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fix: 修复 5 项确定 bug + Provider UX 重做 + 文档统一

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Bug fixes:
- fix(dao): AsyncSession.delete 补齐漏掉的 await（provider/user/individual 共 4 处）
- fix(worker): result.data.output → result.output.output（pydantic-ai 1.x API 适配）
- fix(api): 删除 create_worker_from_template 死端点（ORM 字段不匹配必崩）
- fix(api): /provider/test 按 provider_type 分支适配 Anthropic/Gemini/OpenAI 三种协议
- fix(chat): SSE 流式聊天在 distributed 模式 fallback 到非流式，避免 asyncio.Queue 序列化崩溃

Features (previously unstaged):
- feat(provider): Provider 管理页重做（品牌图标、5 种类型、Test Connection、编辑模式）
- feat(provider): 新增 Gemini provider_type 支持
- feat(workflow): Finalize 节点输出 blackboard 摘要 + 失败原因；步骤完成/失败实时推送 SSE
- feat(i18n): regulatory_node 提示词从路由模式改为直接对话模式（中英双语）
- feat(consciousness): dynamic_prompt 支持 locale 国际化
- feat(logs): SystemLogsView 自动刷新 + 暂停按钮

Docs:
- docs: README/README-EN 统一为"开源通用多 Agent 协作平台"口径
- docs: ROADMAP 按 v0.1.x / v0.2.x / v0.3.x 重组
- docs: project.md 重写为结构化项目介绍

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
zhaoxi
2026-06-14 08:49:38 +00:00
parent c0fcbe2849
commit 9b73ae4db4
27 changed files with 858 additions and 214 deletions
Download Patch File Download Diff File Expand all files Collapse all files
kilostar/core/individual/consciousness_node/consciousness_node.py
+40 -17
View File
@@ -39,6 +39,7 @@ class ConsciousnessNode:
self.logger = get_logger("consciousness_node")
self.agent: None | Agent = None
self.locale: str = "zh"
async def create_agent(
self,
@@ -51,6 +52,7 @@ class ConsciousnessNode:
custom_system_prompt: str | None = None,
) -> None:
system_prompt: str = agent_prompt("consciousness_node", locale=locale, custom_system_prompt=custom_system_prompt)
self.locale = locale or "zh"
output_type = Union[ForregulatoryNode, ForWorkflow, ForWorkflowEngine]
from kilostar.core.global_state_machine.gsm_snapshot import fetch_snapshot
@@ -74,23 +76,43 @@ class ConsciousnessNode:
@self.agent.system_prompt
async def dynamic_prompt(ctx: RunContext[ConsciousnessNodeDeps]):
locale = ctx.deps.locale
prompt = system_prompt + "\n\n"
prompt += (
f"=== 当前任务上下文 ===\n"
f"- 当前指令 (Command): {ctx.deps.command}\n"
f"- 原始用户命令 (Original Command): {ctx.deps.original_command}\n"
)
if ctx.deps.available_skills:
prompt += "\n=== 当前可用 Skill Individual ===\n"
prompt += "你可以直接将以下 Skill Individual 安排进工作流的步骤中（设置 node 为 skill_individual，并将 agent_id 设置为对应 Skill Individual 的真实 agent_id，不要用名称！），作为可调用的工具。\n"
for skill in ctx.deps.available_skills:
prompt += f"- 真实 agent_id: {skill.get('agent_id')}\n 名称: {skill['name']}\n 描述: {skill['description']}\n"
if locale == "en":
prompt += (
f"=== Current Task Context ===\n"
f"- Command: {ctx.deps.command}\n"
f"- Original User Command: {ctx.deps.original_command}\n"
)
if ctx.deps.available_skills:
prompt += "\n=== Available Skill Individuals ===\n"
prompt += "You may assign the following Skill Individuals to workflow steps (set node to skill_individual, and set agent_id to the real agent_id below — never use the name!).\n"
for skill in ctx.deps.available_skills:
prompt += f"- agent_id: {skill.get('agent_id')}\n Name: {skill['name']}\n Description: {skill['description']}\n"
else:
prompt += "\n=== IMPORTANT: No Worker Individuals Available ===\n"
prompt += "No Worker Individuals are registered. When generating a workflow, you have exactly two options:\n"
prompt += "1. Assign the step to consciousness_node itself (set node to consciousness_node, agent_id to null).\n"
