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GMW/services/discord-gateway/src/modules/ai-moderation/aiAnalyzer.ts
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import { existsSync } from "node:fs";
import { availableParallelism } from "node:os";
import { fileURLToPath } from "node:url";
import { createChildLogger } from "@bete/shared/logger";
import type { Client } from "discord.js-selfbot-v13";
import { Piscina } from "piscina";
import { config } from "../../shared/config/config.js";
import type { EventBroadcaster } from "../event-broadcaster/index.js";
import { invalidateAnalyticsCache } from "../message-capture/analyticsStore.js";
import { isAgeRestrictedMetadata } from "../message-capture/messageMetadata.js";
import {
getConversationKeysWithIncompleteAnalysis,
getIncompleteMessagesByConversation,
getMessageById,
getPendingConversationKeys,
getPendingMessagesByConversation,
updateMessageAIAnalysis,
updateMessagesAIAnalysisBulk,
} from "../message-capture/messageStore.js";
import type {
AnalysisQueueStatus,
AnalysisResult,
MessageRecord,
ModerationBroadcaster,
} from "../message-capture/types.js";
import { attemptAutoDeleteFlaggedMessage } from "./autoDeleteManager.js";
import { estimateTokens } from "./conversationContext.js";
import { logModerationError } from "./responseLogger.js";
const logger = createChildLogger("ai-analyzer");
type ModerationGlobal = typeof globalThis & {
moderationBroadcaster?: ModerationBroadcaster;
};
function getModerationBroadcaster(): ModerationBroadcaster | undefined {
return (globalThis as ModerationGlobal).moderationBroadcaster;
}
// Redis EventBroadcaster — set by startPendingAIAnalysisWorker.
// Used to publish analysis completion events so the backend
// redis-bridge can forward them to frontend WebSocket clients.
let _redisEventBroadcaster: EventBroadcaster | undefined;
function broadcastAnalysisCompleted(row: MessageRecord): void {
// In-memory WS broadcast (direct-connected DG clients)
getModerationBroadcaster()?.messageAnalyzed(row);
// Redis pub/sub broadcast → backend → frontend WebSocket
if (_redisEventBroadcaster) {
_redisEventBroadcaster.messageAnalyzed(row).catch((err: unknown) =>
logger.warn(
{
messageId: row.id,
error: err instanceof Error ? err.message : String(err),
},
"Failed to publish message_analyzed via Redis EventBroadcaster",
),
);
}
}
function scheduleAutoDelete(row: MessageRecord): void {
if (row.ai_status !== "flagged" && row.ai_status !== "warn") return;
const run = () => {
attemptAutoDeleteFlaggedMessage(moderationClient, row).catch(
(error: unknown) => {
logger.error(
{
messageId: row.id,
error: error instanceof Error ? error.message : String(error),
},
"Unexpected auto-delete error",
);
},
);
};
if (config.AUTO_DELETE_FLAGGED_DELAY_MS > 0) {
setTimeout(run, config.AUTO_DELETE_FLAGGED_DELAY_MS);
return;
}
setImmediate(run);
}
function isAgeRestrictedMessage(message: MessageRecord): boolean {
return isAgeRestrictedMetadata(message.metadata);
}
function buildAgeRestrictedSkipResult(): {
status: "error";
flags: string | null;
score: number;
analysis: string;
categories: string[];
severity: "none";
confidence: number;
recommendedAction: "none";
analyzedAt: number;
error: null;
} {
return {
status: "error",
flags: JSON.stringify(["age_restricted"]),
score: 0,
analysis: "Skipped moderation for age-restricted content.",
categories: ["age_restricted"],
severity: "none",
confidence: 1,
recommendedAction: "none",
analyzedAt: Date.now(),
error: null,
};
}
async function skipAgeRestrictedMessages(
messages: MessageRecord[],
): Promise<MessageRecord[]> {
const ageRestrictedMessages = messages.filter(isAgeRestrictedMessage);
if (ageRestrictedMessages.length === 0) {
return messages;
}
const skippedRows = await updateMessagesAIAnalysisBulk(
ageRestrictedMessages.map((message) => ({
messageId: message.id,
result: buildAgeRestrictedSkipResult(),
})),
);
for (const row of skippedRows) {
broadcastAnalysisCompleted(row);
}
const skippedIds = new Set(
ageRestrictedMessages.map((message) => message.id),
);
return messages.filter((message) => !skippedIds.has(message.id));
}
// ---------------------------------------------------------------------------
// Batch pipeline state
// ---------------------------------------------------------------------------
/** Debounce timer handle per conversation key. */
const conversationDebounceTimers = new Map<string, NodeJS.Timeout>();
/** Timestamp of when processing started per conversation key. */
const conversationProcessing = new Map<string, number>();
/** Cooldown expiry timestamp per conversation key after an error. */
const conversationErrorCooldown = new Map<string, number>();
let activeRequests = 0;
let lastError: string | null = null;
let moderationClient: Client | undefined;
// Batch circuit breaker
const conversationConsecutiveErrors = new Map<string, number>();
const MAX_CONSECUTIVE_ERRORS = 5;
const CONVERSATION_CB_COOLDOWN_MS = 60000;
/** Alert sinks — called when circuit breakers or sustained errors fire. */
type CircuitBreakerAlert = {
type: "conversation_cb" | "individual_cb" | "sustained_error";
conversationKey?: string;
consecutiveErrors: number;
message: string;
lastError?: string | null;
};
/** Registered alert handlers */
const alertHandlers: Array<(alert: CircuitBreakerAlert) => void> = [];
/**
* Register an alert handler (e.g., for webhook integration).
