RRepoGEO

REPOGEO REPORT · LITE

thunlp/LLMxMapReduce

Default branch main · commit 0e93cc9e · scanned 6/2/2026, 10:13:07 AM

GitHub: 876 stars · 62 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 thunlp/LLMxMapReduce, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A divide-and-conquer framework for Large Language Models (LLMs) to efficiently process and generate long sequences, inspired by MapReduce.
  • mediumhomepage#2
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    https://surveygo.modelbest.cn/

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 thunlp/LLMxMapReduce
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Pinecone · recommended 1×
  4. Weaviate · recommended 1×
  5. Chroma · recommended 1×
  • CATEGORY QUERY
    How to process very long texts efficiently using large language models?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Pinecone
    4. Weaviate
    5. Chroma
    6. OpenAI API (GPT-4, GPT-3.5 Turbo)
    7. Anthropic Claude (e.g., Claude 3 Opus/Sonnet/Haiku)
    8. Google Gemini 1.5 Pro

    AI recommended 8 alternatives but never named thunlp/LLMxMapReduce. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for divide-and-conquer processing of long documents with LLMs.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset/Haystack)
    4. OpenAI Functions/Tools
    5. DSPy
    6. Semantic Kernel

    AI recommended 6 alternatives but never named thunlp/LLMxMapReduce. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 thunlp/LLMxMapReduce?
    pass
    AI named thunlp/LLMxMapReduce explicitly

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

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

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

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thunlp/LLMxMapReduce — 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