feat: Go backend, enhanced search, new widgets, Docker deploy
Major changes: - Add Go backend (backend/) with microservices architecture - Enhanced master-agents-svc: reranker, content-classifier, stealth-crawler, proxy-manager, media-search, fastClassifier, language detection - New web-svc widgets: KnowledgeCard, ProductCard, ProfileCard, VideoCard, UnifiedCard, CardGallery, InlineImageGallery, SourcesPanel, RelatedQuestions - Improved discover-svc with discover-db integration - Docker deployment improvements (Caddyfile, vendor.sh, BUILD.md) - Library-svc: project_id schema migration - Remove deprecated finance-svc and travel-svc - Localization improvements across services Made-with: Cursor
This commit is contained in:
home
@@ -1,12 +1,136 @@
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import type { LlmClient } from '../llm-client.js';
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import SessionManager from '../session.js';
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import type { TextBlock } from '../types.js';
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import type { TextBlock, Chunk } from '../types.js';
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import type { ClassifierOutput } from '../actions/types.js';
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import { detectLanguage } from '../prompts/detectLanguage.js';
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import { getClassifierPrompt } from '../prompts/classifier.js';
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import { getWriterPrompt } from '../prompts/writer.js';
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import { classify } from './classifier.js';
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import { fastClassify, generateSearchQueries } from './fastClassifier.js';
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import { research } from './researcher.js';
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import { executeAllWidgets } from '../widgets/index.js';
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import { searchMedia, type MediaSearchResult } from '../media-search.js';
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import { searchSearxng } from '../searxng.js';
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import { rerankBM25, computeAdaptiveTopK, estimateQueryComplexity } from '../reranker.js';
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const DISCOVER_SVC_URL = (process.env.DISCOVER_SVC_URL ?? '').replace(/\/$/, '');
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interface DigestResponse {
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summaryRu: string;
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citations: { index: number; url: string; title: string; domain: string }[];
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followUp: string[];
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sourcesCount: number;
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clusterTitle: string;
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}
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async function fetchPreGeneratedDigest(articleUrl: string): Promise<DigestResponse | null> {
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if (!DISCOVER_SVC_URL) return null;
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try {
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const res = await fetch(
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`${DISCOVER_SVC_URL}/api/v1/discover/digest?url=${encodeURIComponent(articleUrl)}`,
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{ signal: AbortSignal.timeout(3000) },
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);
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if (!res.ok) return null;
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const data = (await res.json()) as DigestResponse;
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if (data.summaryRu && data.citations?.length > 0) return data;
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return null;
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} catch {
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return null;
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}
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}
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const CRAWL4AI_URL = (process.env.CRAWL4AI_URL ?? 'http://crawl4ai:11235').replace(/\/$/, '');
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function extractCrawl4aiMarkdown(md: unknown): string {
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if (typeof md === 'string') return md;
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if (md && typeof md === 'object') {
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const obj = md as Record<string, unknown>;
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if (typeof obj.raw_markdown === 'string') return obj.raw_markdown;
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if (typeof obj.markdown_with_citations === 'string') return obj.markdown_with_citations;
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if (typeof obj.fit_markdown === 'string' && (obj.fit_markdown as string).length > 0) return obj.fit_markdown;
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}
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return '';
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}
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async function preScrapeArticleUrl(url: string): Promise<{ title: string; content: string; url: string } | null> {
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try {
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const res = await fetch(`${CRAWL4AI_URL}/crawl`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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urls: [url],
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crawler_config: { type: 'CrawlerRunConfig', params: { cache_mode: 'default', page_timeout: 20000 } },
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}),
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signal: AbortSignal.timeout(25000),
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});
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if (!res.ok) throw new Error(`Crawl4AI HTTP ${res.status}`);
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const data = (await res.json()) as {
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results?: { markdown?: unknown; metadata?: { title?: string }; success?: boolean }[];
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};
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const first = data.results?.[0];
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const md = extractCrawl4aiMarkdown(first?.markdown);
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if (first?.success && md.length > 100) {
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return {
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title: first.metadata?.title ?? url,
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content: md.slice(0, 15000),
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url,
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};
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}
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} catch {
