RRepoGEO

REPOGEO REPORT · LITE

rahulnyk/graph_maker

Default branch main · commit da00dc8d · scanned 6/7/2026, 2:33:10 AM

GitHub: 640 stars · 68 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 rahulnyk/graph_maker, 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
  • highreadme#1
    Reconcile the README's description of the repo's identity

    Why:

    CURRENT
    A Python library that can convert any text into a graph of knowedge given an ontology.

... This project is an example notebook that demonstrates the use of the knowledge graph maker library.

> Note: I have moved the graph maker library to a pip package.
    COPY-PASTE FIX
    This repository *is* the `graph_maker` Python library, designed to convert any text into a knowledge graph given an ontology. It includes example notebooks to demonstrate its usage. The library is also available as a pip package.
  • highabout#2
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    A Python library for converting unstructured text into knowledge graphs, enabling advanced analysis and Graph Retrieval Augmented Generation (GRAG).

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 rahulnyk/graph_maker
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stardog
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Stardog · recommended 2×
  2. explosion/spaCy · recommended 1×
  3. networkx/networkx · recommended 1×
  4. thunlp/OpenNRE · recommended 1×
  5. stanfordnlp/CoreNLP · recommended 1×
  • CATEGORY QUERY
    How can I programmatically extract knowledge graphs from unstructured text documents?
    you: not recommended
    AI recommended (in order):
    1. spaCy (explosion/spaCy)
    2. NetworkX (networkx/networkx)
    3. OpenNRE (thunlp/OpenNRE)
    4. Stanford OpenIE (stanfordnlp/CoreNLP)
    5. Haystack (deepset-ai/haystack)
    6. GraphDB
    7. Stardog
    8. Neo4j (neo4j/neo4j)
    9. APOC Procedures (neo4j-contrib/neo4j-apoc-procedures)

    AI recommended 9 alternatives but never named rahulnyk/graph_maker. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries are available for building knowledge graphs from text for RAG?
    you: not recommended
    AI recommended (in order):
    1. Haystack
    2. LlamaIndex
    3. LangChain
    4. spaCy
    5. NetworkX
    6. Stardog Python Client
    7. Neo4j Python Driver
    8. neo4j library
    9. Stardog
    10. Neo4j
    11. RDFLib

    AI recommended 11 alternatives but never named rahulnyk/graph_maker. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 rahulnyk/graph_maker?
    pass
    AI named rahulnyk/graph_maker explicitly

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

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

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

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rahulnyk/graph_maker — 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