Cosmos

Performs semantic image search by analyzing visual content locally without cloud transmission, using embedded vision models to generate image embeddings that are compared against a local index of media files. The system builds a searchable vector database of image features during initial indexing, enabling fast similarity matching against reference images without requiring internet connectivity or API calls.

Identifies visually similar scenes within video files by extracting frame embeddings at regular intervals and comparing them against a reference image or video segment using local vision models. The system samples frames from videos, generates embeddings for each frame, and performs nearest-neighbor search to locate matching or similar scenes without uploading video content to external services.

Converts spoken audio in video files to text using local speech-to-text models that process audio streams without sending data to cloud transcription services. The system extracts audio from video files, applies local speech recognition models (likely using frameworks like Whisper or similar), and generates timestamped transcripts that can be indexed and searched.

Builds and maintains a local vector database that indexes all media files (images and videos) by their visual content embeddings, enabling fast retrieval across the entire collection. The system manages the lifecycle of embeddings — generating them during initial indexing, updating them when files change, and organizing them in a searchable index structure that supports similarity queries without requiring re-processing of source files.

Provides a single search interface that works across multiple image and video formats by normalizing file handling and embedding generation across different codecs and containers. The system abstracts format-specific parsing (JPEG, PNG, MP4, WebM, etc.) behind a unified API, allowing users to search heterogeneous media collections without worrying about format compatibility or conversion.