e0a5f57793
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.
143 lines
4.3 KiB
TypeScript
143 lines
4.3 KiB
TypeScript
import { describe, expect, it } from 'vitest'
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import { getMarkdown, parseMarkdownToStructure } from '../src'
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const streamContent = `\`\`\`python
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# 添加用户消息到 memory
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agent.memory.add_message(CoreMessage.user_message("帮我查询今天的天气"))
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# 开始执行循环
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step = 0
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while step < agent.max_steps:
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# ========== Think 阶段 ==========
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should_act = await agent.think()
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# 调用 LLM 分析用户请求,决定是否需要调用工具
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last_msg = agent.memory.messages[-1]
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# 发送 thinking 事件(如果有)
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if last_msg.thinking:
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yield {
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"type": "thinking",
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"data": {"content": last_msg.thinking},
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"task_id": task_id
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}
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# 如果不需要执行工具,任务完成
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if not should_act:
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yield {
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"type": "message",
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"data": {"content": last_msg.content, "role": "assistant"},
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"task_id": task_id
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}
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break
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# ========== Act 阶段 ==========
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# 发送工具调用事件
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if agent.tool_calls:
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for tool_call in agent.tool_calls:
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yield {
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"type": "tool_call",
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"data": {
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"tool_name": tool_call.function.name,
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"tool_input": tool_call.function.arguments,
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"tool_call_id": tool_call.id
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},
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"task_id": task_id
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}
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# 执行工具
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result = await agent.act()
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# 发送工具结果事件
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yield {
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"type": "tool_result",
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"data": {
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"tool_name": tool_call.function.name,
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"tool_output": result,
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"success": True
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},
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"task_id": task_id
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}
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step += 1
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\`\`\``
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function findFirstCodeBlock(nodes: any[]): any | undefined {
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const stack: any[] = Array.isArray(nodes) ? [...nodes] : []
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while (stack.length) {
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const node = stack.shift()
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if (!node || typeof node !== 'object')
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continue
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if (node.type === 'code_block')
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return node
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if (Array.isArray(node.children))
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stack.unshift(...node.children)
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if (node.type === 'list' && Array.isArray(node.items)) {
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for (const item of node.items) {
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if (Array.isArray(item?.children))
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stack.unshift(...item.children)
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}
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}
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}
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return undefined
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}
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describe('parseMarkdownToStructure - fence regression', () => {
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it('does not drop content after "<" inside fenced code', () => {
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const md = getMarkdown('fence-regression')
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const nodes = parseMarkdownToStructure(streamContent, md)
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const codeNode = findFirstCodeBlock(nodes)
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expect(codeNode).toBeTruthy()
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expect(codeNode.loading).toBe(false)
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expect(String(codeNode.code)).toContain('while step < agent.max_steps:')
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expect(String(codeNode.code)).toContain('tool_call_id')
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expect(String(codeNode.code)).toContain('step += 1')
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})
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it('does not drop content in blockquote fenced code', () => {
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const md = getMarkdown('fence-regression')
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const markdown = [
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'> ```python',
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'> step = 0',
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'> while step < agent.max_steps:',
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'> step += 1',
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'> ```',
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'',
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].join('\n')
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const nodes = parseMarkdownToStructure(markdown, md)
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const codeNode = findFirstCodeBlock(nodes)
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expect(codeNode).toBeTruthy()
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expect(String(codeNode.code)).toContain('while step < agent.max_steps:')
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expect(String(codeNode.code)).toContain('step += 1')
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})
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it('does not drop content in list-indented fenced code', () => {
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const md = getMarkdown('fence-regression')
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const markdown = [
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'- item',
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' ',
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' ```python',
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' step = 0',
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' while step < agent.max_steps:',
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' step += 1',
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' ```',
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'',
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].join('\n')
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const nodes = parseMarkdownToStructure(markdown, md)
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const codeNode = findFirstCodeBlock(nodes)
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expect(codeNode).toBeTruthy()
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expect(String(codeNode.code)).toContain('while step < agent.max_steps:')
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expect(String(codeNode.code)).toContain('step += 1')
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})
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it('still strips dangling html-like tail outside fences', () => {
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const md = getMarkdown('fence-regression')
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const nodes = parseMarkdownToStructure('Hello\\n<think', md)
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expect(JSON.stringify(nodes)).not.toContain('<think')
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expect(JSON.stringify(nodes)).toContain('Hello')
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})
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})
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