Skill UI

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

A comprehensive guide and implementation patterns for constructing sophisticated autonomous AI agents. It details the core 'Observe-Think-Decide-Act' agent loop, multi-model orchestration strategies, and robust tool design principles (like file I/O and function calling). Ideal for building complex, highly capable agents that require advanced planning, reasoning, and external tool interaction.

A comprehensive guide and implementation patterns for constructing sophisticated autonomous AI agents. It details the core 'Observe-Think-Decide-Act' agent loop, multi-model orchestration strategies, and robust tool design principles (like file I/O and function calling). Ideal for building complex, highly capable agents that require advanced planning, reasoning, and external tool interaction.

Activation-aware weight quantization that compresses 7B-70B models to 4-bit with minimal accuracy loss, delivering ~3x inference speedups and memory reductions for constrained GPU deployments while integrating with vLLM and Marlin kernels.

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.

A comprehensive TypeScript client for the Azure Document Intelligence REST API. This SDK allows developers to programmatically extract structured data, text, and tables from various document types, including PDFs and images. It supports using prebuilt models (like invoices, receipts, and IDs) and building custom models for specialized document processing workflows.

A comprehensive TypeScript client for the Azure Document Intelligence REST API. This SDK allows developers to programmatically extract structured data, text, and tables from various document types, including PDFs and images. It supports using prebuilt models (like invoices, receipts, and IDs) and building custom models for specialized document processing workflows.

This comprehensive Python SDK serves as a client library for managing the full lifecycle of Machine Learning resources on Azure Machine Learning. It allows users to programmatically handle workspaces, register data and models, provision compute clusters, define complex training jobs, and build reproducible, end-to-end MLOps pipelines.

This comprehensive Python SDK serves as a client library for managing the full lifecycle of Machine Learning resources on Azure Machine Learning. It allows users to programmatically handle workspaces, register data and models, provision compute clusters, define complex training jobs, and build reproducible, end-to-end MLOps pipelines.

The official client library for interacting with Azure OpenAI services in .NET. It provides comprehensive access to various OpenAI models, including GPT-4o, embeddings, image generation (DALL-E), and audio transcription (Whisper). Use cases include building advanced chat applications, generating structured JSON outputs, and implementing Retrieval-Augmented Generation (RAG).

This is a high-level Software Development Kit (SDK) designed for managing the full lifecycle of AI projects within Azure AI Foundry. Built for Java, it provides robust client interfaces for core AI operations, including managing connections to Azure resources, uploading and managing datasets, creating and updating search indexes, running model evaluations, and handling deployments. It streamlines complex AI workflow orchestration.