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

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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>
This commit is contained in:
Kjell Tore Guttormsen 2026-04-10 09:13:24 +02:00
commit 6645e93205
104 changed files with 1986 additions and 520 deletions
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17
skills/ms-ai-engineering/references/rag-architecture/vector-indexing-techniques.md
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@ -1,6 +1,6 @@
# Vector Indexing - Techniques and Configuration
**Last updated:** 2026-02
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Status:** GA (Hybrid search), Preview (Scalar quantization)
**Category:** RAG Architecture & Semantic Search
@ -410,3 +410,18 @@ Azure AI Search krever ingen spesifikk Microsoft 365-lisens, men:
- [Semantic ranking in Azure AI Search](https://learn.microsoft.com/en-us/azure/search/semantic-search-overview) — **Verified** (2025-11)
**Konfidensnivå:** Verified (90%) — All info basert på offisiell Microsoft-dokumentasjon og prising per feb 2026.
### Vector Indexing i Hybrid Search-kontekst (oppdatert 2026-04)
Vector fields og tekstfelt coeksisterer i hybrid search-indekser:
- **HNSW** (Hierarchical Navigable Small World) — standard ANN-algoritme, `efSearch` og `maxConnections` tunable
- **eKNN** (exhaustive K-Nearest Neighbors) — fullstendig søk, brukes med `"exhaustive": true` i query
- **Multi-vector fields (preview)** — `Collection(Edm.ComplexType)` støtter opp til 100 vektorer per dokument
- `perDocumentVectorLimit: 1` — én vektor per dokument i resultater
- `perDocumentVectorLimit: 0` — ubegrenset (alle matchende vektorer)
- Nyttig for multimodal data (scene embeddings i video, fragmenter i lange dokumenter)
**Tuning ved overbelastning:**
Reduser `efSearch` (f.eks. 800 → 128-192) og `maxConnections` (64 → 32) FØR du skalerer ut med flere replicas. Hybrid queries med aggressive vector-innstillinger + semantic ranker øker CPU/minne-press betydelig.