Files
GMW/services/discord-gateway/src/modules/ai-moderation/qdrantClient.ts
T
Developer fc475dfbb7
Build & Deploy (Nix) / build-and-deploy (backend) (push) Successful in 3m7s
Build & Deploy (Nix) / build-and-deploy (discord-gateway) (push) Successful in 2m21s
Build & Deploy (Nix) / build-and-deploy (proxy) (push) Successful in 2m33s
feat(automod): store semantic cache embeddings in Qdrant
New qdrantClient.ts (zero-dep fetch REST): ensure collection with cosine
distance (auto-recreate on vector-size change), upsert point w/ verdict
payload, search w/ expires_at filter + score threshold.

textCacheStore: when QDRANT_URL set, embeddings are upserted to Qdrant
(primary) and searched there first; Postgres embedding column remains as
legacy fallback for pre-Qdrant rows. Config: QDRANT_URL/COLLECTION/API_KEY.
QDRANT_URL already in repo .env; added to VPS env + GATEWAY_ENV secret.
2026-07-31 21:30:43 +07:00

222 lines
6.0 KiB
TypeScript

/**
* qdrantClient.ts
*
* Minimal Qdrant REST client (zero dependencies, fetch-based) used by the
* semantic moderation cache. Embedding vectors + verdict payloads live in
* Qdrant instead of the Postgres `embedding` column (legacy, kept for
* backward-compatible fallback reads).
*
* All functions degrade gracefully: failures return null / empty results so
* callers fall back to the LLM — moderation quality is never reduced.
*/
import { createHash } from "node:crypto";
import { createChildLogger } from "@/shared/logger/index";
import { config } from "../../shared/config/config.js";
const log = createChildLogger("qdrant");
export interface QdrantVerdictPayload {
text: string;
flags: string; // JSON string of the full moderation result
analyzed_at: number;
expires_at: number;
}
function baseUrl(): string {
return (config.QDRANT_URL ?? "http://100.121.180.82:6333").replace(
/\/+$/,
"",
);
}
function collectionName(): string {
return config.QDRANT_COLLECTION ?? "gmw_text_moderation";
}
function headers(): Record<string, string> {
const h: Record<string, string> = {
"Content-Type": "application/json",
};
if (config.QDRANT_API_KEY) {
h["api-key"] = config.QDRANT_API_KEY;
}
return h;
}
async function request(
method: string,
path: string,
body?: unknown,
timeoutMs = 10_000,
): Promise<unknown> {
const controller = new AbortController();
const timer = setTimeout(() => controller.abort(), timeoutMs);
try {
const res = await fetch(`${baseUrl()}${path}`, {
method,
headers: headers(),
body: body === undefined ? undefined : JSON.stringify(body),
signal: controller.signal,
});
const text = await res.text();
let json: unknown = null;
try {
json = text ? JSON.parse(text) : null;
} catch {
json = null;
}
if (!res.ok) {
throw new Error(
`Qdrant ${method} ${path} -> ${res.status}: ${text.slice(0, 200)}`,
);
}
return json;
} finally {
clearTimeout(timer);
}
}
/** Deterministic uint64 point id from the exact-hash cache key. */
export function qdrantPointId(cacheKey: string): number {
const digest = createHash("sha256").update(cacheKey).digest();
// First 8 bytes as BigInt, then clamp into Qdrant's uint64 space.
const big = digest.readBigUInt64BE(0);
return Number(big & 0x7fffffffffffffffn);
}
/**
* Ensure the collection exists with the right vector size. If the size
* changed (embedding model swapped), recreate — stale vectors are useless
* anyway and cosine scores would be meaningless across dimensions.
*/
export async function ensureQdrantCollection(
vectorSize: number,
): Promise<boolean> {
try {
// 404 = collection doesn't exist yet → create it.
let existing: {
result?: { config?: { params?: { vectors?: { size?: number } } } };
} | null = null;
try {
existing = (await request("GET", `/collections/${collectionName()}`)) as {
result?: { config?: { params?: { vectors?: { size?: number } } } };
};
} catch (error) {
if (!(error instanceof Error) || !error.message.includes("-> 404")) {
throw error;
}
}
const size = existing?.result?.config?.params?.vectors?.size;
if (size === vectorSize) return true;
if (size !== undefined && size !== vectorSize) {
log.warn(
{ collection: collectionName(), oldSize: size, newSize: vectorSize },
"Qdrant collection vector size changed — recreating collection",
);
await request("DELETE", `/collections/${collectionName()}`);
}
await request("PUT", `/collections/${collectionName()}`, {
vectors: { size: vectorSize, distance: "Cosine" },
});
return true;
} catch (error) {
log.error(
{
error: error instanceof Error ? error.message : String(error),
collection: collectionName(),
},
"Failed to ensure Qdrant collection",
);
return false;
}
}
/** Upsert one embedding + verdict payload point. Returns false on failure. */
export async function upsertQdrantPoint(
cacheKey: string,
vector: number[],
payload: QdrantVerdictPayload,
): Promise<boolean> {
try {
if (!(await ensureQdrantCollection(vector.length))) return false;
await request("PUT", `/collections/${collectionName()}/points`, {
points: [{ id: qdrantPointId(cacheKey), vector, payload }],
wait: true,
});
return true;
} catch (error) {
log.warn(
{ error: error instanceof Error ? error.message : String(error) },
"Qdrant upsert failed — semantic entry skipped",
);
return false;
}
}
export interface QdrantSearchHit {
cacheKey: string;
score: number;
payload: QdrantVerdictPayload;
}
/**
* Search the nearest stored vector. Returns hits sorted by score desc,
* filtered to unexpired payloads. Empty array on failure.
*/
export async function searchQdrant(
vector: number[],
limit: number,
scoreThreshold: number,
): Promise<QdrantSearchHit[]> {
try {
const json = (await request(
"POST",
`/collections/${collectionName()}/points/search`,
{
vector,
limit,
score_threshold: scoreThreshold,
with_payload: true,
filter: {
must: [
{
key: "expires_at",
range: { gte: Date.now() },
},
],
},
},
)) as {
result?: Array<{
id?: number;
score?: number;
payload?: QdrantVerdictPayload;
}>;
};
return (json.result ?? [])
.filter((hit) => hit.payload?.flags)
.map((hit) => ({
cacheKey: `qdrant:${hit.id ?? "?"}`,
score: hit.score ?? 0,
payload: hit.payload as QdrantVerdictPayload,
}));
} catch (error) {
log.warn(
{ error: error instanceof Error ? error.message : String(error) },
"Qdrant search failed — semantic cache skipped",
);
return [];
}
}
/** True when Qdrant is configured (non-empty URL). */
export function isQdrantConfigured(): boolean {
return Boolean(config.QDRANT_URL);
}