456 lines
16 KiB
Python
456 lines
16 KiB
Python
"""Define the configurable parameters for the agent."""
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import asyncio
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import uuid
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from dataclasses import MISSING, dataclass, field, fields
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from typing import Any, get_origin
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from yuxi import config as sys_config
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from yuxi.agents.backends.sandbox.paths import sandbox_workspace_agents_prompt_file
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from yuxi.utils.logging_config import logger
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WORKSPACE_AGENTS_PROMPT_MAX_BYTES = 64 * 1024
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def _role_can_access(auth: str | None, role: str | None) -> bool:
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if not auth:
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return True
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if auth == "admin":
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return role in {"admin", "superadmin"}
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if auth == "superadmin":
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return role == "superadmin"
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return False
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def _load_workspace_agents_prompt(thread_id: str, uid: str) -> str:
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prompt_file = sandbox_workspace_agents_prompt_file(thread_id, uid)
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try:
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with prompt_file.open("rb") as buffer:
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content = buffer.read(WORKSPACE_AGENTS_PROMPT_MAX_BYTES + 1)
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except FileNotFoundError:
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return ""
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except IsADirectoryError:
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logger.warning("读取工作区 AGENTS.md 失败: 路径是目录")
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return ""
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except OSError as exc:
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logger.warning(f"读取工作区 AGENTS.md 失败: {exc}")
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return ""
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prompt = content[:WORKSPACE_AGENTS_PROMPT_MAX_BYTES].decode("utf-8", errors="replace").strip()
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if not prompt:
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return ""
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if len(content) > WORKSPACE_AGENTS_PROMPT_MAX_BYTES:
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return f"{prompt}\n\n[AGENTS.md 内容已截断]"
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return prompt
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async def build_agent_input_context(
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agent_config: dict | None,
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*,
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thread_id: str,
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uid: str,
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run_id: str | None = None,
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request_id: str | None = None,
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) -> dict:
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input_context = dict(agent_config or {})
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agents_prompt = await asyncio.to_thread(_load_workspace_agents_prompt, thread_id, uid)
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if agents_prompt:
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agents_section = f"用户工作区 agents/AGENTS.md 内容：\n{agents_prompt}"
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base_prompt = str(input_context.get("system_prompt") or "").rstrip()
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input_context["system_prompt"] = f"{base_prompt}\n\n{agents_section}" if base_prompt else agents_section
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input_context.update({"uid": uid, "thread_id": thread_id, "run_id": run_id, "request_id": request_id})
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return input_context
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def filter_config_by_role(
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config_json: dict,
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role: str | None,
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context_schema: type["BaseContext"] | None = None,
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) -> dict:
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"""按 Context 字段 metadata.auth 过滤 config_json.context。"""
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if not isinstance(config_json, dict):
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return {}
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schema = context_schema or BaseContext
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restricted_fields = {
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f.name
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for f in fields(schema)
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if f.metadata.get("auth") and not _role_can_access(str(f.metadata.get("auth")), role)
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}
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if not restricted_fields:
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return dict(config_json)
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filtered = dict(config_json)
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context = filtered.get("context")
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if isinstance(context, dict):
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filtered["context"] = {key: value for key, value in context.items() if key not in restricted_fields}
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return filtered
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@dataclass(kw_only=True)
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class BaseContext:
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"""
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定义一个基础 Context 供 各类 graph 继承
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配置优先级:
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1. 运行时配置(RunnableConfig)：最高优先级，直接从函数参数传入
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2. 类默认配置：最低优先级，类中定义的默认值
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"""
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def update(self, data: dict):
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"""更新配置字段"""
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for key, value in data.items():
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if hasattr(self, key):
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setattr(self, key, value)
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thread_id: str = field(
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default_factory=lambda: str(uuid.uuid4()),
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metadata={"name": "线程ID", "configurable": False, "description": "用来唯一标识一个对话线程"},
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)
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uid: str = field(
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default_factory=lambda: str(uuid.uuid4()),
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metadata={"name": "UID", "configurable": False, "description": "用来唯一标识一个用户"},
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)
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run_id: str | None = field(
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default=None,
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metadata={"name": "运行 ID", "configurable": False, "hide": True},
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)
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request_id: str | None = field(
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default=None,
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metadata={"name": "请求 ID", "configurable": False, "hide": True},
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)
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system_prompt: str = field(
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default="You are a helpful assistant.",
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metadata={"name": "系统提示词", "description": "用来描述智能体的角色和行为", "kind": "prompt"},
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)
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model: str = field(
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default=sys_config.default_model,
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metadata={
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"name": "智能体模型",
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"options": [],
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"description": "智能体的驱动模型，建议选择 Agent 能力较强的模型，不建议使用小参数模型。",
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"kind": "llm",
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},
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)
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tools: list[str] | None = field(
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default=None,
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metadata={
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"name": "工具",
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"description": "内置的工具。默认选择当前用户可用的全部工具。",
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"type": "list",
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"kind": "tools",
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},
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)
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knowledges: list[str] | None = field(
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default=None,
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metadata={
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"name": "知识库",
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"description": "知识库列表，可以在左侧知识库页面中创建知识库。默认选择当前用户可访问的全部知识库。",
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"type": "list",
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"kind": "knowledges",