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// fallback to basic fetch
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}
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try {
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const res = await fetch(url, {
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signal: AbortSignal.timeout(10000),
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headers: { 'User-Agent': 'GooSeek-Agent/1.0' },
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});
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const html = await res.text();
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const title = html.match(/<title[^>]*>([^<]+)<\/title>/i)?.[1]?.trim() ?? url;
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const bodyMatch = html.match(/<body[^>]*>([\s\S]*?)<\/body>/i);
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const body = bodyMatch ? bodyMatch[1] : html;
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const text = body
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.replace(/<script[\s\S]*?<\/script>/gi, '')
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.replace(/<style[\s\S]*?<\/style>/gi, '')
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.replace(/<[^>]+>/g, ' ')
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.replace(/\s+/g, ' ')
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.trim()
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.slice(0, 15000);
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if (text.length > 100) return { title, content: text, url };
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} catch {
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// give up
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}
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return null;
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}
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/** Слова запроса (длина >= 2) для подсчёта релевантности */
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function queryTerms(query: string): Set<string> {
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const normalized = query
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.replace(/^Summary:\s*/i, '')
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.replace(/https?:\/\/[^\s]+/g, '')
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.toLowerCase();
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const words = normalized.split(/\s+/).filter((w) => w.length >= 2);
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return new Set(words);
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}
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/** Скор релевантности: сколько слов запроса встречается в title и content */
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function relevanceScore(chunk: Chunk, terms: Set<string>): number {
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const title = String(chunk.metadata?.title ?? '');
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const content = (chunk.content ?? '').toLowerCase();
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const titleLower = title.toLowerCase();
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let score = 0;
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for (const t of terms) {
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if (titleLower.includes(t)) score += 3;
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if (content.includes(t)) score += 1;
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}
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return score;
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}
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function rankByRelevance(chunks: Chunk[], query: string): Chunk[] {
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if (chunks.length === 0) return [];
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const terms = queryTerms(query);
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if (terms.size === 0) return chunks;
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return [...chunks].sort((a, b) => relevanceScore(b, terms) - relevanceScore(a, terms));
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}
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export type SearchOrchestratorConfig = {
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llm: LlmClient;
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@@ -27,20 +151,287 @@ export type SearchOrchestratorInput = {
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config: SearchOrchestratorConfig;
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};
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/**
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* SPEED MODE: Optimized fast path
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* - No LLM classifier (rule-based)
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* - Direct SearXNG search (no researcher loop)
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* - No page scraping (snippets only)
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* - Parallel: search + media + widgets
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* Target: 8-15 seconds (like Perplexity)
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*/
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async function runSpeedMode(
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session: SessionManager,
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input: {
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chatHistory: { role: string; content: string }[];
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followUp: string;
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config: SearchOrchestratorConfig;
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detectedLanguage?: string;
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},
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): Promise<void> {
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const { chatHistory, followUp, config, detectedLanguage } = input;
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const classification = fastClassify(followUp, chatHistory);
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const searchQuery = classification.standaloneFollowUp || followUp;
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const queries = generateSearchQueries(searchQuery);
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const researchBlockId = crypto.randomUUID();
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session.emitBlock({
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id: researchBlockId,
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type: 'research',
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data: { subSteps: [{ id: crypto.randomUUID(), type: 'searching', searching: queries }] },
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});
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const searchPromise = (async () => {
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const results: Chunk[] = [];
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const seenUrls = new Set<string>();
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await Promise.all(
