feat(aia): Complete AIA V2.0 with universal streaming capabilities
Major Updates: - Add StreamingService with OpenAI Compatible format (backend/common/streaming) - Upgrade Chat component V2 with Ant Design X integration - Implement AIA module with 12 intelligent agents - Create AgentHub with 100% prototype V11 restoration - Create ChatWorkspace with streaming response support - Add ThinkingBlock for deep thinking display - Add useAIStream Hook for OpenAI Compatible stream handling Backend Common Capabilities (~400 lines): - OpenAIStreamAdapter: SSE adapter with OpenAI format - StreamingService: unified streaming service - Support content and reasoning_content dual streams - Deep thinking tag processing (<think>...</think>) Frontend Common Capabilities (~2000 lines): - AIStreamChat: modern streaming chat component - ThinkingBlock: collapsible deep thinking display - ConversationList: conversation management with grouping - useAIStream: OpenAI Compatible stream handler Hook - useConversations: conversation state management Hook - Modern design styles (Ultramodern theme) AIA Module Frontend (~1500 lines): - AgentHub: 12 agent cards with timeline design - ChatWorkspace: fullscreen immersive chat interface - AgentCard: theme-colored cards (blue/yellow/teal/purple) - 5 phases, 12 agents configuration - Responsive layout (desktop + mobile) AIA Module Backend (~900 lines): - agentService: 12 agents config with system prompts - conversationService: refactored with StreamingService - attachmentService: file upload skeleton (30k token limit) - 12 API endpoints with authentication - Full CRUD for conversations and messages Documentation: - AIA module status and development guide - Universal capabilities catalog (11 services) - Quick reference card for developers - System overview updates Testing: - Stream response verified (HTTP 200) - Authentication working correctly - Auto conversation creation working - Deep thinking display working - Message input and send working Status: Core features completed (85%), attachment and history loading pending
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backend/src/common/streaming/StreamingService.ts
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backend/src/common/streaming/StreamingService.ts
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/**
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* 通用流式响应服务
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*
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* 封装 LLM 调用 + OpenAI Compatible 输出
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* 支持深度思考、Token 统计、错误处理
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*/
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import { FastifyReply } from 'fastify';
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import { OpenAIStreamAdapter, createOpenAIStreamAdapter } from './OpenAIStreamAdapter';
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import { StreamOptions, StreamCallbacks, THINKING_TAGS, OpenAIMessage } from './types';
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import { LLMFactory } from '../llm/adapters/LLMFactory';
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import type { Message as LLMMessage } from '../llm/adapters/types';
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import { logger } from '../logging/logger';
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/**
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* 深度思考标签处理结果
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*/
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interface ThinkingParseResult {
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content: string;
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thinking: string;
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inThinking: boolean;
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}
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/**
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* 流式响应服务
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*/
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export class StreamingService {
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private adapter: OpenAIStreamAdapter;
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private options: StreamOptions;
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private fullContent: string = '';
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private thinkingContent: string = '';
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private isInThinking: boolean = false;
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constructor(reply: FastifyReply, options: StreamOptions = {}) {
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this.adapter = createOpenAIStreamAdapter(reply, options.model);
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this.options = options;
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}
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/**
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* 执行流式生成
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*/
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async streamGenerate(
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messages: OpenAIMessage[],
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callbacks?: StreamCallbacks
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): Promise<{ content: string; thinking: string; messageId: string }> {
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const { model = 'deepseek-v3', temperature = 0.7, maxTokens = 4096 } = this.options;
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try {
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// 获取 LLM 适配器
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const llm = LLMFactory.getAdapter(model as any);
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// 发送角色开始标识
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this.adapter.sendRoleStart();
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// 流式生成
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const stream = llm.chatStream(
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messages as LLMMessage[],
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{ temperature, maxTokens }
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);
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for await (const chunk of stream) {
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if (chunk.content) {
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// 处理深度思考标签
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const { content, thinking, inThinking } = this.processThinkingTags(
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chunk.content,
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this.options.enableDeepThinking ?? false
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);
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// 发送思考内容
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if (thinking) {
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this.thinkingContent += thinking;
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this.adapter.sendReasoningDelta(thinking);
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callbacks?.onThinking?.(thinking);
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}
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// 发送正文内容
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if (content) {
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this.fullContent += content;
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this.adapter.sendContentDelta(content);
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callbacks?.onContent?.(content);
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}
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}
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}
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// 发送完成标识
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const usage = {
