RRepoGEO

REPOGEO REPORT · LITE

jzbjyb/FLARE

Default branch main · commit ec4b06b5 · scanned 6/12/2026, 5:08:27 AM

GitHub: 669 stars · 62 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 jzbjyb/FLARE, 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 RAG-related topics to the repository

    Why:

    COPY-PASTE FIX
    retrieval-augmented-generation, rag, llm, nlp, information-retrieval, generative-ai
  • highreadme#2
    Reposition the core definition of FLARE to the README's first sentence

    Why:

    CURRENT
    This repository contains the code and data for the paper Active Retrieval Augmented Generation.
    COPY-PASTE FIX
    FLARE is a novel Forward-Looking Active REtrieval-augmented generation (RAG) method that actively decides when and what to retrieve. This repository contains the code and data for the paper Active Retrieval Augmented Generation.
  • mediumhomepage#3
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Add the official project or paper URL (e.g., `https://your-project-homepage.com`)

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 jzbjyb/FLARE
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. gpt-3.5-turbo · recommended 1×
  3. LlamaIndex · recommended 1×
  4. Haystack · recommended 1×
  5. spaCy · recommended 1×
  • CATEGORY QUERY
    How can I make my RAG system actively decide when to retrieve new documents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. gpt-3.5-turbo
    3. LlamaIndex
    4. Haystack
    5. spaCy
    6. NLTK
    7. OpenAI API
    8. gpt-4-turbo
    9. Anthropic API
    10. claude-3-opus
    11. claude-3-sonnet
    12. Sentence Transformers
    13. all-MiniLM-L6-v2
    14. paraphrase-MiniLM-L3-v2
    15. OpenAI Embeddings
    16. text-embedding-3-small
    17. Faiss
    18. Annoy
    19. Hnswlib

    AI recommended 19 alternatives but never named jzbjyb/FLARE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a method to improve RAG query generation by predicting upcoming text content.
    you: not recommended
    AI recommended (in order):
    1. GPT-3.5 Turbo / GPT-4
    2. Llama 2
    3. Mistral 7B / Mixtral 8x7B
    4. T5
    5. BART
    6. LangChain (langchain-ai/langchain)
    7. LlamaIndex (run-llama/llama_index)

    AI recommended 7 alternatives but never named jzbjyb/FLARE. 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 jzbjyb/FLARE?
    pass
    AI named jzbjyb/FLARE explicitly

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

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

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

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jzbjyb/FLARE — 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