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queries.map(async (q) => {
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try {
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const res = await searchSearxng(q, { categories: ['general', 'news'], pageno: 1 });
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for (const r of res.results ?? []) {
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if (r.url && !seenUrls.has(r.url)) {
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seenUrls.add(r.url);
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results.push({
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content: r.content || r.title,
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metadata: { title: r.title, url: r.url },
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});
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}
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}
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} catch { /* ignore search errors */ }
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}),
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);
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return results;
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})();
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const widgetPromise = executeAllWidgets({
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chatHistory,
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followUp,
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classification: classification as ClassifierOutput,
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llm: config.llm,
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}).then((outputs) => {
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for (const o of outputs) {
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session.emitBlock({
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id: crypto.randomUUID(),
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type: 'widget',
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data: { widgetType: o.type, params: o.data ?? {} },
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});
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}
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return outputs;
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});
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const mediaPromise = searchMedia(searchQuery, { maxImages: 6, maxVideos: 4 }).then((mediaResult) => {
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if (mediaResult.images.length > 0) {
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session.emitBlock({
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id: crypto.randomUUID(),
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type: 'widget',
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data: {
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widgetType: 'image_gallery',
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params: { images: mediaResult.images, layout: 'carousel' },
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},
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});
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}
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if (mediaResult.videos.length > 0) {
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session.emitBlock({
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id: crypto.randomUUID(),
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type: 'widget',
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data: {
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widgetType: 'videos',
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params: { items: mediaResult.videos, title: '' },
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},
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});
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}
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return mediaResult;
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});
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const [searchResults, widgetOutputs] = await Promise.all([
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searchPromise,
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widgetPromise,
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mediaPromise,
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]);
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session.emitBlock({
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id: crypto.randomUUID(),
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type: 'source',
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data: searchResults,
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});
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session.emit('data', { type: 'researchComplete' });
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const queryComplexity = estimateQueryComplexity(searchQuery);
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const adaptiveTopK = computeAdaptiveTopK(searchResults.length, queryComplexity, 'speed');
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const rerankableItems = searchResults.map((r) => ({
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content: r.content,
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title: (r.metadata?.title as string) ?? '',
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url: (r.metadata?.url as string) ?? '',
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metadata: r.metadata,
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}));
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const rankedItems = rerankBM25(rerankableItems, searchQuery, adaptiveTopK);
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const findingsForWriter = rankedItems.slice(0, 15).map((item) => ({
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content: item.content,
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metadata: item.metadata ?? { title: item.title, url: item.url },
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}));
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const MAX_CONTENT_PER_RESULT = 250;
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const finalContext = findingsForWriter
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.map((f, index) => {
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const content = f.content.length > MAX_CONTENT_PER_RESULT
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? f.content.slice(0, MAX_CONTENT_PER_RESULT) + '…'
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: f.content;
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return `<result index=${index + 1} title="${String(f.metadata?.title ?? '').replace(/"/g, "'")}">${content}</result>`;
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})
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.join('\n') || '';
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const widgetContext = widgetOutputs
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.map((o) => `<result>${o.llmContext}</result>`)
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.join('\n-------------\n');
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const finalContextWithWidgets =