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},
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)
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mcps: list[str] | None = field(
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default=None,
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metadata={
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"name": "MCP服务器",
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"options": [],
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"description": (
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"MCP服务器列表，默认选择当前用户可用的全部 MCP 服务器。建议使用支持 SSE 的 MCP 服务器，"
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"如果需要使用 uvx 或 npx 运行的服务器，也请在项目外部启动 MCP 服务器，并在项目中配置 MCP 服务器。"
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),
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"type": "list",
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"kind": "mcps",
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},
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)
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skills: list[str] | None = field(
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default=None,
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metadata={
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"name": "Skills",
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"options": [],
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"description": "可选技能列表（由超级管理员维护），默认选择当前用户可用的全部 skills。"
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"技能依赖的工具和 MCP 服务器也会被自动挂载。",
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"type": "list",
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"kind": "skills",
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},
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)
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summary_threshold: int = field(
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default=100,
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metadata={
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"name": "上下文摘要触发阈值 (K)",
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"description": "当上下文大小超过该值时，启用摘要功能以优化上下文使用。单位为 K，默认值为 100K。",
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"type": "number",
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"auth": "admin",
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},
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)
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model_retry_times: int = field(
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default=2,
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metadata={
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"name": "模型重试次数",
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"description": "模型调用失败时的最大重试次数，默认值为 2。",
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"type": "number",
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"auth": "admin",
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},
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)
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@classmethod
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def get_configurable_items(cls, user_role: str | None = None):
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"""实现一个可配置的参数列表，在 UI 上配置时使用"""
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configurable_items = {}
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for f in fields(cls):
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if f.init and not f.metadata.get("hide", False):
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if user_role is not None and not _role_can_access(f.metadata.get("auth"), user_role):
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continue
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if f.metadata.get("configurable", True):
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type_name = cls._get_type_name(f.type)
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options = f.metadata.get("options", [])
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if callable(options):
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options = options()
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configurable_items[f.name] = {
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"type": f.metadata.get("type", type_name),
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"name": f.metadata.get("name", f.name),
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"options": options,
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"default": f.default
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if f.default is not MISSING
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else f.default_factory()
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if f.default_factory is not MISSING
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else None,
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"description": f.metadata.get("description", ""),
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"kind": f.metadata.get("kind", ""),
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}
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return configurable_items
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@classmethod
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def _get_type_name(cls, field_type) -> str:
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"""获取类型名称"""
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origin = get_origin(field_type)
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if origin is not None:
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if hasattr(origin, "__name__"):
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return origin.__name__
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return str(origin)
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elif hasattr(field_type, "__name__"):
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return field_type.__name__
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else:
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return str(field_type)
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def update_from_dict(self, data: dict):
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"""从字典更新配置字段"""
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for key, value in data.items():
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if hasattr(self, key):
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setattr(self, key, value)
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_DEFAULT_ALL_CONTEXT_FIELDS = frozenset({"tools", "knowledges", "mcps", "skills"})
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_EMPTY_ALL_CONTEXT_FIELDS = frozenset({"subagents"})
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_AGENT_RESOURCE_FIELDS = _DEFAULT_ALL_CONTEXT_FIELDS | _EMPTY_ALL_CONTEXT_FIELDS
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def _normalize_selected_resource_keys(value: Any, available: list[str]) -> list[str]:
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if not isinstance(value, list):
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return []
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allowed = set(available)
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normalized: list[str] = []
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seen: set[str] = set()
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for item in value:
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if not isinstance(item, str):
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continue
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key = item.strip()
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if not key or key in seen or key not in allowed:
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continue
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seen.add(key)
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normalized.append(key)
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return normalized
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def _resource_fields_requiring_available_keys(normalized: dict, resource_fields: set[str]) -> set[str]:
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fields_to_load: set[str] = set()
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for field_name in resource_fields:
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current = normalized.get(field_name)
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if current is None:
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if field_name in _DEFAULT_ALL_CONTEXT_FIELDS | _EMPTY_ALL_CONTEXT_FIELDS:
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fields_to_load.add(field_name)
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else:
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normalized[field_name] = []
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elif field_name in _EMPTY_ALL_CONTEXT_FIELDS and current == []:
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normalized[field_name] = None
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fields_to_load.add(field_name)
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elif isinstance(current, list) and current:
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fields_to_load.add(field_name)
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else:
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normalized[field_name] = []
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return fields_to_load
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def _resource_option(key: Any, name: Any = None, description: Any = None) -> dict[str, str]:
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key_value = str(key)
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return {
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"key": key_value,
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"name": str(name or key_value),
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"description": str(description or ""),
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}
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async def resolve_agent_resource_options(
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resource_fields: set[str] | None = None,
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*,
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db,
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user,
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) -> dict[str, list[dict[str, str]]]:
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fields_to_load = _AGENT_RESOURCE_FIELDS if resource_fields is None else resource_fields
