Azure AI Bot Service
An Azure service that provides an integrated environment for bot development.
How to connect a bot created in Copilot Studio to Azure using different microsoft accounts?
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MrsKuaero
0
Reputation points
Thank you in advance for your guidance!I’m trying to create a voice bot to test its functionalities and evaluate if Microsoft is the right choice for my company. I’m currently using my personal Azure account and my company account in Copilot Studio.
From what I understand, the way to connect my bot from Copilot Studio to Azure is to publish it and then ensure the same App ID, Client Secret, and Messaging Endpoint are configured in both places. However, I’m encountering issues:
- I’m unable to use the same App ID in both Copilot Studio and the Azure Bot Resource.
- I cannot find the Messaging Endpoint for my Copilot Studio bot. I’ve been using a default format suggested by ChatGPT:
https://eastus.botframework.com/api/messages.
I’d appreciate help in setting this up properly so I can test my voice bot effectively. Specifically:
- How can I retrieve or set the correct Messaging Endpoint for the Copilot Studio bot?
- What’s the right way to align the bot between Copilot Studio and Azure for voice testing?
Thank you in advance!
Azure AI Bot Service
Microsoft Copilot | Other
1 answer
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Jonathan Harrison 170 Reputation points2024-12-11T13:40:42.6366667+00:00 -
Jonathan Harrison 170 Reputation points
2024-12-11T11:10:57.2866667+00:00 Hi I also encountered this problem with my first copilot but i realized it was easer to go the other direction ie, from azure to copilot and you actually wont need openai because azure and copilot handle everything together.Integrate a Copilot Studio bot
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MrsKuaero 0 Reputation points
2024-12-11T12:08:02.1666667+00:00 But how are you able to connect azure resource to your bot in copilot? That is my struggle right now.
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Jonathan Harrison 170 Reputation points
2024-12-11T13:40:12.5033333+00:00 in the azure foundry after you deploy the bot to the environment you get an option to make it a web app or send it to copilot atttached is the sc of what it looks like
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Jonathan Harrison 170 Reputation points
2024-12-11T13:43:49.6033333+00:00 Header 1 Header 2 Cell 1 Cell 2 Cell 3 Cell 4 |
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Jonathan Harrison 170 Reputation points
2024-12-11T13:45:24.0866667+00:00 youll need a azure resource created for ai Azure OpenAI
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Jonathan Harrison 170 Reputation points
2024-12-11T13:47:03.6766667+00:00 Quick Guide: Deploying AI from Azure to Copilot Studios
Prerequisites
- Azure Account: Ensure you have an active Azure account.
- Copilot Studios Account: Ensure you have access to Copilot Studios.
- AI Model: Have your AI model trained and ready in Azure.
Step 1: Export the AI Model from Azure
- Navigate to Azure Portal: Go to the Azure portal and locate your AI model.
- Export the Model:
- For Azure Machine Learning models, navigate to the Models section.
- Select the model you want to export.
- Click on Export and choose the format (e.g., ONNX, TensorFlow, PyTorch).
- Download the exported model file to your local machine.
- Click on Export and choose the format (e.g., ONNX, TensorFlow, PyTorch).
- Select the model you want to export.
- For Azure Machine Learning models, navigate to the Models section.
Step 2: Prepare the Model for Deployment
- Verify Model Format: Ensure the model is in a format supported by Copilot Studios (e.g., ONNX, TensorFlow).
- Dependencies: Ensure you have all necessary dependencies and libraries required to run the model.
Step 3: Set Up Copilot Studios
- Log In: Log in to your Copilot Studios account.
- Create a New Project:
- Navigate to the Projects section.
- Click on Create New Project.
- Provide a name and description for your project.
- Click on Create New Project.
- Navigate to the Projects section.
Step 4: Upload the Model to Copilot Studios
- Navigate to the Project: Go to the project you created.
- Upload the Model:
- Click on Upload Model.
- Select the model file you exported from Azure.
- Follow the prompts to upload the model.
- Select the model file you exported from Azure.
- Click on Upload Model.
Step 5: Configure the Model
- Set Up Environment:
- Specify the runtime environment (e.g., Python version, required libraries).
