RRepoGEO

REPOGEO REPORT · LITE

ChuckHend/pg_vectorize

Default branch main · commit 1a3b5047 · scanned 6/6/2026, 4:52:15 PM

GitHub: 831 stars · 40 forks

AI VISIBILITY SCORE
35 /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
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 ChuckHend/pg_vectorize, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highreadme#2
    Refine README H1 and opening sentence to emphasize Postgres RAG/search automation

    Why:

    CURRENT
    <h1 align="center">
 <b>pg_vectorize: a VectorDB on Postgres</b>
</h1>

A Postgres server and extension that automates the transformation and orchestration of text to embeddings and provides hooks into the most popular LLMs.
    COPY-PASTE FIX
    <h1 align="center">
 <b>pg_vectorize: Automate RAG & Semantic Search on Postgres</b>
</h1>

pg_vectorize is a Postgres server and extension that automates the transformation and orchestration of text to embeddings, providing a complete solution for building RAG and search engines directly on Postgres.
  • mediumtopics#3
    Expand repository topics for better categorization

    Why:

    CURRENT
    ai, rag, vectordb
    COPY-PASTE FIX
    ai, rag, vectordb, postgresql, embeddings, full-text-search, hybrid-search, automation

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 ChuckHend/pg_vectorize
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pgvector
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pgvector · recommended 2×
  2. pg_embedding · recommended 2×
  3. Supabase · recommended 2×
  4. PostgresML · recommended 1×
  5. Neon · recommended 1×
  • CATEGORY QUERY
    How can I integrate semantic search and RAG directly into my PostgreSQL database?
    you: not recommended
    AI recommended (in order):
    1. pgvector
    2. PostgresML
    3. pg_embedding
    4. Supabase
    5. Neon
    6. TimescaleDB
    7. OpenAI API
    8. Anthropic Claude
    9. Hugging Face Inference API
    10. Llama 2
    11. Ollama
    12. vLLM

    AI recommended 12 alternatives but never named ChuckHend/pg_vectorize. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's the best way to automate text embedding and hybrid search within Postgres?
    you: not recommended
    AI recommended (in order):
    1. pg_embedding
    2. pgvector
    3. Lantern
    4. Supabase
    5. Pinecone
    6. Weaviate
    7. Qdrant

    AI recommended 7 alternatives but never named ChuckHend/pg_vectorize. 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 ChuckHend/pg_vectorize?
    pass
    AI named ChuckHend/pg_vectorize explicitly

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

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