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Merge pull request #2704 from MicrosoftDocs/release-agents-foundry
Release agents foundry -> main -- 11:00 PM PST of 2/06
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.openpublishing.redirection.json

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"redirect_url": "/azure/search/search-how-to-dotnet-sdk",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/ai-services/agents/how-to/tools/overview.md",
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"redirect_url": "/azure/ai-services/agents/overview",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/search/search-howto-index-csv-blobs.md",
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"redirect_url": "/azure/search/search-how-to-index-csv-blobs",
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"redirect_url": "/azure/search/search-how-to-large-index",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/ai-services/agents/concepts/agents.md",
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"redirect_url": "/azure/ai-services/agents/overview",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/ai-services/openai/how-to/use-your-data-securely.md",
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"redirect_url": "/azure/ai-services/openai/how-to/on-your-data-configuration",

articles/ai-services/agents/concepts/agents.md

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articles/ai-services/agents/concepts/model-region-support.md

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| westus3 |||| - || - || - | - | - || - |
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## More models
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## Non-Microsoft models
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The Azure AI Agent Service also supports the following models from the Azure AI Foundry model catalog.
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* Cohere-command-r-plus
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* Cohere-command-r
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To use these models, you can use Azure AI Foundry portal to make a deployment, and then reference it in your agent.
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1. Go to the [Azure AI Foundry portal](https://ai.azure.com/) and select **Model catalog** in the left navigation menu, and scroll down to **Meta-Llama-3-70B-Instruct**. You can also find and use one of the models listed previously.
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1. Select **Deploy**.
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1. In the Deployment options screen that appears, select **Serverless API** with Azure AI Content Safety.
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:::image type="content" source="../media/llama/llama-deployment.png" alt-text="An image of the llama model project selection screen.":::
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1. Select your project and then select **Subscribe and deploy**.
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:::image type="content" source="../media/llama/llama-deployment-2.png" alt-text="An image of the llama model deployment screen.":::
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1. Add the serverless connection to your hub/project. The deployment name you choose is the one that you reference in your code.
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1. When calling agent creation API, set the `models` parameter to your deployment name. For example:
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# [Python](#tab/python)
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```python
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agent = project_client.agents.create_agent( model="llama-3", name="my-agent", instructions="You are a helpful agent" )
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```
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# [C#](#tab/csharp)
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```csharp
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Response<Agent> agentResponse = await client.CreateAgentAsync(
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model: "llama-3",
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name: "My agent",
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instructions: "You are a helpful agent"
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```
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---
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To use these models, you can use Azure AI Foundry portal to make a deployment, and then reference the deployment name in your agent. For example:
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```python
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agent = project_client.agents.create_agent( model="llama-3", name="my-agent", instructions="You are a helpful agent" )
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```
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## Next steps
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articles/ai-services/agents/how-to/tools/azure-ai-search.md

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**Index with semantic configuration**
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- By default, the Azure AI Search tool runs hybrid + semantic search on all text fields.
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## Usage support
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|Azure AI foundry support | Python SDK | C# SDK | JavaScript SDK | Basic agent setup | Standard agent setup |
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|---------|---------|---------|---------|---------|---------|
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| ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
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[!INCLUDE [setup](../../includes/azure-search/setup.md)]
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::: zone-end

articles/ai-services/agents/how-to/tools/azure-functions.md

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You can find the template and code used here on [GitHub](https://github.com/Azure-Samples/azure-functions-ai-services-agent-python).
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## Usage support
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|Azure AI foundry support | Python SDK | C# SDK | Basic agent setup | Standard agent setup |
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|---------|---------|---------|---------|---------|
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| | ✔️ | | | ✔️ |
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### Create Azure resources for local and cloud dev-test
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Once you have your Azure subscription, run the following in a new terminal window to create Azure OpenAI and other resources needed:

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