Vector Retrieval and RAG · Public

turbovec

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

Vector databaseRAGEmbeddingsSemantic searchData boundaries

Best fitDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Check whether this project matches your task before installing it.

What it can doVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Review the portable capability path.

Before continuingVerify in a sandbox

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

GitHub snapshot12k stars

1.1k forks · 4 contributors

Doramagic.ai Last verification date: 2026-06-23 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-06-23

What is turbovec?

turbovec is a vector database, retrieval, or RAG storage component for AI applications.
Best fit: Developers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.
Not for: Not for one-off model API calls or environments that cannot isolate indexed data, credentials, and persistence paths.
Capability added to an AI workflow: Vector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance
First safe verification step: Verify create, query, delete, and rollback with a small public text sample before using real data.
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/RyanCodrai/turbovec, https://github.com/RyanCodrai/turbovec#readme, Human Manual, Pitfall Log

Quick decision

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

Best forDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Match the project to your task before installing it.

CapabilityVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

RepositoryRyanCodrai/turbovec

12k stars · 1.1k forks

What it can do

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

Architecture & TurboQuant Algorithm

Related topics: Index API, File Formats & Filtered Search, SIMD Search Kernels, Benchmarks & Multi-Language Bindings

Source: https://github.com/RyanCodrai/turbovec / Human Manual

Index API, File Formats & Filtered Search

Related topics: Architecture & TurboQuant Algorithm, Python Bindings & Framework Integrations

Source: https://github.com/RyanCodrai/turbovec / Human Manual

Python Bindings & Framework Integrations

Related topics: Index API, File Formats & Filtered Search, SIMD Search Kernels, Benchmarks & Multi-Language Bindings

Source: https://github.com/RyanCodrai/turbovec / Human Manual

SIMD Search Kernels, Benchmarks & Multi-Language Bindings

Related topics: Architecture & TurboQuant Algorithm, Python Bindings & Framework Integrations

Source: https://github.com/RyanCodrai/turbovec / 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/RyanCodrai/turbovec, 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.

Stars12k stars

Forks1.1k forks

Contributors4 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

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

01
✨ Node.js / TypeScript bindings (turbovec-node)
github / github_issue
02
Public Read/Write I/O API + pub TurboQuantIndex::from_parts
github / github_issue
03
ask about project
github / github_issue
04
TurboVec on Snowpark Container Services (SPCS)
github / github_issue
05
x86_64 scalar fallback (no AVX2 at runtime) returns incorrect results: r
github / github_issue
06
Security fixes: integer overflow on .tv load + Python NaN query validati
github / github_issue
07
bug: agno insert() orphans vectors on intra-batch duplicate derived doc_
github / github_issue
08
TQ+ calibration: silent identity-freeze on small first add; fit from a c
github / github_issue
09
Docs: API reference drift around bit widths and file formats
github / github_issue
10
Proposal: official Node.js binding (turbovec-node)
github / github_issue
11
proposal: bindings for ruby
github / github_issue
12
Add insertion + removal speed benchmarks to benchmarks/suite/
github / 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 turbovec

Official start command · https://github.com/RyanCodrai/turbovec#readme · verified: yes

Human Manual

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

8+ sections · Human Manual

turbovec Manual

turbovec is a Rust vector search library implementing Google Research's TurboQuant algorithm (ICLR 2026). It compresses float32 embeddings to 2–4 bits per dimension with near-optimal disto...

Open the full manual

https://github.com/RyanCodrai/turbovec Project Manual
Table of Contents
Architecture & TurboQuant Algorithm
Related Pages
Overview
High-Level Architecture
The TurboQuant Pipeline
Data Flow: Ingest → Search → Persist

Architecture & TurboQuant Algorithm

Related topics: Index API, File Formats & Filtered Search, SIMD Search Kernels, Benchmarks & Multi-Language Bindings

Source: https://github.com/RyanCodrai/turbovec / Human Manual

Index API, File Formats & Filtered Search

Related topics: Architecture & TurboQuant Algorithm, Python Bindings & Framework Integrations

Source: https://github.com/RyanCodrai/turbovec / Human Manual

Python Bindings & Framework Integrations

Related topics: Index API, File Formats & Filtered Search, SIMD Search Kernels, Benchmarks & Multi-Language Bindings

Source: https://github.com/RyanCodrai/turbovec / Human Manual

SIMD Search Kernels, Benchmarks & Multi-Language Bindings

Related topics: Architecture & TurboQuant Algorithm, Python Bindings & Framework Integrations

Source: https://github.com/RyanCodrai/turbovec / 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.