feat: expand secure runtime and workspace UX
This commit is contained in:
lofyer
@@ -1,4 +1,8 @@
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import { describe, expect, it, vi } from 'vitest'
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import type {
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ModelToolDefinition,
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ModelToolProviderLike
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} from './model-tool-provider'
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import { ModelAgentRuntime } from './model-runtime'
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function createEventStream(text: string): string {
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@@ -36,6 +40,62 @@ function createEventStream(text: string): string {
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].join('\n')
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}
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function createResponsesEventStream(text: string): string {
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return [
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'event: response.output_text.delta',
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`data: ${JSON.stringify({
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type: 'response.output_text.delta',
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delta: text
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})}`,
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'',
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'event: response.completed',
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`data: ${JSON.stringify({
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type: 'response.completed',
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response: {
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id: 'resp-provider-1',
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model: 'gpt-5-provider',
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usage: {
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input_tokens: 29,
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output_tokens: 8,
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total_tokens: 37,
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input_tokens_details: { cached_tokens: 11 }
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}
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}
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})}`,
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'',
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''
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].join('\n')
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}
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function createToolProvider(
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overrides: Partial<ModelToolProviderLike> = {}
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): ModelToolProviderLike {
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const tool: ModelToolDefinition = {
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name: 'workspace_read_text',
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displayName: '读取工作区文本',
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description: 'Read text',
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inputSchema: {
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type: 'object',
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properties: { path: { type: 'string' } },
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required: ['path']
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},
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source: 'builtin'
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}
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return {
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listTools: vi.fn(async () => [tool]),
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getApproval: vi.fn((_definition, _arguments, summary) => ({
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scopeKey: 'model:builtin:workspace_read_text',
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title: '允许读取工作区文本？',
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description: '读取文件',
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toolName: '读取工作区文本',
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argumentSummary: summary
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})),
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callTool: vi.fn(async () => 'tool result'),
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dispose: vi.fn(async () => {}),
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...overrides
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}
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}
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describe('ModelAgentRuntime', () => {
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it('performs a real minimal request when testing the connection', async () => {
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const fetcher = vi.fn<typeof fetch>(async () =>
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@@ -78,7 +138,7 @@ describe('ModelAgentRuntime', () => {
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skillInstructions: '# 文档写作',
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fetcher
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})
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const events = []
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const events: Array<{ type: string; state?: string }> = []
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for await (const event of runtime.run(
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{
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@@ -231,12 +291,14 @@ describe('ModelAgentRuntime', () => {
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headers: { 'content-type': 'text/event-stream' }
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})
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)
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const toolProvider = createToolProvider()
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const runtime = new ModelAgentRuntime({
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baseUrl: 'http://127.0.0.1:11434/v1',
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model: 'qwen3',
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protocol: 'openai-chat-completions',
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authentication: 'none',
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fetcher
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fetcher,
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toolProvider
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})
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const events = []
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@@ -292,6 +354,451 @@ describe('ModelAgentRuntime', () => {
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])
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expect(events.at(-2)).toMatchObject({ type: 'model-usage' })
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expect(events.at(-1)).toMatchObject({ type: 'done' })
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expect(toolProvider.listTools).not.toHaveBeenCalled()
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})
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it('uses the OpenAI Responses endpoint and streams output text', async () => {
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const fetcher = vi.fn<typeof fetch>(async () =>
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new Response(createResponsesEventStream('Responses 回答'), {
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status: 200,
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headers: { 'content-type': 'text/event-stream' }
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})
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)
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const runtime = new ModelAgentRuntime({
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apiKey: 'test-key',
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baseUrl: 'https://api.openai.com/v1',
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model: 'gpt-5',
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protocol: 'openai-responses',