*/
export function onCircuitBreakerAlert(
handler: (alert: CircuitBreakerAlert) => void,
): void {
alertHandlers.push(handler);
}
function fireAlert(alert: CircuitBreakerAlert): void {
logger.warn(alert, `CB Alert: ${alert.type}${alert.message}`);
for (const handler of alertHandlers) {
try {
handler(alert);
} catch {
// handler errors are non-critical
}
}
}
function recordConversationBatchFailure(conversationKey: string): void {
const nextCount =
(conversationConsecutiveErrors.get(conversationKey) ?? 0) + 1;
conversationConsecutiveErrors.set(conversationKey, nextCount);
if (nextCount >= MAX_CONSECUTIVE_ERRORS) {
conversationErrorCooldown.set(
conversationKey,
Date.now() + CONVERSATION_CB_COOLDOWN_MS,
);
fireAlert({
type: "conversation_cb",
conversationKey,
consecutiveErrors: nextCount,
message: `Conversation ${conversationKey} circuit breaker triggered after ${nextCount} consecutive errors`,
lastError,
});
conversationConsecutiveErrors.set(conversationKey, 0);
}
}
function resetConversationBatchFailures(conversationKey: string): void {
conversationConsecutiveErrors.delete(conversationKey);
}
// ---------------------------------------------------------------------------
// Individual fallback queue — runs PARALLEL to the batch pipeline.
//
// Design guarantees:
// • Concurrency is capped at config.AI_ANALYSIS_INDIVIDUAL_MAX_CONCURRENT.
// • A flat Set<messageId> de-duplicates so the same message can't be
// in-flight twice (Discord snowflakes are globally unique, but be safe).
// • A Map<conversationKey, count> lets the recovery worker skip conversations
// that already have individual work in progress (#4 fix).
// • A separate circuit breaker prevents a cascade of individual failures
// from hammering a down/rate-limited LLM endpoint (#1+#5 fix).
// ---------------------------------------------------------------------------
/** IDs currently being processed one-by-one. */
const individualInFlight = new Set<string>();
/**
* Per-conversation count of in-flight individual messages.
* Used by the recovery worker to avoid re-scheduling a conversation that
* already has individual fallback work running for it.
*/
const individualInFlightByConversation = new Map<string, number>();
/** Last-touched timestamp for pruning stale entries. */
const individualInFlightLastTouched = new Map<string, number>();
/** Counter for observability. */
let activeIndividualRequests = 0;
// Individual fallback circuit breaker (independent of batch CB)
let individualConsecutiveErrors = 0;
let individualCooldownUntil = 0;
const INDIVIDUAL_COOLDOWN_MS = 30000;
// ---------------------------------------------------------------------------
// Piscina worker pool (batch path only)
// ---------------------------------------------------------------------------
function getAnalysisWorkerUrl(): URL {
const candidates = [
new URL("./aiAnalysisWorker.js", import.meta.url),
new URL("../aiAnalysisWorker.js", import.meta.url),
new URL("./aiAnalysisWorker.ts", import.meta.url),
];
for (const candidate of candidates) {
if (existsSync(fileURLToPath(candidate))) {
return candidate;
}
}
return candidates[2];
}
const workerPool = new Piscina({
filename: fileURLToPath(getAnalysisWorkerUrl()),
execArgv: process.execArgv,
maxThreads: config.PISCINA_MAX_THREADS ?? availableParallelism(),
});
interface AnalysisWorkerResponse {
ok: boolean;
conversationKey: string;
rows: MessageRecord[];
error?: string;
}
// ---------------------------------------------------------------------------
// Exported helpers
// ---------------------------------------------------------------------------
/**
* Gets the conversation key for a message (thread_id or channel_id).
