Focus on document and vector storage and Azure OpenAI rebranding

diskann/02_Overview_Cosmos_DB/README.md

@@ -1,17 +1,16 @@
 # Overview of Azure Cosmos DB
 
-[Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/introduction) is a globally distributed, multi-model database service for both NoSQL and relational workloads.
-It supports multiple APIs: NoSQL, MongoDB, PostgreSQL, Cassandra, Gremlin, and Table—covering document, relational, column-family, graph, and key-value data models.
+[Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/introduction) is Microsoft's cloud-native, serverless NoSQL and vector database for mission-critical and globally distributed applications. It provides a native JSON document model with a SQL-like query language, combining flexible data modeling with familiar query semantics. With built-in vector search capabilities, Azure Cosmos DB enables AI-powered scenarios such as semantic search, retrieval-augmented generation, and intelligent applications. As an Azure-native service, it integrates deeply with Azure and delivers predictable performance and availability backed by industry-leading service level agreements (SLAs).
 The service offers turnkey global distribution with elastic scaling of throughput and storage.
 It delivers single-digit millisecond latencies at the 99th percentile and guarantees high availability through multi-homing capabilities.
-Azure Cosmos DB provides comprehensive service level agreements (SLAs) covering throughput, latency, availability, and consistency—a unique combination among cloud database services.
+Azure Cosmos DB provides comprehensive SLAs covering throughput, latency, availability, and consistency—a unique combination among cloud database services.
 
 ## Azure Cosmos DB and AI
 
 The surge of AI-powered applications has led to the need to integrate data from multiple data stores, introducing another layer of complexity as each data store tends to have its own workflow and operational performance.
-Azure Cosmos DB simplifies this process by providing a unified platform for all data types, including AI data.
-Azure Cosmos DB supports relational, document, vector, key-value, graph, and table data models, making it an ideal platform for AI applications.
-The wide array of data model support combined with guaranteed high availability, high throughput, low latency, and tunable consistency are huge advantages when building these types of applications.
+Azure Cosmos DB simplifies this process by providing a unified platform for document and vector data.
+Azure Cosmos DB provides a native JSON document model combined with built-in vector search capabilities, making it an ideal platform for AI applications.
+This document and vector support combined with guaranteed high availability, high throughput, low latency, and tunable consistency are huge advantages when building these types of applications.
 
 ## Azure Cosmos DB for NoSQL
 

diskann/03_Overview_Azure_OpenAI/README.md

@@ -1,4 +1,4 @@
-# Overview of Azure OpenAI and Foundry Tools
+# Overview of Azure OpenAI in Foundry Models and Foundry Tools
 
 Azure OpenAI is a collaboration between Microsoft Azure and OpenAI, a leading research organization in artificial intelligence.
 It is a cloud-based platform that enables developers and data scientists to build and deploy AI models quickly and easily.
@@ -17,28 +17,31 @@ Here are ways that Azure OpenAI can help developers:
 These measures help ensure that AI models are unbiased, explainable, trustworthy, and used in a responsible and compliance manner.
 - **Community support** - With an active developer community developers can seek help via forums and other community support channels.
 
-## Comparison of Azure OpenAI and OpenAI
+## Comparison of Azure OpenAI in Foundry Models and OpenAI
 
 Azure OpenAI Service gives customers advanced language AI with OpenAI GPT-4, GPT-3, Codex, DALL-E, and Whisper models with the security and enterprise promise of Azure.
 Azure OpenAI co-develops the APIs with OpenAI, ensuring compatibility and a smooth transition from one to the other.
 
 With Azure OpenAI, customers get the security capabilities of Microsoft Azure while running the same models as OpenAI.
 Azure OpenAI offers private networking, regional availability, and responsible AI content filtering.
 
-## Azure OpenAI Data Privacy and Security
+## Data Privacy and Security
 
-Azure OpenAI stores and processes data to provide the service and to monitor for uses that violate the applicable product terms.
-Azure OpenAI is fully controlled by Microsoft.
-Microsoft hosts the OpenAI models in Microsoft Azure for the usage of Azure OpenAI, and does not interact with any services operated by OpenAI.
+Azure OpenAI in Foundry Models stores and processes data to provide the service and monitor for uses that violate applicable product terms. Azure OpenAI in Foundry Models is fully controlled by Microsoft. Microsoft hosts the OpenAI models in Microsoft Azure and does not interact with any services operated by OpenAI.
 
