fastembed
Fast, light, accurate library built for retrieval embedding generation
Decision gist · record as of 2026-08-14
Yes. FastEmbed is actively maintained, has no known vulnerabilities, installs with low friction, and solves a real problem—generating 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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10.0; models are downloaded on first use and cached locally.
- Low friction: pure Python wheel with no compiled dependencies beyond ONNX Runtime.
- Active maintenance (last commit 2026-08-12, 3140 GitHub stars) and supports current Python versions (3.10–3.14).
License · maintenance · safety
Apache License (permissive) — Licensed under Apache License with permissive treatment, allowing commercial and private use with minimal restrictions.
last release 2026-03-23 (144 days) · last repo commit 2026-08-12 · 3,140 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 8,608,559 downloads/mo, #1,603 on PyPI
Alternatives
Verify before relying
pip install fastembed
from fastembed import TextEmbedding
documents = ["Example text 1", "Example text 2"]
model = TextEmbedding()
embeddings = list(model.embed(documents))- Whether model download size and cache location are configurable for constrained environments.
- Performance benchmarks comparing ONNX Runtime speed claims against PyTorch-based alternatives.
- Memory footprint for concurrent embedding operations on large datasets.
What it is and 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
FastEmbed is actively maintained, has no known vulnerabilities, installs with low friction, and solves a real problem—generating 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.
Install
fastembed on PyPI
Before you install
Low friction: pure Python wheel with no compiled dependencies beyond ONNX Runtime. Active maintenance (last commit 2026-08-12, 3140 GitHub stars) and supports current Python versions (3.10–3.14). Requires Python >=3.10.0.
Requires Python >=3.10.0; models are downloaded on first use and cached locally.
License in practice
Licensed under Apache License with permissive treatment, allowing commercial and private use with minimal restrictions.
Quickstart
pip install fastembed
from fastembed import TextEmbedding
documents = ["Example text 1", "Example text 2"]
model = TextEmbedding()
embeddings = list(model.embed(documents))Verify before relying
- Whether model download size and cache location are configurable for constrained environments.
- Performance benchmarks comparing ONNX Runtime speed claims against PyTorch-based alternatives.
- Memory footprint for concurrent embedding operations on large datasets.
Package facts
| License | Apache License permissive |
| Python support | Supports the current Python release >=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packageshuggingface-hublogurummh3numpyonnxruntimepillowpy-rust-stemmersrequeststokenizerstqdm |
| Maintenance | Actively maintained 144 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 8,608,559 / month, #1,603 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: Other/Proprietary LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: fastembed-0.8.0-py3-none-any.whl
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See also model2vec · mteb · voyageai · FlagEmbedding · llama-index-embeddings-ollama · colpali-engine · sentence-transformers · InstructorEmbedding · llama-index-vector-stores-qdrant · pymilvus.model