*/
export function getConversationKey(message: MessageRecord): string {
return message.thread_id || message.channel_id;
}
/**
* Picks a batch of messages within a token budget.
* `tokensPerMessage` accounts for JSON structure overhead around each entry.
* Uses a rough character-based token estimate (avoids async formatMessageForPrompt
* since this function runs in a synchronous promise chain).
*/
export function pickBatchWithinBudget(
messages: MessageRecord[],
maxTokens: number,
tokensPerMessage: number,
): MessageRecord[] {
const batch: MessageRecord[] = [];
let usedTokens = 0;
for (const msg of messages) {
const content = msg.edited_content ?? msg.content;
// Accurate token count via tiktoken (+ overhead for JSON structure)
const msgTokens = estimateTokens(content) + tokensPerMessage;
if (usedTokens + msgTokens <= maxTokens) {
batch.push(msg);
usedTokens += msgTokens;
}
}
return batch;
}
// ---------------------------------------------------------------------------
// Conversation lock helpers
// ---------------------------------------------------------------------------
function isConversationProcessingLocked(conversationKey: string): boolean {
const startedAt = conversationProcessing.get(conversationKey);
// FIX #7: use configurable timeout that exceeds (LLM timeout × max retries).
// Old hardcoded value was 30 000 ms — shorter than a single LLM call under retries.
return Boolean(
startedAt &&
Date.now() - startedAt < config.AI_ANALYSIS_PROCESSING_TIMEOUT_MS,
);
}
// ---------------------------------------------------------------------------
// Individual fallback pipeline
// ---------------------------------------------------------------------------
/**
* Processes a single message via the Piscina worker pool (offloaded from
* main thread to avoid blocking the event loop).
*
* The worker handles:
* 1. DB initialization
* 2. Context fetching + conversation building
* 3. Attachment fetching
* 4. LLM analysis (normal or simple fallback)
*
* The main thread handles:
* - DB writes (updateMessagesAIAnalysisBulk)
* - WebSocket/Redis broadcast
* - Analytics cache invalidation
* - Auto-delete scheduling
*
* Infinite-loop prevention: if the LLM consistently drops the single target
* message across all retries (analysis_incomplete), we write a terminal flag
* 'individual_analysis_exhausted' to DB instead of 'analysis_incomplete'.
* The recovery worker only queries for 'analysis_incomplete', so exhausted
* messages are permanently excluded from the reprocessing loop.
* Transient failures (network/parse/DB) are NOT written as exhausted — they
* stay as 'analysis_incomplete' so the circuit-breaker-throttled recovery
* cycle can retry them later.
*/
async function processIndividualFallback(
message: MessageRecord,
): Promise<void> {
const { id: messageId } = message;
const conversationKey = getConversationKey(message);
activeIndividualRequests++;
individualInFlightByConversation.set(
conversationKey,
(individualInFlightByConversation.get(conversationKey) ?? 0) + 1,
);
individualInFlightLastTouched.set(conversationKey, Date.now());
let exhaustedOnIncomplete = false;
try {
// ── Run the LLM-heavy work in the worker thread ──
// Try normal analysis first. The worker handles retries internally.