- Ensure all dependencies are installed.
- Configure Model Settings:
- Set any necessary configuration parameters for your model.
- Define input and output formats.
- Set any necessary configuration parameters for your model.
- Specify the runtime environment (e.g., Python version, required libraries).
Step 6: Deploy the Model
- Deploy:
- Click on Deploy to start the deployment process.
- Monitor the deployment status to ensure it completes successfully.
- Test the Deployment:
- Once deployed, test the model to ensure it is working as expected.
- Use sample inputs to verify the outputs.
- Once deployed, test the model to ensure it is working as expected.
- Click on Deploy to start the deployment process.
Step 7: Integrate with Copilot Studios
- API Integration:
- If your model provides an API, integrate it with Copilot Studios.
- Use the provided API endpoints to connect your model with Copilot Studios features.
- Custom Integration:
- If custom integration is required, follow the Copilot Studios documentation to integrate your model.
- If your model provides an API, integrate it with Copilot Studios.
Step 8: Monitor and Maintain
- Monitor Performance:
- Regularly monitor the performance of your deployed model.
- Use Copilot Studios' monitoring tools to track usage and performance metrics.
- Update and Retrain:
- Periodically update and retrain your model as needed.
- Redeploy updated models following the same steps.
- Periodically update and retrain your model as needed.
- Regularly monitor the performance of your deployed model.
Summary
Deploying an AI model from Azure to Copilot Studios involves exporting the model from Azure, preparing it for deployment, setting up a project in Copilot Studios, uploading and configuring the model, deploying it, and integrating it with Copilot Studios. Regular monitoring and maintenance ensure the model continues to perform well.
If you have any specific questions or need further details on any of these steps, feel free to ask! Quick Guide: Deploying AI from Azure to Copilot Studios
Prerequisites
- Azure Account: Ensure you have an active Azure account.
- Copilot Studios Account: Ensure you have access to Copilot Studios.
- AI Model: Have your AI model trained and ready in Azure.
Step 1: Export the AI Model from Azure
- Navigate to Azure Portal: Go to the Azure portal and locate your AI model.
- Export the Model:
- For Azure Machine Learning models, navigate to the Models section.
- Select the model you want to export.
- Click on Export and choose the format (e.g., ONNX, TensorFlow, PyTorch).
- Download the exported model file to your local machine.
- Click on Export and choose the format (e.g., ONNX, TensorFlow, PyTorch).
- Select the model you want to export.
- For Azure Machine Learning models, navigate to the Models section.
Step 2: Prepare the Model for Deployment
- Verify Model Format: Ensure the model is in a format supported by Copilot Studios (e.g., ONNX, TensorFlow).
- Dependencies: Ensure you have all necessary dependencies and libraries required to run the model.
Step 3: Set Up Copilot Studios
- Log In: Log in to your Copilot Studios account.
- Create a New Project:
- Navigate to the Projects section.
- Click on Create New Project.
- Provide a name and description for your project.
- Click on Create New Project.
- Navigate to the Projects section.
Step 4: Upload the Model to Copilot Studios
- Navigate to the Project: Go to the project you created.
- Upload the Model:
- Click on Upload Model.
- Select the model file you exported from Azure.
- Follow the prompts to upload the model.
- Select the model file you exported from Azure.
- Click on Upload Model.
Step 5: Configure the Model
- Set Up Environment:
- Specify the runtime environment (e.g., Python version, required libraries).
- Ensure all dependencies are installed.
- Configure Model Settings:
- Set any necessary configuration parameters for your model.
- Define input and output formats.
- Set any necessary configuration parameters for your model.
- Specify the runtime environment (e.g., Python version, required libraries).
Step 6: Deploy the Model
- Deploy:
- Click on Deploy to start the deployment process.
- Monitor the deployment status to ensure it completes successfully.
- Test the Deployment:
- Once deployed, test the model to ensure it is working as expected.
- Use sample inputs to verify the outputs.
- Once deployed, test the model to ensure it is working as expected.
- Click on Deploy to start the deployment process.
Step 7: Integrate with Copilot Studios
- API Integration:
- If your model provides an API, integrate it with Copilot Studios.
