RRepoGEO

REPOGEO REPORT · LITE

apple/ml-clara

Default branch main · commit 857c3cf3 · scanned 5/23/2026, 2:03:17 PM

GitHub: 1,137 stars · 93 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
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 apple/ml-clara, 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
  • highabout#1
    Add a concise 'About' description for the repository

    Why:

    COPY-PASTE FIX
    CLaRa: An end-to-end Retrieval-Augmented Generation (RAG) model designed to bridge retrieval and generation with continuous latent reasoning, addressing long contexts and disjoint optimization.
  • mediumreadme#2
    Clarify the repository's license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under the terms found in the [LICENSE](LICENSE) file. Please review the file for specific details regarding usage and distribution.

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 apple/ml-clara
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Cohere Rerank
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Cohere Rerank · recommended 2×
  2. Sentence-BERT · recommended 1×
  3. all-MiniLM-L6-v2 · recommended 1×
  4. all-mpnet-base-v2 · recommended 1×
  5. FAISS · recommended 1×
  • CATEGORY QUERY
    How can I improve RAG performance with very long contexts and better integrate retrieval?
    you: not recommended
    AI recommended (in order):
    1. Sentence-BERT
    2. all-MiniLM-L6-v2
    3. all-mpnet-base-v2
    4. FAISS
    5. HNSWLib
    6. LangChain
    7. LlamaIndex
    8. Cohere Rerank
    9. BGE-Reranker
    10. HyDE
    11. LLMChainExtractor
    12. Multi-Query Retriever

    AI recommended 12 alternatives but never named apple/ml-clara. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best end-to-end RAG models for complex reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. OpenAI's `text-embedding-ada-002`
    3. Cohere's Embed v3
    4. Claude 3 Opus
    5. Llama 3
    6. `bge-large-en-v1.5`
    7. `E5-large-v2`
    8. Mistral Large
    9. Cohere Command R+
    10. Gemma
    11. Cohere Rerank
    12. `bge-reranker-large`

    AI recommended 12 alternatives but never named apple/ml-clara. 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 apple/ml-clara?
    pass
    AI did not name apple/ml-clara — 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 apple/ml-clara in production, what risks or prerequisites should they evaluate first?
    pass
    AI named apple/ml-clara 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 apple/ml-clara solve, and who is the primary audience?
    pass
    AI named apple/ml-clara explicitly

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

Embed your GEO score

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MARKDOWN (README)
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apple/ml-clara — 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