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.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/qdrant/fastembed |
| Publication time | Tue, 28 Apr 2026 19:50:12 +0000 |
| Retrieval time | 2026-04-28T20:14:39.778Z |
| Last seen | 2026-04-28T20:14:39.778Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | _A3i_3zcUesu |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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.