64 lines
1.9 KiB
TypeScript
64 lines
1.9 KiB
TypeScript
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import { embedText } from "@/modules/ai-moderation/embeddingClient.js";
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import {
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ARCHIVE_COLLECTION,
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qdrantPointId,
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upsertQdrantPointV2,
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} from "@/modules/ai-moderation/qdrantClient.js";
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import { config } from "@/shared/config/config.js";
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import { createChildLogger } from "@/shared/logger/index";
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const log = createChildLogger("archive-embedder");
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export interface ArchiveMessage {
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id: string;
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content: string;
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username: string;
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channel_id: string;
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guild_id: string;
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created_at: number;
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}
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/**
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* Fire-and-forget: embed a captured message and upsert it into the persistent
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* archive collection so the public web can semantic-search the corpus.
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*
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* Failures are swallowed — searching is a nice-to-have, never a precondition
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* for capture or moderation. The message text is kept in the payload so the
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* search endpoint can return results even for deleted messages.
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*/
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export function archiveMessageEmbedded(message: ArchiveMessage): void {
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if (!config.AI_LLM_EMBEDDING_MODEL) return; // embeddings disabled → skip
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const text = message.content?.trim();
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if (!text || text.length < 3) return;
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void (async () => {
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try {
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const vector = await embedText(text);
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if (!vector) return;
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const ok = await upsertQdrantPointV2(
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ARCHIVE_COLLECTION,
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qdrantPointId(`archive:${message.id}`),
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vector,
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{
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text: text.slice(0, 4000),
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flags: "",
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analyzed_at: Date.now(),
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// 5-year persistent window (archive is NOT a TTL cache).
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expires_at: Date.now() + 1000 * 60 * 60 * 24 * 365 * 5,
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content_hash: message.id,
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},
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);
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if (!ok) return;
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log.debug({ messageId: message.id }, "Archived message embedding");
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} catch (err) {
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log.debug(
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{
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messageId: message.id,
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error: err instanceof Error ? err.message : String(err),
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},
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"archive embed skipped",
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);
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}
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})();
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}
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