Fastembed – Lightweight Python Embedding Library
FastEmbed is a lightweight Python library designed for efficient embedding generation. It supports various text models and is optimized for speed and accuracy, outperforming some existing models. The library can be easily installed and used with or without GPU support, making it suitable for serverless environments.
- ▪FastEmbed is built for embedding generation and supports popular text models.
- ▪The library is lightweight and does not require a GPU, utilizing the ONNX Runtime instead.
- ▪FastEmbed is faster than PyTorch and offers better accuracy than OpenAI Ada-002.
Opening excerpt (first ~120 words) tap to expand
⚡️ What is FastEmbed? FastEmbed is a lightweight, fast, Python library built for embedding generation. We support popular text models. Please open a GitHub issue if you want us to add a new model. The default text embedding (TextEmbedding) model is Flag Embedding, presented in the MTEB leaderboard. It supports "query" and "passage" prefixes for the input text. Here is an example for Retrieval Embedding Generation and how to use FastEmbed with Qdrant. 📈 Why FastEmbed? Light: FastEmbed is a lightweight library with few external dependencies. We don't require a GPU and don't download GBs of PyTorch dependencies, and instead use the ONNX Runtime. This makes it a great candidate for serverless runtimes like AWS Lambda. Fast: FastEmbed is designed for speed.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.