Based on your description, you are encountering a 500 Server Error when trying to use the Langchain ChatDatabricks integration with a Databricks Serving Endpoint connected to an external OpenAI GPT-4 Turbo model on Azure. This error usually indicates an issue on the server side or with your endpoint configuration, not just the client code. Hereโs how to troubleshoot:
Possible Causes and Solutions
1. Endpoint Name Mismatch
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Double-check if your Databricks serving endpoint name matches exactly in both the Databricks UI and your code. In your error, the URL ends with /serving-endpoints/testpocC/invocations, but you used endpoint="test" in your code. This mismatch can result in a failed request.
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Solution: Make sure endpoint="testpocC" if your actual endpoint is testpocC.
2. Model Registration and Permissions
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Make sure the model is properly registered on Databricks and the serving endpoint is running and healthy.
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Check that your Azure OpenAI key and endpoint are correctly configured in the Databricks UI when setting up the external model. Double-check secretsโ scopes and keys, if you are using Databricks secrets.
3. API Key and Endpoint Configuration
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Ensure the Azure OpenAI API key, resource name, and deployment name are correct.
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If thereโs a typo in the key or the endpoint, authentication will fail internally when Databricks tries to forward the request.
4. Databricks Workspace/Token Permissions
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Confirm that your Databricks workspace user has permission to invoke the serving endpoint.
5. Langchain Integration
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Your code snippet seems structurally correct. The main culprit is likely upstream (endpoint naming, API configuration, or permissions).
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If the endpoint requires a different way of sending messages (e.g., invoke() expects a dict vs a string), check the Langchain documentation for the required message structure.
6. Serving Endpoint Logs
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Check logs on the Databricks endpoint for more detailed error messages. Typically, a 500 error will be accompanied by a more descriptive log message within the Databricks "Serving Endpoints" UI.
Example Checklist
| Step |
What to Check |
| Endpoint Name |
Correct name in both code and Databricks UI? |
| Endpoint URL |
Correctly formed (no typos, fully qualified domain)? |
| Azure Key/Config |
Secrets and keys match those provided by Azure OpenAI? |
| Permissions/Access |
User/service principal has access to serving endpoint? |
| Langchain Message Format |
messages structured per Langchain API expectations? |
| Endpoint Health |
Endpoint status in Databricks UI is "Healthy"? |
| Logs |
Look for specific error details in Databricks serving endpoint logs? |
Example Code Correction
# Example of consistent endpoint naming:
model = ChatDatabricks(target_uri="databricks", endpoint="testpocC", temperature=0.99)
response = model.invoke(messages)
When to Contact Support
If after all checks (endpoint, keys, permissions, logs) you still get a 500, consider:
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There may be a misconfiguration in Databricks external model setup.
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Contact Databricks support with endpoint logs and Azure OpenAI validation screenshots for deeper assistance.
Summary:
The most common causes are endpoint naming mismatches, authorization/configuration errors on Databricks or Azure, or misformatted messages to Langchain. Verify your endpoint name, configuration, permissions, and examine endpoint logs for actionable errors.
