feat: v2.2.92 → v2.2.158 — god-file 분해 + Stocks feature + 대화 연속성
R56–R59: agent.ts 2731→1529줄 god-file 분해 (25 modules) · attrParsers + LLM 메서드 8개 (callNonStreaming, streamChatOnce 등) · executeActions 415줄 → 8 handler 그룹 (file/run/list/brain/calendar/sheets/tasks) · handlePrompt 1100줄 → 7 phase 모듈 (system prompt + budget + autoContinue 등) R50–R55: extension.ts 1145→349줄 (telegram/settings/provider commands 분리) Stocks feature 신규: /stocks slash command (v2.2.152~158) · .astra/stocks.json 저장소 + Yahoo Finance 현재가 갱신 · 8 키워드 필터 (ROE/성장성/유동성/수익성/영업효율/기술력/안정성/PBR) · Naver 시가총액 페이지 JSON API (m.stock.naver.com) 발굴 · LLM Top 5 매력도 분석 + Telegram 자동 보고서 · KST 09:00/15:00 watcher 자동 모니터링 대화 연속성 (v2.2.150~157): · [PRIOR TURN CONCLUSION] block 으로 직전 결론 anchor · thin follow-up 분류 → boilerplate 헤더 suppression · slash 명령 결과 chatHistory mirror (capture wrapper) · echo/parrot 금지 system prompt rule 기타: /stocks 슬래시 자동완성 dropdown UI, Naver JSON API 전환 (cheerio 제거) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
g1nation
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import { getConfig } from '../../config';
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import { logError, resolveEngine } from '../../utils';
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import { estimateMessagesTokens, computeOutputBudget } from '../../lib/contextManager';
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import { lmStudioSamplingFromConfig, lmStudioRespondExtrasFromConfig } from '../../lib/contextBuilders/lmStudioSampling';
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import { AGENT_PROMPTS, type AgentRole, type AgentExecutorOptions, type ChatMessage } from '../../agent';
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export interface CallRoleAgentDeps {
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getAbortSignal: () => AbortSignal | undefined;
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createStreamingRequest: (params: {
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baseUrl: string;
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modelName: string;
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reqMessages: ChatMessage[];
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temperature: number;
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maxTokens?: number;
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contextLength?: number;
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}) => Promise<{ response: Response; engine: 'lmstudio' | 'ollama'; apiUrl: string }>;
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options: AgentExecutorOptions;
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}
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export async function callRoleAgent(deps: CallRoleAgentDeps, role: AgentRole, prompt: string, modelName: string, options: any): Promise<string> {
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const persona = AGENT_PROMPTS[role];
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const { ollamaUrl, contextLength, maxOutputTokens, contextSafetyMargin, contextOverflowPolicy } = getConfig();
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const messages: ChatMessage[] = [
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{ role: 'system', content: persona },
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{ role: 'user', content: prompt }
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];
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// Dynamic output cap so input + output stays within the context window.
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const inputTokens = estimateMessagesTokens(messages);
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const { maxOutputTokens: subMaxTokens } = computeOutputBudget(inputTokens, {
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contextLength, maxOutputTokens, safetyMargin: contextSafetyMargin, minOutputTokens: 512,
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});
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const engine = resolveEngine(ollamaUrl);
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let responseText = '';
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if (engine === 'lmstudio' && deps.options.lmStudioStreamer) {
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try {
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const stream = deps.options.lmStudioStreamer.stream({
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modelName,
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messages: messages.map((m) => ({ role: m.role, content: m.content })),
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temperature: 0.3,
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maxTokens: subMaxTokens,
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contextOverflowPolicy,
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...lmStudioSamplingFromConfig(),
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...lmStudioRespondExtrasFromConfig(),
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signal: deps.getAbortSignal(),
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});
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let subStopReason: string | undefined;
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for await (const { token, stopReason } of stream) {
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if (token) responseText += token;
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if (stopReason) subStopReason = stopReason;
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}
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// Sub-agent answers that got cut mid-sentence corrupt the pipeline silently
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// (Planner produces a half-step, Writer can't recover). Surface a warn log so
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// the operator can raise subMaxTokens or pick a less aggressive output budget.
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if (subStopReason && /maxPredicted|context|truncat/i.test(subStopReason)) {
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logError('Sub-agent answer hit a generation limit.', {
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role, model: modelName, stopReason: subStopReason,
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chars: responseText.length, maxTokens: subMaxTokens,
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});
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}
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return responseText;
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} catch (err: any) {
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if (err?.name === 'AbortError' || deps.getAbortSignal()?.aborted) return responseText;
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logError('LM Studio SDK callAgent stream failed.', { role, error: err?.message ?? String(err) });
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throw err;
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}
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}
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const request = await deps.createStreamingRequest({
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baseUrl: ollamaUrl,
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modelName: modelName,
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reqMessages: messages,
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temperature: 0.3, // Use lower temperature for planning and research
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maxTokens: subMaxTokens,
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contextLength
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});
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const reader = request.response.body?.getReader();
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if (!reader) throw new Error("Agent response body is not readable.");
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const decoder = new TextDecoder();
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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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const chunk = decoder.decode(value, { stream: true });
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const lines = chunk.split('\n');
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for (const line of lines) {
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const trimmed = line.trim();
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if (!trimmed || trimmed === 'data: [DONE]') continue;
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try {
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const json = JSON.parse(trimmed.startsWith('data: ') ? trimmed.slice(6) : trimmed);
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const content = json.choices?.[0]?.delta?.content || json.message?.content || '';
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responseText += content;
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} catch (e) { }
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}
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}
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} finally {
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try { reader.releaseLock(); } catch { /* already released */ }
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}
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return responseText;
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}
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@@ -0,0 +1,117 @@
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import * as vscode from 'vscode';
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import { findBrainFiles, getActiveBrainProfile, logError } from '../../utils';
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import { getConfig } from '../../config';
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import { AgentWorkflowManager } from '../../agents/AgentWorkflowManager';
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import { ErrorTranslator } from '../../core/errorHandler';
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import { StatusBarManager, AgentStatus } from '../../core/statusBar';
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import { stripMarkdownFormatting } from '../../core/responseRecovery';
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import type { AgentExecutorOptions, ChatMessage } from '../../agent';
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export interface WorkflowDeps {
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emitHistoryChanged: () => void;
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chatHistory: ChatMessage[];
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options: AgentExecutorOptions;
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statusBarManager: StatusBarManager;
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getWebview: () => vscode.Webview | undefined;
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getAbortSignal: () => AbortSignal | undefined;
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}
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export async function executeMultiAgentWorkflow(
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deps: WorkflowDeps,
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prompt: string,
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modelName: string,
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options: any
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) {
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if (!deps.getWebview()) return;
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// NOTE: 호출자 (AgentExecutor wrapper) 가 stop() + new AbortController() 를
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// *먼저* 마쳐야 한다 — extracted fn 내부에서 stop 을 부르면 호출자가 막
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// 만든 controller 가 즉시 폐기되기 때문. getAbortSignal() 은 그 새 controller 의
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// signal 을 반환해야 함.
