The model uniqueness constraint now considers (provider, name, type)
instead of just (provider, name). This allows users to add the same
model name for different purposes (e.g., language vs embedding).
Fixes#391
* feat: prevent duplicate model names under same provider
Implement case-insensitive validation to prevent users from creating
duplicate model names under the same provider. This validation is
implemented both in the backend API and the frontend UI.
Changes:
- Backend: Add duplicate check in create_model endpoint (case-insensitive)
- Frontend: Add client-side validation in AddModelForm
- Frontend: Improve error message display from backend
- Tests: Add unit tests for duplicate model validation
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* refactor: optimize duplicate model validation and improve error handling
- Replace O(n) model iteration with efficient SurrealDB query for duplicate check
- Improve error message to include model name and provider for better UX
- Remove frontend duplicate validation (backend-only enforcement)
- Fix test authentication by setting OPEN_NOTEBOOK_PASSWORD before imports
- Update test mocking to use repo_query instead of Model.get_all()
- Add pytest fixture for TestClient to ensure proper test isolation
All 11 tests passing.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* remove unnecessary package
* fix: replace any with unknown type in error handler
- Change error type from 'any' to 'unknown' to satisfy ESLint
- Add proper type assertion for error object structure
- Maintains same runtime behavior with better type safety
---------
Co-authored-by: Claude <noreply@anthropic.com>
* fix text
* remove lint from docker publish workflow
* gemini base url docs
* feat: add multimodal support for openai-compatible providers
- Add helper function to check OpenAI-compatible provider availability per mode
- Update provider detection to support language, embedding, STT, and TTS modalities
- Implement mode-specific environment variable detection (LLM, EMBEDDING, STT, TTS)
- Maintain backward compatibility with generic OPENAI_COMPATIBLE_BASE_URL
- Add comprehensive unit tests for all configuration scenarios
- Update .env.example with mode-specific environment variables
- Update provider support matrix in ai-models.md
- Create comprehensive openai-compatible.md setup guide
This enables users to configure different OpenAI-compatible endpoints for
different AI capabilities (e.g., LM Studio for language models, dedicated
server for embeddings) while maintaining full backward compatibility.
* upgrade
* chore: change docker release strategy
