RRepoGEO

REPOGEO REPORT · LITE

re-search/DocProduct

Default branch master · commit 4fee5e69 · scanned 6/5/2026, 7:22:57 PM

GitHub: 571 stars · 155 forks

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 re-search/DocProduct, 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
    Clarify DocProduct's role as a medical Q&A system in the README intro

    Why:

    CURRENT
    We wanted to use TensorFlow 2.0 to explore how well state-of-the-art natural language processing models like BERT and GPT-2 could respond to medical questions by retrieving and conditioning on relevant medical data, and this is the result.
    COPY-PASTE FIX
    DocProduct is a complete medical question-answering system built with TensorFlow 2.0, demonstrating how state-of-the-art NLP models like BERT and GPT-2 can retrieve and synthesize answers from medical data.
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    [Insert URL to a live demo, project page, or relevant documentation here]
  • lowreadme#3
    Add a clear 'Intended Use' section to the README

    Why:

    CURRENT
    The purpose of this project is to explore the capabilities of deep learning language models for scientific encoding and retrieval IT SHOULD NOT TO BE USED FOR ACTIONABLE MEDICAL ADVICE.
    COPY-PASTE FIX
    ## Intended Use
DocProduct is designed as a research prototype and educational tool to demonstrate the potential of deep learning for medical question answering. It is suitable for AI/ML researchers, data scientists, and developers exploring advanced NLP applications in healthcare. IT SHOULD NOT BE USED FOR ACTIONABLE MEDICAL ADVICE.

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 re-search/DocProduct
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
BioBERT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. BioBERT · recommended 2×
  2. ClinicalBERT · recommended 2×
  3. PubMedBERT · recommended 2×
  4. BERT · recommended 1×
  5. GPT-3 · recommended 1×
  • CATEGORY QUERY
    What are effective deep learning approaches for building a healthcare question-answering system?
    you: not recommended
    AI recommended (in order):
    1. BERT
    2. BioBERT
    3. ClinicalBERT
    4. PubMedBERT
    5. GPT-3
    6. GPT-4
    7. LLaMA
    8. Falcon
    9. RoBERTa
    10. ELECTRA
    11. Retrieval-Augmented Generation (RAG)
    12. REALM
    13. Hugging Face Transformers
    14. DPR
    15. BM25
    16. T5
    17. BART
    18. spaCy
    19. Flair
    20. SQuAD
    21. GraphSAGE
    22. GCN
    23. SNOMED CT
    24. UMLS
    25. ICD codes

    AI recommended 25 alternatives but never named re-search/DocProduct. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I leverage large language models for medical information retrieval and synthesis?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. MedLM
    3. Vertex AI Search
    4. Microsoft Azure OpenAI Service
    5. Hugging Face Transformers (huggingface/transformers)
    6. BioBERT
    7. ClinicalBERT
    8. PubMedBERT
    9. Amazon Comprehend Medical
    10. Cohere
    11. Command models
    12. Embed models

    AI recommended 12 alternatives but never named re-search/DocProduct. 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 re-search/DocProduct?
    pass
    AI named re-search/DocProduct explicitly

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

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

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

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