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:
@@ -82,6 +82,9 @@ class ChatRequest(BaseModel):
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model: Optional[str] = None
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temperature: Optional[float] = None
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max_iterations: Optional[int] = None
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streamlined: bool = Field(default=False, description="启用工具结果流式美化")
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prompt_sections_enabled: bool = Field(default=True, description="启用系统提示词分层装配")
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system_prompt_override: Optional[str] = Field(default=None, description="覆盖 Agent 的 System Prompt")
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class ChatResponse(BaseModel):
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@@ -92,6 +95,8 @@ class ChatResponse(BaseModel):
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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, description="执行追踪步骤")
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streamlined_summary: Optional[str] = Field(default=None, description="流式美化摘要（streamlined 模式）")
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token_usage: Optional[Dict[str, Any]] = Field(default=None, description="Token 预算摘要")
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class OrchestrateAgentItem(BaseModel):
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@@ -249,11 +254,35 @@ async def chat_bare(
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),
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user_id=uid,
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memory_scope_id=bare_scope,
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memory=AgentMemoryConfig(
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memory_dir_enabled=True,
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memory_dir_path="",
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persist_to_db=True,
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vector_memory_enabled=True,
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learning_enabled=True,
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),
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tools=AgentToolConfig(
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permission_level="acceptEdits",
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),
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)
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if not req.prompt_sections_enabled:
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config.prompt_sections.enabled = False
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if req.system_prompt_override:
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config.system_prompt = req.system_prompt_override
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on_llm_call = _make_llm_logger(db, agent_id=None, user_id=current_user.id)
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runtime = AgentRuntime(config=config, on_llm_call=on_llm_call)
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runtime = AgentRuntime(config=config, on_llm_call=on_llm_call, streamlined=req.streamlined)
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result = await runtime.run(req.message)
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# 流式美化：为 steps 生成累计摘要
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streamlined_summary = None
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if req.streamlined and result.steps:
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from app.core.streamlined_output import ToolCounts, categorize_tool, get_tool_summary_text
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counts = ToolCounts()
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for s in result.steps:
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if s.type == "tool_result" and s.tool_name:
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counts.add(categorize_tool(s.tool_name))
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streamlined_summary = get_tool_summary_text(counts)
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return ChatResponse(
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content=result.content,
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iterations_used=result.iterations_used,
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@@ -261,6 +290,8 @@ async def chat_bare(
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truncated=result.truncated,
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session_id=runtime.context.session_id,
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steps=result.steps,
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streamlined_summary=streamlined_summary,
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token_usage=result.token_usage.model_dump() if result.token_usage else None,
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)
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@@ -286,9 +317,23 @@ async def chat_bare_stream(
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),
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user_id=uid,
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memory_scope_id=bare_scope,
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memory=AgentMemoryConfig(
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memory_dir_enabled=True,
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memory_dir_path="",
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persist_to_db=True,
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vector_memory_enabled=True,
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learning_enabled=True,
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),
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tools=AgentToolConfig(
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permission_level="acceptEdits",
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),
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)
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if not req.prompt_sections_enabled:
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config.prompt_sections.enabled = False
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if req.system_prompt_override:
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config.system_prompt = req.system_prompt_override
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on_llm_call = _make_llm_logger(db, agent_id=None, user_id=current_user.id)
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runtime = AgentRuntime(config=config, on_llm_call=on_llm_call)
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runtime = AgentRuntime(config=config, on_llm_call=on_llm_call, streamlined=req.streamlined)
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return StreamingResponse(
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_sse_stream(runtime.run_stream(req.message)),
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media_type="text/event-stream",
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@@ -339,6 +384,8 @@ async def chat_with_agent(
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uid = current_user.id
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mem_scope = f"{uid}:{agent_id}" if uid else str(agent_id)
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memory_cfg = _build_memory_config_from_node(agent_node_cfg)
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if getattr(agent, "parent_agent_id", None):
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memory_cfg.parent_agent_id = agent.parent_agent_id
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config = AgentConfig(
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name=agent.name,
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system_prompt=system_prompt,
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@@ -347,21 +394,42 @@ async def chat_with_agent(
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model=req.model or agent_node_cfg.get("model", "gpt-4o-mini"),
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temperature=req.temperature or float(agent_node_cfg.get("temperature", 0.7)),
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max_iterations=req.max_iterations or int(agent_node_cfg.get("max_iterations", 10)),
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# 计划模式 (P2)
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plan_mode_enabled=bool(agent_node_cfg.get("plan_mode_enabled", False)),
