Skill UI

This SDK provides programmatic access to Azure AI Content Understanding, enabling the extraction of structured and semantic content from diverse multimodal sources. It supports analyzing documents (PDF, etc.), images, audio, and video files. Users can leverage prebuilt analyzers (like invoice or document search) or create custom analyzers with specific field schemas for advanced RAG and automated workflow implementations.

This SDK provides programmatic access to Azure AI Content Understanding, enabling the extraction of structured and semantic content from diverse multimodal sources. It supports analyzing documents (PDF, etc.), images, audio, and video files. Users can leverage prebuilt analyzers (like invoice or document search) or create custom analyzers with specific field schemas for advanced RAG and automated workflow implementations.

A comprehensive SDK for the Azure AI Document Intelligence service, designed for .NET developers. This tool allows you to programmatically extract text, structured data, and tables from various document types, including invoices, receipts, ID cards, and general documents. It supports both powerful prebuilt models and custom model training.

A comprehensive SDK for the Azure AI Document Intelligence service, designed for .NET developers. This tool allows you to programmatically extract text, structured data, and tables from various document types, including invoices, receipts, ID cards, and general documents. It supports both powerful prebuilt models and custom model training.

This SDK enables developers to build robust document analysis applications using Azure AI Document Intelligence. It provides powerful capabilities to extract structured data from diverse document types, including receipts, invoices, ID cards, and general forms. Developers can analyze documents from local files or URLs to extract key-value pairs, tables, and specific fields automatically.

This SDK enables developers to build robust document analysis applications using Azure AI Document Intelligence. It provides powerful capabilities to extract structured data from diverse document types, including receipts, invoices, ID cards, and general forms. Developers can analyze documents from local files or URLs to extract key-value pairs, tables, and specific fields automatically.

A comprehensive TypeScript SDK for integrating Azure AI Translator services. It provides robust clients for both real-time text translation (including language detection, transliteration) and large-scale document translation. Users can translate single documents or manage complex batch translation jobs using REST-style API calls, making multilingual support easy to integrate into applications.

This SDK provides comprehensive connectivity to Azure AI Search for .NET developers. It enables building sophisticated search applications, supporting full-text search, vector search, semantic understanding, and hybrid query capabilities. Developers can manage indexes, perform document CRUD operations, and implement advanced search patterns like faceted search and autocomplete.

The official SDK for interacting with Azure AI Search service using Python. It provides comprehensive functionality for advanced search retrieval, including full-text search, vector similarity search (HNSW), hybrid search, and semantic ranking. Use this tool to build sophisticated enterprise search applications that require deep content understanding and precise information retrieval.

Clarity Gate ensures documents entering RAG knowledge bases retain epistemic quality by generating Clarity-Gated Documents, validating Source of Truth tables, and running deterministic claim/document hash scripts before ingestion.

This skill details advanced techniques for enhancing the effective context capacity of LLMs. It outlines four core strategies—Compaction, Observation Masking, KV-Cache Optimization, and Context Partitioning—to prevent context limits from constraining task complexity. By strategically managing token usage, users can reduce operational costs, minimize latency, and implement robust, long-running agent systems for handling large documents or conversations.

Extends transformer context windows using RoPE, YaRN, ALiBi, and position interpolation so long documents (32k-128k+ tokens) can be processed, pre-trained models lengthened, and efficient positional encodings or extrapolation strategies deployed.