RRepoGEO

REPOGEO REPORT · LITE

0hq/tinyvector

Default branch main · commit 3d216419 · scanned 6/16/2026, 8:47:58 AM

GitHub: 772 stars · 22 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 0hq/tinyvector, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    vector-database, embeddings, nearest-neighbor, sqlite, pytorch, python, ai, machine-learning
  • highreadme#2
    Reposition README opening to clearly state it's a vector database

    Why:

    CURRENT
    <p align="center"> <b>tinyvector - the tiny, least-dumb, speedy vector embedding database</b>. <br /> No, you don't need a vector database. You need tinyvector. </p>
    COPY-PASTE FIX
    <p align="center"> <b>tinyvector is a tiny, speedy vector embedding database built with SQLite and Pytorch.</b> <br /> It's the simple, efficient vector database you actually need, without the unnecessary complexity. </p>
  • mediumabout#3
    Refine repository description for clearer positioning

    Why:

    CURRENT
    A tiny nearest-neighbor embedding database built with SQLite and Pytorch. (In development!)
    COPY-PASTE FIX
    A tiny, fast nearest-neighbor embedding database built with SQLite and Pytorch, designed for simplicity and efficiency.

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 0hq/tinyvector
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
facebookresearch/faiss
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. facebookresearch/faiss · recommended 2×
  2. spotify/annoy · recommended 2×
  3. chroma-core/chroma · recommended 1×
  4. lancedb/lancedb · recommended 1×
  5. unum-cloud/usearch · recommended 1×
  • CATEGORY QUERY
    What's a lightweight vector database solution for Python projects using SQLite?
    you: not recommended
    AI recommended (in order):
    1. ChromaDB (chroma-core/chroma)
    2. LanceDB (lancedb/lancedb)
    3. Faiss (facebookresearch/faiss)
    4. Annoy (spotify/annoy)
    5. USearch (unum-cloud/usearch)

    AI recommended 5 alternatives but never named 0hq/tinyvector. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an embedded nearest-neighbor search engine for managing AI embeddings efficiently.
    you: not recommended
    AI recommended (in order):
    1. Faiss (facebookresearch/faiss)
    2. Hnswlib (nmslib/hnswlib)
    3. ScaNN (google-research/google-research)
    4. Annoy (spotify/annoy)
    5. NMSLIB (nmslib/nmslib)

    AI recommended 5 alternatives but never named 0hq/tinyvector. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 0hq/tinyvector?
    pass
    AI named 0hq/tinyvector explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts 0hq/tinyvector in production, what risks or prerequisites should they evaluate first?
    pass
    AI named 0hq/tinyvector 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 0hq/tinyvector solve, and who is the primary audience?
    pass
    AI did not name 0hq/tinyvector — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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0hq/tinyvector — 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