prompt += "2. If the task truly requires specialized tools, refuse and explain that a Worker must be created first.\n"
prompt += "NEVER fabricate non-existent agent_ids!\n"
else:
prompt += "\n=== 重要：当前无可用 Worker Individual ===\n"
prompt += "系统中当前没有注册任何 Worker Individual。在生成工作流时，你有且仅有以下两种选择：\n"
prompt += "1. 将步骤分配给 consciousness_node 自己完成（设置 node 为 consciousness_node，agent_id 为 null）。\n"
prompt += "2. 如果任务确实需要专用工具或技能才能完成，则拒绝执行并在输出中说明需要先创建对应的 Worker。\n"
prompt += "绝对禁止编造不存在的 agent_id！\n"
prompt += (
f"=== 当前任务上下文 ===\n"
f"- 当前指令: {ctx.deps.command}\n"
f"- 原始用户命令: {ctx.deps.original_command}\n"
)
if ctx.deps.available_skills:
prompt += "\n=== 当前可用 Skill Individual ===\n"
prompt += "你可以直接将以下 Skill Individual 安排进工作流的步骤中（设置 node 为 skill_individual，并将 agent_id 设置为对应 Skill Individual 的真实 agent_id，不要用名称！）。\n"
for skill in ctx.deps.available_skills:
prompt += f"- 真实 agent_id: {skill.get('agent_id')}\n 名称: {skill['name']}\n 描述: {skill['description']}\n"
else:
prompt += "\n=== 重要：当前无可用 Worker Individual ===\n"
prompt += "系统中当前没有注册任何 Worker Individual。在生成工作流时，你有且仅有以下两种选择：\n"
prompt += "1. 将步骤分配给 consciousness_node 自己完成（设置 node 为 consciousness_node，agent_id 为 null）。\n"
prompt += "2. 如果任务确实需要专用工具或技能才能完成，则拒绝执行并在输出中说明需要先创建对应的 Worker。\n"
prompt += "绝对禁止编造不存在的 agent_id！\n"
return prompt
@@ -127,7 +149,6 @@ class ConsciousnessNode:
original_command=command, available_skills=available_skills
)
# 通知 SSE 正在生成图结构（pending 队列：节点端写入 → API SSE 读取，单向下行）
global_workflow_manager = ray_actor_hook(
"global_workflow_manager"
).global_workflow_manager
@@ -135,7 +156,6 @@ class ConsciousnessNode:
trace_id, "正在为您构建并规划工作流任务节点，请稍候..."
)
# 实际构建过程
result = await self.working(payload)
if result and isinstance(result, ForWorkflowEngine):
@@ -197,6 +217,7 @@ class ConsciousnessNode:
original_command=payload.original_command,
command="自主分析并拆解原始命令，生成严密可执行的工作流",
available_skills=payload.available_skills,
locale=self.locale,
)
self.logger.debug("ConsciousnessNode: 开始生成工作流 (原生重试开启)")
prompt = "根据original_command制定严密的可执行workflow"
@@ -207,6 +228,7 @@ class ConsciousnessNode:
deps = ConsciousnessNodeDeps(
original_command=payload.original_command,
command="完成workflow step中分配给意识节点的特定任务或指导",
locale=self.locale,
)
self.logger.debug(
"ConsciousnessNode: 开始处理工作流节点任务 (原生重试开启)"
@@ -221,6 +243,7 @@ class ConsciousnessNode:
deps = ConsciousnessNodeDeps(
original_command=payload.original_command,
command="对于工作流整体执行结果进行检查，并且生成一份专业的技术性总结报告",
locale=self.locale,
)
self.logger.debug(
"ConsciousnessNode: 开始生成技术总结报告 (原生重试开启)"
kilostar/core/individual/consciousness_node/template.py
+2 -1
View File
@@ -28,7 +28,8 @@ class ConsciousnessNodeDeps(DepsModel):
"""ConsciousnessNode 在 pydantic-ai Agent 中使用的依赖：原始指令、当前指令以及可用 Skill 列表。"""
original_command: str
command: str
available_skills: Optional[List[str]] = None
available_skills: Optional[List[dict]] = None
locale: str = "zh"
class ConsciousnessNodeInput(RequestModel):
"""ConsciousnessNode 各类入参的共同基类，仅用于打 schema 标签。"""
kilostar/core/individual/regulatory_node/regulatory_node.py
+5 -9
View File
@@ -30,10 +30,9 @@ from kilostar.utils.i18n import agent_prompt
@actor_class
class RegulatoryNode:
"""RegulatoryNode（监管节点）：用户请求的入口路由 Actor。
"""RegulatoryNode（监管节点）：用户请求的直接对话节点。
负责对消息做意图识别：闲聊 → 直接回 ``ForUser``；复杂任务 → 走
``ForConsciousnessNode`` 移交给意识节点；工作流回执 → 转译成对用户的总结回复。
负责理解用户需求并提供回复；如果收到工作流执行报告则转化为用户友好的总结。
"""
def __init__(self) -> None:
@@ -100,12 +99,9 @@ class RegulatoryNode:
f"- 用户名 (User): {ctx.deps.user_name}\n"
f"- 当前时间 (Time): {ctx.deps.time}\n"
)
# 修改 system_prompt 变量
prompt += (
