RRepoGEO

REPOGEO REPORT · LITE

honeyandme/RAGQnASystem

Default branch main · commit bd32bbaf · scanned 5/9/2026, 11:07:48 AM

GitHub: 1,210 stars · 128 forks

AI VISIBILITY SCORE
23 /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
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 honeyandme/RAGQnASystem, 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 a concise English summary to the README's introduction

    Why:

    COPY-PASTE FIX
    This project is a medical question answering system leveraging Retrieval Augmented Generation (RAG) with a knowledge graph built on Neo4j and DiseaseKG, designed to enhance reliability in medical AI consultations.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    medical-qa, rag, knowledge-graph, neo4j, llm, bert, nlp, healthcare-ai, streamlit
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Choose and add a standard open-source license file (e.g., MIT, Apache-2.0, GPL-3.0) to the repository root.

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 honeyandme/RAGQnASystem
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. run-llama/llama_index · recommended 2×
  3. GPT-4 · recommended 1×
  4. Claude 3 Opus · recommended 1×
  5. Med-PaLM 2 · recommended 1×
  • CATEGORY QUERY
    How to build a reliable medical question answering system using large language models?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Med-PaLM 2
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. Pinecone
    7. Weaviate (weaviate/weaviate)
    8. Chroma (chroma-core/chroma)
    9. PubMed/PubMed Central
    10. ClinicalTrials.gov
    11. UMLS
    12. MeSH
    13. RxNorm
    14. Label Studio (heartexlabs/label-studio)
    15. Prodigy
    16. Ragas (explodinggradients/ragas)
    17. TruLens (truera/trulens)
    18. Guardrails AI (guardrails-ai/guardrails)
    19. Hugging Face Transformers (huggingface/transformers)

    AI recommended 19 alternatives but never named honeyandme/RAGQnASystem. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a RAG framework that leverages knowledge graphs for enhanced medical information retrieval.
    you: not recommended
    AI recommended (in order):
    1. Neo4j (neo4j/neo4j)
    2. LangChain (langchain-ai/langchain)
    3. LlamaIndex (run-llama/llama_index)
    4. TypeDB (vaticle/typedb)
    5. Amazon Neptune
    6. Stardog
    7. Ontotext GraphDB

    AI recommended 7 alternatives but never named honeyandme/RAGQnASystem. 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 honeyandme/RAGQnASystem?
    pass
    AI did not name honeyandme/RAGQnASystem — 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?

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