azure-ai-contentunderstanding_1.2.0b3

1.2.0b3 (2026-08-11)

Features Added

• Added support for selecting 2025-11-01 or 2026-06-01-preview through the api_version keyword argument on ContentUnderstandingClient; this beta package defaults to 2026-06-01-preview. See the README service API version examples.

• Added support for 2026-06-01-preview service. Features included:

  • Analyze smaller inputs without long-running operation polling with analyze_inline and analyze_binary_inline. See sample_analyze_inline.py and sample_analyze_binary_inline.py.
  • Extract fields whose answers must be built from evidence across a document, such as multistep reasoning or calculations, with agentic analyzer workflows by setting ContentAnalyzerConfig.workflow to ContentAnalyzerWorkflow.AGENTIC. See sample_create_analyzer_workflow.py.
  • Classify mixed document packets with boundaries within a page by enabling ContentAnalyzerConfig.allow_in_page_segments. See sample_classify_in_page_segments.py.
  • Prepare documents for retrieval and LLM workflows with semantic chunks by configuring ContentAnalyzerConfig.chunking_strategy with SemanticChunkingStrategy and reading DocumentContent.chunks. See sample_analyze_chunking.py.
  • Identify signatures and their locations in documents with DocumentSignature and DocumentContent.signatures. See sample_detect_signatures.py and sample_analyze_configs.py.
  • Preserve source document context in retrieval and LLM output with AnalysisContent.metadata and to_llm_input. See sample_extract_document_metadata.py and sample_to_llm_input.py.
  • Troubleshoot analyses with diagnostic information from AnalysisResult.infos. See sample_analysis_diagnostics.py.
  • Track inline page usage and agentic workflow token consumption with expanded UsageDetails, available from AnalyzeLROPoller.usage, AnalyzeAsyncLROPoller.usage, and ContentAnalyzerInlineResponse.usage. See sample_analyze_invoice.py, sample_analyze_inline.py, and sample_analyze_binary_inline.py.

Other Changes

• Renamed the optional to_llm_input caller dictionary from metadata to custom_metadata; it is emitted under a nested customMetadata: front-matter block.
• Added advanced samples for field grounding sources and long-running operation (LRO) continuation-token rehydration, with async counterparts.
• Updated README, samples, env templates, and Copilot skills to recommend gpt-5.2 and text-embedding-3-large, including prebuilt analyzer deployment aliases and a model retirement schedule note.
• Added experimental GitHub Copilot skills under .github/skills/ for user feedback on iterative custom-analyzer authoring in VS Code:
  • cu-sdk-author-analyzer — author and refine a custom document analyzer for a single document type (layout extraction → schema drafting → validation → batch test → agent review → refine cycle).
  • cu-sdk-author-analyzer-classify-route — author and refine a classify-and-route pipeline for mixed-document packets (e.g. invoice + bank statement + loan application in one PDF), with per-category review of both the outer classifier descriptions and each inner schema's field descriptions.