What this error means

DNS or TLS error when connecting to Azure OpenAI — example code uses https://example-resource.azure.openai.com/ instead of correct https://example-endpoint.openai.azure.com/ is a OpenAI API failure pattern reported for developers trying to fix connection errors caused by incorrect azure openai endpoint format in official python sdk examples. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

PR #3298 submitted 2026-05-22 fixes swapped Azure endpoint hostname in examples/azure.py. Users copy-pasting the example will hit DNS resolution or TLS handshake failures. Commercial value: medium-high — Azure OpenAI users hitting auth/connection errors. Category: OpenAI API per mapping rules.

Common causes

  • PR #3298 submitted 2026-05-22 fixes swapped Azure endpoint hostname in examples/azure.py. Users copy-pasting the example will hit DNS resolution or TLS handshake failures. Commercial value: medium-high — Azure OpenAI users hitting auth/connection errors. Category: OpenAI API per mapping rules.

Quick fixes

  1. Confirm the exact error signature matches DNS or TLS error when connecting to Azure OpenAI — example code uses https://example-resource.azure.openai.com/ instead of correct https://example-endpoint.openai.azure.com/.
  2. Check the OpenAI API account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

Platform/tool-specific checks

  • Verify the command, editor, extension, or API client that produced the error.
  • Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
  • Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. Keep the working fix documented for the team or deployment environment.

How to prevent it

  • Keep provider/tool configuration documented.
  • Record non-secret diagnostics such as tool version, provider name, model name, and command path.
  • Add a lightweight check before CI or production workflows depend on the tool.