"\n\n注意：你必须调用且只能调用一个函数（工具）来输出结果。"
"如果你想直接回复用户，请调用 ForUser；"
"如果你想移交给工作流，请调用 ForConsciousnessNode。"
"严禁返回纯文本，必须使用工具格式！"
"\n\n注意：请基于上下文信息为用户提供准确、专业的回复。"
"如果你有可用工具，可在需要时主动调用。"
)
if ctx.deps.error_history:
prompt += (
@@ -130,7 +126,7 @@ class RegulatoryNode:
"规则：\n"
"1. 直接、详细地回答用户问题，像一个专业且友好的助手。\n"
"2. 如果你有可用工具，可以调用工具来辅助回答（如搜索、读文件等）。\n"
"3. 不要输出内部思考过程，不要做路由判断，不要提及 ForUser/ForConsciousnessNode 等格式。\n"
"3. 不要输出内部思考过程，直接给出回复内容。\n"
"4. 回复应当完整、有帮助，避免过于简短。\n"
)
kilostar/core/postgres_database/module/individual.py
+1 -1
View File
@@ -104,7 +104,7 @@ class IndividualDatabase:
individual = results.scalar_one_or_none()
if not individual:
return False
session.delete(individual)
await session.delete(individual)
await session.commit()
return True
kilostar/core/postgres_database/module/provider.py
+1 -1
View File
@@ -90,7 +90,7 @@ class ProviderDatabase:
async with self.async_session_maker() as session:
provider = await session.get(ProviderModel, provider_id)
if provider is not None:
session.delete(provider)
await session.delete(provider)
await session.commit()
@database_exception
kilostar/core/postgres_database/module/user.py
+2 -2
View File
@@ -78,7 +78,7 @@ class AuthDatabase:
user = results.scalar_one_or_none()
if user is None:
raise UserNotExistError()
session.delete(user)
await session.delete(user)
await session.commit()
@database_exception
@@ -88,7 +88,7 @@ class AuthDatabase:
user = await session.get(User, user_id)
if user is None:
raise UserNotExistError()
session.delete(user)
await session.delete(user)
await session.commit()
@database_exception
kilostar/core/work/workflow/workflow_engine.py
+31 -6
View File
@@ -223,11 +223,28 @@ class Finalize(BaseNode[WorkflowGraphState, WorkflowDeps, str]):
) -> End[str]:
ctx.state.final_status = self.status
await ctx.deps.update_workflow_status(ctx.state.trace_id, self.status)
msg = (
"工作流执行完成！"
if self.status == WorkflowStatus.COMPLETED.value
else "工作流执行失败。"
)
if self.status == WorkflowStatus.COMPLETED.value:
summary_parts = []
for key, val in ctx.state.blackboard.items():
text = str(val)[:200]
summary_parts.append(f"• {key}: {text}")
summary = "\n".join(summary_parts) if summary_parts else ""
msg = f"工作流执行完成！\n{summary}" if summary else "工作流执行完成！"
else:
failed_logs = [
entry for entry in ctx.state.logs
if any(
isinstance(v, (list, tuple)) and len(v) >= 2 and v[1] == "failed"
for v in (entry.values() if isinstance(entry, dict) else [])
)
]
msg = "工作流执行失败。"
if failed_logs:
last = list(failed_logs[-1].values())[0]
if isinstance(last, (list, tuple)) and len(last) >= 3:
msg += f"\n失败原因: {last[2][:300]}"
await ctx.deps.put_pending(ctx.state.trace_id, msg)
return End(self.status)
@@ -295,9 +312,13 @@ async def _execute_step(
state.logs[-1][str(state.current_step_index)] = [
str(datetime.datetime.now()),
"completed",
f"成功: {step_data.get('action', '')}",
output_text,
]
await _persist_context(ctx, status=WorkflowStatus.RUNNING.value)
await ctx.deps.put_pending(
state.trace_id,
f"✅ 步骤 {state.current_step_index + 1} ({step_data.get('name', '')}) 完成:\n{output_text[:500]}",
)
logic_gate = step_data.get("logic_gate") or {}
if logic_gate.get("if_pass") == "exit":
@@ -314,6 +335,10 @@ async def _execute_step(
"failed",
output_text,
]
await ctx.deps.put_pending(
state.trace_id,
f"❌ 步骤 {state.current_step_index + 1} ({step_data.get('name', '')}) 失败:\n{output_text[:300]}",
)
logic_gate = step_data.get("logic_gate") or {}
fail_target = logic_gate.get("if_fail")
if fail_target and "jump_to_step_" in fail_target: