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01_Azure_Overview/README.md

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#### PowerShell Module
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The Azure portal and Windows PowerShell can be used for managing Azure Cosmos DB for NoSQL. To get started with Azure PowerShell, install the [Azure PowerShell cmdlets](https://learn.microsoft.com/powershell/module/az.cosmosdb/) for Cosmos DB with the following PowerShell command in an administrator-level PowerShell window:
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The Azure portal and Windows PowerShell can be used for managing Azure Cosmos DB for NoSQL. To get started with Azure PowerShell, install the [Azure PowerShell cmdlets](https://learn.microsoft.com/powershell/module/az.cosmosdb/) for Azure Cosmos DB with the following PowerShell command in an administrator-level PowerShell window:
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```PowerShell
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Install-Module -Name Az.CosmosDB

02_Overview_Cosmos_DB/README.md

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## Azure Cosmos DB and AI
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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. In particular, its support for vector storage and retrieval is a game-changer for generative AI applications. By representing complex data elements like text, images, or sound as high-dimensional vectors, Cosmos DB allows for efficient storage, indexing, and querying of these vectors, which is crucial for many generative AI tasks.
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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. In particular, its support for vector storage and retrieval is a game-changer for generative AI applications. By representing complex data elements like text, images, or sound as high-dimensional vectors, Azure Cosmos DB allows for efficient storage, indexing, and querying of these vectors, which is crucial for many generative AI tasks.
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Unlike traditional databases requiring separate workarounds for different data types, Azure Cosmos DB supports relational, document, vector, key-value, graph, and table data models within a single, integrated environment. This simplification means you can leverage the same robust platform for all your AI data needs. Many AI applications rely on external stand-alone vector stores, which can be cumbersome to manage and maintain. Azure Cosmos DB's native support for vector storage and retrieval eliminates the need for these external stores as all the application's data is located in a single place thus streamlining the development and deployment of AI applications. These features enable the building, deploying, and scaling of AI applications to be more efficient and reliable, making Azure Cosmos DB an ideal choice for handling the complex data requirements of modern generative AI solutions.
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07_Create_First_Cosmos_DB_Project/README.md

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# Create your first Cosmos DB project
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# Create your first Azure Cosmos DB project
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This section will cover how to create your first Cosmos DB project. We'll use a notebook to demonstrate the basic CRUD operations. We'll also cover using the Azure Cosmos DB Emulator to test code locally.
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This section will cover how to create your first Azure Cosmos DB project. We'll use a notebook to demonstrate the basic CRUD operations. We'll also cover using the Azure Cosmos DB Emulator to test code locally.
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## Emulator support
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Authentication to Azure Cosmos DB for NoSQL uses a connection string. The connection string is a URL that contains the authentication information for the Azure Cosmos DB account or local emulator.
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### Retrieving the connection string from the Cosmos DB Emulator
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### Retrieving the connection string from the Azure Cosmos DB Emulator
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The splash screen or **Quickstart** section of the Cosmos DB Emulator will display the connection string. Access this screen through the following URL: `https://localhost:8081/_explorer/index.html`.
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The splash screen or **Quickstart** section of the Azure Cosmos DB Emulator will display the connection string. Access this screen through the following URL: `https://localhost:8081/_explorer/index.html`.
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![The Azure Cosmos DB emulator screen displays with the local host url, the Quickstart tab, and the connection string highlighted.](media/emulator_connection_string.png)
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![The Azure Cosmos DB for NoSQL Connection strings screen displays with the copy button next to the connection string highlighted.](media/azure_connection_string.png)
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## Lab - Create your first Cosmos DB for the NoSQL application
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## Lab - Create your first Azure Cosmos DB for the NoSQL application
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Using a notebook, we'll create a Cosmos DB for the NoSQL application in this lab using the **azure-cosmos** library and the Python language. Both the Azure Cosmos DB Emulator and Azure Cosmos DB account in Azure are supported for completion of this lab.
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Using a notebook, we'll create an Azure Cosmos DB for the NoSQL application in this lab using the **azure-cosmos** library and the Python language. Both the Azure Cosmos DB Emulator and Azure Cosmos DB account in Azure are supported for completion of this lab.
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>**Note**: It is highly recommended to use a [virtual environment](https://python.land/virtual-environments/virtualenv) for all labs.
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### Creating a database client
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The `azure-cosmos` library is used to create a Cosmos DB for NoSQL database client. The client enables both DDL (data definition language) and DML (data manipulation language) operations.
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The `azure-cosmos` library is used to create an Azure Cosmos DB for NoSQL database client. The client enables both DDL (data definition language) and DML (data manipulation language) operations.
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```python
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# Initialize the Cosmos DB client
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# Initialize the Azure Cosmos DB client
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client = CosmosClient.from_connection_string(CONNECTION_STRING)
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```
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09_Vector_Search_Cosmos_DB/README.md

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# Use vector search on embeddings in Azure Cosmos DB for NoSQL
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## Cosmos DB indexing
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## Azure Cosmos DB indexing
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Azure Cosmos DB automatically indexes all properties for all items in a container. However, the creation of additional indexes can improve performance and add functionality such as spatial querying and vector search.
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## Enabling vector search in Azure Cosmos DB for NoSQL
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The Cosmos DB account that has been deployed with this guide is already enabled with the capability for vector search. For reference, the following Azure CLI command can be used to enable vector search on an existing Cosmos DB account:
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The Azure Cosmos DB account that has been deployed with this guide is already enabled with the capability for vector search. For reference, the following Azure CLI command can be used to enable vector search on an existing Azure Cosmos DB account:
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```bash
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az cosmosdb update --resource-group <resource-group-name> --name <account-name> --capabilities EnableNoSQLVectorSearch

