Merge pull request #265568 from MicrosoftDocs/main

Merge main to live, 4 AM

Author: v-ccolin

articles/ai-studio/how-to/prompt-flow-tools/prompt-flow-tools-overview.md

@@ -23,18 +23,25 @@ The following table provides an index of tools in prompt flow.
 | [Prompt](./prompt-tool.md) | Craft a prompt by using Jinja as the templating language. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Python](./python-tool.md) | Run Python code. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Azure OpenAI GPT-4 Turbo with Vision](./azure-open-ai-gpt-4v-tool.md) | Use AzureOpenAI GPT-4 Turbo with Vision model deployment to analyze images and provide textual responses to questions about them. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
-| [Vector Index Lookup](./vector-index-lookup-tool.md) | Search text or a vector-based query from a vector index. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
 | [Content Safety (Text)](./content-safety-tool.md) | Use Azure AI Content Safety to detect harmful content. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
+| [Index Lookup](./index-lookup-tool.md) | Search an Azure Machine Learning Vector Index for relevant results using one or more text queries. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
+| [Vector Index Lookup](./vector-index-lookup-tool.md) | Search text or a vector-based query from a vector index. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
 | [Faiss Index Lookup](./faiss-index-lookup-tool.md) | Search a vector-based query from the Faiss index file. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
 | [Vector DB Lookup](./vector-db-lookup-tool.md) | Search a vector-based query from an existing vector database. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
 | [Embedding](./embedding-tool.md) | Use Azure Open AI embedding models to create an embedding vector that represents the input text. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Serp API](./serp-api-tool.md) | Use Serp API to obtain search results from a specific search engine. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 
+The following table shows an index of custom tools created by the community to extend prompt flow's capabilities for specific use cases. They aren't officially maintained or endorsed by prompt flow team. For questions or issues when using a tool, please see the support contact in the description.
+
+| Tool name | Description | Environment | Package name | 
+|-----------|-----------|-------------|--------------|
+| [Azure AI Language tools](https://microsoft.github.io/promptflow/integrations/tools/azure-ai-language-tool.html) | This collection of tools is a wrapper for various Azure AI Language APIs, which can help effectively understand and analyze documents and conversations. The capabilities currently supported include: Abstractive Summarization, Extractive Summarization, Conversation Summarization, Entity Recognition, Key Phrase Extraction, Language Detection, PII Entity Recognition, Conversational PII, Sentiment Analysis, Conversational Language Understanding, Translator. You can learn how to use them by the [Sample flows](https://github.com/microsoft/promptflow/tree/e4542f6ff5d223d9800a3687a7cfd62531a9607c/examples/flows/integrations/azure-ai-language). Support contact: [email protected] | Custom | [promptflow-azure-ai-language](https://pypi.org/project/promptflow-azure-ai-language/) |
+
 To discover more custom tools developed by the open-source community, see [More custom tools](https://microsoft.github.io/promptflow/integrations/tools/index.html).
 
 ## Remarks
 - If existing tools don't meet your requirements, you can [develop your own custom tool and make a tool package](https://microsoft.github.io/promptflow/how-to-guides/develop-a-tool/create-and-use-tool-package.html).
-- To install the custom tools, if you are using the automatic runtime, you can readily install the package by adding the custom tool package name into the `requirements.txt` file in the flow folder. Then select the **Save and install** button to start installation. After completion, you can see the custom tools displayed in the tool list. To learn more, see [How to create and manage a runtime](../create-manage-runtime.md).
+- To install the custom tools, if you're using the automatic runtime, you can readily install the package by adding the custom tool package name into the `requirements.txt` file in the flow folder. Then select the **Save and install** button to start installation. After completion, you can see the custom tools displayed in the tool list. To learn more, see [How to create and manage a runtime](../create-manage-runtime.md).
 :::image type="content" source="../../media/prompt-flow/install-package-on-automatic-runtime.png" alt-text="Screenshot of how to install packages on automatic runtime."lightbox = "../../media/prompt-flow/install-package-on-automatic-runtime.png":::
 
 ## Next steps

articles/aks/use-pod-sandboxing.md

@@ -20,7 +20,7 @@ This article helps you understand this new feature, and how to implement it.
 
 - Register the `KataVMIsolationPreview` feature in your Azure subscription.
 
-- AKS supports Pod Sandboxing (preview) on version 1.24.0 and higher.
+- AKS supports Pod Sandboxing (preview) on version 1.24.0 and higher with all AKS network plugins.
 