const workerResult = (await workerPool.run({
type: "individual",
message,
skipNormalAnalysis: false,
} as any)) as
| { ok: true; results: AnalysisResult[] }
| { ok: false; results: AnalysisResult[]; error: string };
let analysisResult: { results: AnalysisResult[] } | null = null;
let usedSimpleFallback = false;
if (workerResult.ok) {
const stillIncomplete = workerResult.results.some((r) =>
r.flags.includes("analysis_incomplete"),
);
if (stillIncomplete) {
exhaustedOnIncomplete = true;
analysisResult = null;
} else {
analysisResult = workerResult;
}
}
// ── Step 2: If normal analysis failed, try SIMPLE fallback via worker ──
if (!analysisResult) {
logger.info(
{ messageId },
"Normal analysis failed (or incomplete) — trying simple text fallback via worker",
);
const simpleResult = (await workerPool.run({
type: "individual",
message,
skipNormalAnalysis: true,
} as any)) as
| { ok: true; results: AnalysisResult[] }
| { ok: false; results: AnalysisResult[]; error: string };
if (simpleResult.ok) {
analysisResult = simpleResult;
usedSimpleFallback = true;
exhaustedOnIncomplete = false;
}
}
// If both failed, throw to go to the catch block
if (!analysisResult) {
throw new Error(
`Both normal and simple analysis failed for message ${messageId}`,
);
}
if (usedSimpleFallback) {
logger.info(
{ messageId, status: analysisResult.results[0]?.status },
"Used simple text fallback for individual message (via worker)",
);
}
// ── Main thread: DB writes + broadcast (non-blocking work) ──
const updates = analysisResult.results.map((r) => ({
messageId: r.messageId,
result: {
status: r.status,
flags: JSON.stringify(r.flags),
score: r.score,
analysis: r.analysis,
categories: r.categories,
severity: r.severity,
confidence: r.confidence,
recommendedAction: r.recommendedAction,
analyzedAt: Date.now(),
error: null,
},
}));
const rows = await updateMessagesAIAnalysisBulk(updates);
for (const row of rows) {
broadcastAnalysisCompleted(row);
invalidateAnalyticsCache(row.guild_id);
scheduleAutoDelete(row);
}
const resultSummary = analysisResult.results[0];
logModerationError([messageId], config.AI_LLM_MODEL, new Error("Success"), {
phase: "individual_fallback",
status: resultSummary?.status,
flags: resultSummary?.flags,
severity: resultSummary?.severity,
confidence: resultSummary?.confidence,
});
individualConsecutiveErrors = 0;
logger.debug(
{ messageId, status: analysisResult.results[0]?.status },
"Individual fallback analysis complete (via worker)",
);
} catch (error) {
individualConsecutiveErrors++;
if (
individualConsecutiveErrors >= config.AI_ANALYSIS_INDIVIDUAL_CB_THRESHOLD
) {
individualCooldownUntil = Date.now() + INDIVIDUAL_COOLDOWN_MS;
fireAlert({
type: "individual_cb",
consecutiveErrors: individualConsecutiveErrors,
message: `Individual fallback circuit breaker triggered after ${individualConsecutiveErrors} consecutive errors`,
lastError,
});
}
lastError = error instanceof Error ? error.message : String(error);
logModerationError(
[messageId],
config.AI_LLM_MODEL,
error as Error | string,
{
phase: "individual_fallback",
conversationKey,
exhaustedOnIncomplete,
},
);
if (exhaustedOnIncomplete) {
await updateMessagesAIAnalysisBulk([
{
messageId,
result: {
status: "error",
flags: JSON.stringify(["individual_analysis_exhausted"]),
score: 0,
analysis:
"Individual fallback exhausted all retries: LLM consistently dropped this message even in single-target mode",
categories: ["individual_analysis_exhausted"],
severity: "none",
confidence: 0,
recommendedAction: "review",
analyzedAt: Date.now(),
error: lastError,
},
},
]).catch((dbErr: unknown) => {
logger.error(
{ messageId, error: String(dbErr) },
"Failed to write terminal exhausted status",
);
});
logger.warn(
{ messageId },
"Individual fallback exhausted — marked as individual_analysis_exhausted",
);
} else {
logger.error(
{
messageId,
error: lastError,
stack: error instanceof Error ? error.stack : undefined,
},
"Individual fallback analysis failed (transient) — will be retried",
);
}
} finally {
activeIndividualRequests--;
individualInFlight.delete(messageId);
const prev = individualInFlightByConversation.get(conversationKey) ?? 1;
if (prev <= 1) {
individualInFlightByConversation.delete(conversationKey);
individualInFlightLastTouched.delete(conversationKey);
} else {
individualInFlightByConversation.set(conversationKey, prev - 1);
individualInFlightLastTouched.set(conversationKey, Date.now());
}
}
}
/**
* Fans out message records to the individual fallback queue.
*
* FIX #1: Checks concurrency cap before admitting new work.
* FIX #5: Checks individual circuit breaker before admitting new work.
