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fix: organize imports and apply linting fixes

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This commit is contained in:
MythEclipse
2026-05-14 15:02:23 +07:00
parent 1623c612c3
commit d1282f2f57
15 changed files with 477 additions and 199 deletions
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src/moderation/aiAnalyzer.ts
+105 -42
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@@ -2,7 +2,11 @@ import { config } from "../config";
import { createChildLogger } from "../logger";
import type { SqliteDatabase } from "../muxer-queue";
import { retryWithBackoff } from "../retry";
import { getMessageById, getPendingAIAnalysisMessages, updateMessageAIAnalysis } from "./messageStore";
import {
getMessageById,
getPendingAIAnalysisMessages,
updateMessageAIAnalysis,
} from "./messageStore";
import type { MessageRecord } from "./types";
const logger = createChildLogger("ai-analyzer");
@@ -37,7 +41,10 @@ function estimateTokens(text: string): number {
return Math.ceil(text.length / 4);
}
function formatMessageForAnalysis(message: MessageRecord, index: number): string {
function formatMessageForAnalysis(
message: MessageRecord,
index: number,
): string {
const text = getAnalysisText(message);
const time = new Date(message.created_at).toISOString();
return `${index + 1}. id=${message.id} time=${time} user=${message.username}: ${text}`;
@@ -49,7 +56,10 @@ function estimateMessageTokens(message: MessageRecord): number {
async function fetchJson(url: string, init: RequestInit): Promise<unknown> {
const controller = new AbortController();
const timeout = setTimeout(() => controller.abort(), config.AI_ANALYSIS_TIMEOUT_MS);
const timeout = setTimeout(
() => controller.abort(),
config.AI_ANALYSIS_TIMEOUT_MS,
);
try {
const response = await fetch(url, { ...init, signal: controller.signal });
@@ -85,10 +95,16 @@ function parseLLMAnalysis(content: string): LLMAnalysis {
if (jsonStart >= 0 && jsonEnd > jsonStart) {
try {
const parsed = JSON.parse(content.slice(jsonStart, jsonEnd + 1));
const status = parsed.status === "flagged" ? "flagged" : parsed.status === "warn" ? "warn" : "clean";
const status =
parsed.status === "flagged"
? "flagged"
: parsed.status === "warn"
? "warn"
: "clean";
const flags = Array.isArray(parsed.flags) ? parsed.flags.map(String) : [];
const score = Math.max(0, Math.min(1, Number(parsed.score) || 0));
const analysis = typeof parsed.analysis === "string" ? parsed.analysis : content;
const analysis =
typeof parsed.analysis === "string" ? parsed.analysis : content;
return { status, flags, score, analysis };
} catch {
// Fall through to text-only parsing.
@@ -96,27 +112,37 @@ function parseLLMAnalysis(content: string): LLMAnalysis {
}
return {
status: /flagged|bahaya|berisiko|toxic|hate|harassment|violence|sexual|self-harm|illegal|scam|hacking/i.test(content) ? "flagged" : /warn|provokasi|hinaan|menyerang/i.test(content) ? "warn" : "clean",
status:
/flagged|bahaya|berisiko|toxic|hate|harassment|violence|sexual|self-harm|illegal|scam|hacking/i.test(
content,
)
? "flagged"
: /warn|provokasi|hinaan|menyerang/i.test(content)
? "warn"
: "clean",
flags: [],
score: 0,
analysis: content.trim() || "Tidak ada analisis dari LLM.",
};
}
async function runLLMAnalysis(messages: MessageRecord[]): Promise<{ results: LLMAnalysis[]; raw: unknown }> {
const response = await retryWithBackoff(
() => fetchJson(`${config.AI_LLM_BASE_URL}/chat/completions`, {
method: "POST",
headers: {
"Authorization": `Bearer ${config.AI_LLM_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: config.AI_LLM_MODEL,
messages: [
{
role: "system",
content: `Kamu moderator Discord komunitas. Analisis setiap pesan dengan 3 kategori:
async function runLLMAnalysis(
messages: MessageRecord[],
): Promise<{ results: LLMAnalysis[]; raw: unknown }> {
const response = (await retryWithBackoff(
() =>
fetchJson(`${config.AI_LLM_BASE_URL}/chat/completions`, {
method: "POST",
headers: {
Authorization: `Bearer ${config.AI_LLM_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: config.AI_LLM_MODEL,
messages: [
{
role: "system",
content: `Kamu moderator Discord komunitas. Analisis setiap pesan dengan 3 kategori:
- CLEAN: Pesan normal, tidak melanggar aturan
- WARN: Melanggar aturan minor yang menarget orang lain (tone menyerang, hinaan ringan, konflik kecil) - butuh peringatan tapi tidak dihapus
- FLAGGED: Melanggar aturan berat (NSFW, ilegal, hacking, scam, harassment, violence, SARA, gore, spam, promosi judi) - butuh review moderator untuk penghapusan
@@ -166,18 +192,18 @@ PENENTUAN STATUS:
Balas JSON array dengan schema: [{"status":"clean|warn|flagged","flags":["..."],"score":0..1,"analysis":"ringkasan Bahasa Indonesia + alasan + aksi disarankan"}]
Satu JSON object per pesan dalam array.`,
},
{
role: "user",
content: `Analisis ${messages.length} pesan berikut sebagai satu alur percakapan. Tetap kembalikan satu hasil per pesan dengan urutan yang sama:\n${messages.map(formatMessageForAnalysis).join("\n")}`,
