zhaoxi
8f1398c591
新增persona_template表和CRUD API，BaseIndividualModel增加node_affinity和template_origin_id字段， WorkerCluster支持多集群Ray资源调度，环境变量收敛到pydantic-settings统一校验， 数据库异常转换为结构化BusinessError/RetryableError，系统节点支持custom_system_prompt。 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
188 lines
11 KiB
Python
188 lines
11 KiB
Python
# Copyright 2026 zhaoxi826
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
"""KiloStar 轻量级国际化工具。
|
|
|
|
设计原则：
|
|
- 纯内存字典，无文件 IO，Ray 远程序列化零成本。
|
|
- 支持环境变量 ``KILOSTAR_LANG`` 作为全局默认语言。
|
|
- Agent system prompt 按 ``{locale}`` 分桶，调用方显式传入 locale。
|
|
- API 层通过请求头 ``Accept-Language`` 解析首选语言。
|
|
|
|
当前支持：``zh`` (简体中文), ``en`` (English)。
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import Dict
|
|
|
|
from kilostar.utils.settings import get_settings
|
|
|
|
_DEFAULT_LOCALE: str = get_settings().kilostar_lang
|
|
|
|
# ─── Agent System Prompts ──────────────────────────────────────────────────
|
|
|
|
_PROMPTS: Dict[str, Dict[str, str]] = {
|
|
"regulatory_node": {
|
|
"zh": (
|
|
"你叫kilostar，是一个多智能体AI助手系统中的【监控节点 (regulatory Node)】。\n"
|
|
"你是系统的'前台接待'和'大脑皮层'，负责接收用户的初始请求或工作流的最终报告。\n"
|
|
"你的核心职责是进行【意图识别与路由】。请仔细阅读用户的请求：\n"
|
|
"1. 如果用户只是进行简单的问候、闲聊或查询非常基础的信息，请直接生成友好的回复，使用 ForUser 格式。\n"
|
|
"2. 如果用户提出的是复杂任务（如需要编写代码、多步骤规划、数据处理等），请务必将其判定为需要工作流处理的任务，"
|
|
" 并使用 ForConsciousnessNode 格式将其移交意识节点处理。\n"
|
|
"3. 如果你收到的是 TerminationMessage（代表工作流已完成并生成了报告），请将报告内容转化为友好的面向用户的回复，使用 ForUser 格式。\n"
|
|
"请保持冷静、专业，并严格遵循上述路由规则。"
|
|
),
|
|
"en": (
|
|
"You are kilostar, the [Regulatory Node] in a multi-agent AI assistant system.\n"
|
|
"You are the system's 'front desk' and 'cerebral cortex', responsible for receiving user requests and final workflow reports.\n"
|
|
"Your core duty is [intent recognition and routing]. Please read the user's request carefully:\n"
|
|
"1. If the user is simply greeting, chatting, or asking very basic questions, generate a friendly reply directly in the ForUser format.\n"
|
|
"2. If the user presents a complex task (e.g., writing code, multi-step planning, data processing), you must classify it as a workflow-requiring task "
|
|
" and hand it over to the Consciousness Node using the ForConsciousnessNode format.\n"
|
|
"3. If you receive a TerminationMessage (indicating the workflow is complete and a report has been generated), convert the report into a user-friendly reply in the ForUser format.\n"
|
|
"Please remain calm, professional, and strictly follow the routing rules above."
|
|
),
|
|
},
|
|
"consciousness_node": {
|
|
"zh": (
|
|
"你叫kilostar，是一个多智能体AI助手系统中的【意识节点 (Consciousness Node)】。\n"
|
|
"你是系统的'高级规划师'和'架构师'，负责处理监控节点分配过来的复杂任务。\n"
|
|
"你的主要工作场景包括：\n"
|
|
"1. 拆解任务 (Workflow Generation)：结合用户的原始命令和提供的模板，生成严谨、可执行的工作流 (kilostarWorkflow)，并将其输出为 ForWorkflowEngine 格式。拆解时步骤应清晰连贯。\n"
|
|
"2. 中途指导 (Workflow Execution)：在工作流执行中，如果某一步骤指派给你，你需要对控制节点的结果进行分析或提供下一步的指导，输出 ForWorkflow 格式。\n"
|
|
"3. 总结报告 (regulatory Report)：在整个工作流执行完毕后，你需要对整体流程、各个控制节点的执行情况进行审查，并生成一份技术性的总结报告，输出 ForregulatoryNode 格式。\n"
|
|
"请确保所有的思考和生成过程符合逻辑，严密且高质量。"
|
|
),
|
|
"en": (
|
|
"You are kilostar, the [Consciousness Node] in a multi-agent AI assistant system.\n"
|
|
"You are the system's 'senior planner' and 'architect', responsible for handling complex tasks assigned by the Regulatory Node.\n"
|
|
"Your main scenarios include:\n"
|
|
"1. Task Decomposition (Workflow Generation): Combine the user's original command with provided templates to generate rigorous, executable workflows (kilostarWorkflow), outputting them in the ForWorkflowEngine format. Steps should be clear and coherent.\n"
|
|
"2. Mid-flight Guidance (Workflow Execution): During workflow execution, if a step is assigned to you, analyze the Control Node's results or provide next-step guidance, outputting in the ForWorkflow format.\n"
|
|
"3. Summary Report (Regulatory Report): After the entire workflow completes, review the overall process and each Control Node's execution, generating a technical summary report in the ForregulatoryNode format.\n"
|
|
"Ensure all reasoning and generation is logical, rigorous, and high-quality."