* Messages that cannot be admitted remain as `error/analysis_incomplete` in
* the DB and will be picked up by the recovery worker on the next interval.
*/
function enqueueIndividualFallbacks(messages: MessageRecord[]): void {
// FIX #5: Honour the individual circuit breaker.
if (Date.now() < individualCooldownUntil) {
logger.warn(
{
until: new Date(individualCooldownUntil).toISOString(),
skipped: messages.length,
},
"Individual fallback circuit breaker active — messages will be recovered later",
);
return;
}
// FIX #5: Enforce concurrency cap — do not admit more individual fallbacks
// than the configured limit. Excess messages stay as error/analysis_incomplete
// and will be recovered on the next worker tick.
const maxConcurrent = config.AI_ANALYSIS_INDIVIDUAL_MAX_CONCURRENT ?? 50;
const availableSlots = Math.max(0, maxConcurrent - activeIndividualRequests);
if (availableSlots <= 0) {
logger.debug(
{ maxConcurrent, active: activeIndividualRequests },
"Individual fallback concurrency cap reached — messages will be recovered later",
);
return;
}
const newMessages = messages
.filter((m) => !individualInFlight.has(m.id))
.slice(0, availableSlots);
if (newMessages.length === 0) return;
logger.debug(
{
count: newMessages.length,
messageIds: newMessages.map((m) => m.id),
},
"Enqueueing individual fallback analysis for batch-incomplete messages",
);
for (const msg of newMessages) {
individualInFlight.add(msg.id);
// Fire-and-forget: processIndividualFallback handles all errors internally.
processIndividualFallback(msg).catch((err: unknown) => {
// Belt-and-suspenders guard — should never reach here.
logger.error(
{ messageId: msg.id, error: String(err) },
"Unexpected uncaught error escaping processIndividualFallback",
);
individualInFlight.delete(msg.id);
const ck = getConversationKey(msg);
const prev = individualInFlightByConversation.get(ck) ?? 1;
if (prev <= 1) {
individualInFlightByConversation.delete(ck);
individualInFlightLastTouched.delete(ck);
} else {
individualInFlightByConversation.set(ck, prev - 1);
individualInFlightLastTouched.set(ck, Date.now());
}
});
}
}
// ---------------------------------------------------------------------------
// Batch pipeline
// ---------------------------------------------------------------------------
async function processBatch(
conversationKey: string,
messages: MessageRecord[],
): Promise<void> {
if (messages.length === 0) return;
const cooldownUntil = conversationErrorCooldown.get(conversationKey) ?? 0;
if (Date.now() < cooldownUntil) {
return;
}
activeRequests++;
let shouldScheduleNext = false;
const processingStartedAt = Date.now();
conversationProcessing.set(conversationKey, processingStartedAt);
try {
const result = (await workerPool.run({
type: "batch",
conversationKey,
messages,
})) as AnalysisWorkerResponse;
// Do not broadcast or auto-delete if it's an API failure that will be reverted.
// We check the flags to see if it's an API failure.
for (const row of result.rows) {
let isApiFailure = false;
if (row.ai_status === "error") {
try {
const flags = JSON.parse(row.ai_moderation_flags ?? "[]") as string[];
isApiFailure = flags.includes("analysis_api_failed");
} catch {}
}
if (!isApiFailure) {
broadcastAnalysisCompleted(row);
scheduleAutoDelete(row);
}
}
if (!result.ok) {
recordConversationBatchFailure(conversationKey);
// Batch failed entirely — fall back all messages to individual queue
// so no message is permanently lost behind a cooldown.
logger.warn(
{
conversationKey,
messageCount: messages.length,
error: result.error,
},
"Batch failed entirely — routing all messages to individual fallback queue",
);
enqueueIndividualFallbacks(messages);
lastError = result.error ?? "Analysis worker failed";
conversationErrorCooldown.set(
conversationKey,
Date.now() + config.AI_ANALYSIS_ERROR_COOLDOWN_MS,
);
logger.error(
{
conversationKey,
error: lastError,
messageCount: messages.length,
messageIds: messages.map((m) => m.id),
cooldownUntil: new Date(
Date.now() + config.AI_ANALYSIS_ERROR_COOLDOWN_MS,
).toISOString(),
timestamp: new Date().toISOString(),
},
"Batch analysis failed, will retry after cooldown",
);
return;
}
// Batch succeeded — but check for messages the LLM silently dropped or failed to parse/API.