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`<search_results note="These are the search results and assistant can cite these">\n${finalContext}\n</search_results>\n` +
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`<widgets_result noteForAssistant="Its output is already showed to the user, assistant can use this information to answer the query but do not CITE this as a source">\n${widgetContext}\n</widgets_result>`;
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const writerPrompt = getWriterPrompt(
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finalContextWithWidgets,
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config.systemInstructions,
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'speed',
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config.locale,
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config.memoryContext,
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config.answerMode,
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config.responsePrefs,
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config.learningMode,
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detectedLanguage,
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false,
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);
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const answerStream = config.llm.streamText({
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messages: [
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{ role: 'system', content: writerPrompt },
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...chatHistory,
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{ role: 'user', content: followUp },
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],
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options: { maxTokens: 2048 },
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});
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let responseBlockId = '';
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let hasContent = false;
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let accumulatedText = '';
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for await (const chunk of answerStream) {
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const chunkText = chunk.contentChunk ?? '';
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if (!chunkText && !responseBlockId) continue;
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if (!responseBlockId) {
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accumulatedText = chunkText;
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const block: TextBlock = {
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id: crypto.randomUUID(),
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type: 'text',
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data: chunkText,
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};
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session.emitBlock(block);
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responseBlockId = block.id;
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if (chunkText) hasContent = true;
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} else if (chunkText) {
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accumulatedText += chunkText;
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hasContent = true;
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session.emit('data', {
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type: 'textChunk',
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blockId: responseBlockId,
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chunk: chunkText,
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});
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const block = session.getBlock(responseBlockId) as TextBlock | null;
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if (block) {
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block.data = accumulatedText;
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}
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}
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}
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if (responseBlockId) {
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session.updateBlock(responseBlockId, [{ op: 'replace', path: '/data', value: accumulatedText }]);
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}
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if (!hasContent && findingsForWriter.length > 0) {
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const lines = findingsForWriter.slice(0, 8).map((f, i) => {
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const title = (f.metadata?.title as string) ?? 'Без названия';
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const excerpt = f.content.length > 100 ? f.content.slice(0, 100) + '…' : f.content;
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return `${i + 1}. **${title}** — ${excerpt}`;
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});
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session.emitBlock({
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id: crypto.randomUUID(),
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type: 'text',
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data: `## По найденным источникам\n\n${lines.join('\n\n')}\n\n*Ответ LLM недоступен.*`,
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});
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}
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|
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session.emit('end', {});
|
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}
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export async function runSearchOrchestrator(
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session: SessionManager,
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input: SearchOrchestratorInput,
|
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): Promise<void> {
|
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const { chatHistory, followUp, config } = input;
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const detectedLanguage = detectLanguage(followUp);
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const isArticleSummary = followUp.trim().startsWith('Summary: ') && followUp.trim().length > 9;
|
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|
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const classification = await classify({
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// SPEED MODE: Fast path — no LLM classifier, direct search, no scraping
|
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if (config.mode === 'speed' && !isArticleSummary) {
|
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await runSpeedMode(session, { chatHistory, followUp, config, detectedLanguage });
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return;
|
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}
|
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|
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let preScrapedArticle: { title: string; content: string; url: string } | null = null;
|
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|
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if (isArticleSummary) {
|
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const articleUrl = followUp.trim().slice(9).trim();
