fastembed

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轻量级 Python 向量化库，无需 API 调用即可在本地快速生成高质量文本嵌入，适合 RAG 和向量检索场景。

Fast, Accurate, Lightweight Python library to make State of the Art Embedding

v0.9.1发布于 2026-10-24 18:32 UTC3,288star +4 / 7 天302fork同步于 2026-11-10 05:40 UTC

向量化文本嵌入 RAG 向量检索本地推理

对比 GitHub 官网 embeddings openai rag retrieval retrieval-augmented-generation

怎么上手

由 README 提炼，执行前请确认

原文

pip install fastembed
# 或 GPU 版本
pip install fastembed-gpu

硬件要求按 README 说明

README 明确说明不要求 GPU，可在 CPU 上运行，适合 AWS Lambda 等 serverless 环境；GPU 需安装 fastembed-gpu 包并指定 CUDAExecutionProvider，README 提到支持 CUDA 12.x，未列出具体显卡型号与显存要求。

纯 CPU 可运行需要 CUDA

下载：源码 zip·GitHub Releases（安装包）本站不镜像安装包

项目说明

由 README 自动翻译提炼

没人工点评？写下你的经验

轻量快速的 Python 嵌入库，用 ONNX Runtime 生成文本、图像和多模态向量，无需 GPU 也能跑

轻量级 Python 向量化库，无需 API 调用即可在本地快速生成高质量文本嵌入，适合 RAG 和向量检索场景。

核心能力

基于 ONNX Runtime，不依赖 PyTorch 的 GB 级依赖
支持稠密、稀疏（SPLADE++）、ColBERT 晚交互等多种文本嵌入
提供图像嵌入与 ColPali 多模态晚交互嵌入
内置 CrossEncoder 重排器，可直接对候选文档打分
支持 add_custom_model 接入自定义或量化 ONNX 模型

容易踩的坑

首次调用 TextEmbedding() 会触发模型下载和初始化，需保证网络可访问模型源；GPU 支持要单独安装 fastembed-gpu 包并显式传入 providers=["CUDAExecutionProvider"]；embed() 返回生成器，需 list() 转换后才能取长度或转 numpy；在 zsh 下安装 qdrant-client[fastembed] 需加引号。

内容由模型根据 README 生成，可能不完整或不准确，以官方文档为准。看原文 README

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README 原文

未翻译

在 GitHub 打开

⚡️ 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. We use the ONNX Runtime, which is faster than PyTorch. We also use data parallelism for encoding large datasets.
Accurate: FastEmbed is better than OpenAI Ada-002. We also support an ever-expanding set of models, including a few multilingual models.

🚀 Installation

To install the FastEmbed library, pip works best. You can install it with or without GPU support:

pip install fastembed

# or with GPU support

pip install fastembed-gpu

📖 Quickstart

from fastembed import TextEmbedding


# Example list of documents
documents: list[str] = [
    "This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.",
    "fastembed is supported by and maintained by Qdrant.",
]

# This will trigger the model download and initialization
embedding_model = TextEmbedding()
print("The model BAAI/bge-small-en-v1.5 is ready to use.")

embeddings_generator = embedding_model.embed(documents)  # reminder this is a generator
embeddings_list = list(embedding_model.embed(documents))
  # you can also convert the generator to a list, and that to a numpy array
len(embeddings_list[0]) # Vector of 384 dimensions

Fastembed supports a variety of models for different tasks and modalities. The list of all the available models can be found here

🎒 Dense text embeddings

from fastembed import TextEmbedding

model = TextEmbedding(model_name="BAAI/bge-small-en-v1.5")
embeddings = list(model.embed(documents))

# [
#   array([-0.1115,  0.0097,  0.0052,  0.0195, ...], dtype=float32),
#   array([-0.1019,  0.0635, -0.0332,  0.0522, ...], dtype=float32)
# ]

Dense text embedding can also be extended with models which are not in the list of supported models.

from fastembed import TextEmbedding
from fastembed.common.model_description import PoolingType, ModelSource

TextEmbedding.add_custom_model(
    model="intfloat/multilingual-e5-small",
    pooling=PoolingType.MEAN,
    normalization=True,
    sources=ModelSource(hf="intfloat/multilingual-e5-small"),  # can be used with an `url` to load files from a private storage
    dim=384,
    model_file="onnx/model.onnx",  # can be used to load an already supported model with another optimization or quantization, e.g. onnx/model_O4.onnx
)
model = TextEmbedding(model_name="intfloat/multilingual-e5-small")
embeddings = list(model.embed(documents))

🔱 Sparse text embeddings

SPLADE++

from fastembed import SparseTextEmbedding

model = SparseTextEmbedding(model_name="prithivida/Splade_PP_en_v1")
embeddings = list(model.embed(documents))

# [
#   SparseEmbedding(indices=[ 17, 123, 919, ... ], values=[0.71, 0.22, 0.39, ...]),
#   SparseEmbedding(indices=[ 38,  12,  91, ... ], values=[0.11, 0.22, 0.39, ...])
# ]

🦥 Late interaction models (aka ColBERT)

from fastembed import LateInteractionTextEmbedding

model = LateInteractionTextEmbedding(model_name="colbert-ir/colbertv2.0")
embeddings = list(model.embed(documents))

# [
#   array([
#       [-0.1115,  0.0097,  0.0052,  0.0195, ...],
#       [-0.1019,  0.0635, -0.0332,  0.0522, ...],
#   ]),
#   array([
#       [-0.9019,  0.0335, -0.0032,  0.0991, ...],
#       [-0.2115,  0.8097,  0.1052,  0.0195, ...],
#   ]),  
# ]

