#similarity search

The project offers a browser-based solution for sorting and searching images on local devices using OpenAI's CLIP model. Images can be compared to user-entered text without needing to upload files to external servers, maintaining user privacy and simplifying usage. The project leverages the File System Access API and a browser-ported CLIP model to provide advanced image management tools within the browser.

The project provides an efficient search engine for vector and text similarity, showcasing advancements over conventional FAISS solutions. It is compatible with multiple programming languages and metrics, adaptable across diverse systems, and features real-time clustering and customizable metrics for specific applications. The engine emphasizes hardware independence and optimized memory usage for enhanced performance.

Faiss is a C++ library by Meta's AI Research team for similarity search and clustering of dense vectors. It supports Python wrappers and utilizes GPU for faster processing, handling billions of vectors. Faiss includes L2, dot product, and cosine similarity algorithms, with indexing structures like HNSW and NSG for efficiency. Compatible with CPU and GPU, it integrates easily with Anaconda, making it essential for managing high-dimensional vector data.

Vearch is a distributed vector database focused on effective similarity searches for embedding vectors in AI solutions. It integrates hybrid search functions, including vector search and scalar filtering, to deliver rapid retrieval of vectors within milliseconds. With features supporting scalability and reliability through replication and elastic expansion, Vearch is apt for various uses such as visual search systems or acting as a memory backend. Deployment options via Kubernetes, Docker, or source code compilation offer flexible infrastructure compatibility.