feat: CIS number in embeddings, tenancy dropdown in consult, text filter for exact CIS search

- Chunk findings CSV: includes CIS Recommendation number, Section, Status in document header
- Consult embeddings: tenancy dropdown (OCI config selector) for filtered search
- CIS number detection: regex extracts "cis X.Y" from query → TEXT LIKE filter for exact match
- Dynamic top_k: 10 per table when CIS filter active (vs 3 default), 15 global results
- Vector search text_filter: combined vector similarity + TEXT LIKE for precise results
- Purged 121174 legacy docs without tenancy metadata from all CIS tables
- Re-embedded 4364 docs across 7 tables with full CIS metadata
- GPT-5.2 for consult (was GPT-4.1), max_tokens 8000

frontend-react/src/api/endpoints/embeddings.ts

@@ -88,10 +88,11 @@ export const embeddingsApi = {
     }) as unknown as Promise<PurgeResult>,
 
   /** Consult embeddings with a question */
-  consult: (query: string, tableName?: string, topK = 10) =>
+  consult: (query: string, tableName?: string, topK = 10, ociConfigId?: string) =>
     client.post('/embeddings/consult', {
       query,
       table_name: tableName || '',
       top_k: topK,
+      oci_config_id: ociConfigId || '',
     }) as unknown as Promise<ConsultResult>,
 };