feat(ai): audit + harden embedding pipeline

- Normalize text before embedding (strip mentions/URLs/emoji/markdown/control chars, lowercase, truncate) on both write and query sides so vectors aren't diluted and tokens aren't wasted
- embeddingClient: retry embeddings (maxRetries 2), validate batch dimension consistency, preserve index alignment for empty-normalized texts
- archiveEmbedder: store normalized text in archive payload, skip empty-normalized content
- backend: normalize search queries, make archive search similarity threshold configurable (AI_LLM_EMBEDDING_ARCHIVE_MIN_SIMILARITY, default 0.6)

services/backend/src/modules/messages/embed.ts

@@ -3,6 +3,26 @@ import { createChildLogger } from "@/shared/logger/index";
 
 const logger = createChildLogger("messages-embed");
 
+/** Max chars for a search query fed to the embedding model. */
+const MAX_QUERY_CHARS = 300;
+
+/**
+ * Normalize a user search query before embedding so it lands in the same
+ * vector space as the archived content (which is normalized the same way on
+ * write). Mirrors the gateway's normalizer: strip control/zero-width chars,
+ * lowercase, collapse whitespace, cap length. Readable punctuation is kept —
+ * a search query is already compact.
+ */
+export function normalizeEmbeddingQuery(raw: string): string {
+  if (!raw) return "";
+  return raw
+    .replace(/[\p{Cc}\p{Cf}]/gu, " ")
+    .toLowerCase()
+    .replace(/\s+/g, " ")
+    .trim()
+    .slice(0, MAX_QUERY_CHARS);
+}
+
 /**
  * Embed a search query with the configured OpenAI-compatible embedding model.
  * Uses raw fetch (the backend has no openai SDK dependency) and returns null
@@ -10,8 +30,10 @@ const logger = createChildLogger("messages-embed");
  *
  * encoding_format: "float" is REQUIRED — Nvidia-backed models reject base64.
  */
-export async function embedQuery(text: string): Promise<number[] | null> {
+export async function embedQuery(rawQuery: string): Promise<number[] | null> {
   if (!config.AI_LLM_API_KEY || !config.AI_LLM_EMBEDDING_MODEL) return null;
+  const text = normalizeEmbeddingQuery(rawQuery);
+  if (!text) return null;
   try {
     const res = await fetch(`${config.AI_LLM_BASE_URL}/embeddings`, {
       method: "POST",

services/backend/src/modules/messages/messages.service.ts

@@ -1,3 +1,4 @@
+import { config } from "@/shared/config/index";
 import { NotFoundError, ValidationError } from "@/shared/errors/index";
 import { createChildLogger } from "@/shared/logger/index";
 import { embedQuery } from "./embed.js";
@@ -100,7 +101,11 @@ export class MessagesService {
       );
       return { results: [], nextCursor: null };
     }
-    const hits = await searchArchive(vector, input.limit, 0.6);
+    const hits = await searchArchive(
+      vector,
+      input.limit,
+      config.AI_LLM_EMBEDDING_ARCHIVE_MIN_SIMILARITY,
+    );
     const results = hits.map((h) => mapSearchHit(h));
     return { results, nextCursor: null };
   }

services/backend/src/shared/config/index.ts

@@ -135,6 +135,14 @@ export const configSchema = z
     AI_LLM_MODEL: z.string().default("text"),
     AI_LLM_VISION_MODEL: z.string().optional(),
     AI_LLM_EMBEDDING_MODEL: z.string().optional(),
+    // Minimum cosine similarity for the public archive semantic search. Lower
+    // = more (noisier) results; raise it to tighten precision. Tuned for a 1B
+    // embedding model — re-tune if the model's dimensionality changes.
+    AI_LLM_EMBEDDING_ARCHIVE_MIN_SIMILARITY: z.coerce
+      .number()
+      .min(0)
+      .max(1)
+      .default(0.6),
     AI_LLM_MAX_CONCURRENT: z.coerce.number().int().positive().default(5),
     AI_LLM_IMAGE_MAX_DIMENSION: z.coerce
       .number()