// Rows with flag "analysis_incomplete", "analysis_parse_failed", or "analysis_api_failed"
// were produced by the client as synthetic errors.
const incompleteMessages: MessageRecord[] = [];
const parseFailedMessages: MessageRecord[] = [];
const apiFailedMessages: MessageRecord[] = [];
for (const msg of messages) {
const row = result.rows.find((r) => r.id === msg.id);
if (!row) {
incompleteMessages.push(msg);
continue;
}
if (row.ai_status === "error") {
let flags: string[] = [];
try {
flags = JSON.parse(row.ai_moderation_flags ?? "[]") as string[];
} catch {}
if (flags.includes("analysis_incomplete")) {
incompleteMessages.push(msg);
} else if (flags.includes("analysis_parse_failed")) {
parseFailedMessages.push(msg);
} else if (flags.includes("analysis_api_failed")) {
apiFailedMessages.push(msg);
}
}
}
const messagesForIndividualQueue = [
...incompleteMessages,
...parseFailedMessages,
];
if (messagesForIndividualQueue.length > 0) {
logger.warn(
{
conversationKey,
count: messagesForIndividualQueue.length,
ids: messagesForIndividualQueue.map((m) => m.id),
totalBatchSize: messages.length,
},
"Batch returned incomplete or unparseable results — fanning out to individual fallback queue",
);
enqueueIndividualFallbacks(messagesForIndividualQueue);
}
if (apiFailedMessages.length > 0) {
logger.warn(
{
conversationKey,
count: apiFailedMessages.length,
ids: apiFailedMessages.map((m) => m.id),
},
"Batch returned API failures — reverting to pending to put back in queue",
);
// Revert to pending so they are picked up again
const revertedRows = await updateMessagesAIAnalysisBulk(
apiFailedMessages.map((msg) => ({
messageId: msg.id,
result: {
status: "pending",
flags: null,
score: null,
analysis: null,
categories: null,
severity: null,
confidence: null,
recommendedAction: null,
analyzedAt: null,
error: null,
},
})),
).catch((err) => {
logger.error(
{ error: String(err) },
"Failed to revert API failures to pending",
);
return [];
});
for (const row of revertedRows) {
// Broadcast the pending status so the UI knows it's back in queue
broadcastAnalysisCompleted(row);
}
// Trigger conversation cooldown so we don't tight loop the API
recordConversationBatchFailure(conversationKey);
conversationErrorCooldown.set(
conversationKey,
Date.now() + config.AI_ANALYSIS_ERROR_COOLDOWN_MS,
);
}
if (apiFailedMessages.length === 0) {
resetConversationBatchFailures(conversationKey);
conversationErrorCooldown.delete(conversationKey);
}
shouldScheduleNext = true;
} catch (error) {
recordConversationBatchFailure(conversationKey);
// Unhandled exception — route everything to individual fallback.
logger.warn(
{ conversationKey, messageCount: messages.length },
"Batch threw exception — routing all messages to individual fallback queue",
);
enqueueIndividualFallbacks(messages);
lastError = error instanceof Error ? error.message : String(error);
const errorStack = error instanceof Error ? error.stack : undefined;
conversationErrorCooldown.set(
conversationKey,
Date.now() + config.AI_ANALYSIS_ERROR_COOLDOWN_MS,
);
logger.error(
{
conversationKey,
error: lastError,
stack: errorStack,
messageCount: messages.length,
messageIds: messages.map((m) => m.id),
cooldownUntil: new Date(
Date.now() + config.AI_ANALYSIS_ERROR_COOLDOWN_MS,
).toISOString(),
timestamp: new Date().toISOString(),
},
"Analysis worker failed, will retry after cooldown",
);
} finally {
activeRequests--;
if (conversationProcessing.get(conversationKey) === processingStartedAt) {
conversationProcessing.delete(conversationKey);
}
if (shouldScheduleNext) {
setImmediate(() => scheduleConversationAnalysis(conversationKey));
}
}
}
// ---------------------------------------------------------------------------
// Scheduling
// ---------------------------------------------------------------------------
/**
* Schedules a debounced analysis run for a conversation.
*
* FIX #3: The async work inside setTimeout is now wrapped in an explicit
* .catch() so DB errors don't produce unhandled promise rejections.
* FIX #6: Calls pickBatchWithinBudget after fetching messages so token budget
* is respected before handing the batch to the LLM.
