Observability and Evaluation · Public

fastembed

Observability and evaluation project for turning logs, quality metrics, drift, or experiment results into reviewable signals.

ObservabilityEvaluationQuality metricsData driftExperiment tracking

Best fitDevelopers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.

Check whether this project matches your task before installing it.

What it can doObservability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage

Review the portable capability path.

Before continuingVerify in a sandbox

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

GitHub snapshot3.0k stars

200 forks · 29 contributors

Official first step Read manual preview Source repository

Publication status · 2026-05-24

What is fastembed?

fastembed helps developers observe, evaluate, or monitor AI/data application behavior and quality.
Best fit: Developers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.
Not for: Not for users without logs/sample data, privacy boundaries, or those who only need a chat UI.
Capability added to an AI workflow: Observability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage
First safe verification step: Verify collection, metric interpretation, export, and deletion paths with redacted sample data first.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: The main risk is sending sensitive logs, user data, or misleading metrics into production observability.
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 forDevelopers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.

Match the project to your task before installing it.

CapabilityObservability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage

Observability and evaluation project for turning logs, quality metrics, drift, or experiment results into reviewable signals.

Repositoryqdrant/fastembed

3.0k stars · 200 forks

What it can do

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

Introduction to FastEmbed

Related topics: Installation Guide, System Architecture, Quick Start Guide

Sources: [README.md](https://github.com/qdrant/fastembed/blob/main/README.md)

Installation Guide

Related topics: Introduction to FastEmbed, GPU Support and Acceleration

Sources: [README.md:1-100]()

Quick Start Guide

Related topics: Introduction to FastEmbed, Text Embedding Module, Image Embedding Module

Sources: [fastembed/text/onnx_embedding.py](https://github.com/qdrant/fastembed/blob/main/fastembed/text/onnx_embedding.py)

System Architecture

Related topics: Introduction to FastEmbed, ONNX Model Infrastructure

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

Text Embedding Module

Related topics: System Architecture, GPU Support and Acceleration

Sources: [fastembed/text/onnx_embedding.py:1-200]()

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.

Stars3.0k stars

Forks200 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
[Bug]: Segmentation Fault or AssertionError during initialization on Pyt
github / github_issue
02
[Bug]: No timeout on model download — requests.get() can hang indefinite
github / github_issue
03
[Bug]: license error in pypi metadata
github / github_issue
04
[Bug]: Unable to load 'Qdrant/bm25' on macOS python3.14
github / github_issue
05
[Bug]: Loading models with additional files fails with onnxruntime 1.24.
github / github_issue
06
The dependency `py-rust-stemmers` cannot be downloaded in a pure Python
github / github_issue
07
[Bug]: Tar path traversal (Zip Slip) in decompress_to_cache — arbitrary
github / github_issue
08
v0.8.0
github / github_release
09
v0.7.4
github / github_release
10
v0.7.2
github / github_release
11
v0.7.1
github / github_release
12
v0.7.0
github / github_release

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

FastEmbed serves as an embedding generation engine optimized for production use cases, particularly in vector search applications. The library emphasizes:

Open the full manual

fastembed Human Manual
Table of Contents
Introduction to FastEmbed
Related Pages
Overview
Architecture
Core Components
Supported Models

Introduction to FastEmbed

Related topics: Installation Guide, System Architecture, Quick Start Guide

Sources: [README.md](https://github.com/qdrant/fastembed/blob/main/README.md)

Installation Guide

Related topics: Introduction to FastEmbed, GPU Support and Acceleration

Sources: [README.md:1-100]()

Quick Start Guide

Related topics: Introduction to FastEmbed, Text Embedding Module, Image Embedding Module

Sources: [fastembed/text/onnx_embedding.py](https://github.com/qdrant/fastembed/blob/main/fastembed/text/onnx_embedding.py)

System Architecture

Related topics: Introduction to FastEmbed, ONNX Model Infrastructure

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

Text Embedding Module

Related topics: System Architecture, GPU Support and Acceleration

Sources: [fastembed/text/onnx_embedding.py:1-200]()

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 ManifestPROJECT_PACK.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.

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.

high

Review upstream issue

The source signal needs review before production use.

high

Review upstream issue

The source signal needs review before production use.

high

Review upstream issue

Developers may expose sensitive permissions or credentials: [Bug]: Tar path traversal (Zip Slip) in decompress_to_cache — arbitrary file write outside cache directory

medium

Review upstream issue

Developers may fail before the first successful local run: The dependency `py-rust-stemmers` cannot be downloaded in a pure Python environment.

medium

Review upstream issue

Developers may fail before the first successful local run: [Bug]: Segmentation Fault or AssertionError during initialization on Python 3.14.2

medium

Review upstream issue

Developers may fail before the first successful local run: [Bug]: Unable to load 'Qdrant/bm25' on macOS python3.14

medium

Review upstream issue

Upgrade or migration may change expected behavior: v0.5.1

medium

Review upstream issue

The source signal needs review before production use.