 - To manage a Kubernetes cluster, use the Kubernetes command-line client [kubectl][kubectl]. Azure Cloud Shell comes with `kubectl`. You can install kubectl locally using the [az aks install-cli][az-aks-install-cmd] command.
 

articles/azure-monitor/essentials/diagnostic-settings.md

@@ -6,7 +6,7 @@ ms.author: robb
 services: azure-monitor
 ms.topic: conceptual
 ms.custom:
-ms.date: 10/19/2023
+ms.date: 02/06/2023
 ms.reviewer: lualderm
 ---
 
@@ -66,7 +66,7 @@ When you use category groups, you:
 Currently, there are two category groups:
 
 - **All**: Every resource log offered by the resource.
-- **Audit**: All resource logs that record customer interactions with data or the settings of the service. Audit logs are an attempt by each resource provider to provide the most relevant audit data, but might not be considered sufficient from an auditing standards perspective.
+- **Audit**: All resource logs that record customer interactions with data or the settings of the service. Audit logs are an attempt by each resource provider to provide the most relevant audit data, but might not be considered sufficient from an auditing standards perspective depending on your use case. As mentioned above, what's collected is dynamic, and Microsoft may change it over time as new resource log categories become available.
 
 The "Audit" category is a subset of "All", but the Azure portal and REST API consider them separate settings. Selecting "All" does collect all audit logs regardless of if the "Audit" category is also selected.  
 

articles/azure-monitor/overview.md

@@ -225,12 +225,24 @@ You may need to integrate Azure Monitor with other systems or to build custom so
 |---------|---------|
 |[Event Hubs](../event-hubs/event-hubs-about.md)|Azure Event Hubs is a streaming platform and event ingestion service. It can transform and store data by using any real-time analytics provider or batching/storage adapters. Use Event Hubs to stream Azure Monitor data to partner SIEM and monitoring tools.|
 |[Azure Storage](../storage/common/storage-introduction.md)| Export data to Azure storage for less expensive, long-term archival of monitoring data for auditing or compliance purposes. 
-|Hosted and Managed Partners | Many external partners integrate with Azure Monitor. Azure Monitor has partnered with other monitoring providers to provide an [Azure-hosted version of their products](/azure/partner-solutions/) to make interoperability easier. Examples include Elastic, Datadog, Logz.io, and Dynatrace. 
+|Hosted and Managed Partners | Many external partners integrate with Azure Monitor. Azure Monitor has partnered with other monitoring providers to provide an [Azure-hosted version of their products](/azure/partner-solutions/partners) to make interoperability easier. Examples include Elastic, Datadog, Logz.io, and Dynatrace. 
 |[API](/rest/api/monitor/)|Multiple APIs are available to read and write metrics and logs to and from Azure Monitor in addition to accessing generated alerts. You can also configure and retrieve alerts. With APIs, you have unlimited possibilities to build custom solutions that integrate with Azure Monitor.|
 |[Azure Logic Apps](../logic-apps/logic-apps-overview.md)|Azure Logic Apps is a service you can use to automate tasks and business processes by using workflows that integrate with different systems and services with little or no code. Activities are available that read and write metrics and logs in Azure Monitor. You can use Logic Apps to [customize responses and perform other actions in response to Azure Monitor alerts](alerts/alerts-logic-apps.md).  You can also perform other [more complex actions](logs/logicapp-flow-connector.md) when the Azure Monitor infrastructure doesn't already supply a built-it method.|
 |[Azure Functions](../azure-functions/functions-overview.md)| Similar to Azure Logic Apps, Azure Functions give you the ability to preprocess and post process monitoring data and perform complex action beyond the scope of typical Azure Monitor alerts. Azure Functions uses code however providing additional flexibility over Logic Apps.
 |Azure DevOps and GitHub | Azure Monitor Application Insights gives you the ability to create [Work Item Integration](app/release-and-work-item-insights.md?tabs=work-item-integration) with monitoring data embedding in it. Additional options include [release annotations](app/release-and-work-item-insights.md?tabs=release-annotations) and [continuous monitoring](app/release-and-work-item-insights.md?tabs=continuous-monitoring). |
 
+
+Additional integrations not shown in the diagram that may be of interest. 
+
+| Integration | Description |
+|----|----|
+| [Defender for the Cloud](../defender-for-cloud/defender-for-cloud-introduction.md) | Collect and analyze security events and perform threat analysis. See [Data collection in Defender for the Cloud](../defender-for-cloud/monitoring-components.md). |
+| [Microsoft Sentinel](../sentinel/index.yml) | Connect to different sources including Office 365 and Amazon Web Services Cloud Trail. See [Connect data sources](../sentinel/connect-data-sources.md). |
+| [Microsoft Intune](/intune/) | Create a diagnostic setting to send logs to Azure Monitor. See [Send log data to storage, Event Hubs, or log analytics in Intune (preview)](/mem/intune/fundamentals/review-logs-using-azure-monitor).  |
+| [ITSM](alerts/itsmc-overview.md) | The [IT Service Management (ITSM) Connector](./alerts/itsmc-overview.md) allows you to connect Azure and a supported ITSM product/service.   |
+
+These are just a few options. There are many more third party companies that integrate with Azure and Azure Monitor at various levels. Use your favorite search engine to locate them. 
+
 ## Frequently asked questions
 
 This section provides answers to common questions.

articles/defender-for-cloud/defender-for-storage-malware-scan.md

@@ -161,9 +161,9 @@ By default, a limit of 5 TB (5,000 GB) is established if no specific capping mec
 
 Follow [these steps](tutorial-enable-storage-plan.md#set-up-and-configure-microsoft-defender-for-storage) to configure the capping mechanism.
 