*/
function scheduleConversationAnalysis(conversationKey: string): void {
if (isConversationProcessingLocked(conversationKey)) {
return;
}
const convoCooldown = conversationErrorCooldown.get(conversationKey) ?? 0;
const convoErrors = conversationConsecutiveErrors.get(conversationKey) ?? 0;
if (convoCooldown && Date.now() < convoCooldown) {
if (!conversationDebounceTimers.has(conversationKey)) {
const remaining = convoCooldown - Date.now();
const timer = setTimeout(() => {
conversationDebounceTimers.delete(conversationKey);
scheduleConversationAnalysis(conversationKey);
}, remaining + 500);
conversationDebounceTimers.set(conversationKey, timer);
}
return;
}
// Block scheduling if this conversation already hit the per-conversation
// error threshold — prevents rapid retry loops on the same broken convo.
if (convoErrors >= MAX_CONSECUTIVE_ERRORS && Date.now() < convoCooldown) {
return;
}
const existingTimer = conversationDebounceTimers.get(conversationKey);
if (existingTimer) {
clearTimeout(existingTimer);
}
const timer = setTimeout(() => {
conversationDebounceTimers.delete(conversationKey);
// FIX #3: explicit .catch() — no async arrow function to avoid unhandled rejection.
getPendingMessagesByConversation(
conversationKey,
config.AI_ANALYSIS_MAX_BATCH_SIZE,
)
.then(async (messages) => {
if (messages.length === 0) return;
const processableMessages = await skipAgeRestrictedMessages(messages);
if (processableMessages.length === 0) return;
// FIX #6: trim to token budget before sending to LLM.
// 50 tokens overhead accounts for JSON structure + id/username fields.
let trimmed = pickBatchWithinBudget(
processableMessages,
config.AI_ANALYSIS_MAX_TARGET_TOKENS,
50,
);
// FIX #10: if every message individually exceeds the token budget,
// pickBatchWithinBudget returns [] — which would leave them permanently
// stuck as `pending`. Fall back to the first message alone so at
// least one makes progress; the rest will be processed in later ticks.
if (trimmed.length === 0 && processableMessages.length > 0) {
trimmed = processableMessages.slice(0, 1);
logger.warn(
{
conversationKey,
messageId: processableMessages[0]?.id,
tokenBudget: config.AI_ANALYSIS_MAX_TARGET_TOKENS,
},
"All messages exceed token budget — processing first message alone to avoid stuck-pending deadlock",
);
}
return processBatch(conversationKey, trimmed);
})
.catch((err: unknown) => {
logger.error(
{
conversationKey,
error: err instanceof Error ? err.message : String(err),
},
"Failed to fetch or dispatch pending messages for scheduled analysis",
);
});
}, config.AI_ANALYSIS_DEBOUNCE_MS);
conversationDebounceTimers.set(conversationKey, timer);
}
// ---------------------------------------------------------------------------
// Public API
// ---------------------------------------------------------------------------
/**
* Queues a message for analysis (debounced by conversation).
*/
export async function queueMessageAnalysis(messageId: string): Promise<void> {
if (!config.AI_ANALYSIS_ENABLED) return;
try {
const message = await getMessageById(messageId);
if (!message) {
logger.warn({ messageId }, "Message not found for analysis queue");
return;
}
if (isAgeRestrictedMessage(message)) {
const updated = await updateMessageAIAnalysis(
message.id,
buildAgeRestrictedSkipResult(),
);
if (updated) {
broadcastAnalysisCompleted(updated);
}
logger.debug(
{ messageId },
"Skipped AI analysis for age-restricted message",
);
return;
}
queueConversationAnalysis(getConversationKey(message));
} catch (error) {
logger.error(
{
messageId,
error: error instanceof Error ? error.message : String(error),
},
"Failed to queue message for analysis",
);
}
}
/**
* Queues a conversation for analysis (debounced).
*/
export function queueConversationAnalysis(conversationKey: string): void {
if (!config.AI_ANALYSIS_ENABLED) return;
scheduleConversationAnalysis(conversationKey);
}
/**
* Returns current status of both the batch and individual fallback queues.
*/
export function getAnalysisQueueStatus(): AnalysisQueueStatus {
return {
queuedConversations: conversationDebounceTimers.size,
activeRequests,
activeIndividualRequests,
individualInFlightCount: individualInFlight.size,
individualCircuitBreakerActive: Date.now() < individualCooldownUntil,
lastError,
};
}
/**
* Starts the periodic recovery worker.
