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/semantic-ranker-reranking.md

@@ -1,6 +1,6 @@
 # Semantic Ranker and Reranking Models
 
-**Last updated:** 2026-02
+**Last updated:** 2026-04 | Verified: MCP 2026-04
 **Status:** GA (core), Preview (query rewrite, prerelease models)
 **Category:** RAG Architecture & Semantic Search
 
@@ -276,3 +276,25 @@ Preview-funksjon (2025) som integrerer iterativ søk med semantic ranking.
 ### Baseline (modellkunnskap)
 - Cross-encoder-eksempler basert på Sentence Transformers-dokumentasjon
 - Offentlig sektor-anbefalinger basert på norsk kontekst
+
+
+### Semantic Ranker i Hybrid Search (oppdatert 2026-04)
+
+Semantic ranker (L2 reranking) fungerer optimalt i hybrid search-kontekst:
+
+- Aksepterer opp til **50 resultater** fra RRF-merger som input
+- Bruker maskinlesningsforståelse (MRC) for å re-ranke basert på semantisk relevans
+- `@search.rerankerScore` erstatter `@search.score` som primær rankingmetrikk
+- Valgfritt: `captions` (ekstraktiv) og `answers` (ekstraktiv) fra verbatim tekst
+
+**Konfigurasjon:**
+```json
+{
+  "queryType": "semantic",
+  "semanticConfiguration": "min-konfig",
+  "captions": "extractive",
+  "answers": "extractive"
+}
+```
+
+**Viktig:** Sett `k=50` i vectorQueries — semantic ranker trenger tilstrekkelig input. Pre-filtre som er for strenge kan redusere antall input-dokumenter og svekke reranking-kvaliteten.