RRepoGEO

REPOGEO REPORT · LITE

timescale/pgai

Default branch main · commit 47d74aff · scanned 6/23/2026, 2:01:32 PM

GitHub: 5,801 stars · 312 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/pgai, 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
    Add prominent deprecation notice to README

    Why:

    CURRENT
    The current H1 "As of February 2026, this project is no longer being maintained or supported." appears after a large banner and some links.
    COPY-PASTE FIX
    Add the following as the absolute first line in the README: `**NOTICE: This project will no longer be maintained or supported as of February 2026. Please plan accordingly.**`
  • mediumhomepage#2
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    [URL to an official Timescale page detailing pgai's status, alternatives, or archive information]
  • lowreadme#3
    Clarify differentiation from pgvector in README

    Why:

    CURRENT
    A Python library that transforms PostgreSQL into a robust, production-ready retrieval engine for RAG and Agentic applications.
    COPY-PASTE FIX
    A Python library that transforms PostgreSQL into a robust, production-ready retrieval engine for RAG and Agentic applications. Built on `pgvector`, `pgai` extends PostgreSQL with advanced capabilities like HNSW indexing and automated embedding synchronization.

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/pgai
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PostgreSQL
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PostgreSQL · recommended 1×
  2. pgvector · recommended 1×
  3. OpenAI · recommended 1×
  4. Cohere · recommended 1×
  5. Sentence Transformers · recommended 1×
  • CATEGORY QUERY
    How can I use PostgreSQL as a robust retrieval engine for RAG applications?
    you: not recommended
    AI recommended (in order):
    1. PostgreSQL
    2. pgvector
    3. OpenAI
    4. Cohere
    5. Sentence Transformers

    AI recommended 5 alternatives but never named timescale/pgai. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help manage vector embeddings and enable natural language to SQL on PostgreSQL?
    you: not recommended
    AI recommended (in order):
    1. pgvector (pgvector/pgvector)
    2. Supabase (supabase/supabase)
    3. PostgresML (PostgresML/postgresml)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. pg_embedding (neondatabase/pg_embedding)
    7. OpenAI's GPT-4
    8. Anthropic's Claude

    AI recommended 8 alternatives but never named timescale/pgai. 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/pgai?
    pass
    AI named timescale/pgai 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/pgai in production, what risks or prerequisites should they evaluate first?
    pass
    AI named timescale/pgai 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/pgai solve, and who is the primary audience?
    pass
    AI named timescale/pgai explicitly

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

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timescale/pgai — 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