feat(gmw): moderation explainability + semantic message search
- Persist structured verdict (flags/severity/confidence/evidence) on moderation_actions so the public web can show WHY a message was moderated. - Add a persistent Qdrant archive collection (gmw_message_archive); embed every captured message at capture time (fire-and-forget, best-effort). - Public semantic search over the archive (backend oRPC + FE toggle on the messages view). Both features are read-only/public and fully automatic. Migration: 0015_add_moderation_explainability.sql
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import { config } from "@/shared/config/index.js";
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import { createChildLogger } from "@/shared/logger/index.js";
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const logger = createChildLogger("messages-embed");
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/**
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* Embed a search query with the configured OpenAI-compatible embedding model.
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* Uses raw fetch (the backend has no openai SDK dependency) and returns null
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* when embeddings are not configured (search unavailable).
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*
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* encoding_format: "float" is REQUIRED — Nvidia-backed models reject base64.
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*/
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export async function embedQuery(text: string): Promise<number[] | null> {
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if (!config.AI_LLM_API_KEY || !config.AI_LLM_EMBEDDING_MODEL) return null;
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try {
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const res = await fetch(`${config.AI_LLM_BASE_URL}/embeddings`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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Authorization: `Bearer ${config.AI_LLM_API_KEY}`,
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},
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body: JSON.stringify({
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model: config.AI_LLM_EMBEDDING_MODEL,
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input: text,
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encoding_format: "float",
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}),
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});
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if (!res.ok) {
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logger.warn({ status: res.status }, "query embed HTTP error");
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return null;
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}
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const json = (await res.json()) as {
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data?: Array<{ embedding?: number[] }>;
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};
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return json.data?.[0]?.embedding ?? null;
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} catch (error) {
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logger.warn(
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{ error: error instanceof Error ? error.message : String(error) },
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"query embed failed",
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);
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return null;
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}
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}
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