryConfig(
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max_history_messages=int(agent_node_cfg.get("memory_max_history", 20)),
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vector_memory_top_k=int(agent_node_cfg.get("memory_vector_top_k", 5)),
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persist_to_db=bool(agent_node_cfg.get("memory_persist", True)),
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vector_memory_enabled=bool(agent_node_cfg.get("memory_vector_enabled", True)),
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learning_enabled=bool(agent_node_cfg.get("memory_learning", True)),
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memory_dir_enabled=bool(agent_node_cfg.get("memory_dir_enabled", False)),
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memory_dir_path=str(agent_node_cfg.get("memory_dir_path", "")),
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compaction=compaction,
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)
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# ──────────────────────────── 请求/响应模型 ────────────────────────────
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class BranchCreateRequest(BaseModel):
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session_id: str = Field(..., description="要分叉的原会话 ID")
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title: Optional[str] = Field(default=None, description="自定义分支标题")
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agent_id: Optional[str] = Field(default=None, description="关联 Agent ID")
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class BranchItem(BaseModel):
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id: str
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title: str
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agent_name: Optional[str] = None
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parent_session_id: str
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message_count: int
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first_user_message: Optional[str] = None
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created_at: str
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class Config:
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from_attributes = True
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class BranchListResponse(BaseModel):
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branches: List[BranchItem]
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total: int
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class BranchDetailResponse(BaseModel):
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id: str
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title: str
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agent_name: Optional[str] = None
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parent_session_id: str
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branch_session_id: str
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message_count: int
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first_user_message: Optional[str] = None
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messages: Optional[List[Dict[str, Any]]] = None
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created_at: str
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class Config:
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from_attributes = True
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class BranchResumeRequest(BaseModel):
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message: str = Field(..., description="在分支基础上继续的新消息")
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class BranchResumeResponse(BaseModel):
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content: str
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iterations_used: int
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tool_calls_made: int
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truncated: bool
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session_id: str
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agent_id: Optional[str] = None
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steps: List[AgentStep] = Field(default_factory=list)
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# ──────────────────────────── API 端点 ────────────────────────────
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@router.post("/branches", response_model=BranchDetailResponse)
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async def create_conversation_branch(
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req: BranchCreateRequest,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""从指定会话创建对话分支（快照完整消息列表）。"""
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from app.services.conversation_branch_service import (
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create_branch,
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get_messages_from_session,
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)
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messages = get_messages_from_session(db, req.session_id)
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if not messages:
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raise HTTPException(status_code=404, detail="找不到该会话的消息记录（请确保会话已产生对话）")
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agent_name = None
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if req.agent_id:
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agent = db.query(Agent).filter(Agent.id == req.agent_id).first()
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if agent:
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agent_name = agent.name
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branch = create_branch(
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db=db,
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user_id=current_user.id,
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parent_session_id=req.session_id,
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messages=messages,
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agent_id=req.agent_id,
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agent_name=agent_name,
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custom_title=req.title,
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)
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return BranchDetailResponse(
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id=branch.id,
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title=branch.title,
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agent_name=branch.agent_name,
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parent_session_id=branch.parent_session_id,
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branch_session_id=branch.branch_session_id,
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message_count=branch.message_count,
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first_user_message=branch.first_user_message,
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messages=branch.messages,
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created_at=branch.created_at.isoformat() if branch.created_at else "",
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)
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@router.get("/branches", response_model=BranchListResponse)
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async def list_conversation_branches(
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agent_id: Optional[str] = None,
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limit: int = 50,
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offset: int = 0,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""列出当前用户的所有对话分支。"""
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from app.services.conversation_branch_service import list_branches
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branches = list_branches(
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db=db, user_id=current_user.id, agent_id=agent_id, limit=limit, offset=offset,
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)
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return BranchListResponse(
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branches=[
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BranchItem(
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id=b.id, title=b.title, agent_name=b.agent_name,
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parent_session_id=b.parent_session_id,
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message_count=b.message_count,
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first_user_message=b.first_user_message,
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created_at=b.created_at.isoformat() if b.created_at else "",
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)
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for b in branches
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],
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total=len(branches),
