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zhenxun_bot/zhenxun/builtin_plugins/llm_manager/presenters.py
T
80fc5b86a7 ✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架 (#2146)
* ✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架

- 【重构】将原 services/llm 重构并迁移至全新的 services/ai 架构,提供向下兼容垫片
- 【新增】引入 Agent、Team、Workflow 三大智能体与工作流编排范式
- 【新增】引入基于 RAG 的长期向量记忆与中期槽位记忆系统
- 【新增】引入基于 Docker 的安全代码执行沙箱环境
- 【新增】支持 MCP 协议,允许动态管理和调用 MCP 服务
- 【新增】引入输入输出安全合规护栏与自愈反思机制
- 【优化】重构并优化多厂商 API 适配器 (Gemini, OpenAI, DeepSeek, GLM 等)
- 【优化】优化日志脱敏与 Token 预估机制
- 【移除】移除旧版 llm default 和 llm reset-key 命令,新增 llm mcp 管理命令

* 🔧 chore(deps): 更新项目依赖与配置

- 添加 mcp、jieba 和 aiodocker 依赖到配置文件及 requirements.txt
- 在 pyright 配置中设置 reportMissingImports 为 none
- 调整 .gitignore 中 resources 目录的忽略规则

* ♻️ refactor(tools): 重构工具终止机制并清理知识库日志输出

- 统一使用 `context.state["__end_run__"]` 替代 `EndRunResult` 控制任务结束
- 移除文件系统和向量知识库检索工具中 `ToolResult` 的 `.with_log` 调用
- 调整指令处理器(Directive)的返回值为 `tool_res.output`
- 修复部分类型检查警告并优化联合类型判断语法

* ♻️ refactor(tools): 重构工具副作用指令与控制流熔断机制

- 引入 `DirectivePayload` 及 `ToolResult` 的子类以结构化表达工具副作用
- 移除通过 `context.state` 传递魔术变量的隐式控制流设计
- 重构 `DirectiveManager` 处理器接口,直接在处理器中修改 `AgentState` 并构建 `AgentRunResult`
- 在 `StandardAgentExecutor` 中统一通过 `directive_manager` 调度工具返回的副作用指令
- 补全 `MessageBuilder` 中部分核心方法的文档注释

* 🐛 fix(sandbox): 修复 Docker 沙箱容器状态检测与会话清理逻辑

-【修复】修正 `is_alive` 中直接读取私有属性的问题,改用 `show()` 返回值
-【修复】解决 `execute_code` 中缓存的执行器与当前会话不一致的问题
-【优化】在清理工作区前增加容器存活检测,避免向已死容器发送请求
-【优化】创建容器时增加运行状态校验,若已停止则自动从缓存中移除并重建
-【优化】优化容器销毁和清理逻辑,静默处理容器不存在 (404) 的异常

* 📝 docs(core): 补充核心模块初始化方法的文档注释

* 🚨 auto fix by pre-commit hooks

---------

Co-authored-by: webjoin111 <455457521@qq.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
2026-07-03 08:53:56 +08:00

