{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"}],"enrichment":{"capability":"FastEmbed generates vector embeddings for text, images, and multimodal content using ONNX Runtime models, supporting dense, sparse, late-interaction, and reranking approaches without requiring GPU or large PyTorch dependencies.","skillfed_tags":["embeddings","vector-search","onnx-runtime"],"use_cases":["Build semantic search systems by embedding documents and queries, then finding nearest neighbors in a vector database.","Embed product images and descriptions for multimodal e-commerce search without GPU infrastructure.","Rerank search results using cross-encoders to improve relevance without recomputing full embeddings.","Deploy embedding pipelines in serverless functions where PyTorch dependencies would exceed size limits.","Generate sparse embeddings for hybrid search combining keyword and semantic matching.","Embed PDF document screenshots with ColPali for document retrieval and question-answering."],"what_it_does":"FastEmbed is a Python library for generating vector embeddings from text, images, and multimodal documents using pre-trained ONNX models. It avoids the overhead of PyTorch by using ONNX Runtime, making it lightweight enough for serverless environments like AWS Lambda. The library supports multiple embedding types: dense embeddings (default Flag Embedding model), sparse embeddings (SPLADE++), late-interaction models (ColBERT), image embeddings (CLIP-based), and multimodal embeddings (ColPali). It also includes reranking via cross-encoders.\n\nThe package is built around a simple API: instantiate an embedding model by name, call its embed method with a list of inputs, and iterate over the resulting vectors. Models are downloaded from Hugging Face Hub on first use and cached locally. The library supports custom models and GPU acceleration via the optional fastembed-gpu variant. It integrates directly with Qdrant vector database for retrieval workflows.","worth_installing":"Yes. FastEmbed is actively maintained, has no known vulnerabilities, installs with low friction, and solves a real problem\u2014generating embeddings without PyTorch overhead. The permissive Apache license and broad model support make it a practical choice for retrieval systems, serverless deployments, and CPU-only environments. Start with it if you need embeddings without GPU or large dependencies."},"id":"fastembed","links":{"html":"https://skillfed.io/packages/fastembed","md":"https://skillfed.io/packages/fastembed.md","pypi":"https://pypi.org/project/fastembed/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-23","license_spdx":null,"license_treatment":"permissive","name":"fastembed","python_support":"supports_current","summary":"Fast, light, accurate library built for retrieval embedding generation"},"popularity":{"monthly_downloads":8608559,"position":1603,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.8.0"}