* fix: auto-detect normalized embeddings and use cosine distance
- Add automatic detection for normalized embedding models (OpenAI, Voyage AI, Cohere)
- Automatically set distance_metric='cosine' for normalized embeddings
- Add warnings when using non-optimal distance metrics
- Implement manual L2 normalization in HNSW backend (custom Faiss build lacks normalize_L2)
- Fix DiskANN zmq_port compatibility with lazy loading strategy
- Add documentation for normalized embeddings feature
This fixes the low accuracy issue when using OpenAI text-embedding-3-small model with default MIPS metric.
* style: format
* feat: add OpenAI embeddings support to google_history_reader_leann.py
- Add --embedding-model and --embedding-mode arguments
- Support automatic detection of normalized embeddings
- Works correctly with cosine distance for OpenAI embeddings
* feat: add --use-existing-index option to google_history_reader_leann.py
- Allow using existing index without rebuilding
- Useful for testing pre-built indices
* fix: Improve OpenAI embeddings handling in HNSW backend
- Fix ambiguous fullwidth characters (commas, parentheses) in strings and comments
- Replace Chinese comments with English equivalents
- Fix unused imports with proper noqa annotations for intentional imports
- Fix bare except clauses with specific exception types
- Fix redefined variables and undefined names
- Add ruff noqa annotations for generated protobuf files
- Add lint and format check to GitHub Actions CI pipeline