237 lines
9.0 KiB
Python

import time
from typing import Any, Literal
from zhenxun import ui
from zhenxun.services import renderer_service
from zhenxun.services.ai.core.models import ModelModality
from zhenxun.ui.models import StatusBadgeCell, TextCell
def _format_seconds(seconds: int) -> str:
"""将秒数格式化为 'Xm Ys' 或 'Xh Ym' 的形式"""
if seconds <= 0:
return "0s"
if seconds < 60:
return f"{seconds}s"
minutes, seconds = divmod(seconds, 60)
if minutes < 60:
return f"{minutes}m {seconds}s"
hours, minutes = divmod(minutes, 60)
return f"{hours}h {minutes}m"
class Presenters:
"""格式化LLM管理插件的输出 (图片格式)"""
@staticmethod
async def format_model_list_as_image(
models: list[dict[str, Any]], show_all: bool
) -> bytes:
"""将模型列表格式化为表格图片"""
title = "LLM模型列表" + (" (所有已配置模型)" if show_all else " (仅可用)")
if not models:
table = ui.table(title=title, tip="当前没有配置任何LLM模型。").set_headers(
["提供商", "模型名称", "API类型", "状态"]
)
return await renderer_service.render(table)
column_name = ["提供商", "模型名称", "API类型", "状态"]
rows_data = []
for model in models:
is_available = model.get("is_available", True)
embed_tag = " (Embed)" if model.get("is_embedding_model", False) else ""
rows_data.append(
[
TextCell(content=model.get("provider_name", "N/A")),
TextCell(content=f"{model.get('model_name', 'N/A')}{embed_tag}"),
TextCell(content=model.get("api_type", "N/A")),
StatusBadgeCell(
text="可用" if is_available else "不可用",
status_type="ok" if is_available else "error",
),
]
)
table = ui.table(
title=title, tip="使用 `llm info <Provider/ModelName>` 查看详情"
)
table.set_headers(column_name)
table.set_column_alignments(["left", "left", "left", "center"])
table.add_rows(rows_data)
return await renderer_service.render(table, use_cache=True)
@staticmethod
async def format_model_details_as_markdown_image(details: dict[str, Any]) -> bytes:
"""将模型详情格式化为Markdown图片"""
provider = details["provider_config"]
model = details["model_detail"]
caps = details["capabilities"]
cap_list = []
if ModelModality.IMAGE in caps.input_modalities:
cap_list.append("图片")
if ModelModality.VIDEO in caps.input_modalities:
cap_list.append("视频")
if ModelModality.AUDIO in caps.input_modalities:
cap_list.append("音频")
if caps.supports_tool_calling:
cap_list.append("工具调用")
if caps.is_embedding_model:
cap_list.append("文本嵌入")
md = ui.markdown("")
md.head(f"🔎 模型详情: {provider.name}/{model.model_name}", 1)
md.text("---")
md.head("提供商信息", 2)
md.text(f"- **名称**: {provider.name}")
md.text(f"- **API 类型**: {provider.api_type}")
md.text(f"- **API Base**: {provider.api_base or '默认'}")
md.head("模型详情", 2)
temp_value = model.temperature or provider.temperature or "未设置"
input_tokens = caps.max_input_tokens
context_window = (
f"{int(input_tokens / 1000)}K"
if input_tokens >= 1000
else str(input_tokens)
)
md.text(f"- **名称**: {model.model_name}")
md.text(f"- **默认温度**: {temp_value}")
md.text(f"- **上下文窗口**: {context_window}")
md.text(f"- **核心能力**: {', '.join(cap_list) or '纯文本'}")
return await renderer_service.render(md.with_style("light"))
@staticmethod
async def format_key_status_as_image(
provider_name: str, sorted_stats: list[dict[str, Any]]
) -> bytes:
"""将已排序的、详细的API Key状态格式化为表格图片"""
title = f"🔑 '{provider_name}' API Key 状态"
data_list = []
for key_info in sorted_stats:
status_str = key_info.get("status", "HEALTHY")
successes = key_info.get("successes", 0)
failures = key_info.get("failures", 0)
total_calls = successes + failures
if total_calls == 0 and status_str == "HEALTHY":
status_str = "UNUSED"
if status_str == "COOLDOWN":
cooldown_seconds = max(
0, int(key_info.get("cooldown_until", 0) - time.time())
)
formatted_time = _format_seconds(cooldown_seconds)
status_cell = StatusBadgeCell(
text=f"冷却中({formatted_time})", status_type="info"
)
else:
status_map: dict[
str,
tuple[str, Literal["ok", "error", "warning", "info", "success"]],
] = {
"DISABLED": ("永久禁用", "error"),
"ERROR": ("错误", "error"),
"WARNING": ("告警", "warning"),
"HEALTHY": ("健康", "ok"),
"UNUSED": ("未使用", "info"),
}
text, status_type = status_map.get(status_str, ("未知", "info"))
status_cell = StatusBadgeCell(text=text, status_type=status_type)
total_calls_text = (
f"{successes}/{total_calls}" if total_calls > 0 else "0/0"
)
success_rate = (successes / total_calls * 100) if total_calls > 0 else 100.0
success_rate_text = f"{success_rate:.1f}%" if total_calls > 0 else "N/A"
rate_color = None
if total_calls > 0:
if success_rate < 80:
rate_color = "#F56C6C"
elif success_rate < 95:
rate_color = "#E6A23C"
success_rate_cell = TextCell(content=success_rate_text, color=rate_color)
avg_latency_text = "N/A"
last_error = key_info.get("last_error") or "-"
if len(last_error) > 25:
last_error = last_error[:22] + "..."
suggested_action = "-"
if status_str == "DISABLED":
suggested_action = "检查配额或换Key"
elif status_str == "COOLDOWN":
suggested_action = "等待恢复"
data_list.append(
[
TextCell(content=key_info["key_id"]),
status_cell,
TextCell(content=total_calls_text),
success_rate_cell,
TextCell(content=avg_latency_text),
TextCell(content=last_error),
TextCell(content=suggested_action),
]
)
table = ui.table(title=title, tip="使用 `llm reset-key <Provider>` 重置Key状态")
table.set_headers(
[
"Key (部分)",
"状态",
"总调用",
"成功率",
"平均延迟(s)",
"上次错误",
"建议操作",
]
)
table.add_rows(data_list)
return await renderer_service.render(table, use_cache=False)
@staticmethod
async def format_mcp_list_as_image(mcp_list: list[dict[str, Any]]) -> bytes:
"""将MCP列表格式化为表格图片"""
title = "MCP 服务管理列表"
if not mcp_list:
table = ui.table(title=title, tip="当前未配置任何 MCP 服务。").set_headers(
["ID", "MCP名称", "协议", "状态", "目标"]
)
return await renderer_service.render(table)
column_name = ["ID", "MCP名称", "协议", "状态", "目标"]
rows_data = []
for mcp in mcp_list:
is_enable = mcp["enabled"]
status_type = "success" if is_enable else "info"
status_text = "开启" if is_enable else "关闭"
rows_data.append(
[
TextCell(content=str(mcp["id"])),
TextCell(content=mcp["name"]),
TextCell(content=mcp["transport"]),
StatusBadgeCell(text=status_text, status_type=status_type),
TextCell(content=mcp["target"]),
]
)
table = ui.table(
title=title,
tip="使用 `llm mcp 开启/关闭 <ID/名称>` 来修改状态,支持批量操作",
)
table.set_headers(column_name)
table.set_column_alignments(["center", "left", "left", "center", "left"])
table.add_rows(rows_data)
return await renderer_service.render(table, use_cache=False)