Files
connectai/src/core/services.ts
T
2026-05-10 22:57:03 +09:00

165 lines
6.5 KiB
TypeScript

import * as fs from 'fs';
import * as path from 'path';
import { getConfig } from '../config';
import { buildApiUrl, logError, logInfo, resolveEngine, summarizeText, _getBrainDir } from '../utils';
/**
* IAIService: AI 모델 호출에 대한 인터페이스.
*
* `call(prompt)` 는 plain user 메시지 1개만 보내는 legacy shortcut이고,
* `chat({ system, user })` 는 role-aware 호출이다. Telegram 핸들러처럼
* 모델을 grounding 해야 하는 경로에서는 system을 반드시 채워야 한다 —
* gemma 같은 작은 모델은 system이 없으면 짧은/모호한 입력에 대해
* "시는 못 써드려요" 같은 환각 거절을 하는 경향이 있다.
*/
export interface IAIService {
call(prompt: string): Promise<string>;
chat(req: AIChatRequest): Promise<AIChatResult>;
}
export interface AIChatRequest {
/** Optional system prompt. Strongly recommended for short / ambiguous user inputs. */
system?: string;
/** Required. The user message. */
user: string;
/** Optional override (default = config.defaultModel). */
model?: string;
/** Optional override (default = config.timeout). */
timeoutMs?: number;
}
export interface AIChatResult {
content: string;
/** Engine that actually returned the content. */
engine: 'lmstudio' | 'ollama';
model: string;
/** True iff content came back empty after all retries. Caller decides UX. */
empty: boolean;
}
/**
* IBrainService: 지식 베이스(Brain) 조작에 대한 인터페이스
*/
export interface IBrainService {
inject(title: string, markdown: string): Promise<string>;
}
/**
* AIService: Ollama 및 LM Studio 폴백 로직을 포함한 AI 호출 구현체.
*
* Behavior:
* 1. Try the user-configured engine first; on transport / 5xx / empty response,
* fall through to the other engine.
* 2. Empty responses are treated as a soft failure: we log + retry the other
* engine before giving up. Pure exceptions (network blip) trigger the same
* fallback path.
* 3. The legacy `call(prompt)` is preserved as a thin wrapper around `chat()`
* for callers that don't have a system prompt — but new code should pass
* a system prompt explicitly.
*/
export class AIService implements IAIService {
public async call(prompt: string): Promise<string> {
const result = await this.chat({ user: prompt });
return result.content;
}
public async chat(req: AIChatRequest): Promise<AIChatResult> {
const config = getConfig();
const model = (req.model || config.defaultModel || '').trim() || 'gemma4:e2b';
const timeoutMs = req.timeoutMs ?? config.timeout;
const primaryEngine = resolveEngine(config.ollamaUrl);
const engines = primaryEngine === 'lmstudio'
? ['lmstudio', 'ollama'] as const
: ['ollama', 'lmstudio'] as const;
const messages: Array<{ role: 'system' | 'user' | 'assistant'; content: string }> = [];
if (req.system && req.system.trim()) {
messages.push({ role: 'system', content: req.system });
}
messages.push({ role: 'user', content: req.user });
let lastError: Error | null = null;
let lastEmptyEngine: typeof engines[number] | null = null;
for (const engine of engines) {
const apiUrl = buildApiUrl(config.ollamaUrl, engine, 'chat');
const payload = {
model,
messages,
stream: false,
...(engine === 'ollama' ? { options: { temperature: 0.7 } } : { temperature: 0.7 }),
};
try {
logInfo('[AIService] Request started.', {
engine, apiUrl, model,
hasSystem: !!req.system, userChars: req.user.length,
});
const res = await fetch(apiUrl, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload),
signal: AbortSignal.timeout(timeoutMs),
});
const rawText = await res.text();
if (!res.ok) {
lastError = new Error(`AI call failed: ${res.status} ${summarizeText(rawText, 250)}`);
logError(`[AIService] ${engine} HTTP ${res.status}`, { body: summarizeText(rawText, 250) });
continue;
}
const data = rawText ? JSON.parse(rawText) as any : {};
const content = engine === 'lmstudio'
? (data.choices?.[0]?.message?.content || '')
: (data.message?.content || data.response || '');
if (!content || !content.trim()) {
// Treat empty as soft failure so the other engine gets a chance.
lastEmptyEngine = engine;
lastError = new Error(`AI engine '${engine}' returned an empty response.`);
logError(`[AIService] ${engine} empty response — falling through.`, { model });
continue;
}
return { content, engine, model, empty: false };
} catch (error: any) {
lastError = error instanceof Error ? error : new Error(String(error));
logError(`[AIService] ${engine} failed:`, lastError.message);
}
}
// Both engines exhausted. Surface a result with empty=true so the
// caller (e.g. Telegram handler) can produce a user-visible reply
// instead of swallowing the failure.
if (lastEmptyEngine) {
return { content: '', engine: lastEmptyEngine, model, empty: true };
}
throw lastError || new Error('All AI engines failed.');
}
}
/**
* BrainService: 지식 베이스 파일 시스템 저장 및 관리 구현체
*/
export class BrainService implements IBrainService {
public async inject(title: string, markdown: string): Promise<string> {
const brainDir = _getBrainDir();
if (!fs.existsSync(brainDir)) {
fs.mkdirSync(brainDir, { recursive: true });
}
const today = new Date().toISOString().split('T')[0];
const datePath = path.join(brainDir, '00_Raw', today);
if (!fs.existsSync(datePath)) {
fs.mkdirSync(datePath, { recursive: true });
}
const safeTitle = title.replace(/[^a-zA-Z0-9가-힣]/gi, '_');
const filePath = path.join(datePath, `${safeTitle}.md`);
fs.writeFileSync(filePath, markdown, 'utf-8');
return filePath;
}
}