RRepoGEO

REPOGEO REPORT · LITE

HKUDS/VideoRAG

Default branch main · commit c412a093 · scanned 5/11/2026, 3:17:49 AM

GitHub: 2,986 stars · 420 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 HKUDS/VideoRAG, 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
    Reposition README opening to prioritize VideoRAG as a framework

    Why:

    CURRENT
    <h1>
      <strong>VideoRAG: Chat with Your Videos</strong> • <strong>Vimo Desktop</strong>
    </h1>
    ... Vimo is a revolutionary desktop application that lets you **chat with your videos** using cutting-edge AI technology. Built on the powerful VideoRAG framework...
    COPY-PASTE FIX
    <h1>
      <strong>VideoRAG: A Framework for Chatting with Videos</strong>
    </h1>
    <p>
      VideoRAG is a powerful framework enabling Retrieval-Augmented Generation (RAG) specifically designed for video content, allowing you to "chat with your videos." It underpins applications like Vimo Desktop, a revolutionary desktop application that leverages VideoRAG to understand and analyze videos of any length, answering questions with remarkable accuracy.
    </p>
  • mediumtopics#2
    Expand topics with video-specific keywords

    Why:

    CURRENT
    large-language-models, llms, long-video-understanding, multi-modal-llms, rag, retrieval-augmented-generation
    COPY-PASTE FIX
    large-language-models, llms, long-video-understanding, multi-modal-llms, rag, retrieval-augmented-generation, video-processing, video-analysis, multimedia-retrieval, video-qa, video-llm
  • lowlicense#3
    Add a license clarification to the README

    Why:

    COPY-PASTE FIX
    Add a section to the README: `## License
This project is licensed under the terms specified in the [LICENSE](LICENSE) file. Please refer to that file for full details.`

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 HKUDS/VideoRAG
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. LlamaIndex · recommended 2×
  3. OpenAI Whisper · recommended 1×
  4. Faiss · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to build an application for querying information from long video archives?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Whisper
    2. Faiss
    3. Pinecone
    4. Weaviate
    5. LangChain
    6. LlamaIndex
    7. OpenAI Embeddings
    8. Cohere
    9. Sentence-BERT
    10. GPT-4
    11. Claude
    12. Llama 2
    13. Deepgram
    14. AssemblyAI
    15. Elasticsearch
    16. Kibana
    17. Google Cloud Video Intelligence API
    18. AWS Rekognition Video
    19. Google Cloud Search
    20. AWS OpenSearch Service
    21. Hugging Face Transformers
    22. Chroma
    23. Qdrant
    24. Veed.io
    25. Happy Scribe
    26. Trint

    AI recommended 26 alternatives but never named HKUDS/VideoRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework can I use for retrieval-augmented generation on extensive video content?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. OpenCV
    5. PyTorch
    6. TensorFlow

    AI recommended 6 alternatives but never named HKUDS/VideoRAG. 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 HKUDS/VideoRAG?
    pass
    AI named HKUDS/VideoRAG explicitly

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

  • If a team adopts HKUDS/VideoRAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HKUDS/VideoRAG 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 HKUDS/VideoRAG solve, and who is the primary audience?
    pass
    AI named HKUDS/VideoRAG 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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MARKDOWN (README)
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HKUDS/VideoRAG — 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