#IndexedDB

A lightweight vector database using OpenAI embeddings for semantic searches in IndexedDB. Efficient storage management and enhanced search precision with cosine similarity make it ideal for managing document vectors with filtering options.

The client-vector-search library delivers an efficient solution for embedding and vector searching with caching options, designed for both browser and server-side use. Notably faster than alternatives such as OpenAI's text-embedding-ada-002 and Pinecone, it utilizes transformer models to embed documents and compute cosine similarity between embeddings. Users can manage and cache indexes directly on the client side. Upcoming enhancements include integrating an HNSW index and a comprehensive testing framework, accommodating thousands of vectors for versatile application performance.

The library enhances web apps' offline capabilities using asynchronous storage with a localStorage-like API, supporting IndexedDB, WebSQL, and localStorage for flexible data management. Integration is simple with just one script, allowing use of callbacks, Promises, or async/await for storage access. It handles diverse data types such as Blobs and TypedArrays, ensuring broad applicability. Versatile through comprehensive configuration and multi-instance support, it also integrates with popular frameworks and allows custom driver creation.