#FastEmbed

FastEmbed is a Python library for generating text and image embeddings. It supports various popular models and uses ONNX Runtime instead of PyTorch, which is optimized for serverless environments and provides significant speed and accuracy improvements over competitors like OpenAI's Ada-002. The library can be installed via pip, with GPU support if needed, and is suitable for large datasets using data parallelism. FastEmbed supports multiple embeddings types including dense, sparse, and late interaction models, and integrates with Qdrant.

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.