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if not fields_to_load:
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return {}
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options: dict[str, list[dict[str, str]]] = {}
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if "tools" in fields_to_load:
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from yuxi.agents.toolkits.service import get_tool_metadata
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options["tools"] = [
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_resource_option(tool["slug"], tool.get("name"), tool.get("description"))
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for tool in get_tool_metadata(category="buildin")
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if tool.get("slug")
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]
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if "knowledges" in fields_to_load:
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from yuxi.knowledge import knowledge_base
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databases = (await knowledge_base.get_databases_by_user(user)).get("databases", [])
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options["knowledges"] = [
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_resource_option(item.get("kb_id"), item.get("name"), item.get("description"))
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for item in databases
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if isinstance(item, dict) and item.get("kb_id")
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]
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if "mcps" in fields_to_load:
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from yuxi.agents.mcp.service import get_all_mcp_servers
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servers = await get_all_mcp_servers(db)
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options["mcps"] = [
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_resource_option(server.slug, server.name, server.description)
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for server in servers
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if server.enabled and server.slug
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]
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if "skills" in fields_to_load:
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from yuxi.agents.skills.service import list_accessible_skills
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skills = await list_accessible_skills(db, user)
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options["skills"] = [
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_resource_option(skill.slug, skill.name, skill.description) for skill in skills if skill.slug
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]
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if "subagents" in fields_to_load:
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from yuxi.repositories.agent_repository import AgentRepository
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subagents = await AgentRepository(db).list_visible_subagents(user=user)
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options["subagents"] = [
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_resource_option(agent.slug, agent.name, agent.description) for agent in subagents if agent.slug
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]
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return options
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async def normalize_agent_context_config(
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context: dict | None,
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*,
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db,
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user,
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context_schema: type[BaseContext] | None = None,
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) -> dict:
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schema = context_schema or BaseContext
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raw_context = dict(context) if isinstance(context, dict) else {}
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filtered = filter_config_by_role({"context": raw_context}, getattr(user, "role", None), schema)
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normalized = dict(filtered.get("context") or {})
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field_names = {item.name for item in fields(schema)}
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resource_fields = _AGENT_RESOURCE_FIELDS & field_names
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if not resource_fields:
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return normalized
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fields_to_load = _resource_fields_requiring_available_keys(normalized, resource_fields)
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if not fields_to_load:
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return normalized
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resource_options = await resolve_agent_resource_options(fields_to_load, db=db, user=user)
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available = {
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field_name: [option["key"] for option in field_options]
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for field_name, field_options in resource_options.items()
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}
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for field_name, available_keys in available.items():
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current = normalized.get(field_name)
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if current is None:
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normalized[field_name] = available_keys
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else:
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normalized[field_name] = _normalize_selected_resource_keys(current, available_keys)
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return normalized
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async def prepare_agent_runtime_context(
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context: BaseContext,
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*,
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context_schema: type[BaseContext] | None = None,
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) -> BaseContext:
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"""准备 Agent 运行时上下文，主要是根据 context 中的 uid 加载用户可访问的资源列表，并进行规范化处理。"""
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schema = context_schema or type(context)
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uid = str(getattr(context, "uid", "") or "").strip()
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if not uid:
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return context
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from yuxi.agents.backends.knowledge_base_backend import resolve_visible_knowledge_bases_for_context
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from yuxi.agents.middlewares.skills import resolve_runtime_skills_for_context
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from yuxi.repositories.user_repository import UserRepository
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from yuxi.storage.postgres.manager import pg_manager
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resource_fields = _AGENT_RESOURCE_FIELDS
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async with pg_manager.get_async_session_context() as db:
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user = await UserRepository().get_by_uid_with_db(db, uid)
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if user is None:
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for field_name in resource_fields:
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if hasattr(context, field_name):
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setattr(context, field_name, [])
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setattr(context, "_visible_knowledge_bases", [])
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setattr(context, "_prompt_skills", [])
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setattr(context, "_readable_skills", [])
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setattr(context, "_runtime_skill_metadata", {})
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setattr(context, "_runtime_skill_dependency_map", {})
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return context
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raw_resources = {
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field_name: getattr(context, field_name, None)
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for field_name in resource_fields
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if hasattr(context, field_name)
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}
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normalized = await normalize_agent_context_config(
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raw_resources,
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db=db,
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user=user,
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context_schema=schema,
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)
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for field_name in resource_fields:
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if hasattr(context, field_name):
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setattr(context, field_name, normalized.get(field_name, []))
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await resolve_visible_knowledge_bases_for_context(context)
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skill_scope = await resolve_runtime_skills_for_context(context, db=db, user=user)
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context.skills = skill_scope["context_skills"]
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setattr(context, "_prompt_skills", skill_scope["prompt_skills"])
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setattr(context, "_readable_skills", skill_scope["readable_skills"])
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setattr(context, "_runtime_skill_metadata", skill_scope["runtime_skill_metadata"])
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setattr(context, "_runtime_skill_dependency_map", skill_scope["runtime_skill_dependency_map"])
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return context
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