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plan_approval_required=bool(agent_node_cfg.get("plan_approval_required", True)),
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),
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tools=AgentToolConfig(
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include_tools=agent_node_cfg.get("tools", []),
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exclude_tools=agent_node_cfg.get("exclude_tools", []),
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# 工具安全分级 (P3)
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permission_level=str(agent_node_cfg.get("permission_level", "default")),
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deny_tools=agent_node_cfg.get("deny_tools", []),
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auto_approve_rules=agent_node_cfg.get("auto_approve_rules", []),
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),
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memory=memory_cfg,
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budget=budget,
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user_id=uid,
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memory_scope_id=mem_scope,
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)
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if not req.prompt_sections_enabled:
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config.prompt_sections.enabled = False
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if req.system_prompt_override:
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config.system_prompt = req.system_prompt_override
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on_llm_call = _make_llm_logger(db, agent_id=agent_id, user_id=current_user.id)
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runtime = AgentRuntime(config=config, on_llm_call=on_llm_call)
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runtime = AgentRuntime(config=config, on_llm_call=on_llm_call, streamlined=req.streamlined)
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result = await runtime.run(req.message)
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# 流式美化：为 steps 生成累计摘要
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streamlined_summary = None
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if req.streamlined and result.steps:
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from app.core.streamlined_output import ToolCounts, categorize_tool, get_tool_summary_text
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counts = ToolCounts()
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for s in result.steps:
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if s.type == "tool_result" and s.tool_name:
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counts.add(categorize_tool(s.tool_name))
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streamlined_summary = get_tool_summary_text(counts)
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return ChatResponse(
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content=result.content,
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iterations_used=result.iterations_used,
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@@ -370,6 +438,8 @@ async def chat_with_agent(
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session_id=runtime.context.session_id,
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agent_id=agent_id,
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steps=result.steps,
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streamlined_summary=streamlined_summary,
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token_usage=result.token_usage.model_dump() if result.token_usage else None,
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)
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@@ -408,6 +478,8 @@ async def chat_with_agent_stream(
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uid = current_user.id
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mem_scope = f"{uid}:{agent_id}" if uid else str(agent_id)
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memory_cfg = _build_memory_config_from_node(agent_node_cfg)
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if getattr(agent, "parent_agent_id", None):
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memory_cfg.parent_agent_id = agent.parent_agent_id
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config = AgentConfig(
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name=agent.name,
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system_prompt=system_prompt,
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@@ -416,19 +488,30 @@ async def chat_with_agent_stream(
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model=req.model or agent_node_cfg.get("model", "gpt-4o-mini"),
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temperature=req.temperature or float(agent_node_cfg.get("temperature", 0.7)),
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max_iterations=req.max_iterations or int(agent_node_cfg.get("max_iterations", 10)),
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# 计划模式 (P2)
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plan_mode_enabled=bool(agent_node_cfg.get("plan_mode_enabled", False)),
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plan_approval_required=bool(agent_node_cfg.get("plan_approval_required", True)),
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),
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tools=AgentToolConfig(
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include_tools=agent_node_cfg.get("tools", []),
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exclude_tools=agent_node_cfg.get("exclude_tools", []),
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# 工具安全分级 (P3)
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permission_level=str(agent_node_cfg.get("permission_level", "default")),
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deny_tools=agent_node_cfg.get("deny_tools", []),
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auto_approve_rules=agent_node_cfg.get("auto_approve_rules", []),
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),
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memory=memory_cfg,
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budget=budget,
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user_id=uid,
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memory_scope_id=mem_scope,
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)
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if not req.prompt_sections_enabled:
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config.prompt_sections.enabled = False
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if req.system_prompt_override:
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config.system_prompt = req.system_prompt_override
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on_llm_call = _make_llm_logger(db, agent_id=agent_id, user_id=current_user.id)
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runtime = AgentRuntime(config=config, on_llm_call=on_llm_call)
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runtime = AgentRuntime(config=config, on_llm_call=on_llm_call, streamlined=req.streamlined)
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return StreamingResponse(
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_sse_stream(runtime.run_stream(req.message)),
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media_type="text/event-stream",
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@@ -453,10 +536,24 @@ def _find_agent_node_config(nodes: list) -> Dict[str, Any]:
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def _build_memory_config_from_node(agent_node_cfg: dict) -> AgentMemoryConfig:
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"""从 Agent 工作流节点配置中提取记忆配置。"""
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from app.core.compaction_config import CompactionConfig
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# 压缩配置
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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() # 默认配置
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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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# 文件式记忆 (MEMORY.md)
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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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# 对话压缩
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compaction=compaction,
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)
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