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authentication: 'api-key',
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fetcher
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})
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const events = []
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for await (const event of runtime.run(
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{
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requestId: 'a431666e-5ec8-45e6-beb4-654132eed133',
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conversationId: 'conversation-responses',
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prompt: '你好'
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},
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new AbortController().signal
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)) {
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events.push(event)
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}
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const [input, init] = fetcher.mock.calls[0] ?? []
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expect(input?.toString()).toBe('https://api.openai.com/v1/responses')
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expect(init?.headers).toEqual({
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authorization: 'Bearer test-key',
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'content-type': 'application/json'
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})
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expect(JSON.parse(init?.body as string)).toMatchObject({
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model: 'gpt-5',
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max_output_tokens: 4096,
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stream: true,
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instructions: expect.stringContaining('GoodBuddy'),
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input: [
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expect.objectContaining({ role: 'user', content: '你好' })
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]
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})
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expect(events).toContainEqual(
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expect.objectContaining({
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type: 'text',
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delta: 'Responses 回答'
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})
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)
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expect(events.filter((event) => event.type === 'model-usage')).toEqual([
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{
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requestId: 'a431666e-5ec8-45e6-beb4-654132eed133',
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type: 'model-usage',
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callId: 'resp-provider-1',
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runtime: 'model',
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provider: 'openai',
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model: 'gpt-5-provider',
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inputTokens: 29,
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outputTokens: 8,
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cacheReadTokens: 11,
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cacheWriteTokens: 0,
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reportedTotalTokens: 37
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}
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])
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expect(events.at(-1)).toMatchObject({ type: 'done' })
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})
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it('tests an OpenAI Responses connection with Responses request fields', async () => {
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const fetcher = vi.fn<typeof fetch>(async () =>
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Response.json({ id: 'resp-test', output: [] })
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)
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const runtime = new ModelAgentRuntime({
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apiKey: 'test-key',
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baseUrl: 'https://api.openai.com/v1/',
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model: 'gpt-5',
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protocol: 'openai-responses',
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authentication: 'api-key',
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fetcher
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})
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await expect(runtime.testConnection()).resolves.toMatchObject({
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available: true,
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detail: expect.stringContaining('已验证')
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})
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expect(fetcher.mock.calls[0]?.[0]?.toString()).toBe(
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'https://api.openai.com/v1/responses'
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)
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expect(
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JSON.parse(fetcher.mock.calls[0]?.[1]?.body as string)
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).toEqual({
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model: 'gpt-5',
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max_output_tokens: 16,
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stream: false,
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input: 'Reply OK.'
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})
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})
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it('runs approved direct-model tools and returns their results to OpenAI', async () => {
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const responses = [
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{
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id: 'chatcmpl-tool-1',
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model: 'qwen3',
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choices: [
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{
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message: {
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role: 'assistant',
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content: null,
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tool_calls: [
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{
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id: 'call-1',
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type: 'function',
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function: {
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name: 'workspace_read_text',
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arguments: '{"path":"README.md"}'
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}
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}
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]
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}
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}
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],
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usage: { prompt_tokens: 10, completion_tokens: 4 }
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},
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{
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id: 'chatcmpl-tool-2',
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model: 'qwen3',
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choices: [
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{
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message: {
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role: 'assistant',
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content: '文件内容已读取。'
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}
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}
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],