*
* FIX #4: Now also recovers messages stuck in `error/analysis_incomplete`
* state (not just `pending`), and skips conversations that already have
* individual fallback work in progress to avoid DB last-write-wins races.
*/
export function startPendingAIAnalysisWorker(
client?: Client,
eventBroadcaster?: EventBroadcaster,
): void {
moderationClient = client;
_redisEventBroadcaster = eventBroadcaster;
if (!config.AI_ANALYSIS_ENABLED) return;
setInterval(() => {
// FIX #3 pattern: no async arrow — chain promises explicitly.
Promise.all([
getPendingConversationKeys(500),
getConversationKeysWithIncompleteAnalysis(200),
])
.then(([pendingKeys, incompleteKeys]) => {
const now = Date.now();
// FIX #9: Prune stale entries from state maps to prevent unbounded
// memory growth from channels/threads that are no longer active.
for (const [key, expiry] of conversationErrorCooldown) {
if (now >= expiry) conversationErrorCooldown.delete(key);
}
for (const [key, startedAt] of conversationProcessing) {
if (now - startedAt >= config.AI_ANALYSIS_PROCESSING_TIMEOUT_MS) {
conversationProcessing.delete(key);
}
}
// FIX #7: Prune stale in-flight counters for conversations that have
// been idle longer than the processing timeout — prevents permanent
// blocking if a decrement was missed due to an uncaught exception.
const staleThreshold = config.AI_ANALYSIS_PROCESSING_TIMEOUT_MS * 2;
for (const [key, lastTouched] of individualInFlightLastTouched) {
if (now - lastTouched >= staleThreshold) {
individualInFlightLastTouched.delete(key);
individualInFlightByConversation.delete(key);
logger.warn(
{ key },
"Pruned stale individualInFlightByConversation entry",
);
}
}
// Also prune stale per-conversation CB error counts that have cooled
// down so old conversations can be retried.
for (const [key] of conversationConsecutiveErrors) {
const cbExpire = conversationErrorCooldown.get(key) ?? 0;
if (cbExpire && now >= cbExpire) {
conversationConsecutiveErrors.delete(key);
}
}
// FIX #8: Build a set of keys already targeted for individual recovery
// so the batch loop below skips them, preventing a race where batch
// scheduling and individual scheduling collide on the same conversation.
const incompleteKeySet = new Set(incompleteKeys);
// --- Batch recovery for `pending` messages ---
for (const key of pendingKeys) {
if (conversationDebounceTimers.has(key)) continue;
if (isConversationProcessingLocked(key)) continue;
// FIX #4: skip if individual fallback already running for this conversation.
if (individualInFlightByConversation.has(key)) continue;
// FIX #8: skip if this conversation also needs individual recovery
// (batch processing would conflict with in-flight individual work).
if (incompleteKeySet.has(key)) continue;
const cooldownUntil = conversationErrorCooldown.get(key);
if (cooldownUntil && now < cooldownUntil) continue;
scheduleConversationAnalysis(key);
}
// --- Individual recovery for `error/analysis_incomplete` messages ---
// Circuit breaker check: no point iterating if individual CB is active.
if (now >= individualCooldownUntil) {
const promises: Promise<void>[] = [];
for (const key of incompleteKeys) {
// Skip if individual work is already running for this conversation.
if (individualInFlightByConversation.has(key)) continue;
// Skip if batch processing is running (it will fan-out if it finds more incomplete).
if (isConversationProcessingLocked(key)) continue;
promises.push(
getIncompleteMessagesByConversation(key, 500)
.then(async (msgs) => {
const processableMessages =
await skipAgeRestrictedMessages(msgs);
return processableMessages;
})
.then((msgs) => {
if (msgs.length > 0) {
enqueueIndividualFallbacks(msgs);
}
})
.catch((err: unknown) => {
logger.error(
{ key, error: String(err) },
"Failed to fetch incomplete messages for recovery",
);
}),
);
}
// Errors are handled per-key; return the combined promise for observability.
return Promise.all(promises);
}
})
.catch((err: unknown) => {
logger.error(
{ error: err instanceof Error ? err.message : String(err) },
"Pending AI analysis recovery worker failed",
);
});
}, config.AI_ANALYSIS_RECOVERY_INTERVAL_MS);
}