},
],
temperature: 0.2,
},
{
role: "user",
content: `Analisis ${messages.length} pesan berikut sebagai satu alur percakapan. Tetap kembalikan satu hasil per pesan dengan urutan yang sama:\n${messages.map(formatMessageForAnalysis).join("\n")}`,
},
],
temperature: 0.2,
}),
signal: AbortSignal.timeout(config.AI_ANALYSIS_TIMEOUT_MS),
}),
signal: AbortSignal.timeout(config.AI_ANALYSIS_TIMEOUT_MS),
}),
{ retries: 2, logger },
) as ChatCompletionResponse;
)) as ChatCompletionResponse;
const content = response.choices?.[0]?.message?.content?.trim() || "";
@@ -191,12 +217,18 @@ Satu JSON object per pesan dalam array.`,
const parsed = JSON.parse(content.substring(jsonStart, jsonEnd + 1));
if (Array.isArray(parsed)) {
results = parsed.map((item: any) => {
const status = item.status === "flagged" ? "flagged" : item.status === "warn" ? "warn" : "clean";
const status =
item.status === "flagged"
? "flagged"
: item.status === "warn"
? "warn"
: "clean";
return {
status,
flags: Array.isArray(item.flags) ? item.flags.map(String) : [],
score: Math.max(0, Math.min(1, Number(item.score) || 0)),
analysis: typeof item.analysis === "string" ? item.analysis : content,
analysis:
typeof item.analysis === "string" ? item.analysis : content,
};
});
}
@@ -213,10 +245,15 @@ Satu JSON object per pesan dalam array.`,
return { results, raw: response };
}
async function analyzeAndStoreBatch(db: SqliteDatabase, messages: MessageRecord[]): Promise<void> {
async function analyzeAndStoreBatch(
db: SqliteDatabase,
messages: MessageRecord[],
): Promise<void> {
if (messages.length === 0) return;
const analyzableMessages = messages.filter((message) => getAnalysisText(message).length > 0);
const analyzableMessages = messages.filter(
(message) => getAnalysisText(message).length > 0,
);
if (analyzableMessages.length === 0) return;
activeRequests++;
@@ -228,7 +265,12 @@ async function analyzeAndStoreBatch(db: SqliteDatabase, messages: MessageRecord[
const result = results[i] || parseLLMAnalysis("");
const row = updateMessageAIAnalysis(db, message.id, {
status: result.status as "pending" | "clean" | "warn" | "flagged" | "error",
status: result.status as
| "pending"
| "clean"
| "warn"
| "flagged"
| "error",
flags: JSON.stringify(result.flags),
score: result.score,
raw: JSON.stringify(raw),
@@ -242,7 +284,11 @@ async function analyzeAndStoreBatch(db: SqliteDatabase, messages: MessageRecord[
if (analyzableMessages.length > 1) {
const midpoint = Math.ceil(analyzableMessages.length / 2);
logger.warn(
{ count: analyzableMessages.length, nextBatchSizes: [midpoint, analyzableMessages.length - midpoint], error },
{
count: analyzableMessages.length,
nextBatchSizes: [midpoint, analyzableMessages.length - midpoint],
error,
},
"AI batch failed, splitting into smaller batches",
);
await analyzeAndStoreBatch(db, analyzableMessages.slice(0, midpoint));
@@ -288,7 +334,11 @@ async function drainQueue(db: SqliteDatabase): Promise<void> {
if (!message) continue;
const messageTokens = estimateMessageTokens(message);
if (batch.length > 0 && (batch.length >= MAX_AI_BATCH_MESSAGES || tokenEstimate + messageTokens > batchTokenLimit)) {
if (
batch.length > 0 &&
(batch.length >= MAX_AI_BATCH_MESSAGES ||
tokenEstimate + messageTokens > batchTokenLimit)
) {
queuedMessageIds.add(messageId);
break;
}
@@ -298,7 +348,10 @@ async function drainQueue(db: SqliteDatabase): Promise<void> {
}
if (batch.length > 0) {
logger.info({ count: batch.length, tokenEstimate }, "Processing AI analysis batch");
logger.info(
{ count: batch.length, tokenEstimate },
"Processing AI analysis batch",
);
await analyzeAndStoreBatch(db, batch);
}
}
@@ -307,12 +360,17 @@ async function drainQueue(db: SqliteDatabase): Promise<void> {
}
}
export function queueMessageAnalysis(db: SqliteDatabase, messageId: string): void {
export function queueMessageAnalysis(
db: SqliteDatabase,
messageId: string,
): void {
if (!config.AI_ANALYSIS_ENABLED) return;
logger.debug({ messageId }, "Queueing AI analysis");
queuedMessageIds.add(messageId);
setImmediate(() => {
drainQueue(db).catch((error) => logger.error({ error }, "AI analysis queue failed"));
drainQueue(db).catch((error) =>
logger.error({ error }, "AI analysis queue failed"),
);
});
}
@@ -327,10 +385,15 @@ export function startPendingAIAnalysisWorker(db: SqliteDatabase): void {
if (isProcessing) return;
const pendingMessages = getPendingAIAnalysisMessages(db, 500);
if (pendingMessages.length === 0) return;
logger.info({ count: pendingMessages.length }, "Queueing pending AI analysis messages");
logger.info(
{ count: pendingMessages.length },
"Queueing pending AI analysis messages",
);
for (const message of pendingMessages) {
queuedMessageIds.add(message.id);
}
drainQueue(db).catch((error) => logger.error({ error }, "Pending AI analysis worker failed"));
drainQueue(db).catch((error) =>
logger.error({ error }, "Pending AI analysis worker failed"),
);
}, 15000);
}