-Here are a few important things to know in regards to the security and privacy of prompts (inputs) and completions (outputs), embeddings, and training data when using Azure OpenAI:
+**Your data privacy is protected:**
 
-- are NOT available to other customers.
-- are NOT available to OpenAI.
-- are NOT used to improve OpenAI models.
-- are NOT used to improve any Microsoft or 3rd party products or services.
-- are NOT used for automatically improving Azure OpenAI models for use in the deployed resource (The models are stateless, unless explicitly fine-tuning models with explicitly provided training data).
-- Fine-tuned Azure OpenAI models are available exclusively for the account in which it was created.
+Your prompts (inputs), completions (outputs), embeddings, and training data:
+
+- Are NOT available to other customers
+- Are NOT available to OpenAI
+- Are NOT used to improve OpenAI models
+- Are NOT used to improve any Microsoft or third-party products or services
+- Are NOT used for automatically improving Azure OpenAI models (models are stateless unless you explicitly fine-tune them with your own training data)
+
+**Your fine-tuned models:**
+
+- Fine-tuned Azure OpenAI in Foundry Models models are available exclusively to the account that created them
 
 ## Azure AI Platform
 
@@ -128,27 +131,27 @@ Here are a couple differentiators to help determine which of these to services t
 
 If the solution requires other more task specific AI features, then one of the other Foundry Tools should be considered.
 
-### Azure AI Studio
-
-Azure AI Studio is a web portal that brings together multiple Azure AI-related services into a single, unified development environment.
-
-Specifically, Azure AI Studio combines:
+### Microsoft Foundry
 
-- The model catalog and prompt flow development capabilities of Azure Machine Learning service.
+Microsoft Foundry is a unified Azure platform for building and deploying generative AI applications. It provides developers with an integrated environment that combines models, tools, and infrastructure in one place.
 
-- The generative AI model deployment, testing, and custom data integration capabilities of Azure OpenAI service.
+**Key capabilities:**
 
-- Integration with Foundry Tools for speech, vision, language, document intelligence, and content safety.
+- Access to a comprehensive model catalog with models from OpenAI, Meta, and other providers
+- Interactive playgrounds for testing and refining AI applications
+- Built-in evaluation and monitoring tools for quality assurance
+- Integration with Foundry Tools for speech, vision, language, document intelligence, and content safety
+- Collaboration features for teams working on AI projects
 
-Azure AI Studio enables teams to collaborate efficiently and effectively on AI projects, such as developing custom copilot applications that use large language models (LLMs).
+Microsoft Foundry is designed for building custom copilot applications and other solutions powered by large language models (LLMs).
 
 ![Microsoft AI Foundry screenshot](images/Microsoft_AI_Foundry_2025_11_30.png)
 
-Tasks accomplished using Azure AI Studio include:
+**Common workflows in Microsoft Foundry:**
 
-- Deploying models from the model catalog to real-time inferencing endpoints for client applications to consume.
-- Deploying and testing generative AI models in an Azure OpenAI service.
-- Integrating data from custom data sources to support a retrieval augmented generation (RAG) approach to prompt engineering for generative AI models.
-- Using prompt flow to define workflows that integrate models, prompts, and custom processing.
-- Integrating content safety filters into a generative AI solution to mitigate potential harms.
-- Extending a generative AI solution with multiple AI capabilities using Foundry Tools.
+- Deploy models from the catalog to real-time inferencing endpoints
+- Build and test generative AI applications using interactive playgrounds
+- Implement retrieval augmented generation (RAG) by integrating custom data sources
+- Evaluate model performance and application quality
+- Add content safety filters to mitigate potential harms
+- Extend applications with additional AI capabilities from Foundry Tools

vcore/00_Introduction/README.md

@@ -10,7 +10,7 @@
 - [Docker Desktop](https://www.docker.com/products/docker-desktop/) with [WSL 2 backend (if on Windows)](https://docs.docker.com/desktop/features/wsl/)
 - [Azure CLI](https://learn.microsoft.com/cli/azure/install-azure-cli)
 - [Bicep CLI](https://learn.microsoft.com/azure/azure-resource-manager/bicep/install#install-manually)
-- [Powershell](https://learn.microsoft.com/powershell/scripting/install/installing-powershell?view=powershell-7.3)
+- [Powershell](https://learn.microsoft.com/powershell/scripting/install/installing-powershell?view=powershell-7.5)
 