- Use the provided API endpoints to connect your model with Copilot Studios features.
- Custom Integration:
- If custom integration is required, follow the Copilot Studios documentation to integrate your model.
- If your model provides an API, integrate it with Copilot Studios.
Step 8: Monitor and Maintain
- Monitor Performance:
- Regularly monitor the performance of your deployed model.
- Use Copilot Studios' monitoring tools to track usage and performance metrics.
- Update and Retrain:
- Periodically update and retrain your model as needed.
- Redeploy updated models following the same steps.
- Periodically update and retrain your model as needed.
- Regularly monitor the performance of your deployed model.
Summary
Deploying an AI model from Azure to Copilot Studios involves exporting the model from Azure, preparing it for deployment, setting up a project in Copilot Studios, uploading and configuring the model, deploying it, and integrating it with Copilot Studios. Regular monitoring and maintenance ensure the model continues to perform well.
If you have any specific questions or need further details on any of these steps, feel free to ask!
-
Jonathan Harrison 170 Reputation points
2024-12-11T13:48:23.7533333+00:00 Quick Guide: Deploying AI from Azure to Copilot Studios
Prerequisites
- Azure Account: Ensure you have an active Azure account.
- Copilot Studios Account: Ensure you have access to Copilot Studios.
- AI Model: Have your AI model trained and ready in Azure.
Step 1: Export the AI Model from Azure
- Navigate to Azure Portal: Go to the Azure portal and locate your AI model.
- Export the Model:
- For Azure Machine Learning models, navigate to the Models section.
- Select the model you want to export.
- Click on Export and choose the format (e.g., ONNX, TensorFlow, PyTorch).
- Download the exported model file to your local machine.
- Click on Export and choose the format (e.g., ONNX, TensorFlow, PyTorch).
- Select the model you want to export.
- For Azure Machine Learning models, navigate to the Models section.
Step 2: Prepare the Model for Deployment
- Verify Model Format: Ensure the model is in a format supported by Copilot Studios (e.g., ONNX, TensorFlow).
- Dependencies: Ensure you have all necessary dependencies and libraries required to run the model.
Step 3: Set Up Copilot Studios
- Log In: Log in to your Copilot Studios account.
- Create a New Project:
- Navigate to the Projects section.
- Click on Create New Project.
- Provide a name and description for your project.
- Click on Create New Project.
- Navigate to the Projects section.
Step 4: Upload the Model to Copilot Studios
- Navigate to the Project: Go to the project you created.
- Upload the Model:
- Click on Upload Model.
- Select the model file you exported from Azure.
- Follow the prompts to upload the model.
- Select the model file you exported from Azure.
- Click on Upload Model.
Step 5: Configure the Model
- Set Up Environment:
- Specify the runtime environment (e.g., Python version, required libraries).
- Ensure all dependencies are installed.
- Configure Model Settings:
- Set any necessary configuration parameters for your model.
- Define input and output formats.
- Set any necessary configuration parameters for your model.
- Specify the runtime environment (e.g., Python version, required libraries).
Step 6: Deploy the Model
- Deploy:
- Click on Deploy to start the deployment process.
- Monitor the deployment status to ensure it completes successfully.
- Test the Deployment:
- Once deployed, test the model to ensure it is working as expected.
- Use sample inputs to verify the outputs.
- Once deployed, test the model to ensure it is working as expected.
- Click on Deploy to start the deployment process.
Step 7: Integrate with Copilot Studios
- API Integration:
- If your model provides an API, integrate it with Copilot Studios.
- Use the provided API endpoints to connect your model with Copilot Studios features.
- Custom Integration:
- If custom integration is required, follow the Copilot Studios documentation to integrate your model.
- If your model provides an API, integrate it with Copilot Studios.
Step 8: Monitor and Maintain
- Monitor Performance:
- Regularly monitor the performance of your deployed model.
- Use Copilot Studios' monitoring tools to track usage and performance metrics.
- Update and Retrain:
- Periodically update and retrain your model as needed.
- Redeploy updated models following the same steps.
- Periodically update and retrain your model as needed.
- Regularly monitor the performance of your deployed model.
