Fastembed

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FastEmbed is a lightweight, fast Python library for generating state-of-the-art embeddings. It supports popular text and image models, and is optimized for speed and accuracy using ONNX Runtime.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is fastembed?

FastEmbed is a lightweight and fast Python library designed for generating state-of-the-art embeddings. It supports a variety of popular text and image models, including Flag Embedding for text and CLIP for images. A key differentiator is its use of ONNX Runtime, which eliminates the need for heavy PyTorch dependencies and GPUs, making it suitable for serverless environments like AWS Lambda. FastEmbed offers dense and sparse text embeddings, late interaction models like ColBERT, and multimodal embeddings. It also includes rerankers for improving search relevance. The library is maintained by Qdrant and can be easily integrated with the Qdrant client for vector search applications, with options for GPU support.

Best used for

Ideal for developers and data scientists who need to generate high-quality embeddings for text and images, and integrate them into vector search applications. Especially valuable for building efficient RAG systems and semantic search solutions without heavy GPU dependencies.

Common actions

generate text embeddings

generate image embeddings

optimize embedding performance

integrate vector search

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Capabilities

Key features

Lightweight Python library
Fast embedding generation
ONNX Runtime optimization
Supports diverse text models
Supports image models
Includes rerankers
Qdrant integration

Target Audience

developerdata scientist

Integrations

qdrant

Pricing & Plans

Open Source

Free

FAQs

What types of embeddings can FastEmbed generate?

FastEmbed supports various embedding types, including dense text embeddings (like Flag Embedding), sparse text embeddings (SPLADE++), late interaction models (ColBERT), image embeddings (CLIP), and late interaction multimodal embeddings (ColPali). It also includes rerankers for improved relevance.

Does FastEmbed require a GPU for operation?

No, FastEmbed is designed to be lightweight and does not strictly require a GPU, as it uses the ONNX Runtime. This makes it suitable for serverless environments. However, it does offer optional GPU support for enhanced performance if a compatible GPU is available.

How does FastEmbed compare to other embedding libraries?

FastEmbed is designed to be faster and lighter than many other embedding libraries, such as Transformers or Sentence-Transformers, primarily due to its use of ONNX Runtime and fewer external dependencies. It also aims for high accuracy, often outperforming models like OpenAI Ada-002.

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