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promptTokens: this.estimateTokens(messages.map(m => m.content).join('')),
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completionTokens: this.estimateTokens(this.fullContent),
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totalTokens: 0,
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};
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usage.totalTokens = usage.promptTokens + usage.completionTokens;
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this.adapter.sendComplete(usage);
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this.adapter.end();
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// 完成回调
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callbacks?.onComplete?.(this.fullContent, this.thinkingContent);
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logger.info('[StreamingService] 流式生成完成', {
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conversationId: this.options.conversationId,
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contentLength: this.fullContent.length,
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thinkingLength: this.thinkingContent.length,
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tokens: usage.totalTokens,
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});
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return {
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content: this.fullContent,
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thinking: this.thinkingContent,
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messageId: this.adapter.getMessageId(),
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};
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} catch (error) {
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const errorMessage = error instanceof Error ? error.message : '流式生成失败';
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this.adapter.sendError(errorMessage);
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this.adapter.end();
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callbacks?.onError?.(error instanceof Error ? error : new Error(errorMessage));
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logger.error('[StreamingService] 流式生成失败', {
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error,
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conversationId: this.options.conversationId,
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});
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throw error;
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}
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}
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/**
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* 处理深度思考标签
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*/
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private processThinkingTags(text: string, enableDeepThinking: boolean): ThinkingParseResult {
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if (!enableDeepThinking) {
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return { content: text, thinking: '', inThinking: this.isInThinking };
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}
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let content = '';
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let thinking = '';
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let remaining = text;
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while (remaining.length > 0) {
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if (this.isInThinking) {
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// 在思考模式中，查找结束标签
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const endIndex = remaining.indexOf(THINKING_TAGS.END);
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if (endIndex !== -1) {
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thinking += remaining.substring(0, endIndex);
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remaining = remaining.substring(endIndex + THINKING_TAGS.END.length);
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this.isInThinking = false;
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} else {
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thinking += remaining;
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remaining = '';
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}
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} else {
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// 不在思考模式，查找开始标签
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const startIndex = remaining.indexOf(THINKING_TAGS.START);
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if (startIndex !== -1) {
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content += remaining.substring(0, startIndex);
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remaining = remaining.substring(startIndex + THINKING_TAGS.START.length);
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this.isInThinking = true;
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} else {
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content += remaining;
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remaining = '';
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}
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}
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}
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return { content, thinking, inThinking: this.isInThinking };
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}
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/**
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* 估算 Token 数量（简单实现）
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*/
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private estimateTokens(text: string): number {
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// 中文约 1.5 字符/token，英文约 4 字符/token
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const chineseChars = (text.match(/[\u4e00-\u9fa5]/g) || []).length;
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const otherChars = text.length - chineseChars;
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return Math.ceil(chineseChars / 1.5 + otherChars / 4);
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}
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}
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/**
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* 创建流式响应服务
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*/
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export function createStreamingService(
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reply: FastifyReply,
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options?: StreamOptions
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): StreamingService {
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return new StreamingService(reply, options);
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}
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/**
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* 快捷方法：直接执行流式生成
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*/
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export async function streamChat(
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reply: FastifyReply,
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messages: OpenAIMessage[],
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options?: StreamOptions,
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callbacks?: StreamCallbacks
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) {
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const service = createStreamingService(reply, options);
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return service.streamGenerate(messages, callbacks);
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}
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