fix(moderation): audit fixes — media/vision timeout 120s, Qdrant retry w/ backoff, JSON repair in LLM caller

- AI_LLM_MEDIA_ANALYSIS_TIMEOUT_MS 60s→120s + vision 60s→120s: vision model
  via router regularly exceeded 60s, dropping media batches into the
  individual-fallback chain then exhausting into ai_status=error.
- Qdrant upserts: retryWithRetry() wraps PUT /points with exponential
  backoff (3 attempts, jitter) for transient 408/abort/ECONNRESET — the
  41 six-hour 'Qdrant upsert failed — semantic entry skipped' warnings were
  single-hop timeouts on a healthy-but-loaded Qdrant.
- LLM caller: on parse failure, attempt extractJson() structural repair of
  the raw content (models with thinking disabled sometimes emit JSON as
  plain text) before giving up and re-requesting.
This commit is contained in:
asepharyana
2026-09-09 21:29:01 +07:00
committed by asepharyana
parent 7d4009da9f
commit f85af3952a
4 changed files with 103 additions and 7 deletions
@@ -17,7 +17,10 @@ import { config } from "../../shared/config/config.js";
import { incrementCounterBy } from "../gateway-metrics/index.js";
import type { AnalysisResult } from "../message-capture/types.js";
import { llmChat } from "./llmClient.js";
import { parseModerationResponse } from "./moderationResponseParser.js";
import {
extractJson,
parseModerationResponse,
} from "./moderationResponseParser.js";
import { logModerationError } from "./responseLogger.js";
const log = createChildLogger("llm-caller");
@@ -129,6 +132,35 @@ export async function callModerationLLM(
},
`Failed to parse moderation response (${label})`,
);
// Attempt cheap structural repair before re-throwing: models with
// thinking disabled sometimes emit the JSON object as plain text
// (no code fence, outer prose). extractJson walks the raw content
// for a balanced {…} / […]. If that yields a parseable object,
// use it — the parse is data-driven, never injected into the
// prompt, so it cannot leak into future calls.
try {
const repaired = extractJson(rawContent);
if (repaired) {
return {
parsed: parseModerationResponse(
JSON.stringify(repaired),
targetIds,
),
result: completion,
};
}
} catch (repairError: any) {
log.warn(
{
error:
repairError instanceof Error
? repairError.message
: String(repairError),
label,
},
"JSON repair attempt failed — falling through to retry",
);
}
throw parseError;
}
} catch (apiError: any) {
@@ -78,6 +78,10 @@ export async function runMediaBatch(
const userContent = userBlocks.join("\n\n");
const perMsgTimeout = config.AI_LLM_MEDIA_ANALYSIS_TIMEOUT_MS ?? 60000;
// Batch budget = per-message budget × message count, capped at 5 minutes
// absolute so a single slow vision call cannot stall the whole pipeline
// for a huge batch (the abandonment budget is a hard safety net; messages
// that time out are routed to the individual fallback queue anyway).
const batchTimeout = Math.min(
Math.max(perMsgTimeout, perMsgTimeout * targets.length),
300_000,
@@ -107,6 +107,54 @@ async function request(
}
}
/** True for transient errors worth retrying (408 timeout, ECONNRESET, aborts). */
function isTransientQdrantError(error: unknown): boolean {
if (error instanceof Error && error.name === "AbortError") return true;
const msg = error instanceof Error ? error.message : String(error);
return (
msg.includes("-> 408") ||
msg.includes("aborted") ||
msg.includes("ECONNRESET") ||
msg.includes("ETIMEDOUT") ||
msg.includes("fetch failed")
);
}
/** Retry with exponential backoff around `request`, abort-aware. */
async function requestWithRetry(
method: string,
path: string,
body?: unknown,
timeoutMs = 10_000,
attempts = 3,
): Promise<unknown> {
let lastError: unknown;
for (let attempt = 0; attempt < attempts; attempt++) {
if (attempt > 0) {
// Exponential backoff: 500ms → 1s → 2s (jittered ±20%).
const base = 500 * 2 ** (attempt - 1);
const delayMs = base + Math.floor(Math.random() * base * 0.2);
await new Promise((resolve) => setTimeout(resolve, delayMs));
}
try {
return await request(method, path, body, timeoutMs);
} catch (error) {
lastError = error;
if (!isTransientQdrantError(error)) throw error;
log.debug(
{
error: error instanceof Error ? error.message : String(error),
attempt: attempt + 1,
method,
path,
},
"Qdrant transient error — retrying with backoff",
);
}
}
throw lastError;
}
/** Deterministic uint64 point id from the exact-hash cache key. */
export function qdrantPointId(cacheKey: string): number {
const digest = createHash("sha256").update(cacheKey).digest();
@@ -185,7 +233,7 @@ export async function upsertQdrantPoint(
): Promise<boolean> {
try {
if (!(await ensureQdrantCollection(vector.length))) return false;
await request(
await requestWithRetry(
"PUT",
`/collections/${collectionName()}/points`,
{
@@ -193,6 +241,7 @@ export async function upsertQdrantPoint(
wait: true,
},
30_000,
3,
);
return true;
} catch (error) {
@@ -470,7 +519,7 @@ export async function upsertQdrantPointV2(
): Promise<boolean> {
try {
if (!(await ensureQdrantCollectionV2(name, vector.length))) return false;
await request(
await requestWithRetry(
"PUT",
`/collections/${name}/points`,
{
@@ -478,6 +527,7 @@ export async function upsertQdrantPointV2(
wait: true,
},
30_000,
3,
);
return true;
} catch (error) {
@@ -105,6 +105,14 @@ export const configSchema = z
// to POSTGRES_POOL_MAX; min:0 only drops idle clients after
// idleTimeoutMillis. This both trims RSS and frees PgBouncer slots.
POSTGRES_POOL_MIN: z.coerce.number().int().min(0).default(0),
// Ceiling for the gateway's per-process pg Pool. Each Piscina worker
// thread owns its own pool (main + 4 workers = 5 pools), so this value
// is the per-thread cap. Kept at 10 (2026-09-09 audit): the real
// bottleneck is PgBouncer's per-(user,db) default_pool_size on imrnes —
// raising this ceiling without raising the Bouncer pool just makes more
// clients queue at the same 20 slots. pool_mode=session means each pg
// Pool client occupies a Bouncer slot for the whole transaction; min:0
// + idleTimeoutMillis frees idle slots automatically.
POSTGRES_POOL_MAX: z.coerce.number().int().positive().default(10),
// ── Redis ────────────────────────────────────────────────────────────
@@ -211,16 +219,18 @@ export const configSchema = z
.number()
.int()
.positive()
.default(60000),
.default(120_000),
// Standalone image/sticker/emoji vision analysis (analyzeSingleMediaImage
// → llmVision → llmChat). Decoupled from the media *batch* timeout above so
// a single vision call can be tuned independently. 1 minute by default —
// vision models (especially behind a router) need headroom for large images.
// a single vision call can be tuned independently. 2 minutes by default —
// vision models (especially behind a router) need headroom for large images
// and the media batch budget grew to 120s (2026-09-09) so single-image calls
// must not be the bottleneck in the fallback chain.
AI_LLM_VISION_ANALYSIS_TIMEOUT_MS: z.coerce
.number()
.int()
.positive()
.default(60000),
.default(120_000),
// Text-only moderation batches are cheaper than media (no downloads /
// vision pre-pass), so they get their own (shorter) timeout instead of
// being tied to the media budget.