Summary
Deploying an AI model from Azure to Copilot Studios involves exporting the model from Azure, preparing it for deployment, setting up a project in Copilot Studios, uploading and configuring the model, deploying it, and integrating it with Copilot Studios. Regular monitoring and maintenance ensure the model continues to perform well.
If you have any specific questions or need further details on any of these steps, feel free to ask!
Quick Guide: Deploying AI from Azure to Copilot Studios
Prerequisites
- Azure Account: Ensure you have an active Azure account.
- Copilot Studios Account: Ensure you have access to Copilot Studios.
- AI Model: Have your AI model trained and ready in Azure.
Step 1: Export the AI Model from Azure
- Navigate to Azure Portal: Go to the Azure portal and locate your AI model.
- Export the Model:
- For Azure Machine Learning models, navigate to the Models section.
- Select the model you want to export.
- Click on Export and choose the format (e.g., ONNX, TensorFlow, PyTorch).
- Download the exported model file to your local machine.
- Click on Export and choose the format (e.g., ONNX, TensorFlow, PyTorch).
- Select the model you want to export.
- For Azure Machine Learning models, navigate to the Models section.
Step 2: Prepare the Model for Deployment
- Verify Model Format: Ensure the model is in a format supported by Copilot Studios (e.g., ONNX, TensorFlow).
- Dependencies: Ensure you have all necessary dependencies and libraries required to run the model.
Step 3: Set Up Copilot Studios
- Log In: Log in to your Copilot Studios account.
- Create a New Project:
- Navigate to the Projects section.
- Click on Create New Project.
- Provide a name and description for your project.
- Click on Create New Project.
- Navigate to the Projects section.
Step 4: Upload the Model to Copilot Studios
- Navigate to the Project: Go to the project you created.
- Upload the Model:
- Click on Upload Model.
- Select the model file you exported from Azure.
- Follow the prompts to upload the model.
- Select the model file you exported from Azure.
- Click on Upload Model.
Step 5: Configure the Model
- Set Up Environment:
- Specify the runtime environment (e.g., Python version, required libraries).
- Ensure all dependencies are installed.
- Configure Model Settings:
- Set any necessary configuration parameters for your model.
- Define input and output formats.
- Set any necessary configuration parameters for your model.
- Specify the runtime environment (e.g., Python version, required libraries).
Step 6: Deploy the Model
- Deploy:
- Click on Deploy to start the deployment process.
- Monitor the deployment status to ensure it completes successfully.
- Test the Deployment:
- Once deployed, test the model to ensure it is working as expected.
- Use sample inputs to verify the outputs.
- Once deployed, test the model to ensure it is working as expected.
- Click on Deploy to start the deployment process.
Step 7: Integrate with Copilot Studios
- API Integration:
- If your model provides an API, integrate it with Copilot Studios.
- Use the provided API endpoints to connect your model with Copilot Studios features.
- Custom Integration:
- If custom integration is required, follow the Copilot Studios documentation to integrate your model.
- If your model provides an API, integrate it with Copilot Studios.
Step 8: Monitor and Maintain
- Monitor Performance:
- Regularly monitor the performance of your deployed model.
- Use Copilot Studios' monitoring tools to track usage and performance metrics.
- Update and Retrain:
- Periodically update and retrain your model as needed.
- Redeploy updated models following the same steps.
- Periodically update and retrain your model as needed.
- Regularly monitor the performance of your deployed model.
Summary
Deploying an AI model from Azure to Copilot Studios involves exporting the model from Azure, preparing it for deployment, setting up a project in Copilot Studios, uploading and configuring the model, deploying it, and integrating it with Copilot Studios. Regular monitoring and maintenance ensure the model continues to perform well.
I
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Jonathan Harrison 170 Reputation points
2024-12-11T13:55:43.33+00:00 here you go another walkthrough
Publish an agent to Azure Bot Service channels - Microsoft Copilot Studio
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santoshkc 15,600 Reputation points Microsoft External Staff Moderator2024-12-12T14:06:20.3433333+00:00 Hi @MrsKuaero,
Just checking in to see if the above response provided by @Jonathan Harrison helped.
Thank you.
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