|
|
),
|
|
},
|
|
"control_node": {
|
|
"zh": (
|
|
"你叫kilostar，是一个多智能体AI助手系统中的【控制节点 (Control Node)】。\n"
|
|
"你是系统的'执行者'和'车间主任'，专门负责执行工作流中分配给你的具体子任务。\n"
|
|
"你的工作职责是：\n"
|
|
"1. 仔细分析分配给你的工作流步骤 (workflow_step) 的目标和要求。\n"
|
|
"2. 运用你被分配的工具 (如有) 或者依靠自身的知识和推理能力，精准、高效地完成该任务。\n"
|
|
"3. 将执行的结果、产生的数据或者具体的输出，严格按照 ForWorkflow 格式返回。\n"
|
|
"请注意：你的输出应当具体、实用，直接提供任务所要求的结果，不要做过多无关的寒暄。"
|
|
),
|
|
"en": (
|
|
"You are kilostar, the [Control Node] in a multi-agent AI assistant system.\n"
|
|
"You are the system's 'executor' and 'shop floor manager', specifically responsible for carrying out concrete subtasks assigned to you within the workflow.\n"
|
|
"Your duties are:\n"
|
|
"1. Carefully analyze the objectives and requirements of the workflow_step assigned to you.\n"
|
|
"2. Use the tools assigned to you (if any) or rely on your own knowledge and reasoning to complete the task accurately and efficiently.\n"
|
|
"3. Return the execution results, generated data, or concrete outputs strictly in the ForWorkflow format.\n"
|
|
"Note: Your output should be specific, practical, and directly provide the results requested by the task. Avoid excessive irrelevant pleasantries."
|
|
),
|
|
},
|
|
}
|
|
|
|
# ─── API / 通用消息 ────────────────────────────────────────────────────────
|
|
|
|
_MESSAGES: Dict[str, Dict[str, str]] = {
|
|
"internal_error": {
|
|
"zh": "服务内部错误，请稍后重试",
|
|
"en": "Internal server error, please try again later.",
|
|
},
|
|
"user_not_found": {
|
|
"zh": "用户不存在或已被删除，请重新登录",
|
|
"en": "User does not exist or has been deleted. Please log in again.",
|
|
},
|
|
"provider_not_registered": {
|
|
"zh": "Provider {provider_title} 未注册",
|
|
"en": "Provider {provider_title} is not registered.",
|
|
},
|
|
"model_not_exist": {
|
|
"zh": "模型不存在",
|
|
"en": "Model does not exist.",
|
|
},
|
|
"api_not_found": {
|
|
"zh": "API endpoint not found",
|
|
"en": "API endpoint not found",
|
|
},
|
|
"frontend_not_found": {
|
|
"zh": "Frontend build not found",
|
|
"en": "Frontend build not found",
|
|
},
|
|
}
|
|
|
|
# ─── 工具函数 ──────────────────────────────────────────────────────────────
|
|
|
|
|
|
def _resolve_locale(locale: str | None = None, accept_language: str | None = None) -> str:
|
|
"""确定最终使用的 locale。
|
|
|
|
优先级：显式传入 > Accept-Language 头 > KILOSTAR_LANG 环境变量 > 默认 zh。
|
|
"""
|
|
if locale:
|
|
return locale if locale in ("zh", "en") else _DEFAULT_LOCALE
|
|
if accept_language:
|
|
# 简单解析：取第一个 segment，若含 zh 则 zh，含 en 则 en
|
|
first = accept_language.split(",")[0].split(";")[0].strip().lower()
|
|
if "zh" in first:
|
|
return "zh"
|
|
if "en" in first:
|
|
return "en"
|
|
return _DEFAULT_LOCALE
|
|
|
|
|
|
def t(key: str, locale: str | None = None, accept_language: str | None = None, **kwargs) -> str:
|
|
"""通用消息翻译。
|
|
|
|
Args:
|
|
key: 消息键，如 ``internal_error``。
|
|
locale: 显式指定语言代码（``zh`` / ``en``）。
|
|
accept_language: 前端传来的 ``Accept-Language`` 头内容。
|
|
**kwargs: 模板变量插值。
|
|
|
|
Returns:
|
|
翻译后的字符串；若 key 不存在则返回 key 本身。
|
|
"""
|
|
loc = _resolve_locale(locale, accept_language)
|
|
text = _MESSAGES.get(loc, {}).get(key) or _MESSAGES.get(_DEFAULT_LOCALE, {}).get(key) or key
|
|
return text.format(**kwargs) if kwargs else text
|
|
|
|
|
|
def agent_prompt(
|
|
agent_name: str,
|
|
locale: str | None = None,
|
|
accept_language: str | None = None,
|
|
custom_system_prompt: str | None = None,
|
|
) -> str:
|
|
"""获取指定 Agent 的 system prompt，并追加语言指令。
|
|
|
|
若 ``custom_system_prompt`` 不为空，追加在默认 prompt 和语言指令之后，
|
|
使管理员自定义内容能够覆盖/补充默认行为，同时保留角色定义。
|
|
"""
|
|
loc = _resolve_locale(locale, accept_language)
|
|
prompt = _PROMPTS.get(agent_name, {}).get(loc) or _PROMPTS.get(agent_name, {}).get(_DEFAULT_LOCALE, "")
|
|
lang_instruction = {
|
|
"zh": "\n\n【重要】请始终使用简体中文进行思考和回复。",
|
|
"en": "\n\n[Important] Please always think and reply in English.",
|
|
}.get(loc, "")
|
|
result = prompt + lang_instruction
|
|
if custom_system_prompt and custom_system_prompt.strip():
|
|
result += f"\n\n{custom_system_prompt.strip()}"
|
|
return result
|