RAG・知識ベース
zvec
Lightweight, lightning-fast in-process vector database from Alibaba that embeds in your app.
★ 16.1k スターApache-2.0
ガイドを見る →fastembedは、qdrantが公開しているオープンソースのRAG・知識ベースプロジェクトです（GitHub: qdrant/fastembed）。GitHubスターは3,237、フォークは266で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月7日です。
Fast, lightweight Python embeddings from Qdrant — dense, sparse, ColBERT, image and rerankers.
Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.
Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.
Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.
Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.
State-of-the-art dense vectors with automatic model download on first use.
Sparse representations for hybrid lexical/semantic retrieval.
ColBERT-style multi-vector embeddings for high-quality ranking.
Embed images, and use ColPali late-interaction multimodal models for document pages.
Built-in cross-encoder reranking to refine retrieval results.
Designed to run well on CPU, with an optional GPU package, and native Qdrant integration.
Get the source so you can inspect, configure and run it locally.
git clone https://github.com/qdrant/fastembed.git
cd fastembedInstall the standard package, or the GPU variant.
pip install fastembed
# or with GPU support: pip install fastembed-gpuCreate a TextEmbedding model and embed documents — the first call downloads and initialises the model.
from fastembed import TextEmbedding
model = TextEmbedding()
embeddings = list(model.embed(documents)) # see README quickstartIt is maintained by qdrant on GitHub (qdrant/fastembed), where the source, issues and releases live.
Yes — it is free and open source under Apache-2.0, which permits use, modification and commercial redistribution, with licence/notice retention.
No — models run locally after a one-time download, so there is no API key or per-call cost.
Compared with calling a hosted embeddings API such as OpenAI's, fastembed's distinguishing focus is: fast, lightweight Python embeddings from Qdrant — dense, sparse, ColBERT, image and rerankers. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.
Clone https://github.com/qdrant/fastembed (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.
Lightweight, lightning-fast in-process vector database from Alibaba that embeds in your app.
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