RRepoGEO

REPOGEO REPORT · LITE

PrithivirajDamodaran/FlashRank

Default branch main · commit 92c3a29f · scanned 6/16/2026, 3:11:58 AM

GitHub: 982 stars · 70 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 PrithivirajDamodaran/FlashRank, 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
  • highabout#1
    Update 'About' description to highlight unique value

    Why:

    CURRENT
    Lite & Super-fast re-ranking for your search & retrieval pipelines. Supports SoTA Listwise and Pairwise reranking based on LLMs and cross-encoders and more. Created by Prithivi Da, open for PRs & Collaborations.
    COPY-PASTE FIX
    FlashRank: Ultra-lite & Super-fast Python library for re-ranking search results. Leverage SoTA Listwise/Pairwise rerankers with the world's tiniest models (~4MB), optimized for CPU and minimal dependencies. Boost your RAG and retrieval pipelines efficiently.
  • highhomepage#2
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://pypi.org/project/FlashRank/
  • mediumreadme#3
    Refine README opening paragraph to emphasize unique value

    Why:

    CURRENT
    Re-rank your search results with SoTA Pairwise or Listwise rerankers before feeding into your LLMs
    COPY-PASTE FIX
    FlashRank: Ultra-lite & Super-fast Python library for efficient, CPU-optimized re-ranking of search results. Leverage state-of-the-art Listwise and Pairwise rerankers with the world's tiniest models (~4MB) for your RAG and retrieval pipelines, without heavy dependencies like PyTorch or Transformers.

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 PrithivirajDamodaran/FlashRank
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. Cohere Rerank · recommended 1×
  3. bert-base-uncased-mnli · recommended 1×
  4. msmarco-distilbert-base-v4 · recommended 1×
  5. sentence-transformers library · recommended 1×
  • CATEGORY QUERY
    How can I quickly re-rank search results for better relevance in my RAG pipeline?
    you: not recommended
    AI recommended (in order):
    1. Cohere Rerank
    2. Hugging Face Transformers
    3. bert-base-uncased-mnli
    4. msmarco-distilbert-base-v4
    5. sentence-transformers library
    6. BM25
    7. pyserini
    8. rank_bm25
    9. OpenAI's gpt-3.5-turbo
    10. gpt-4
    11. Sentence-BERT (SBERT)
    12. Elasticsearch's Learning to Rank (LTR) plugin

    AI recommended 12 alternatives but never named PrithivirajDamodaran/FlashRank. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient Python libraries for LLM-based re-ranking of search results?
    you: not recommended
    AI recommended (in order):
    1. Haystack
    2. LangChain
    3. Sentence Transformers
    4. Hugging Face Transformers
    5. RankGPT
    6. Faiss

    AI recommended 6 alternatives but never named PrithivirajDamodaran/FlashRank. 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 PrithivirajDamodaran/FlashRank?
    pass
    AI named PrithivirajDamodaran/FlashRank explicitly

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

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