OtterSearch
AI search for PDFs & images. Better than Spotlight.
OtterSearch launched on January 30, 2026 with 6 votes, #2,272 of 14,584 launches that month and more than 75% of that year's launches.
What it does
Unlike Spotlight, OtterSearch understands content, not just filenames. Search "beach sunset" and find IMG_2847.jpg. Search "contract terms" and find Document_Final_v3.pdf. Key Features: Semantic search with AI query expansion Indexes PDF content (first 3 pages), not just titles 100% local—no cloud, no data collection 30-second setup, runs anywhere Lightning fast with vector indexing Perfect for anyone drowning in unlabeled PDFs and images.
Contribute to khushwant18/OtterSearch development by creating an account on GitHub.
Find PDFs and images in your laptop using semantic understanding with image embeddings and AI-powered multimodal search. Just download the right version for your Mac, open the DMG, and drag to Applications! This is normal for unsigned apps. The app is safe - it's open source! Note: Scanned PDFs are automatically detected and indexed as images for better search accuracy! # Setup with uv (fastest) bash setup_uv.sh # Run python __main__.py # Open in browser # http://localhost:8000 📦 Requirements Python 3.9+ ~1GB disk space (for ML models) uv (or standard pip) 🚀 Setup Options Option 1: uv (Recommended - Fastest) python -m venv venv source venv/bin/activate pip install -r requirements.txt…from github.com
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I built an AI-Powered semantic search for Mac2025 · github.com · ▲6After getting frustrated with macOS's Spotlight search, e.g., typing "driver license" doesn't give me anything unless the file name matches exactly, I thought, why not index my entire Documents folder? This way, I can find that one PDF or image buried deep in subfolders using natural language queries. So I built SmartSearch; it uses SentenceTransformers for embeddings and FAISS for fast similarity search. Best of all, it runs locally on your computer. Github: https://github.com/neberej/smart-search/ Demo:…
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