Docfinder v2.1.0

What's Changed

Added

• Cross-encoder reranking: search results are re-scored with cross-encoder/ms-marco-MiniLM-L-2-v2 for better precision; the bi-encoder fetches 3× candidates and the cross-encoder selects the best matches.
• Parallel document ingestion: files are parsed in parallel via ProcessPoolExecutor (up to 4 workers) when indexing 4+ documents; embedding and storage stay on the main process for thread safety.
• Balanced hardware auto-selection for embeddings: runtime selection is now more robust (e.g. NVIDIA systems without ONNX CUDA provider fall back to PyTorch CUDA instead of CPU).
• Balanced adaptive indexing workers: parser worker count now adapts automatically to available CPU/RAM.
• Runtime diagnostics in Settings/System Info: backend, device, GPU, and indexing mode are exposed for easier troubleshooting.
• Hybrid RAG context strategy: small documents (≤ 20 chunks) are loaded fully into context, while larger documents use a sliding window around the matched chunk.
• Table extraction from PDFs: PyMuPDF find_tables() tables are extracted and converted to Markdown for better embedding quality.
• New Reranker class with lazy model loading (index/reranker.py).
• New storage methods get_document_chunk_count() and get_all_chunks() for hybrid RAG context.
• Reranker model preloaded at startup alongside the embedding model.
• Search folder filters: Search tab now includes selectable indexed directory filters (All/None + checkboxes), applied at query time without re-indexing.

Changed

• Spotlight panel (spotlight.html) redesigned to better match the main app visual style.
• Spotlight quick-search input is now rounded with less aggressive filtering (minimum 2 chars, softer threshold, fallback rendering).
• ONNX fallback now preserves the best available torch device (cuda/mps) instead of forcing CPU.

📦 Downloads

Platform	Download
macOS	DocFinder-macOS.dmg
Windows	DocFinder-Windows-Setup.exe
Linux	DocFinder-Linux-x86_64.AppImage

Full Changelog: v2.0.0...v2.1.0