Posts tagged with "RAG"

Cohere Parse v5 landed in Microsoft Foundry on 27 August 2026 at $1.50 per 1,000 pages, the same price as Document Intelligence Read and under a third of Content Understanding's layout meter. We price all four Azure document parsers in Sweden Central at 100k, 1M and 10M pages a month, then weigh the saving against Parse's missing confidence scores, absent Swedish language support and preview-only status.

At its 122nd plenary on 7 July 2026 the EDPB adopted draft Guidelines 03/2026 on web scraping for generative AI and draft Guidelines 02/2026 on anonymisation, the first comprehensive GDPR framework for large-scale scraping of public web data into AI pipelines. We walk through the legitimate interest test, the new legal weight of robots.txt and ai.txt, the Article 9 lifecycle safeguards, and what it means for teams ingesting web content into RAG on Azure.

Azure HorizonDB reached public preview at Build 2026: a PostgreSQL-compatible, scale-out engine with vector search built in, up to 128 TB of storage and Sweden Central among the five launch regions. For RAG embeddings on Azure the choice now runs three ways, decided by where the source data lives, how much retrieval quality matters and how close you are to Flexible Server's vertical-scaling ceiling.

Microsoft Build 2026 turned Foundry into a full production-agent stack: Foundry IQ for unified retrieval, Toolboxes for managed tool access, agent memory, Voice Live and the experimental Scout Autopilot. Foundry IQ knowledge bases and Voice Live are generally available now, Toolboxes and memory sit in public preview, and Scout remains experimental, which sets the build, pilot and watch lanes for an Azure-first EU team.

The vector-database market settled in 2025. The 2026 question is less "which novel DB do we adopt" and more "do we need a separate vector store at all." A decision framework across Azure AI Search, pgvector on managed Postgres, and the dedicated managed vector platforms, with the workloads each one handles well.

Generic LLM copilots are a liability in manufacturing. Technicians need answers that cite the exact procedure, not plausible-sounding text. Retrieval-augmented generation grounded in Azure AI Search solves this when architected correctly. This is the pattern that holds up under service-bay pressure.

A practical comparison of SQL Server 2025 vector search against Azure AI Search and pgvector for hybrid RAG — when collocating vectors with your transactional data is the right call, and when a dedicated search layer still wins.

Classic RAG hits a ceiling when questions require multi-hop reasoning or query refinement. Agentic RAG — treating retrieval as a tool, decomposing queries, adding self-correction loops — routinely wins where classic RAG plateaus.

Agents need context. Discover Foundry IQ—the knowledge layer connecting AI agents to enterprise data with multi-source RAG orchestration, retrieval steering, dynamic security controls, and agentic RAG delivering 36% higher accuracy than traditional search.

Microsoft Ignite BRK193: Build agents with Azure AI Search knowledge features. Connect to SharePoint, web, blob. Hybrid search (keyword+vector+semantic), agentic retrieval with query planning, reasoning effort modes, Foundry IQ with MCP protocol. Code-focused implementation guide.