docs(architect): weekly KB update — 106 files refreshed (2026-04)

Updates across all 5 skills: ms-ai-advisor, ms-ai-engineering,
ms-ai-governance, ms-ai-security, ms-ai-infrastructure.

Key changes:
- Language Services (Custom Text Classification, Text Analytics, QnA):
  retirement warning 2029-03-31, migration guides to Foundry/GPT-4o
- Agentic Retrieval: 50M free reasoning tokens/month (Public Preview)
- Computer Use: Claude Sonnet 4.5 (preview) + OpenAI CUA models
- Agent Registry: Risks column (M365 E7), user-shared/org-published types
- Declarative agents: schema v1.5 → v1.6, Store validation requirements
- MLflow 3: 13 built-in LLM judges, production monitoring, Genie Code
- AG-UI HITL: ApprovalRequiredAIFunction (C#) + @tool(approval_mode) (Python)
- Entra ID Ignite 2025: Agent ID Admin/Developer RBAC roles, Conditional Access
- Security Copilot: 400 SCU/month per 1000 M365 E5 licenses, auto-provisioned
- Fast Transcription API: phrase lists, 14-language multi-lingual transcription
- Azure Monitor Workbooks: Bicep support, RBAC specifics
- Power Platform Copilot: data residency (Norway/Europe → EU DB, Bing → USA)
- RAG security-rbac: 4-approach table (GA + 3 preview access control methods)
- IaC MLOps: Well-Architected OE:05 principles, Bicep/Terraform patterns
- Translator: image file batch translation Preview (JPEG/PNG/BMP/WebP)

All 106 files: Last updated 2026-04 | Verified: MCP 2026-04

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

skills/ms-ai-engineering/references/rag-architecture/rag-core-patterns.md

@@ -1,6 +1,6 @@
 # RAG Core Patterns and Architecture
 
-**Last updated:** 2026-02
+**Last updated:** 2026-04 | Verified: MCP 2026-04
 **Status:** GA
 **Category:** RAG Architecture & Semantic Search
 
@@ -417,3 +417,25 @@ results = search_client.search(
 ---
 
 **For Cosmo:** Når kunde spør om RAG, start med "Naive vs Advanced vs Agentic"-beslutningstreet. Identifiser data source, query complexity, og latency-krav først. Hvis offentlig sektor: alltid spør om GDPR/Schrems II/AI Act compliance før du foreslår arkitektur. Hvis customer mangler evaluation strategy: stopp og definer retrieval recall/precision targets før du går videre med implementation.
+
+
+### Hybrid Search — Kjernemønster (oppdatert 2026-04)
+
+Hybrid search er standardmønsteret for RAG i Azure AI Search:
+
+```json
+{
+  "search": "historisk hotell nær restauranter",
+  "vectorQueries": [{"kind": "vector", "vector": [...], "k": 50, "fields": "DescriptionVector"}],
+  "queryType": "semantic",
+  "semanticConfiguration": "my-semantic-config"
+}
+```
+
+**Hvorfor hybrid:**
+- Vector search: finner konseptuelt like dokumenter uten nøyaktige nøkkelord-treff
+- Full-text search: presis matching for produktkoder, navn, datoer
+- RRF merger: normaliserer scores fra BM25 og HNSW/eKNN
+- Semantic ranker (L2): re-ranker opp til 50 resultater med maskinlesningsforståelse
+
+**Best practice:** Sett `k=50` ved bruk av semantic ranker.