RRepoGEO

REPOGEO REPORT · LITE

timescale/pgvectorscale

Default branch main · commit 57c88b7b · scanned 5/22/2026, 8:33:08 AM

GitHub: 3,025 stars · 138 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)

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

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 timescale/pgvectorscale, 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 the repository

    Why:

    COPY-PASTE FIX
    vector-database, postgresql-extension, vector-search, approximate-nearest-neighbor, diskann, pgvector, ai, machine-learning, embedding-search, timescaledb
  • highreadme#2
    Reposition the README's H3 to explicitly state its core offering

    Why:

    CURRENT
    <h3>pgvectorscale builds on pgvector with higher performance embedding search and cost-efficient storage for AI applications. </h3>
    COPY-PASTE FIX
    <h3>pgvectorscale is a high-performance, scalable vector database solution built directly into PostgreSQL, enhancing pgvector for AI applications. </h3>
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://www.timescale.com/pgvectorscale

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 timescale/pgvectorscale
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pgvector/pgvector
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. pgvector/pgvector · recommended 3×
  2. qdrant/qdrant · recommended 2×
  3. weaviate/weaviate · recommended 2×
  4. facebookresearch/faiss · recommended 2×
  5. milvus-io/milvus · recommended 2×
  • CATEGORY QUERY
    How to improve vector search performance and throughput in a SQL database?
    you: not recommended
    AI recommended (in order):
    1. PostgreSQL with pgvector (pgvector/pgvector)
    2. Chroma (chroma-core/chroma)
    3. Qdrant (qdrant/qdrant)
    4. Weaviate (weaviate/weaviate)
    5. Faiss (facebookresearch/faiss)
    6. Milvus/Zilliz (milvus-io/milvus)

    AI recommended 6 alternatives but never named timescale/pgvectorscale. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best options for scalable vector indexing and compression in a database?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate (weaviate/weaviate)
    3. Qdrant (qdrant/qdrant)
    4. Faiss (Facebook AI Similarity Search) (facebookresearch/faiss)
    5. PostgreSQL
    6. pgvector (pgvector/pgvector)
    7. Cassandra
    8. Milvus (milvus-io/milvus)
    9. Vald (vdaas/vald)
    10. PostgreSQL
    11. pgvector (pgvector/pgvector)

    AI recommended 11 alternatives but never named timescale/pgvectorscale. 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 timescale/pgvectorscale?
    pass
    AI named timescale/pgvectorscale explicitly

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

  • If a team adopts timescale/pgvectorscale in production, what risks or prerequisites should they evaluate first?
    pass
    AI named timescale/pgvectorscale 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 timescale/pgvectorscale solve, and who is the primary audience?
    pass
    AI named timescale/pgvectorscale 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 timescale/pgvectorscale. 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)
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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
timescale/pgvectorscale — RepoGEO report