feat: 实现协议双向映射，支持 Live 模式调用 OpenAI API
核心改动： - 新增 src/utils/import.ts：OpenAI 响应 → Protocol Message 反向映射 + StreamingImporter 流式增量累加器 - 新增 src/services/api.ts：薄封装层，exportToOpenAIFormat → fetch → importFromOpenAIResponse - 新增 src/services/api-config.ts：API 配置 localStorage 持久化 - 新增 src/components/ApiSettings.tsx：API 设置模态框 - 改造 ChatContext：新增 isLive / sendMessage / 流式状态管理 - 改造 ChatView/ChatInput：Live 模式下启用输入，支持回车发送和 loading 动画 - 改造 App.tsx：Demo/Live 模式切换 + 设置入口 - 新增 19 个 import.test.ts 测试用例，全部 48 测试通过
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carry
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/**
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* API 调用服务 —— 薄封装层，负责 HTTP 通信和格式转换。
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*
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* 数据流：
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* envelope → exportToOpenAIFormat() → POST JSON → API
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* API → SSE/JSON response → importFromOpenAIResponse() → Message[]
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*
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* 依赖：
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* - src/utils/export.ts (Protocol → OpenAI)
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* - src/utils/import.ts (OpenAI → Protocol)
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* - src/services/api-config.ts (配置)
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*/
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import type { PromptEnvelope, Message } from '../types/protocol'
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import type { ApiConfig } from './api-config'
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import { exportToOpenAIFormat, type OpenAIExport, type OpenAIMessage } from '../utils/export'
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import {
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importFromOpenAIResponse,
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StreamingImporter,
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type OpenAIResponse,
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type OpenAIStreamChunk,
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} from '../utils/import'
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// ============================================================
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// 非流式请求
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// ============================================================
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/**
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* 发送非流式 Chat Completion 请求。
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* @returns 导入后的 Protocol Message 数组（通常只有一条 assistant 消息）
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*/
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export async function sendChatRequest(
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envelope: PromptEnvelope,
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config: ApiConfig
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): Promise<Message[]> {
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const body = buildRequestBody(envelope, config)
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const response = await fetch(`${config.baseUrl}/chat/completions`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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Authorization: `Bearer ${config.apiKey}`,
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},
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body: JSON.stringify(body),
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})
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if (!response.ok) {
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const errorText = await response.text().catch(() => '无法读取错误详情')
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let errorMsg: string
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try {
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const errJson = JSON.parse(errorText)
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errorMsg = errJson.error?.message || errorText
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} catch {
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errorMsg = errorText || `HTTP ${response.status}`
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}
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throw new Error(`API 请求失败 (${response.status}): ${errorMsg}`)
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}
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const data: OpenAIResponse = await response.json()
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const choice = data.choices?.[0]
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if (!choice) {
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throw new Error('API 响应中没有有效的 choices')
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}
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const msg = importFromOpenAIResponse(choice.message, choice.finish_reason, {
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timestamp: Date.now(),
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})
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return [msg]
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}
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// ============================================================
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// 流式请求
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// ============================================================
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export interface StreamCallbacks {
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/** 每收到 text delta 时回调部分 Message（用于实时打字效果） */
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onToken?: (partial: Message) => void
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/** tool_call delta 变化时回调（用于展示即将执行的 tool） */
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onToolCall?: (partial: Message) => void
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/** 流式完成时回调最终 Message 数组 */
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onDone: (final: Message[]) => void
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/** 发生错误时回调 */
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onError: (err: Error) => void
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}
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/**
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* 发送流式 Chat Completion 请求（SSE）。
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*
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* 使用 StreamingImporter 逐 chunk 累积 delta，
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* 通过 onToken/onToolCall 回调部分 Message 供 UI 实时渲染。
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*/
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export async function sendChatRequestStream(
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envelope: PromptEnvelope,
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config: ApiConfig,
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callbacks: StreamCallbacks
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): Promise<void> {
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const body = buildRequestBody(envelope, config)
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body.stream = true
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// 流式模式下需要 stream_options 来获取 usage（可选）
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body.stream_options = { include_usage: true }
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let response: Response
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try {
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response = await fetch(`${config.baseUrl}/chat/completions`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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Authorization: `Bearer ${config.apiKey}`,
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},
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body: JSON.stringify(body),
