fix: improve embedding model validation logic for dataset operations #3235
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
What problem does this PR solve? When creating or updating datasets with custom embedding models (e.g., Ollama), the validation logic was too restrictive and prevented valid models from being used. The previous implementation would reject valid custom models if they weren't in the predefined list, even when they existed in TenantLLMService. Type of change: Bug Fix Changes: - Simplify and improve the embedding model validation flow in create/update endpoints - Check TenantLLMService for custom models before rejecting - Make validation logic more consistent between create and update operations This fix allows users to successfully create and update datasets with custom embedding models while maintaining proper validation checks.
Feiue
reviewed
Nov 7, 2024
Co-authored-by: liuhua <10215101452@stu.ecnu.edu.cn>
Co-authored-by: liuhua <10215101452@stu.ecnu.edu.cn>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
What problem does this PR solve?
When creating or updating datasets with custom embedding models (e.g., Ollama), the validation logic was too restrictive and prevented valid models from being used. The previous implementation would reject valid custom models if they weren't in the predefined list, even when they existed in TenantLLMService.
Changes:
What problem does this PR solve?
This fix allows users to successfully create and update datasets with custom embedding models while maintaining proper validation checks.
Type of change