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

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 2×
Cohere Rerank · recommended 1×
bert-base-uncased-mnli · recommended 1×
msmarco-distilbert-base-v4 · recommended 1×
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

Drop this badge into the README of PrithivirajDamodaran/FlashRank. 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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HTML

<a href="https://repogeo.com/en/r/PrithivirajDamodaran/FlashRank"><img src="https://repogeo.com/badge/PrithivirajDamodaran/FlashRank.svg" alt="RepoGEO" /></a>

Pro

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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