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usage: { prompt_tokens: 18, completion_tokens: 7 }
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}
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]
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const fetcher = vi.fn<typeof fetch>(async () =>
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Response.json(responses.shift())
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)
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const toolProvider = createToolProvider()
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const runtime = new ModelAgentRuntime({
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baseUrl: 'http://127.0.0.1:11434/v1',
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model: 'qwen3',
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protocol: 'openai-chat-completions',
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authentication: 'none',
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fetcher,
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toolProvider
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})
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const authorize = vi.fn(async () => 'once' as const)
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const events = []
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for await (const event of runtime.run(
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{
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requestId: 'a431666e-5ec8-45e6-beb4-654132eed130',
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conversationId: 'conversation-tools',
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prompt: '读取 README',
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workMode: 'execute'
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},
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new AbortController().signal,
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authorize
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)) {
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events.push(event)
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}
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expect(fetcher).toHaveBeenCalledTimes(2)
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const firstBody = JSON.parse(
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fetcher.mock.calls[0]?.[1]?.body as string
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) as Record<string, unknown>
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expect(firstBody).toMatchObject({
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stream: false,
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tools: [
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{
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type: 'function',
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function: { name: 'workspace_read_text' }
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}
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]
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})
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const secondBody = JSON.parse(
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fetcher.mock.calls[1]?.[1]?.body as string
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) as { messages: Array<Record<string, unknown>> }
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expect(secondBody.messages).toContainEqual({
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role: 'tool',
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tool_call_id: 'call-1',
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content: 'tool result'
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})
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expect(authorize).toHaveBeenCalledWith(
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expect.objectContaining({
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scopeKey: 'model:builtin:workspace_read_text'
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})
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)
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expect(toolProvider.callTool).toHaveBeenCalledWith(
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'workspace_read_text',
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{ path: 'README.md' },
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expect.any(AbortSignal)
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)
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expect(
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events
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.filter((event) => event.type === 'tool')
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.map((event) => event.state)
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).toEqual(['pending', 'running', 'completed'])
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expect(events).toContainEqual(
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expect.objectContaining({
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type: 'text',
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delta: '文件内容已读取。'
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})
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)
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expect(events.at(-1)).toMatchObject({ type: 'done' })
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await runtime.dispose()
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expect(toolProvider.dispose).toHaveBeenCalledOnce()
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})
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it('continues OpenAI Responses with function_call_output', async () => {
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const responses = [
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{
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id: 'resp-tool-1',
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model: 'gpt-5',
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output: [
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{
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type: 'function_call',
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call_id: 'call-responses-1',
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name: 'workspace_read_text',
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arguments: '{"path":"README.md"}'
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}
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],
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usage: { input_tokens: 14, output_tokens: 3 }
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},
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{
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id: 'resp-tool-2',
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model: 'gpt-5',
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output: [
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{
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type: 'message',
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role: 'assistant',
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content: [
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{
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type: 'output_text',
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text: 'Responses 工具调用完成。'
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}
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]
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}
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],
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usage: { input_tokens: 21, output_tokens: 6 }
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}
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]
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const fetcher = vi.fn<typeof fetch>(async () =>
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Response.json(responses.shift())
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)
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const runtime = new ModelAgentRuntime({
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apiKey: 'test-key',
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baseUrl: 'https://api.openai.com/v1',
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model: 'gpt-5',
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protocol: 'openai-responses',
