Azure AI Deployment Testing results in Type Error

Question

Hello, I am trying to deploy an endpoint using Azure Ai Studio.

Here is what I did:

1. Go to chat playground, and pick index I want.
2. Asked some questions and saw answers generating properly
3. Then went to prompt flow and ran compute session.
4. Using chat functionality, I asked question and saw answers being generated correctly again.
5. Deployed the prompt flow into endpoint.
6. Using Test UI in the endpoint, I asked questions for testing.
7. Keep receiving following error:
8. I also tested through Postman, but I receive the same error.

Its always TypeError for the first query and afterwards is keyError.

I am not sure what is the problem since it worked in chat playground, and also in prompt flow.

Does anyone have had similar experience or any ideas to fix this?

Answer 1

The issue you're encountering during Azure AI deployment testing seems to be related to how variables or inputs are being handled within your prompt flow after deployment, specifically regarding NoneType objects and missing keys like 'reply'.

1. Check Input Variables in Prompt Flow:

• Ensure that all variables used in your prompt flow have valid default values, especially the ones involved in the generateReply and formatRewriteIntentInputs functions. The error indicates that NoneType values are being passed where a string or other data type is expected.
  • The TypeError: unsupported operand type(s) for +: 'NoneType' and 'NoneType' could mean that two variables (possibly string concatenation or list operations) are None, so ensure all variables used in those steps are properly initialized.

2. Inspect KeyError:

• The KeyError: 'reply' suggests that the key 'reply' is missing from the dictionary being accessed. Double-check how this key is being set in the prompt flow. There might be a logical step or condition missing before trying to access or modify the reply key.
  • Make sure that the reply variable is being correctly generated or assigned a value in your flow. If it's optional, add error handling to provide a default value when it is missing.

3. Check for Differences Between Chat Playground and Endpoint:

• It’s possible that the environment used for the chat playground is handling certain inputs or data in a more flexible way than the endpoint deployment. Review how the variables are handled in the playground versus the endpoint, especially around input sanitization and default value assignments.
  • The endpoint might not have access to some initial values or context that were available in the playground. Try adding debugging steps to log the state of important variables like reply to help trace the issue.

4. Test with Simplified Inputs:

• Try testing the endpoint with very simple inputs to isolate whether it's a specific query or input format causing the issue. This could help identify whether the problem lies in a specific part of your logic.

5. Review Postman Calls:

• If you're receiving the same errors through Postman, review the exact payload and headers you're sending. Ensure the input format matches what your model expects, especially regarding how variables like reply are being passed.

6. Error Handling in Functions:

• Add error handling in the functions like generateReply and formatRewriteIntentInputs to catch and log any potential NoneType or missing key issues. This can prevent the deployment from failing entirely and provide useful debugging information.

If the issue persists after these checks, you may need to review the full prompt flow and ensure all variables are properly handled and initialized throughout the flow.