RRepoGEO

REPOGEO REPORT · LITE

AnswerDotAI/RAGatouille

Default branch main · commit e75b8a96 · scanned 5/22/2026, 1:46:56 PM

GitHub: 3,924 stars · 270 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
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 AnswerDotAI/RAGatouille, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    rag, retrieval-augmented-generation, colbert, late-interaction, information-retrieval, nlp, machine-learning, deep-learning, python
  • highreadme#2
    Strengthen README's opening to emphasize late-interaction retrieval

    Why:

    CURRENT
    # Welcome to RAGatouille

_Easily use and train state of the art retrieval methods in any RAG pipeline. Designed for modularity and ease-of-use, backed by research._
    COPY-PASTE FIX
    # RAGatouille: State-of-the-Art Late-Interaction Retrieval (ColBERT) for RAG

_Easily use and train advanced late-interaction retrieval methods like ColBERT in any RAG pipeline. Designed for modularity and ease-of-use, RAGatouille offers a powerful alternative to dense embeddings for superior accuracy and generalization._
  • mediumhomepage#3
    Add project homepage URL

    Why:

    COPY-PASTE FIX
    https://ben.clavie.eu/ragatouille/

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 AnswerDotAI/RAGatouille
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
UKP-LAB/sentence-transformers
Recommended in 6 of 2 queries
COMPETITOR LEADERBOARD
  1. UKP-LAB/sentence-transformers · recommended 6×
  2. BM25 · recommended 2×
  3. facebookresearch/contriever · recommended 2×
  4. intfloat/e5 · recommended 2×
  5. Sentence Transformers · recommended 1×
  • CATEGORY QUERY
    How can I enhance retrieval accuracy in my RAG pipeline for better results?
    you: not recommended
    AI recommended (in order):
    1. Sentence Transformers
    2. Hugging Face Transformers library
    3. Pinecone
    4. Weaviate
    5. Milvus
    6. BM25
    7. Elasticsearch
    8. Apache Lucene
    9. ColBERT
    10. Haystack
    11. cross-encoder/ms-marco-MiniLM-L-6-v2

    AI recommended 11 alternatives but never named AnswerDotAI/RAGatouille. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What retrieval models are effective for RAG in specialized domains or multilingual content?
    you: not recommended
    AI recommended (in order):
    1. Sentence-BERT (SBERT) (UKP-LAB/sentence-transformers)
    2. MiniLM-L6-v2 (UKP-LAB/sentence-transformers)
    3. all-MiniLM-L6-v2 (UKP-LAB/sentence-transformers)
    4. MPNet-base-v2 (UKP-LAB/sentence-transformers)
    5. all-mpnet-base-v2 (UKP-LAB/sentence-transformers)
    6. XLM-RoBERTa (XLM-R) (facebookresearch/XLM-RoBERTa)
    7. xlm-r-large-en-ko-ja-zh-cased-l12-h768 (UKP-LAB/sentence-transformers)
    8. BM25
    9. NLTK (nltk/nltk)
    10. SpaCy (explosion/spaCy)
    11. Contriever (facebookresearch/contriever)
    12. mContriever (facebookresearch/contriever)
    13. E5-large-v2 (intfloat/e5)
    14. multilingual-e5-large (intfloat/e5)
    15. Cohere Embed v3
    16. OpenAI's text-embedding-3-large

    AI recommended 16 alternatives but never named AnswerDotAI/RAGatouille. 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 AnswerDotAI/RAGatouille?
    pass
    AI named AnswerDotAI/RAGatouille explicitly

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

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

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

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AnswerDotAI/RAGatouille — 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