 ## Why use this guide?
 

vcore/02_Overview_Cosmos_DB/README.md

@@ -1,17 +1,16 @@
 # Overview of Azure Cosmos DB
 
-[Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/introduction) is a globally distributed, multi-model database service for both NoSQL and relational workloads.
-It supports multiple APIs: NoSQL, MongoDB, PostgreSQL, Cassandra, Gremlin, and Table—covering document, relational, column-family, graph, and key-value data models.
+[Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/introduction) is Microsoft's cloud-native, serverless NoSQL and vector database for mission-critical and globally distributed applications. It provides a native JSON document model with a SQL-like query language, combining flexible data modeling with familiar query semantics. With built-in vector search capabilities, Azure Cosmos DB enables AI-powered scenarios such as semantic search, retrieval-augmented generation, and intelligent applications. As an Azure-native service, it integrates deeply with Azure and delivers predictable performance and availability backed by industry-leading service level agreements (SLAs).
 The service offers turnkey global distribution with elastic scaling of throughput and storage.
 It delivers single-digit millisecond latencies at the 99th percentile and guarantees high availability through multi-homing capabilities.
-Azure Cosmos DB provides comprehensive service level agreements (SLAs) covering throughput, latency, availability, and consistency—a unique combination among cloud database services.
+Azure Cosmos DB provides comprehensive SLAs covering throughput, latency, availability, and consistency—a unique combination among cloud database services.
 
 ## Azure Cosmos DB and AI
 
 The surge of AI-powered applications has led to the need to integrate data from multiple data stores, introducing another layer of complexity as each data store tends to have its own workflow and operational performance.
-Azure Cosmos DB simplifies this process by providing a unified platform for all data types, including AI data.
-Azure Cosmos DB supports relational, document, vector, key-value, graph, and table data models, making it an ideal platform for AI applications.
-The wide array of data model support combined with guaranteed high availability, high throughput, low latency, and tunable consistency are huge advantages when building these types of applications.
+Azure Cosmos DB simplifies this process by providing a unified platform for document and vector data.
+Azure Cosmos DB provides a native JSON document model combined with built-in vector search capabilities, making it an ideal platform for AI applications.
+This document and vector support combined with guaranteed high availability, high throughput, low latency, and tunable consistency are huge advantages when building these types of applications.
 
 ## Azure DocumentDB
 

vcore/03_Overview_Azure_OpenAI/README.md

@@ -1,4 +1,4 @@
-# Overview of Azure OpenAI and Foundry Tools
+# Overview of Azure OpenAI in Foundry Models and Foundry Tools
 
 Azure OpenAI is a collaboration between Microsoft Azure and OpenAI, a leading research organization in artificial intelligence.
 It is a cloud-based platform that enables developers and data scientists to build and deploy AI models quickly and easily.
@@ -17,28 +17,31 @@ Here are ways that Azure OpenAI can help developers:
 These measures help ensure that AI models are unbiased, explainable, trustworthy, and used in a responsible and compliance manner.
 - **Community support** - With an active developer community developers can seek help via forums and other community support channels.
 
-## Comparison of Azure OpenAI and OpenAI
+## Comparison of Azure OpenAI in Foundry Models and OpenAI
 
 Azure OpenAI Service gives customers advanced language AI with OpenAI GPT-4, GPT-3, Codex, DALL-E, and Whisper models with the security and enterprise promise of Azure.
 Azure OpenAI co-develops the APIs with OpenAI, ensuring compatibility and a smooth transition from one to the other.
 
 With Azure OpenAI, customers get the security capabilities of Microsoft Azure while running the same models as OpenAI.
 Azure OpenAI offers private networking, regional availability, and responsible AI content filtering.
 