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const signal = deps.getAbortSignal();
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if (!signal) return;
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const webview = deps.getWebview();
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if (!webview) return;
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deps.statusBarManager.updateStatus(AgentStatus.Thinking, 'Multi-Agent Workflow Running');
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webview.postMessage({ type: 'streamStart' });
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deps.options.onStreamLifecycle?.start();
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try {
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let brainContext = 'No specific context available';
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try {
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const config = getConfig();
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const activeBrain = options.brainProfileId
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? (config.brainProfiles.find((profile) => profile.id === options.brainProfileId) || getActiveBrainProfile())
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: getActiveBrainProfile();
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const brainFiles = findBrainFiles(activeBrain.localBrainPath);
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brainContext = `Brain: ${activeBrain.name}, Files: ${brainFiles.length}`;
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} catch (ctxErr) {
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logError('Failed to load brain context for agents', ctxErr);
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}
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const selectedAgentContext = options.agentSkillContext
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? `\nSelected Agent Reference:\n${options.agentSkillContext}`
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: '';
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const designerContext = options.designerContext
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? `\nProject Chronicle Guard:\n${options.designerContext}`
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: '';
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// 워크플로우 매니저에게 설정 기반 실행 위임
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// [Clean Stream] 단계 진행 메시지는 채팅 본문(streamChunk) 이 아닌 사이드바
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// 상단의 workflowStage 인디케이터로만 표시한다 → "생각 단계가 본문에 계속 보임"
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// 답답함 제거. 채팅 버블에는 최종 답변만 한 번에 들어간다.
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const rawFinalReport = await AgentWorkflowManager.runStrictWorkflow(
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prompt,
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modelName,
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`${brainContext}${selectedAgentContext}${designerContext}`,
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signal,
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(step, msg) => {
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deps.getWebview()?.postMessage({
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type: 'workflowStage',
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value: { step, message: msg, done: step === '완료' || step === '오류' }
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});
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}
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);
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const wv2 = deps.getWebview();
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if (signal.aborted || !wv2) return;
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// [Plain Text Output] Synthesizer가 잘 따라줬어도 작은 모델은 `##` `**` 를 흘리는 경우가 있어
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// 최종 후처리로 한 번 더 마커를 벗긴다. 채팅 history 에도 정제된 결과만 남겨 다음 턴 컨텍스트에서
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// 마커가 재학습되는 일을 막는다.
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const finalReport = getConfig().outputFormat === 'plain'
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? stripMarkdownFormatting(rawFinalReport)
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: rawFinalReport;
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wv2.postMessage({ type: 'streamChunk', value: finalReport });
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wv2.postMessage({ type: 'workflowStage', value: { step: '완료', message: '', done: true } });
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wv2.postMessage({ type: 'streamEnd' });
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deps.chatHistory.push({ role: 'assistant', content: finalReport });
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deps.emitHistoryChanged();
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deps.statusBarManager.updateStatus(AgentStatus.Success, 'Workflow Complete');
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wv2.postMessage({ type: 'autoContinue', value: '✅ 모든 분석이 성공적으로 완료되었습니다.' });
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} catch (error: any) {
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// 어떤 종료 경로에서든 stage indicator 는 반드시 닫는다 — 안 닫으면 사이드바에 영원히 "③ 자기 검증..." 가 남는다.
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deps.getWebview()?.postMessage({ type: 'workflowStage', value: { step: '완료', message: '', done: true } });
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if (error.name === 'AbortError' || error.message?.includes('cancelled')) {
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deps.statusBarManager.updateStatus(AgentStatus.Idle, 'Workflow Cancelled');
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return;
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}
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const friendly = ErrorTranslator.translate(error);
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logError('Workflow failed', error);
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const wvErr = deps.getWebview();
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wvErr?.postMessage({ type: 'autoContinue', value: '' });
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wvErr?.postMessage({
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type: 'error',
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value: `### ${friendly.title}\n\n**상태:** ${friendly.message}\n\n**해결 방법:** ${friendly.action}`
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});
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deps.statusBarManager.updateStatus(AgentStatus.Idle, 'Error occurred');
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} finally {
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deps.options.onStreamLifecycle?.end();
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}
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}
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