Tool Connections & Integrations · Public

fastembed

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

Best fitUsers who want source-backed project understanding before installing it.

Check whether this project matches your task before installing it.

What it can doPortable AI capability asset

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot3.1k stars

210 forks · 29 contributors

Doramagic.ai Last verification date: 2026-07-08 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-07-08

What is fastembed?

Fast, Accurate, Lightweight Python library to make State of the Art Embedding
Best fit: Users who want source-backed project understanding before installing it.
Not for: Not for users who want to skip sandbox verification or cannot accept configuration, permission, or maintenance overhead.
Capability added to an AI workflow: Portable AI capability asset
First safe verification step: Verify the smallest path in an isolated environment and keep a rollback path.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: May increase setup, validation, or first-run risk for the user.
Evidence base: https://github.com/qdrant/fastembed, https://github.com/qdrant/fastembed#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forUsers who want source-backed project understanding before installing it.

Match the project to your task before installing it.

CapabilityPortable AI capability asset

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

Repositoryqdrant/fastembed

3.1k stars · 210 forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Overview & Dense Text Embeddings

Related topics: Sparse Embeddings (SPLADE, BM25, MiniCOIL, BM42), Late Interaction, Multimodal & Image Embeddings, Custom Models, GPU Support, Rerankers & Known Issues

Sources: [fastembed/text/onnx_embedding.py](fastembed/text/onnx_embedding.py), [fastembed/text/pooled_embedding.py](fastembed/text/pooled_embedding.py), [fastembed/text/pooled_normalized_embedding.py](fastembed/text/pooled_normalized_embedding.py), [fastembed/late_interaction/colbert.py](fastembed/late_interaction/colbert.py).

Sparse Embeddings (SPLADE, BM25, MiniCOIL, BM42)

Related topics: Overview & Dense Text Embeddings, Late Interaction, Multimodal & Image Embeddings

Source: https://github.com/qdrant/fastembed / Human Manual

Late Interaction, Multimodal & Image Embeddings

Related topics: Overview & Dense Text Embeddings, Sparse Embeddings (SPLADE, BM25, MiniCOIL, BM42), Custom Models, GPU Support, Rerankers & Known Issues

Source: https://github.com/qdrant/fastembed / Human Manual

Custom Models, GPU Support, Rerankers & Known Issues

Related topics: Overview & Dense Text Embeddings, Sparse Embeddings (SPLADE, BM25, MiniCOIL, BM42), Late Interaction, Multimodal & Image Embeddings

Source: https://github.com/qdrant/fastembed / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

Sources: https://github.com/qdrant/fastembed, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars3.1k stars

Forks210 forks

Contributors29 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using fastembed with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
Support inference-free SPLADE models (asymmetric doc-side encoding, toke
github / github_issue
02
[Model]: LateOn
github / github_issue
03
[Bug]: Segmentation Fault or AssertionError during initialization on Pyt
github / github_issue
04
[Bug]: Unable to load 'Qdrant/bm25' on macOS python3.14
github / github_issue
05
[Feature]: Add python3.14 support
github / github_issue
06
v0.8.0
github / github_release
07
v0.7.4
github / github_release
08
v0.7.2
github / github_release
09
v0.7.1
github / github_release
10
v0.7.0
github / github_release
11
v0.6.1
github / github_release
12
Capability evidence risk requires verification
GitHub / issue

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install fastembed

Official start command · https://github.com/qdrant/fastembed#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

fastembed Manual

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

Open the full manual

https://github.com/qdrant/fastembed Project Manual
Table of Contents
Overview & Dense Text Embeddings
Related Pages
What is FastEmbed?
High-Level Architecture
Dense Text Embedding Variants
OnnxTextEmbedding (Flag / BGE family)

Overview & Dense Text Embeddings

Related topics: Sparse Embeddings (SPLADE, BM25, MiniCOIL, BM42), Late Interaction, Multimodal & Image Embeddings, Custom Models, GPU Support, Rerankers & Known Issues

Sources: [fastembed/text/onnx_embedding.py](fastembed/text/onnx_embedding.py), [fastembed/text/pooled_embedding.py](fastembed/text/pooled_embedding.py), [fastembed/text/pooled_normalized_embedding.py](fastembed/text/pooled_normalized_embedding.py), [fastembed/late_interaction/colbert.py](fastembed/late_interaction/colbert.py).

Sparse Embeddings (SPLADE, BM25, MiniCOIL, BM42)

Related topics: Overview & Dense Text Embeddings, Late Interaction, Multimodal & Image Embeddings

Source: https://github.com/qdrant/fastembed / Human Manual

Late Interaction, Multimodal & Image Embeddings

Related topics: Overview & Dense Text Embeddings, Sparse Embeddings (SPLADE, BM25, MiniCOIL, BM42), Custom Models, GPU Support, Rerankers & Known Issues

Source: https://github.com/qdrant/fastembed / Human Manual

Custom Models, GPU Support, Rerankers & Known Issues

Related topics: Overview & Dense Text Embeddings, Sparse Embeddings (SPLADE, BM25, MiniCOIL, BM42), Late Interaction, Multimodal & Image Embeddings

Source: https://github.com/qdrant/fastembed / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start EvidenceREPO_INSPECTION.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.