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authentication: 'api-key',
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fetcher,
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toolProvider: createToolProvider()
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})
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const events = []
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for await (const event of runtime.run(
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{
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requestId: 'a431666e-5ec8-45e6-beb4-654132eed134',
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conversationId: 'conversation-responses-tools',
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prompt: '读取 README',
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workMode: 'execute'
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},
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new AbortController().signal,
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async () => 'once'
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)) {
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events.push(event)
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}
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const firstBody = JSON.parse(
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fetcher.mock.calls[0]?.[1]?.body as string
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) as Record<string, unknown>
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expect(firstBody).toMatchObject({
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model: 'gpt-5',
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stream: false,
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tools: [
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{
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type: 'function',
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name: 'workspace_read_text',
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strict: false
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}
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]
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})
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const secondBody = JSON.parse(
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fetcher.mock.calls[1]?.[1]?.body as string
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) as Record<string, unknown>
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expect(secondBody).toMatchObject({
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previous_response_id: 'resp-tool-1',
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input: [
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{
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type: 'function_call_output',
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call_id: 'call-responses-1',
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output: 'tool result'
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}
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]
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})
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expect(
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events
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.filter((event) => event.type === 'tool')
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.map((event) => event.state)
|
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).toEqual(['pending', 'running', 'completed'])
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expect(events).toContainEqual(
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expect.objectContaining({
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type: 'text',
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delta: 'Responses 工具调用完成。'
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})
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)
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expect(events.at(-1)).toMatchObject({ type: 'done' })
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})
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it('fails closed when a direct-model tool is denied', async () => {
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const fetcher = vi.fn<typeof fetch>(async () =>
|
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Response.json({
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choices: [
|
||||
{
|
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message: {
|
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role: 'assistant',
|
||||
content: null,
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tool_calls: [
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{
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id: 'call-denied',
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type: 'function',
|
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function: {
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name: 'workspace_read_text',
|
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arguments: '{"path":"secret.txt"}'
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
})
|
||||
)
|
||||
const toolProvider = createToolProvider()
|
||||
const runtime = new ModelAgentRuntime({
|
||||
baseUrl: 'http://127.0.0.1:11434/v1',
|
||||
model: 'qwen3',
|
||||
protocol: 'openai-chat-completions',
|
||||
authentication: 'none',
|
||||
fetcher,
|
||||
toolProvider
|
||||
})
|
||||
const events: Array<{ type: string; state?: string }> = []
|
||||
const consume = async (): Promise<void> => {
|
||||
for await (const event of runtime.run(
|
||||
{
|
||||
requestId: 'a431666e-5ec8-45e6-beb4-654132eed131',
|
||||
conversationId: 'conversation-denied',
|
||||
prompt: '读取 secret',
|
||||
workMode: 'execute'
|
||||
},
|
||||
new AbortController().signal,
|
||||
async () => 'deny'
|
||||
)) {
|
||||
events.push(event)
|
||||
}
|
||||
}
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||||
|
||||
await expect(consume()).rejects.toThrow('用户拒绝')
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expect(
|
||||
events
|
||||
.filter((event) => event.type === 'tool')
|
||||
.map((event) => event.state)
|
||||
).toEqual(['pending', 'failed'])
|
||||
expect(toolProvider.callTool).not.toHaveBeenCalled()
|
||||
})
|
||||
|
||||
it('uses Anthropic tool_use and tool_result messages in Execute mode', async () => {
|
||||
const responses = [
|
||||
{
|
||||
id: 'message-tool-1',
|
||||
model: 'claude',
|
||||
content: [
|
||||
{
|
||||
type: 'tool_use',
|
||||
id: 'toolu-1',
|
||||
name: 'workspace_read_text',
|
||||
input: { path: 'notes.md' }
|
||||
}
|
||||
],
|
||||
usage: { input_tokens: 12, output_tokens: 3 }
|
||||
},
|
||||
{
|
||||
id: 'message-tool-2',
|
||||
model: 'claude',
|
||||
content: [{ type: 'text', text: '读取完成。' }],
|
||||
usage: { input_tokens: 20, output_tokens: 5 }
|
||||
}
|
||||
]
|
||||
const fetcher = vi.fn<typeof fetch>(async () =>
|
||||
Response.json(responses.shift())
|
||||
)
|
||||
const runtime = new ModelAgentRuntime({
|
||||
apiKey: 'test-key',
|
||||
baseUrl: 'https://bigtoken.ai',
|
||||
model: 'claude',
|
||||
protocol: 'anthropic-messages',
|
||||
authentication: 'api-key',
|
||||
fetcher,
|
||||
toolProvider: createToolProvider()
|
||||
})
|
||||
|
||||
for await (const _event of runtime.run(
|
||||
{
|
||||
requestId: 'a431666e-5ec8-45e6-beb4-654132eed132',
|
||||
conversationId: 'conversation-anthropic-tools',
|
||||
prompt: '读取 notes',
|
||||
workMode: 'execute'
|
||||
},
|
||||
new AbortController().signal,
|
||||
async () => 'once'
|
||||
)) {
|
||||
void _event
|
||||
}
|
||||
|
||||
const firstBody = JSON.parse(
|
||||
fetcher.mock.calls[0]?.[1]?.body as string
|
||||
) as Record<string, unknown>
|
||||
expect(firstBody).toMatchObject({
|
||||
stream: false,
|
||||
tools: [
|
||||
{
|
||||
name: 'workspace_read_text',
|
||||
input_schema: expect.objectContaining({ type: 'object' })
|
||||
}
|
||||
]
|
||||
})
|
||||
const secondBody = JSON.parse(
|
||||
fetcher.mock.calls[1]?.[1]?.body as string
|
||||
) as { messages: Array<Record<string, unknown>> }
|
||||
expect(secondBody.messages.at(-1)).toEqual({
|
||||
role: 'user',
|
||||
content: [
|
||||
{
|
||||
type: 'tool_result',
|
||||
tool_use_id: 'toolu-1',
|
||||
content: 'tool result'
|
||||
}
|
||||
]
|
||||
})
|
||||
})
|
||||
|
||||
it('generates a bounded image through the BigToken-compatible endpoint', async () => {
|
||||
|
||||
Reference in New Issue
Block a user