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)
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@router.get("/branches/{branch_id}", response_model=BranchDetailResponse)
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async def get_conversation_branch(
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branch_id: str,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""获取分支详情（含完整消息列表）。"""
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from app.services.conversation_branch_service import get_branch
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branch = get_branch(db, branch_id, current_user.id)
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if not branch:
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raise HTTPException(status_code=404, detail="分支不存在")
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return BranchDetailResponse(
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id=branch.id, title=branch.title, agent_name=branch.agent_name,
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parent_session_id=branch.parent_session_id,
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branch_session_id=branch.branch_session_id,
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message_count=branch.message_count,
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first_user_message=branch.first_user_message,
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messages=branch.messages,
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created_at=branch.created_at.isoformat() if branch.created_at else "",
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)
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@router.delete("/branches/{branch_id}")
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async def delete_conversation_branch(
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branch_id: str,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""删除（软删除）对话分支。"""
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from app.services.conversation_branch_service import delete_branch
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success = delete_branch(db, branch_id, current_user.id)
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if not success:
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raise HTTPException(status_code=404, detail="分支不存在或无权操作")
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return {"message": "分支已删除", "branch_id": branch_id}
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@router.post("/branches/{branch_id}/resume", response_model=BranchResumeResponse)
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async def resume_from_branch(
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branch_id: str,
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req: BranchResumeRequest,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""从分支恢复对话 — 使用分支保存的消息作为上下文继续对话。"""
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from app.services.conversation_branch_service import get_branch
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branch = get_branch(db, branch_id, current_user.id)
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if not branch:
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raise HTTPException(status_code=404, detail="分支不存在")
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if not branch.messages:
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raise HTTPException(status_code=400, detail="分支没有保存的消息")
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messages = branch.messages
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system_prompt = "你是一个有用的AI助手。"
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history_messages = messages
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if messages and messages[0].get("role") == "system":
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system_prompt = messages[0].get("content", system_prompt)
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history_messages = messages[1:]
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# 构建 Agent 配置
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agent = None
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if branch.agent_id:
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agent = db.query(Agent).filter(Agent.id == branch.agent_id).first()
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if agent:
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wc = agent.workflow_config or {}
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nodes = wc.get("nodes", [])
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agent_node_cfg = _find_agent_node_config(nodes)
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llm_config = AgentLLMConfig(
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provider=agent_node_cfg.get("provider", "deepseek"),
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model=agent_node_cfg.get("model", "deepseek-v4-flash"),
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temperature=float(agent_node_cfg.get("temperature", 0.7)),
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max_iterations=int(agent_node_cfg.get("max_iterations", 10)),
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)
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config = AgentConfig(
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name=agent.name, system_prompt=system_prompt, llm=llm_config,
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tools=AgentToolConfig(include_tools=agent_node_cfg.get("tools", [])),
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memory=_build_memory_config_from_node(agent_node_cfg),
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user_id=current_user.id,
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memory_scope_id=f"{current_user.id}:{branch.agent_id}" if current_user.id else str(branch.agent_id),
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)
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else:
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config = AgentConfig(
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name="branch_resume", system_prompt=system_prompt,
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llm=AgentLLMConfig(model="deepseek-v4-flash", temperature=0.7, max_iterations=10),
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user_id=current_user.id,
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memory_scope_id=f"{current_user.id}:__bare__" if current_user.id else "__bare__",
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)
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# 创建 Runtime 并预加载分支消息
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context = AgentContext(
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system_prompt=system_prompt,
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user_id=current_user.id,
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session_id=branch.branch_session_id,
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)
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for msg in history_messages:
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role = msg.get("role", "")
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content = msg.get("content", "")
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if role == "user":
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context.add_user_message(content)
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elif role == "assistant":
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tool_calls = msg.get("tool_calls")
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context.add_assistant_message(content, tool_calls)
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elif role == "tool":
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context.add_tool_result(
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msg.get("tool_call_id", ""),
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msg.get("name", "unknown"),
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content,
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)
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# 不需要 on_llm_logger 回调（避免与主 logger 重复），仅做执行
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runtime = AgentRuntime(config=config, context=context)
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result = await runtime.run(req.message)
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return BranchResumeResponse(
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content=result.content,
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iterations_used=result.iterations_used,
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tool_calls_made=result.tool_calls_made,
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truncated=result.truncated,
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session_id=branch.branch_session_id,
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agent_id=branch.agent_id,
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steps=result.steps,
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)
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