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/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 };
   }