* 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
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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