🖼️ Image embeddings

from fastembed import ImageEmbedding

images = [
    "./path/to/image1.jpg",
    "./path/to/image2.jpg",
]

model = ImageEmbedding(model_name="Qdrant/clip-ViT-B-32-vision")
embeddings = list(model.embed(images))

# [
#   array([-0.1115,  0.0097,  0.0052,  0.0195, ...], dtype=float32),
#   array([-0.1019,  0.0635, -0.0332,  0.0522, ...], dtype=float32)
# ]

Late interaction multimodal models (ColPali)

from fastembed import LateInteractionMultimodalEmbedding

doc_images = [
    "./path/to/qdrant_pdf_doc_1_screenshot.jpg",
    "./path/to/colpali_pdf_doc_2_screenshot.jpg",
]

query = "What is Qdrant?"

model = LateInteractionMultimodalEmbedding(model_name="Qdrant/colpali-v1.3-fp16")
doc_images_embeddings = list(model.embed_image(doc_images))
# shape (2, 1030, 128)
# [array([[-0.03354, -0.0209, ..., -0.1558, -0.0768]], dtype=float16)]
query_embedding = model.embed_text(query)
# shape (1, 20, 128)
# [array([[-0.002182, 0.1476, ..., -0.02208, 0.1683]], dtype=float16)]

🔄 Rerankers

from fastembed.rerank.cross_encoder import TextCrossEncoder

query = "Who is maintaining Qdrant?"
documents: list[str] = [
    "This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.",
    "fastembed is supported by and maintained by Qdrant.",
]
encoder = TextCrossEncoder(model_name="Xenova/ms-marco-MiniLM-L-6-v2")
scores = list(encoder.rerank(query, documents))

# [-11.48061752319336, 5.472434997558594]

Text cross encoders can also be extended with models which are not in the list of supported models.

from fastembed.rerank.cross_encoder import TextCrossEncoder 
from fastembed.common.model_description import ModelSource

TextCrossEncoder.add_custom_model(
    model="Xenova/ms-marco-MiniLM-L-4-v2",
    model_file="onnx/model.onnx",
    sources=ModelSource(hf="Xenova/ms-marco-MiniLM-L-4-v2"),
)
model = TextCrossEncoder(model_name="Xenova/ms-marco-MiniLM-L-4-v2")
scores = list(model.rerank_pairs(
    [("What is AI?", "Artificial intelligence is ..."), ("What is ML?", "Machine learning is ..."),]
))

⚡️ FastEmbed on a GPU

FastEmbed supports running on GPU devices. It requires installation of the fastembed-gpu package.

pip install fastembed-gpu

Check our example for detailed instructions, CUDA 12.x support and troubleshooting of the common issues.

from fastembed import TextEmbedding

embedding_model = TextEmbedding(
    model_name="BAAI/bge-small-en-v1.5", 
    providers=["CUDAExecutionProvider"]
)
print("The model BAAI/bge-small-en-v1.5 is ready to use on a GPU.")

Usage with Qdrant

Installation with Qdrant Client in Python:

pip install qdrant-client[fastembed]

pip install qdrant-client[fastembed-gpu]

You might have to use quotes pip install 'qdrant-client[fastembed]' on zsh.

from qdrant_client import QdrantClient, models

# Initialize the client
client = QdrantClient("localhost", port=6333) # For production
# client = QdrantClient(":memory:") # For experimentation

model_name = "sentence-transformers/all-MiniLM-L6-v2"
payload = [
    {"document": "Qdrant has Langchain integrations", "source": "Langchain-docs", },
    {"document": "Qdrant also has Llama Index integrations", "source": "LlamaIndex-docs"},
]
docs = [models.Document(text=data["document"], model=model_name) for data in payload]
ids = [42, 2]

client.create_collection(
    "demo_collection",
    vectors_config=models.VectorParams(
        size=client.get_embedding_size(model_name), distance=models.Distance.COSINE)
)

client.upload_collection(
    collection_name="demo_collection",
    vectors=docs,
    ids=ids,
    payload=payload,
)

search_result = client.query_points(
    collection_name="demo_collection",
    query=models.Document(text="This is a query document", model=model_name)
).points
print(search_result)

fastembed

怎么上手

项目说明

核心能力

容易踩的坑

最近发版

Changelog

Features 🏎️

Fixes 🔧

Other

Changelog

Features 🏎️

Fixes 🔧

Upgrade Notes

Changelog

Features 🏎️

Fixes 🔧

Upgrade Notes

Changelog

Features 🏎️

Fixes 🔧

Deprecations

Changelog

Features 🏎️

Fixes 🕸️

用户点评

README 原文

⚡️ What is FastEmbed?

📈 Why FastEmbed?

🚀 Installation

📖 Quickstart

🎒 Dense text embeddings

🔱 Sparse text embeddings

🦥 Late interaction models (aka ColBERT)

🖼️ Image embeddings

Late interaction multimodal models (ColPali)

🔄 Rerankers

⚡️ FastEmbed on a GPU

Usage with Qdrant

怎么上手

项目说明

核心能力

容易踩的坑

最近发版

Changelog

Features 🏎️

Fixes 🔧

Other

Changelog

Features 🏎️

Fixes 🔧

Upgrade Notes

Changelog

Features 🏎️

Fixes 🔧

Upgrade Notes

Changelog

Features 🏎️

Fixes 🔧

Deprecations

Changelog

Features 🏎️

Fixes 🕸️

用户点评

README 原文

⚡️ What is FastEmbed?

📈 Why FastEmbed?

🚀 Installation

📖 Quickstart

🎒 Dense text embeddings

🔱 Sparse text embeddings

🦥 Late interaction models (aka ColBERT)

🖼️ Image embeddings

Late interaction multimodal models (ColPali)

🔄 Rerankers

⚡️ FastEmbed on a GPU

Usage with Qdrant

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