yusyus
bcc2ef6a7f
test: Skip tests requiring optional dependencies
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- Skip test_benchmark.py if psutil not installed
- Skip test_embedding.py if numpy not installed
- Skip test_embedding_pipeline.py if numpy not installed
- Uses pytest.importorskip() for clean dependency handling
- Fixes CI test collection errors for optional features
2026-02-08 14:49:45 +03:00
yusyus
0265de5816
style: Format all Python files with ruff
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- Formatted 103 files to comply with ruff format requirements
- No code logic changes, only formatting/whitespace
- Fixes CI formatting check failures
2026-02-08 14:42:27 +03:00
yusyus
51787e57bc
style: Fix 411 ruff lint issues (Kimi's issue #4 )
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Auto-fixed lint issues with ruff --fix and --unsafe-fixes:
Issue #4 : Ruff Lint Issues
- Before: 447 errors (originally reported as ~5,500)
- After: 55 errors remaining
- Fixed: 411 errors (92% reduction)
Auto-fixes applied:
- 156 UP006: List/Dict → list/dict (PEP 585)
- 63 UP045: Optional[X] → X | None (PEP 604)
- 52 F401: Removed unused imports
- 52 UP035: Fixed deprecated imports
- 34 E712: True/False comparisons → not/bool()
- 17 F841: Removed unused variables
- Plus 37 other auto-fixable issues
Remaining 55 errors (non-critical):
- 39 B904: Exception chaining (best practice)
- 5 F401: Unused imports (edge cases)
- 3 SIM105: Could use contextlib.suppress
- 8 other minor style issues
These remaining issues are code quality improvements, not critical bugs.
Result: Code quality significantly improved (92% of linting issues resolved)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com >
2026-02-08 12:46:38 +03:00
yusyus
b475b51ad1
feat: Add custom embedding pipeline (Task #17 )
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- Multi-provider support (OpenAI, Local)
- Batch processing with configurable batch size
- Memory and disk caching for efficiency
- Cost tracking and estimation
- Dimension validation
- 18 tests passing (100%)
Files:
- embedding_pipeline.py: Core pipeline engine
- test_embedding_pipeline.py: Comprehensive tests
Features:
- EmbeddingProvider abstraction
- OpenAIEmbeddingProvider with pricing
- LocalEmbeddingProvider (simulated)
- EmbeddingCache (memory + disk)
- CostTracker for API usage
- Batch processing optimization
Supported Models:
- text-embedding-ada-002 (1536d, $0.10/1M tokens)
- text-embedding-3-small (1536d, $0.02/1M tokens)
- text-embedding-3-large (3072d, $0.13/1M tokens)
- Local models (any dimension, free)
Week 2: 8/9 tasks complete (89%)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com >
2026-02-07 13:48:05 +03:00