-## Azure OpenAI Data Privacy and Security
+## Data Privacy and Security
 
-Azure OpenAI stores and processes data to provide the service and to monitor for uses that violate the applicable product terms.
-Azure OpenAI is fully controlled by Microsoft.
-Microsoft hosts the OpenAI models in Microsoft Azure for the usage of Azure OpenAI, and does not interact with any services operated by OpenAI.
+Azure OpenAI in Foundry Models stores and processes data to provide the service and monitor for uses that violate applicable product terms. Azure OpenAI in Foundry Models is fully controlled by Microsoft. Microsoft hosts the OpenAI models in Microsoft Azure and does not interact with any services operated by OpenAI.
 
-Here are a few important things to know in regards to the security and privacy of prompts (inputs) and completions (outputs), embeddings, and training data when using Azure OpenAI:
+**Your data privacy is protected:**
 
-- are NOT available to other customers.
-- are NOT available to OpenAI.
-- are NOT used to improve OpenAI models.
-- are NOT used to improve any Microsoft or 3rd party products or services.
-- are NOT used for automatically improving Azure OpenAI models for use in the deployed resource (The models are stateless, unless explicitly fine-tuning models with explicitly provided training data).
-- Fine-tuned Azure OpenAI models are available exclusively for the account in which it was created.
+Your prompts (inputs), completions (outputs), embeddings, and training data:
+
+- Are NOT available to other customers
+- Are NOT available to OpenAI
+- Are NOT used to improve OpenAI models
+- Are NOT used to improve any Microsoft or third-party products or services
+- Are NOT used for automatically improving Azure OpenAI models (models are stateless unless you explicitly fine-tune them with your own training data)
+
+**Your fine-tuned models:**
+
+- Fine-tuned Azure OpenAI in Foundry Models models are available exclusively to the account that created them
 
 ## Azure AI Platform
 
@@ -128,27 +131,27 @@ Here are a couple differentiators to help determine which of these to services t
 
 If the solution requires other more task specific AI features, then one of the other Foundry Tools should be considered.
 
-### Azure AI Studio
-
-Azure AI Studio is a web portal that brings together multiple Azure AI-related services into a single, unified development environment.
-
-Specifically, Azure AI Studio combines:
+### Microsoft Foundry
 
-- The model catalog and prompt flow development capabilities of Azure Machine Learning service.
+Microsoft Foundry is a unified Azure platform for building and deploying generative AI applications. It provides developers with an integrated environment that combines models, tools, and infrastructure in one place.
 
-- The generative AI model deployment, testing, and custom data integration capabilities of Azure OpenAI service.
+**Key capabilities:**
 
-- Integration with Foundry Tools for speech, vision, language, document intelligence, and content safety.
+- Access to a comprehensive model catalog with models from OpenAI, Meta, and other providers
+- Interactive playgrounds for testing and refining AI applications
+- Built-in evaluation and monitoring tools for quality assurance
+- Integration with Foundry Tools for speech, vision, language, document intelligence, and content safety
+- Collaboration features for teams working on AI projects
 
-Azure AI Studio enables teams to collaborate efficiently and effectively on AI projects, such as developing custom copilot applications that use large language models (LLMs).
+Microsoft Foundry is designed for building custom copilot applications and other solutions powered by large language models (LLMs).
 
 ![Microsoft AI Foundry screenshot](images/Microsoft_AI_Foundry_2025_11_30.png)
 
-Tasks accomplished using Azure AI Studio include:
+**Common workflows in Microsoft Foundry:**
 
-- Deploying models from the model catalog to real-time inferencing endpoints for client applications to consume.
-- Deploying and testing generative AI models in an Azure OpenAI service.
-- Integrating data from custom data sources to support a retrieval augmented generation (RAG) approach to prompt engineering for generative AI models.
-- Using prompt flow to define workflows that integrate models, prompts, and custom processing.
-- Integrating content safety filters into a generative AI solution to mitigate potential harms.
-- Extending a generative AI solution with multiple AI capabilities using Foundry Tools.
+- Deploy models from the catalog to real-time inferencing endpoints
+- Build and test generative AI applications using interactive playgrounds
+- Implement retrieval augmented generation (RAG) by integrating custom data sources
+- Evaluate model performance and application quality
+- Add content safety filters to mitigate potential harms
+- Extend applications with additional AI capabilities from Foundry Tools