-## Handling possible false positives
+## Handling possible false positives and false negatives
 
-If you have a file that you suspect might be malware or is being incorrectly detected, you can submit it to us for analysis through the [sample submission portal](/microsoft-365/security/intelligence/submission-guide). Select “Microsoft Defender for Storage” as the source.
+If you have a file that you suspect might be malware but isn't being detected (false negative) or is being incorrectly detected (false positive), you can submit it to us for analysis through the [sample submission portal](/microsoft-365/security/intelligence/submission-guide). Select “Microsoft Defender for Storage” as the source.
 
 Defender for Cloud allows you to [suppress false positive alerts](alerts-suppression-rules.md). Make sure to limit the suppression rule by using the malware name or file hash.
 

articles/machine-learning/prompt-flow/tools-reference/overview.md

@@ -27,14 +27,22 @@ The following table shows an index of tools in prompt flow.
 | [LLM](./llm-tool.md) | Uses Open AI's large language model (LLM) for text completion or chat. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Prompt](./prompt-tool.md) | Crafts a prompt by using Jinja as the templating language. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Embedding](./embedding-tool.md) | Uses Open AI's embedding model to create an embedding vector that represents the input text. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
-| [Open Model LLM](./open-model-llm-tool.md) | Uses an open-source model from the Azure Model catalog, deployed to an Azure Machine Learning online endpoint for large language model Chat or Completion API calls. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
+| [Open Model LLM](./open-model-llm-tool.md) | Enable the utilization of a variety of Open Model and Foundational Models, such as Falcon and Llama 2 from the Azure Model catalog. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Serp API](./serp-api-tool.md) | Uses Serp API to obtain search results from a specific search engine. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Content Safety (Text)](./content-safety-text-tool.md) | Uses Azure Content Safety to detect harmful content. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
-| [Azure OpenAI GPT-4 Turbo with Vision (preview)](./azure-open-ai-gpt-4v-tool.md) | Use AzureOpenAI GPT-4 Turbo with Vision model deployment to analyze images and provide textual responses to questions about them. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
+| [Azure OpenAI GPT-4 Turbo with Vision](./azure-open-ai-gpt-4v-tool.md) | Use AzureOpenAI GPT-4 Turbo with Vision model deployment to analyze images and provide textual responses to questions about them. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
+| [OpenAI GPT-4V](./openai-gpt-4v-tool.md) | Use OpenAI GPT-4V to leverage vision ability. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
+| [Index Lookup](./index-lookup-tool.md) | Search an Azure Machine Learning Vector Index for relevant results using one or more text queries. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
 | [Faiss Index Lookup](./faiss-index-lookup-tool.md) | Searches a vector-based query from the Faiss index file. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
 | [Vector DB Lookup](./vector-db-lookup-tool.md) | Searches a vector-based query from existing vector database. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
 | [Vector Index Lookup](./vector-index-lookup-tool.md) | Searches text or a vector-based query from Azure Machine Learning vector index. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
 
+The following table shows an index of custom tools created by the community to extend prompt flow's capabilities for specific use cases. They aren't officially maintained or endorsed by prompt flow team. For questions or issues when using a tool, please see the support contact provided in the description.
+
+| Tool name | Description | Environment | Package name | 
+|-----------|-----------|-------------|--------------|
+| [Azure AI Language tools](https://microsoft.github.io/promptflow/integrations/tools/azure-ai-language-tool.html) | This collection of tools is a wrapper for various Azure AI Language APIs, which can help effectively understand and analyze documents and conversations. The capabilities currently supported include: Abstractive Summarization, Extractive Summarization, Conversation Summarization, Entity Recognition, Key Phrase Extraction, Language Detection, PII Entity Recognition, Conversational PII, Sentiment Analysis, Conversational Language Understanding, Translator. You can learn how to use them by the [Sample flows](https://github.com/microsoft/promptflow/tree/e4542f6ff5d223d9800a3687a7cfd62531a9607c/examples/flows/integrations/azure-ai-language). Support contact: [email protected] | Custom | [promptflow-azure-ai-language](https://pypi.org/project/promptflow-azure-ai-language/) |
+
 To discover more custom tools developed by the open-source community, see [More custom tools](https://microsoft.github.io/promptflow/integrations/tools/index.html). 
 
 

articles/network-watcher/.openpublishing.redirection.network-watcher.json

@@ -1,5 +1,10 @@
 {
     "redirections": [
+        {
+            "source_path_from_root": "/articles/network-watcher/network-watcher-deep-packet-inspection.md",
+            "redirect_url": "/azure/network-watcher/packet-capture-inspect",
+            "redirect_document_id": true
+        },
         {
             "source_path_from_root": "/articles/network-watcher/network-watcher-troubleshoot-manage-rest.md",
             "redirect_url": "/rest/api/network-watcher/network-watchers/get-troubleshooting",