RRepoGEO

REPOGEO REPORT · LITE

neondatabase/pg_embedding

Default branch main · commit 5d48508a · scanned 6/1/2026, 3:22:38 AM

GitHub: 579 stars · 27 forks

AI VISIBILITY SCORE
85 /100
Healthy
Category recall
2 / 2
Avg rank #2.0 when recommended
Rule findings
1 pass · 1 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 neondatabase/pg_embedding, 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

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

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    postgresql, vector-search, hnsw, approximate-nearest-neighbor, embedding, database-extension, similarity-search
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://neon.tech/docs/extensions/pg-embedding

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
2 / 2
100% of queries surface neondatabase/pg_embedding
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
22%
Of all named tools, what % are you?
Top rival
pgvector
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pgvector · recommended 2×
  2. Lantern · recommended 2×
  3. PostGIS · recommended 1×
  4. TimescaleDB · recommended 1×
  5. PostgresML · recommended 1×
  • CATEGORY QUERY
    How can I implement efficient vector similarity search directly within my PostgreSQL database?
    you: #3
    AI recommended (in order):
    1. pgvector
    2. Lantern
    3. pg_embedding ← you
    4. PostGIS
    5. TimescaleDB
    Show full AI answer
  • CATEGORY QUERY
    What are the best PostgreSQL extensions for fast approximate nearest neighbor search with HNSW?
    you: #1
    AI recommended (in order):
    1. pg_embedding ← you
    2. pgvector
    3. Lantern
    4. PostgresML
    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 neondatabase/pg_embedding?
    pass
    AI named neondatabase/pg_embedding explicitly

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

  • If a team adopts neondatabase/pg_embedding in production, what risks or prerequisites should they evaluate first?
    pass
    AI named neondatabase/pg_embedding 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 neondatabase/pg_embedding solve, and who is the primary audience?
    pass
    AI named neondatabase/pg_embedding explicitly

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

Embed your GEO score

Drop this badge into the README of neondatabase/pg_embedding. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/neondatabase/pg_embedding.svg)](https://repogeo.com/en/r/neondatabase/pg_embedding)
HTML
<a href="https://repogeo.com/en/r/neondatabase/pg_embedding"><img src="https://repogeo.com/badge/neondatabase/pg_embedding.svg" alt="RepoGEO" /></a>
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neondatabase/pg_embedding — 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