feat(desktop): embed BoosAPI Desktop client as a subproject

Vendor the rebranded BoosAPI Desktop client (Tauri + Vue) into metapi as a monorepo subdirectory under desktop/. Source only — node_modules, dist, Cargo target and the upstream .git history are excluded. desktop/ keeps its own independent Cargo workspace (desktop/src-tauri with crates/*); it is not part of the backend boosapi crate, so a `cargo check` at the metapi root still compiles only the backend. Build the desktop app from desktop/src-tauri per desktop/README.md. Note: app icons are still placeholder assets pending the final BoosAPI logo. The updater signing keys live outside the repo and are not included.
2026-06-22 11:32:57 +08:00
parent d9c78ecc73
commit e0a5f57793
2305 changed files with 449355 additions and 0 deletions
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+import { describe, expect, it } from 'vitest'
+import { getMarkdown, parseMarkdownToStructure } from '../src'
+
+const streamContent = `\`\`\`python
+# 添加用户消息到 memory
+agent.memory.add_message(CoreMessage.user_message("帮我查询今天的天气"))
+
+# 开始执行循环
+step = 0
+while step < agent.max_steps:
+    # ========== Think 阶段 ==========
+    should_act = await agent.think()
+    # 调用 LLM 分析用户请求，决定是否需要调用工具
+    
+    last_msg = agent.memory.messages[-1]
+    
+    # 发送 thinking 事件（如果有）
+    if last_msg.thinking:
+        yield {
+            "type": "thinking",
+            "data": {"content": last_msg.thinking},
+            "task_id": task_id
+        }
+    
+    # 如果不需要执行工具，任务完成
+    if not should_act:
+        yield {
+            "type": "message",
+            "data": {"content": last_msg.content, "role": "assistant"},
+            "task_id": task_id
+        }
+        break
+    
+    # ========== Act 阶段 ==========
+    # 发送工具调用事件
+    if agent.tool_calls:
+        for tool_call in agent.tool_calls:
+            yield {
+                "type": "tool_call",
+                "data": {
+                    "tool_name": tool_call.function.name,
+                    "tool_input": tool_call.function.arguments,
+                    "tool_call_id": tool_call.id
+                },
+                "task_id": task_id
+            }
+    
+    # 执行工具
+    result = await agent.act()
+    
+    # 发送工具结果事件
+    yield {
+        "type": "tool_result",
+        "data": {
+            "tool_name": tool_call.function.name,
+            "tool_output": result,
+            "success": True
+        },
+        "task_id": task_id
+    }
+    
+    step += 1
+\`\`\``
+
+function findFirstCodeBlock(nodes: any[]): any | undefined {
+  const stack: any[] = Array.isArray(nodes) ? [...nodes] : []
+  while (stack.length) {
+    const node = stack.shift()
+    if (!node || typeof node !== 'object')
+      continue
+    if (node.type === 'code_block')
+      return node
+    if (Array.isArray(node.children))
+      stack.unshift(...node.children)
+    if (node.type === 'list' && Array.isArray(node.items)) {
+      for (const item of node.items) {
+        if (Array.isArray(item?.children))
+          stack.unshift(...item.children)
+      }
+    }
+  }
+  return undefined
+}
+
+describe('parseMarkdownToStructure - fence regression', () => {
+  it('does not drop content after "<" inside fenced code', () => {
+    const md = getMarkdown('fence-regression')
+    const nodes = parseMarkdownToStructure(streamContent, md)
+
+    const codeNode = findFirstCodeBlock(nodes)
+    expect(codeNode).toBeTruthy()
+    expect(codeNode.loading).toBe(false)
+    expect(String(codeNode.code)).toContain('while step < agent.max_steps:')
+    expect(String(codeNode.code)).toContain('tool_call_id')
+    expect(String(codeNode.code)).toContain('step += 1')
+  })
+
+  it('does not drop content in blockquote fenced code', () => {
+    const md = getMarkdown('fence-regression')
+    const markdown = [
+      '> ```python',
+      '> step = 0',
+      '> while step < agent.max_steps:',
+      '>     step += 1',
+      '> ```',
+      '',
+    ].join('\n')
+
+    const nodes = parseMarkdownToStructure(markdown, md)
+    const codeNode = findFirstCodeBlock(nodes)
+    expect(codeNode).toBeTruthy()
+    expect(String(codeNode.code)).toContain('while step < agent.max_steps:')
+    expect(String(codeNode.code)).toContain('step += 1')
+  })
+
+  it('does not drop content in list-indented fenced code', () => {
+    const md = getMarkdown('fence-regression')
+    const markdown = [
+      '- item',
+      '  ',
+      '    ```python',
+      '    step = 0',
+      '    while step < agent.max_steps:',
+      '        step += 1',
+      '    ```',
+      '',
+    ].join('\n')
+
+    const nodes = parseMarkdownToStructure(markdown, md)
+    const codeNode = findFirstCodeBlock(nodes)
+    expect(codeNode).toBeTruthy()
+    expect(String(codeNode.code)).toContain('while step < agent.max_steps:')
+    expect(String(codeNode.code)).toContain('step += 1')
+  })
+
+  it('still strips dangling html-like tail outside fences', () => {
+    const md = getMarkdown('fence-regression')
+    const nodes = parseMarkdownToStructure('Hello\\n<think', md)
+    expect(JSON.stringify(nodes)).not.toContain('<think')
+    expect(JSON.stringify(nodes)).toContain('Hello')
+  })
+})