Client Vector Search: Efficient Client-Side AI Search Made Simple

Client Vector Search is a powerful AI tool designed for embedding, storing, and searching vectors directly on the client side. It enables fast and efficient semantic search with as little as 5 lines of code, making it accessible for developers and engineers looking to integrate advanced AI capabilities into their applications. The tool delivers superior performance, with embeddings that outperform OpenAI's text-embedding-ada-002 and the ability to search up to 100K vectors in under 100ms. Built for scalability, it supports integration via an embedding API and can be easily installed using npm. The platform includes a Playground for testing with real datasets, such as Wikipedia, and provides functions like getEmbedding and EmbeddingIndex for seamless implementation.

Key Features:

• Client-Side Vector Search: Perform semantic search directly in the browser or client environment.
• High-Performance Embeddings: Outperforms OpenAI's text-embedding-ada-002 on the client side.
• Lightning-Fast Search: Capable of searching up to 100K vectors in less than 100ms.
• Minimal Code Integration: Requires as few as 5 lines of code to implement.
• Scalable via API: Supports scaling with an embedding API for larger datasets.
• Easy Installation: Installable via npm with npm i client-vector-search.
• Interactive Playground: Test embeddings and searches using datasets like Wikipedia.
• Key Functions: Includes getEmbedding and EmbeddingIndex for core operations.
• Persistent Indexing: Allows users to save indexes and use APIs for long-term use.

Pricing: Client Vector Search offers a subscription model with a pricing tier of $20/month for embedding, storing, and searching 10M vectors.

Conclusion: Client Vector Search is a highly efficient and developer-friendly tool that brings advanced AI search capabilities to the client side, enabling fast, scalable, and high-performing semantic search with minimal effort.