RRepoGEO

REPOGEO REPORT · LITE

NovaSearch-Team/RAG-Retrieval

Default branch master · commit 8f30d05c · scanned 5/20/2026, 6:58:07 AM

GitHub: 1,121 stars · 90 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 NovaSearch-Team/RAG-Retrieval, 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
    Strengthen README's opening statement to highlight unified RAG retrieval model framework

    Why:

    CURRENT
    The RAG-Retrieval offers end-to-end code for training, inference, and distillation of the RAG retrieval model.
    COPY-PASTE FIX
    RAG-Retrieval is a comprehensive, end-to-end framework for unified fine-tuning, inference, and distillation of all major RAG retrieval models, including embedding models, ColBERT, and rerankers.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL to `https://github.com/NovaSearch-Team/RAG-Retrieval` (or a dedicated project website if one exists).
  • lowtopics#3
    Expand repository topics with more specific RAG-related keywords

    Why:

    CURRENT
    ai, llm, nlp, rag, retrieval-augmented-generation
    COPY-PASTE FIX
    ai, llm, nlp, rag, retrieval-augmented-generation, rag-finetuning, model-distillation, colbert, reranker, embedding-models

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 NovaSearch-Team/RAG-Retrieval
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. Sentence-Transformers · recommended 2×
  3. Hugging Face Datasets · recommended 1×
  4. Pyserini · recommended 1×
  5. Anserini · recommended 1×
  • CATEGORY QUERY
    How can I efficiently fine-tune various RAG retrieval models like embeddings and rerankers?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Datasets
    3. Sentence-Transformers
    4. Pyserini
    5. Anserini
    6. OpenMatch
    7. Faiss
    8. Weights & Biases
    9. MLflow
    10. DeepSpeed
    11. Hugging Face Accelerate

    AI recommended 11 alternatives but never named NovaSearch-Team/RAG-Retrieval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a unified library for inference and distillation of different RAG ranking models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Sentence-Transformers
    3. Haystack
    4. PyTorch-Llama-recipes (facebookresearch/llama-recipes)
    5. OpenNMT-py
    6. Keras/TensorFlow

    AI recommended 6 alternatives but never named NovaSearch-Team/RAG-Retrieval. 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 NovaSearch-Team/RAG-Retrieval?
    pass
    AI named NovaSearch-Team/RAG-Retrieval explicitly

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

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