#vector search engine

Explore a Python client library designed for the Qdrant vector search engine, offering sync and async API requests with full API method type hints. It provides REST and gRPC support for smooth integration across different environments, including local development. FastEmbed enhances vector operations with efficient CPU/GPU vector embeddings. Comprehensive documentation and examples aid in optimizing server and cloud setups, granting extensive control over collection management.

This JS/TS library delivers essential tools and documentation for developers engaging with the Qdrant vector search engine. It includes a primary SDK package, a lightweight REST client, and a gRPC client, each crafted to seamlessly integrate with Qdrant. With support for Node.js, Deno, Browser, and Cloudflare Workers, the package ensures versatility across various environments. Users can effortlessly connect to both local and cloud-based Qdrant platforms. Regular updates ensure continued improvement, while extensive examples provide a strong foundation for development. Discover the project's roadmap and contribute under the Apache 2.0 License.