RRepoGEO

REPOGEO REPORT · LITE

ImprintLab/Medical-Graph-RAG

Default branch main · commit d8040c74 · scanned 6/8/2026, 11:37:44 PM

GitHub: 792 stars · 139 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 ImprintLab/Medical-Graph-RAG, 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
    Reposition the README's opening to clearly state its purpose and audience

    Why:

    CURRENT
    # Medical-Graph-RAG
We build a Graph RAG System specifically for the medical domain.
    COPY-PASTE FIX
    # Medical-Graph-RAG: An Evidenced-based Graph RAG System for Medical Information Retrieval
Medical-Graph-RAG is a specialized framework for researchers and developers building reliable AI systems in healthcare. It provides an evidence-based Graph RAG solution specifically designed to enhance the accuracy and reliability of medical information retrieval and question-answering.
  • mediumabout#2
    Enhance the repository description to highlight its unique solution

    Why:

    CURRENT
    A Graph RAG System for Evidenced-based Medical Information Retrieval [ACL 2025]
    COPY-PASTE FIX
    A specialized Graph RAG system for accurate, evidence-based medical information retrieval, designed to enhance reliability and reduce hallucinations in medical LLM applications for healthcare AI and research.
  • mediumtopics#3
    Add more specific topics to improve categorization within medical AI

    Why:

    CURRENT
    deep-learning, graph-rag, large-language-model, large-language-models, machine-learning, medical, retrieval-augmented-generation
    COPY-PASTE FIX
    deep-learning, graph-rag, large-language-model, large-language-models, machine-learning, medical, retrieval-augmented-generation, evidence-based-medicine, healthcare-ai

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 ImprintLab/Medical-Graph-RAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Haystack · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. FAISS · recommended 1×
  • CATEGORY QUERY
    How to build an evidence-based medical information retrieval system using RAG?
    you: not recommended
    AI recommended (in order):
    1. Haystack
    2. LlamaIndex
    3. LangChain
    4. Hugging Face Transformers
    5. FAISS
    6. Pinecone
    7. Weaviate
    8. Gensim
    9. Scikit-learn
    10. NLTK
    11. SpaCy
    12. UMLS

    AI recommended 12 alternatives but never named ImprintLab/Medical-Graph-RAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a graph RAG framework for accurate retrieval from complex medical documents.
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. LangChain
    3. LlamaIndex
    4. Amazon Neptune
    5. TypeDB
    6. ArangoDB
    7. Memgraph
    8. TigerGraph

    AI recommended 8 alternatives but never named ImprintLab/Medical-Graph-RAG. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 ImprintLab/Medical-Graph-RAG?
    pass
    AI named ImprintLab/Medical-Graph-RAG explicitly

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

  • If a team adopts ImprintLab/Medical-Graph-RAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ImprintLab/Medical-Graph-RAG 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 ImprintLab/Medical-Graph-RAG solve, and who is the primary audience?
    pass
    AI did not name ImprintLab/Medical-Graph-RAG — likely talking about a different project

    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 ImprintLab/Medical-Graph-RAG. 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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