RRepoGEO

REPOGEO REPORT · LITE

BUAADreamer/EasyRAG

Default branch master · commit a87b6852 · scanned 5/29/2026, 5:23:08 PM

GitHub: 632 stars · 77 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 BUAADreamer/EasyRAG, 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
    Rephrase the 'Overview' section's opening sentence for direct value proposition

    Why:

    CURRENT
    This paper presents EasyRAG, a simple, lightweight, and efficient retrieval-augmented generation framework for automated network operations. Our framework has three advantages.
    COPY-PASTE FIX
    EasyRAG is a simple, lightweight, and highly efficient retrieval-augmented generation (RAG) framework specifically designed for automated network operations. It delivers accurate question answering, simple deployment without fine-tuning, and efficient inference, making it an ideal solution for developers and researchers.
  • mediumcomparison#2
    Add a 'Comparison with Alternatives' section to README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

While frameworks like LangChain and LlamaIndex offer extensive modularity and integrations, EasyRAG prioritizes extreme simplicity, lightweight deployment, and efficient inference specifically for automated network operations. Our framework requires no fine-tuning and minimal VRAM, making it exceptionally easy to deploy and scale for specific use cases where speed and ease of use are paramount.
  • lowabout#3
    Refine repository description for more keyword density

    Why:

    CURRENT
    Easy-to-Use RAG Framework; CCF AIOps International Challenge 2024 Top3 Solution; CCF AIOps 国际挑战赛 2024 季军方案
    COPY-PASTE FIX
    Easy-to-Use, Lightweight, and Efficient RAG Framework for Automated Network Operations. Top3 Solution in CCF AIOps International Challenge 2024; CCF AIOps 国际挑战赛 2024 季军方案.

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 BUAADreamer/EasyRAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. FAISS · recommended 2×
  3. LlamaIndex · recommended 2×
  4. Haystack · recommended 2×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    Seeking an efficient retrieval-augmented generation framework for automated network operations.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pinecone
    3. Weaviate
    4. Chroma
    5. FAISS
    6. LlamaIndex
    7. Haystack
    8. BM25
    9. DPR
    10. Sentence Transformers
    11. RAGatouille
    12. OpenAI Assistants API
    13. PyTorch
    14. TensorFlow
    15. Hugging Face Transformers
    16. ScaNN
    17. PostgreSQL
    18. pgvector
    19. Elasticsearch

    AI recommended 19 alternatives but never named BUAADreamer/EasyRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are simple, lightweight RAG frameworks for accurate question answering?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. Haystack
    3. LangChain
    4. Ragas
    5. Sentence-Transformers
    6. FAISS
    7. Annoy

    AI recommended 7 alternatives but never named BUAADreamer/EasyRAG. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 BUAADreamer/EasyRAG?
    pass
    AI named BUAADreamer/EasyRAG explicitly

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

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