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