Backend/cosmic_works/cosmic_works_ai_agent.py

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AOAI_KEY = os.environ.get("AOAI_KEY")
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AOAI_API_VERSION = "2024-06-01"
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# Initialize the Cosmos DB client, database and product (with vector) container
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# Initialize the Azure Cosmos DB client, database and product (with vector) container
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client = CosmosClient.from_connection_string(CONNECTION_STRING)
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db = client.get_database_client("cosmic_works")
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product_v_container = db.get_container_client("product_v")

Labs/deploy/deploy.md

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> ````powershell
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> Update-AzCosmosDBAccount -ResourceGroupName <resource-group-name> -Name <account-name> -Capabilities @{name="EnableNoSQLVectorSearch"}
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> ````
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## Enroll with the DiskANN early preview
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To enroll in the DiskANN early preview, fill the following form [Azure Cosmos DB for NoSQL DiskANN Early Preview](https://forms.office.com/pages/responsepage.aspx?id=v4j5cvGGr0GRqy180BHbR7M9usLEDhFDufzz-8echE9UREtCOVhQWkZLVlhTRlNBM0lLUkdRTEwxUy4u&route=shorturl) and a member of the Cosmos DB team will reach out to you.

Labs/lab_1_first_application.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# First Cosmos DB for NoSQL application"
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"# First Azure Cosmos DB for NoSQL application"
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]
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},
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"source": [
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"## Create a database\n",
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"\n",
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"Ensure the Cosmos DB account connection string is located in a `.env` file in the root of the project, you will need to create this file. The `.env` file should contain the following value (replace the value with your own connection string):\n",
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"Ensure the Azure Cosmos DB account connection string is located in a `.env` file in the root of the project, you will need to create this file. The `.env` file should contain the following value (replace the value with your own connection string):\n",
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"COSMOS_DB_CONNECTION_STRING=\"cosmos__db__connection_string\"\n",
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"\n",
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">**Note**: If you are running using the **local emulator**, append the following value to the connection string: `&retrywrites=false&tlsallowinvalidcertificates=true`.\n",
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"\n",
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"To create a NoSQL database in Cosmos DB, first instantiate a `CosmosClient` object, use the `create_database_if_not_exists` method to create a database if it does not exist to avoid any exceptions should the database already exist. This method will create a database with the specified name if it does not exist, otherwise it will return the existing database."
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"To create a NoSQL database in Azure Cosmos DB, first instantiate a `CosmosClient` object, use the `create_database_if_not_exists` method to create a database if it does not exist to avoid any exceptions should the database already exist. This method will create a database with the specified name if it does not exist, otherwise it will return the existing database."
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"load_dotenv()\n",
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"CONNECTION_STRING = os.environ.get(\"COSMOS_DB_CONNECTION_STRING\")\n",
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"# Initialize the Cosmos DB client\n",
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"# Initialize the Azure Cosmos DB client\n",
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"# Create or load the cosmic_works database\n",
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"## Create or Update a document (Upsert)\n",
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"\n",
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"Documents in Cosmos DB for NoSQL API are represented as JSON objects. In this lab, the Pydantic library is used to create a model for the document. This model is then used to create a document in the database using built-in serialization methods. Find the models in the `models` folder. Notice the class property definitions include aliases, these aliases can be used to override the serialized property names. This is useful when the property names in the model do not match the property names desired in the database.\n",
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"Documents in Azure Cosmos DB for NoSQL API are represented as JSON objects. In this lab, the Pydantic library is used to create a model for the document. This model is then used to create a document in the database using built-in serialization methods. Find the models in the `models` folder. Notice the class property definitions include aliases, these aliases can be used to override the serialized property names. This is useful when the property names in the model do not match the property names desired in the database.\n",
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"One method of creating a document is using the `create_item` method. This method takes a single document and inserts it into the database, if the item already exists in the container, and exception is thrown. Alternatively, the `upsert_item` method can also be used to insert a document into the database and in this case, if the document already exists, it will be updated."
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"print(\"\\nCast Product from document retrieved from Azure Cosmos DB:\")\n",
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Labs/lab_2_load_data.ipynb

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"# Load data into Azure Cosmos DB for NosQL API\n",
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"This notebook demonstrates how to load data into Cosmos DB from Cosmic Works JSON files from Azure Storage into the database using the NoSQL API."
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"This notebook demonstrates how to load data into Azure Cosmos DB from Cosmic Works JSON files from Azure Storage into the database using the NoSQL API."
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"# Initialize the Azure Cosmos DB client\n",
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Labs/lab_3_cosmosdb_vector_search.ipynb

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"# Initialize the Cosmos DB client\n",
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"# Initialize the Azure Cosmos DB client\n",
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Labs/lab_4_langchain.ipynb

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"# Initialize the Cosmos DB client, database and product (with vector) container\n",
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"# Initialize the Azure Cosmos DB client, database and product (with vector) container\n",
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"client = CosmosClient.from_connection_string(CONNECTION_STRING)\n",
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"db = client.get_database_client(\"cosmic_works\")\n",
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"product_v_container = db.get_container_client(\"product_v\")\n",

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