REPOGEO REPORT · LITE

RyanCodrai/turbovec

Default branch main · commit 1e7200cf · scanned 6/27/2026, 5:17:01 AM

GitHub: 12,237 stars · 1,081 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface RyanCodrai/turbovec, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README opening to clearly state core identity and language

Why:

CURRENT

A 10 million document corpus takes 31 GB of RAM as float32. turbovec fits it in 4 GB - and searches it faster than FAISS.** turbovec is a Rust vector index with Python bindings, built on Google Research's **TurboQuant** algorithm — a data-oblivious quantizer with near-optimal distortion and no separate training phase.

COPY-PASTE FIX

turbovec is a high-performance, memory-efficient vector index for similarity search, implemented in Rust with Python bindings. Unlike low-level C++ SIMD libraries, turbovec provides a complete solution for vector search, built on Google Research's **TurboQuant** algorithm. It can fit a 10 million document corpus (31 GB float32) into 4 GB RAM and searches it faster than FAISS.

highabout#2

Refine the 'About' description for clarity and purpose

Why:

CURRENT

A vector index built on TurboQuant, written in Rust with Python bindings

COPY-PASTE FIX

A high-performance, memory-efficient vector index for similarity search, built on TurboQuant, written in Rust with Python bindings for RAG and AI applications.

mediumfaq#3

Add a FAQ section to address common misconceptions

Why:

COPY-PASTE FIX

## FAQ

**Is turbovec a C++ library?**
No, turbovec is implemented in Rust with Python bindings. It is not a low-level C++ library for SIMD intrinsics.

**Is turbovec just a SIMD optimization library?**
No, turbovec is a complete vector index solution for similarity search, leveraging SIMD for performance but providing a full API for indexing and querying.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface RyanCodrai/turbovec

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Faiss

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Faiss · recommended 1×
Annoy · recommended 1×
Hnswlib · recommended 1×
ScaNN · recommended 1×
NMSLIB · recommended 1×

CATEGORY QUERY
How to perform fast, memory-efficient vector similarity search in Python applications?
you: not recommended
AI recommended (in order):
1. Faiss
2. Annoy
3. Hnswlib
4. ScaNN
5. NMSLIB
6. Milvus
AI recommended 6 alternatives but never named RyanCodrai/turbovec. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a local, performant vector database with online indexing and SIMD optimizations.
you: not recommended
AI recommended (in order):
1. Faiss (facebookresearch/faiss)
2. Hnswlib (nmslib/hnswlib)
3. USearch (unum-cloud/usearch)
4. ScaNN (google-research/google-research)
5. Milvus (milvus-io/milvus)
6. Qdrant (qdrant/qdrant)
AI recommended 6 alternatives but never named RyanCodrai/turbovec. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of RyanCodrai/turbovec?
pass
AI named RyanCodrai/turbovec explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts RyanCodrai/turbovec in production, what risks or prerequisites should they evaluate first?
pass
AI named RyanCodrai/turbovec explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo RyanCodrai/turbovec solve, and who is the primary audience?
pass
AI named RyanCodrai/turbovec explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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RyanCodrai/turbovec — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite