FastEmbed

FastEmbed is a lightweight Python library for generating fast and accurate text and image embeddings.

FastEmbed is a Python library designed for efficient embedding generation. It uses the ONNX Runtime to provide fast performance without downloading heavy PyTorch dependencies or requiring a GPU, making it suitable for serverless environments like AWS Lambda. The default text model is Flag Embedding, and the library also supports sparse text embeddings, late interaction models, image embeddings, multimodal models, and rerankers. It offers data parallelism for encoding large datasets and supports multilingual models. Developers use it to build search and retrieval systems, often integrating it with the Qdrant vector database. Installation is handled via pip, with an optional GPU package available.

FastEmbed at a glance

Best for: Software engineer
Runs on: open source, self-hosted
Pricing: Free
Released: July 2023
Category: AI coding tool
Website: github.com/qdrant/fastembed

Key features

Generates dense text embeddings
Provides sparse text embeddings
Supports late interaction models
Produces image embeddings
Includes rerankers
Utilizes ONNX Runtime for speed
Supports data parallelism for encoding

Use it for

Generate embeddings for retrieval systems
Encode large datasets using data parallelism
Run embedding generation in serverless environments

Alternatives to FastEmbed

Text Embeddings Inference — High-performance serving for open-source embedding models.
MNN — It delivers high-performance inference and training with a lightweight footprint.
AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.
TurboVec — Fits a 10 million document corpus in 4 GB of RAM and searches faster than FAISS.
Floret Torch — Trains word vectors up to twenty times faster than CPU floret.
LMDeploy — Accelerates inference with high-performance engines and quantization tools.
zvec — It is a pure local, serverless vector database providing fast hybrid search.
LanceDB — Provides one table for the whole AI data loop and high-throughput data access.
MTEB — Evaluates embedding models across over 1000 tasks and 1000 languages.
FastMCP — The standard framework for building Model Context Protocol servers and clients.
ModLens — Converts screenshots and charts into structured JSON data for coding agents.
TensorRT — Speeds up inference by 36X compared to CPU-only platforms.

All FastEmbed alternatives