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Quickstart-Document-Permissions-Pull-API/document-permissions-pull-api.ipynb

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"\n",
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"+ Folders and files, where each file has an [access control list specified](https://learn.microsoft.com/azure/storage/blobs/data-lake-storage-access-control). We recommend group IDs.\n",
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"\n",
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"+ [Visual Studio Code](https://code.visualstudio.com/download) with the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and [Jupyter package](https://pypi.org/project/jupyter/).\n",
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"\n",
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"## Permissions\n",
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"\n",
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"This walkthrough uses Microsoft Entra ID authentication and authorization.\n",
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"\n",
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"We recommend creating a virtual environment to run this sample code. In Visual Studio Code, open the control palette (ctrl-shift-p) to create an environment. This notebook was tested on Python 3.10.\n",
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"\n",
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"Once the environment is created, load the environment variables to set up connections and object names."
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"After the environment is created, load the environment variables to set up connections and object names."
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]
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},
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{

Quickstart-Document-Permissions-Push-API/document-permissions-push-api.ipynb

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"source": [
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"## Prerequisites\n",
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"\n",
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"+ Azure AI Search, with [role-based access control](https://learn.microsoft.com/azure/search/search-security-enable-roles).\n",
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"+ Azure AI Search with [role-based access control](https://learn.microsoft.com/azure/search/search-security-enable-roles).\n",
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"\n",
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"+ [Visual Studio Code](https://code.visualstudio.com/download) with the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and [Jupyter package](https://pypi.org/project/jupyter/).\n",
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"\n",
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"## Permissions\n",
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"\n",
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"\n",
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"We recommend creating a virtual environment to run this sample code. In Visual Studio Code, open the control palette (ctrl-shift-p) to create an environment. This notebook was tested on Python 3.10.\n",
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"\n",
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"Once your environment is created, load the environment variables."
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"After your environment is created, load the environment variables."
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]
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Quickstart-RAG/Quickstart-rag.ipynb

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" - Enable role-based access control.\n",
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" - Enable a system identity for Azure AI Search.\n",
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" \n",
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"+ [Visual Studio Code](https://code.visualstudio.com/download) with the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and [Jupyter package](https://pypi.org/project/jupyter/).\n",
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"\n",
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"Make sure you know the name of the deployed model, and have the endpoints for both Azure resources at hand. You will provide this information in the steps that follow.\n",
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"\n",
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"## Configure access\n",

Quickstart/README.md

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## Prerequisites
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* Visual Studio Code with the Python extension (or equivalent tool) and Python 3.10 or later.
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* [Visual Studio Code](https://code.visualstudio.com/download) with the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and [Jupyter package](https://pypi.org/project/jupyter/).
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* The [azure-search-documents package](https://pypi.org/project/azure-search-documents/) from the Azure SDK for Python.
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Tutorial-RAG/Tutorial-rag.ipynb

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"\n",
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"- [Microsoft Foundry resource](https://learn.microsoft.com/azure/ai-services/multi-service-resource), in the same region as Azure AI Search, used for recognizing location entities in the Earth Book.\n",
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"\n",
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"- [Azure AI Search](https://learn.microsoft.com/azure/search/search-create-service-portal), Basic tier or higher is recommended. Choose the same region as Microsoft Foundry."
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"- [Azure AI Search](https://learn.microsoft.com/azure/search/search-create-service-portal), Basic tier or higher is recommended. Choose the same region as Microsoft Foundry.\n",
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"\n",
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"- [Visual Studio Code](https://code.visualstudio.com/download) with the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and [Jupyter package](https://pypi.org/project/jupyter/)."
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{

agentic-retrieval-pipeline-example/agent-example.ipynb

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"\n",
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"+ A [supported LLM](https://learn.microsoft.com/azure/search/search-agentic-retrieval-how-to-create#supported-models) deployed to your project. This notebook uses `gpt-5-mini`. We recommend a minimum token capacity of 100,000. You can find the LLM's capacity and rate limit in the Foundry portal. If you want vectorization at query time, you should also deploy a text embedding model.\n",
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"\n",
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"We recommend creating a virtual environment to run this sample code. In Visual Studio Code, open the control palette (ctrl-shift-p) to create an environment. This notebook was tested on Python 3.13.7."
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"+ [Visual Studio Code](https://code.visualstudio.com/download) with the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and [Jupyter package](https://pypi.org/project/jupyter/)."
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"## Load connections\n",
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"\n",
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"Load the environment variables to set up connections and object names."
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"We recommend creating a virtual environment to run this sample code. In Visual Studio Code, open the control palette (ctrl-shift-p) to create an environment. This notebook was tested on Python 3.13.7.\n",
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"\n",
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"After your environment is created, load the environment variables to set up connections and object names."
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{

cmk-example/cmk-example.ipynb

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"\n",
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"- [Azure AI Search](https://learn.microsoft.com/azure/search/search-create-service-portal)\n",
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"- [Azure Key Vault]()\n",
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"- [Azure Storage](https://learn.microsoft.com/azure/storage/common/storage-account-create) or [Azure Log Analytics](https://learn.microsoft.com/azure/azure-monitor/logs/quick-create-workspace?tabs=azure-portal) for data retention of audit logs.\n"
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"- [Azure Storage](https://learn.microsoft.com/azure/storage/common/storage-account-create) or [Azure Log Analytics](https://learn.microsoft.com/azure/azure-monitor/logs/quick-create-workspace?tabs=azure-portal) for data retention of audit logs.\n",
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"- [Visual Studio Code](https://code.visualstudio.com/download) with the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and [Jupyter package](https://pypi.org/project/jupyter/).\n"
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"cell_type": "markdown",
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"## CREATE INDEX"
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"## Create an index"
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"cell_type": "markdown",
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"metadata": {},
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"## GET INDEX DEFINITION\n"
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"## Get the index definition"
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"metadata": {},
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"## LOAD INDEX"
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"## Load the index"
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"metadata": {},
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"## QUERY ENCRYPTED CONTENT"
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"## Query encrypted content"
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]
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