|
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|
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const [digest, preScrapeResult] = await Promise.all([
|
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fetchPreGeneratedDigest(articleUrl),
|
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preScrapeArticleUrl(articleUrl),
|
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]);
|
||||
|
||||
if (digest) {
|
||||
const sourceBlock = {
|
||||
id: crypto.randomUUID(),
|
||||
type: 'source' as const,
|
||||
data: digest.citations.map((c: { index: number; url: string; title: string; domain: string }) => ({
|
||||
content: c.title,
|
||||
metadata: { url: c.url, title: c.title, domain: c.domain, index: c.index },
|
||||
})),
|
||||
};
|
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session.emitBlock(sourceBlock);
|
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session.emit('data', { type: 'researchComplete' });
|
||||
|
||||
let summaryText = digest.summaryRu;
|
||||
if (digest.followUp && digest.followUp.length > 0) {
|
||||
summaryText += '\n\n---\n' + digest.followUp.map((q: string) => `> ${q}`).join('\n');
|
||||
}
|
||||
session.emitBlock({ id: crypto.randomUUID(), type: 'text', data: summaryText });
|
||||
session.emit('end', {});
|
||||
return;
|
||||
}
|
||||
|
||||
preScrapedArticle = preScrapeResult;
|
||||
}
|
||||
|
||||
let classification = await classify({
|
||||
chatHistory,
|
||||
query: followUp,
|
||||
llm: config.llm,
|
||||
locale: config.locale,
|
||||
detectedLanguage,
|
||||
enabledSources: config.sources,
|
||||
});
|
||||
|
||||
if (isArticleSummary && classification.classification.skipSearch) {
|
||||
classification = {
|
||||
...classification,
|
||||
classification: { ...classification.classification, skipSearch: false },
|
||||
};
|
||||
}
|
||||
|
||||
const widgetPromise = executeAllWidgets({
|
||||
chatHistory,
|
||||
followUp,
|
||||
@@ -57,28 +448,87 @@ export async function runSearchOrchestrator(
|
||||
return outputs;
|
||||
});
|
||||
|
||||
const mediaQuery = classification.standaloneFollowUp || followUp;
|
||||
const mediaPromise = !isArticleSummary
|
||||
? searchMedia(mediaQuery, { maxImages: 8, maxVideos: 6 }).then((mediaResult) => {
|
||||
if (mediaResult.images.length > 0) {
|
||||
session.emitBlock({
|
||||
id: crypto.randomUUID(),
|
||||
type: 'widget',
|
||||
data: {
|
||||
widgetType: 'image_gallery',
|
||||
params: {
|
||||
images: mediaResult.images,
|
||||
layout: 'carousel',
|
||||
},
|
||||
},
|
||||
});
|
||||
}
|
||||
if (mediaResult.videos.length > 0) {
|
||||
session.emitBlock({
|
||||
id: crypto.randomUUID(),
|
||||
type: 'widget',
|
||||
data: {
|
||||
widgetType: 'videos',
|
||||
params: {
|
||||
items: mediaResult.videos,
|
||||
title: '',
|
||||
},
|
||||
},
|
||||
});
|
||||
}
|
||||
return mediaResult;
|
||||
})
|
||||
: Promise.resolve({ images: [], videos: [] } as MediaSearchResult);
|
||||
|
||||
let searchPromise: Promise<{ searchFindings: import('../types.js').Chunk[] }> | null = null;
|
||||
const effectiveFollowUp = (isArticleSummary && preScrapedArticle?.title)
|
||||
? `Summary: ${preScrapedArticle.url}\nArticle title: ${preScrapedArticle.title}`
|
||||
: followUp;
|
||||
|
||||
if (!classification.classification.skipSearch) {
|
||||
searchPromise = research(session, config.llm, {
|
||||
chatHistory,
|
||||
followUp,
|
||||
classification,
|
||||
followUp: effectiveFollowUp,
|
||||
classification: isArticleSummary && preScrapedArticle
|
||||
? { ...classification, standaloneFollowUp: `${preScrapedArticle.title} ${classification.standaloneFollowUp}` }
|
||||
: classification,
|
||||
config: {
|
||||
mode: config.mode,
|
||||
sources: config.sources,
|
||||
fileIds: config.fileIds,
|
||||
locale: config.locale,
|
||||
detectedLanguage,
|
||||
isArticleSummary,
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
const [widgetOutputs, searchResults] = await Promise.all([widgetPromise, searchPromise ?? Promise.resolve({ searchFindings: [] })]);
|
||||
const [widgetOutputs, searchResults] = await Promise.all([
|
||||
widgetPromise,
|
||||
searchPromise ?? Promise.resolve({ searchFindings: [] }),
|
||||
mediaPromise,
|
||||
]);
|
||||
|
||||
session.emit('data', { type: 'researchComplete' });
|
||||
|
||||
const MAX_RESULTS_FOR_WRITER = 15;
|
||||
const MAX_CONTENT_PER_RESULT = 180;
|
||||
const findingsForWriter = (searchResults?.searchFindings ?? []).slice(0, MAX_RESULTS_FOR_WRITER);
|
||||
const MAX_RESULTS_FOR_WRITER = isArticleSummary ? 30 : 25;
|
||||
const MAX_CONTENT_PER_RESULT = isArticleSummary ? 2000 : 320;
|
||||
const rawFindings = searchResults?.searchFindings ?? [];
|
||||
|
||||
if (isArticleSummary && preScrapedArticle) {
|
||||
const alreadyHasUrl = rawFindings.some(
|
||||
(f) => (f.metadata?.url as string)?.includes(preScrapedArticle!.url),
|
||||
);
|
||||
if (!alreadyHasUrl) {
|
||||
rawFindings.unshift({
|
||||
content: preScrapedArticle.content,
|
||||
metadata: { url: preScrapedArticle.url, title: preScrapedArticle.title },
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
const findingsForWriter = rankByRelevance(rawFindings, followUp).slice(0, MAX_RESULTS_FOR_WRITER);
|
||||
const finalContext =
|
||||
findingsForWriter
|
||||
.map((f, index) => {
|
||||
@@ -104,6 +554,8 @@ export async function runSearchOrchestrator(
|
||||
config.answerMode,
|
||||
config.responsePrefs,
|
||||
config.learningMode,
|
||||
detectedLanguage,
|
||||
isArticleSummary,
|
||||
);
|
||||
|
||||
const answerStream = config.llm.streamText({
|
||||
@@ -117,27 +569,43 @@ export async function runSearchOrchestrator(
|
||||
|
||||
let responseBlockId = '';
|
||||
let hasContent = false;
|
||||
let accumulatedText = '';
|
||||
|
||||
for await (const chunk of answerStream) {
|
||||
if (!chunk.contentChunk && !responseBlockId) continue;
|
||||
const chunkText = chunk.contentChunk ?? '';
|
||||
if (!chunkText && !responseBlockId) continue;
|
||||
|
||||
if (!responseBlockId) {
|
||||
accumulatedText = chunkText;
|
||||
const block: TextBlock = {
|
||||
id: crypto.randomUUID(),
|
||||
type: 'text',
|
||||
data: chunk.contentChunk ?? '',
|
||||
data: chunkText,
|
||||
};
|
||||
session.emitBlock(block);
|
||||
responseBlockId = block.id;
|
||||
if (chunk.contentChunk) hasContent = true;
|
||||
} else {
|
||||
if (chunkText) hasContent = true;
|
||||
} else if (chunkText) {
|
||||
accumulatedText += chunkText;
|
||||
hasContent = true;
|
||||
// Отправляем только новый чанк для немедленного отображения
|
||||
session.emit('data', {
|
||||
type: 'textChunk',
|
||||
blockId: responseBlockId,
|
||||
chunk: chunkText
|
||||
});
|
||||
// Также обновляем полный блок для консистентности
|
||||
const block = session.getBlock(responseBlockId) as TextBlock | null;
|
||||
if (block) {
|
||||
block.data += chunk.contentChunk ?? '';
|
||||
if (chunk.contentChunk) hasContent = true;
|
||||
session.updateBlock(block.id, [{ op: 'replace', path: '/data', value: block.data }]);
|
||||
block.data = accumulatedText;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Финальное обновление блока
|
||||
if (responseBlockId) {
|
||||
session.updateBlock(responseBlockId, [{ op: 'replace', path: '/data', value: accumulatedText }]);
|
||||
}
|
||||
|
||||
if (!hasContent && findingsForWriter.length > 0) {
|
||||
const lines = findingsForWriter.slice(0, 10).map((f, i) => {
|
||||
|
||||
Reference in New Issue
Block a user