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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
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17
skills/ms-ai-engineering/references/rag-architecture/contextual-retrieval.md
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@ -1,6 +1,6 @@
# Contextual Retrieval — Kontekstuell berikelse av chunks
**Last updated:** 2026-02
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Status:** GA (custom skill pattern), Preview (agentic retrieval)
**Category:** RAG Architecture & Semantic Search
@ -282,3 +282,18 @@ Hvis contextual retrieval reduserer irrelevante LLM-kall med 30%:
| Custom skill interface (Azure AI Search) | **Verified** | [learn.microsoft.com](https://learn.microsoft.com/en-us/azure/search/cognitive-search-custom-skill-interface) |
| Custom skill example (Python) | **Verified** | [learn.microsoft.com](https://learn.microsoft.com/en-us/previous-versions/azure/search/cognitive-search-custom-skill-python) |
| Retrieval failure benchmarks | **Baseline** | Anthropic research, validert av Microsoft |
### Custom Skill Interface (oppdatert 2026-04)
Custom skills integreres i Azure AI Search enrichment pipeline via `#Microsoft.Skills.Custom.WebApiSkill`.
**Interface-krav:**
- HTTPS endpoint (Azure Functions, containers, eller annen Azure-hosted tjeneste)
- Aksepterer JSON batch: `{"values": [{"recordId": "...", "data": {...}}, ...]}`
- Returnerer JSON batch: `{"values": [{"recordId": "...", "data": {...}, "errors": [], "warnings": []}]}`
- Timeout: default 30s, maks 230s (`PT230S`)
- Autentisering: API-key i URI/header, eller managed identity med `authResourceId`
**Kontekstuell berikelse via custom skill:**
Custom skills brukes for contextual retrieval der hver chunk berikes med kontekst fra omliggende tekst (f.eks. "Dette er fra kapittel 3 om sikkerhet..."). Custom skill kaller LLM med original dokument + chunk, og returnerer kontekstualisert chunk for bedre embedding-kvalitet.