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})
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} catch (err) {
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callbacks.onError(
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new Error(`网络请求失败: ${err instanceof Error ? err.message : String(err)}`)
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)
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return
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}
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if (!response.ok) {
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const errorText = await response.text().catch(() => '无法读取错误详情')
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let errorMsg: string
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try {
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const errJson = JSON.parse(errorText)
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errorMsg = errJson.error?.message || errorText
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} catch {
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errorMsg = errorText || `HTTP ${response.status}`
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}
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callbacks.onError(new Error(`API 请求失败 (${response.status}): ${errorMsg}`))
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return
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}
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const reader = response.body?.getReader()
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if (!reader) {
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callbacks.onError(new Error('响应体不可读（不支持流式）'))
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return
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}
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const decoder = new TextDecoder()
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const importer = new StreamingImporter()
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let buffer = ''
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try {
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while (true) {
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const { done, value } = await reader.read()
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if (done) break
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buffer += decoder.decode(value, { stream: true })
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// 按 SSE 协议解析：event 以 \n\n 为界
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const lines = buffer.split('\n')
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// 最后一个可能不完整，保留在 buffer 中
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buffer = lines.pop() || ''
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for (const line of lines) {
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const trimmed = line.trim()
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if (!trimmed || trimmed.startsWith(':')) {
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// 空行、注释行 —— 跳过
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continue
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}
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if (trimmed.startsWith('data: ')) {
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const dataStr = trimmed.slice(6)
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// 结束标记
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if (dataStr === '[DONE]') {
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break
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}
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try {
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const chunk: OpenAIStreamChunk = JSON.parse(dataStr)
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const delta = chunk.choices?.[0]?.delta
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if (delta) {
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const prevContent = importer.content
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const prevToolCount = importer.hasPendingToolCalls
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importer.ingestDelta(delta)
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// 文本有变化 → 回调当前部分消息
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if (importer.content !== prevContent || importer.hasPendingToolCalls !== prevToolCount) {
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const partial = importer.toPartialMessage()
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callbacks.onToken?.(partial)
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}
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}
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} catch {
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// JSON 解析失败 —— 跳过此 chunk（某些 API 可能返回非标准格式）
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console.warn('[api] SSE chunk 解析失败:', dataStr.slice(0, 80))
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}
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}
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}
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}
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// 构建最终消息
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const finalMsg = importer.toMessage({ timestamp: Date.now() })
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callbacks.onDone([finalMsg])
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} catch (err) {
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// 流式中断时，尝试保留已累积的内容
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if (importer.content.trim() || importer.hasPendingToolCalls) {
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const partial = importer.toPartialMessage({ timestamp: Date.now() })
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// 用部分内容构建最终消息，但追加截断标记
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const truncated = importer.toMessage({ timestamp: Date.now() })
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if (truncated.segments.length > 0 && truncated.segments[0].kind === 'text') {
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callbacks.onDone([truncated])
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} else {
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callbacks.onDone([partial])
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}
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}
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callbacks.onError(
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new Error(`流式连接中断: ${err instanceof Error ? err.message : String(err)}`)
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)
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}
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}
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// ============================================================
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// 请求构造
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// ============================================================
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/**
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* 构建 OpenAI Chat Completions 请求体。
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* 从 envelope 导出并注入 model 配置。
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*/
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function buildRequestBody(
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envelope: PromptEnvelope,
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config: ApiConfig
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): OpenAIExport & { stream?: boolean; stream_options?: { include_usage: boolean } } {
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const exported = exportToOpenAIFormat(envelope)
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return {
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...exported,
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model: config.model || exported.model,
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}
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}
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