FastEmbed is a Python library developed by Qdrant to facilitate efficient and user-friendly embedding generation for data science and machine learning applications. Targeting 80% of NLP embedding use cases, it simplifies the process by offering default workflows and a small, focused set of transformer models, such as BAAI/bge-small-en-v1.5. FastEmbed is optimized for speed and performance by quantizing models and integrating them with ONNX Runtime, enabling fast computations even on CPUs without the need for specialized hardware. The library's design minimizes installation time and resource requirements, making it suitable for environments with storage limitations. FastEmbed seamlessly integrates with Qdrant, a vector store that enhances embedding generation, storage, and retrieval, thereby providing scalable and efficient solutions for large-scale datasets. The fusion of FastEmbed with Qdrant's capabilities offers a streamlined approach to handling text embeddings, reducing latency, and supporting extensive data operations.