RRepoGEO

REPOGEO REPORT · LITE

OpenBMB/VisRAG

Default branch master · commit f35d232d · scanned 6/11/2026, 11:23:10 AM

GitHub: 963 stars · 76 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 OpenBMB/VisRAG, 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
  • highabout#1
    Update repository description to highlight multi-image reasoning

    Why:

    CURRENT
    Parsing-free RAG supported by VLMs
    COPY-PASTE FIX
    Evidence-guided Vision Retrieval-augmented Generation (RAG) framework for multi-image reasoning with VLMs.
  • mediumtopics#2
    Add more specific topics for multi-image reasoning and evidence-guided RAG

    Why:

    CURRENT
    document-retrieval, document-understanding, multi-modal, multi-modality, rag, retrieval, retrieval-augmented-generation, vision-language-model
    COPY-PASTE FIX
    document-retrieval, document-understanding, multi-modal, multi-modality, rag, retrieval, retrieval-augmented-generation, vision-language-model, multi-image-reasoning, evidence-guided-rag, vlm-framework
  • lowhomepage#3
    Add the project homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/OpenBMB/VisRAG

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 OpenBMB/VisRAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 2×
  2. tensorflow/tensorflow · recommended 2×
  3. LlamaIndex · recommended 1×
  4. LangChain · recommended 1×
  5. Chroma · recommended 1×
  • CATEGORY QUERY
    How can I implement retrieval augmented generation for documents containing both text and images?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Chroma
    4. Weaviate
    5. Pinecone
    6. CLIP
    7. BLIP
    8. OpenAI's `text-embedding-3-small`
    9. `text2vec-openai`
    10. `img2vec-clip`
    11. `text-embedding-ada-002`
    12. OpenAI
    13. `all-MiniLM-L6-v2`
    14. Sentence Transformers
    15. FAISS
    16. GPT-4V (GPT-4 with Vision)
    17. Google Gemini Pro Vision

    AI recommended 17 alternatives but never named OpenBMB/VisRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for vision language models to perform evidence-guided multi-image reasoning.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. CLIP (openai/CLIP)
    3. BLIP (salesforce/BLIP)
    4. LLaVA (haotian-liu/LLaVA)
    5. InstructBLIP (salesforce/LAVIS)
    6. PyTorch (pytorch/pytorch)
    7. TensorFlow (tensorflow/tensorflow)
    8. LangChain (langchain-ai/langchain)
    9. OpenAI's GPT-4V
    10. Google's Gemini Pro Vision
    11. LlamaIndex (run-llama/llama_index)
    12. Microsoft's Florence-2 (microsoft/Florence)
    13. PyTorch (pytorch/pytorch)
    14. TensorFlow (tensorflow/tensorflow)
    15. ViT (google-research/vision_transformer)
    16. Swin Transformer (microsoft/Swin-Transformer)
    17. T5 (google-research/text-to-text-transfer-transformer)
    18. GPT-2 (openai/gpt-2)

    AI recommended 18 alternatives but never named OpenBMB/VisRAG. 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 OpenBMB/VisRAG?
    pass
    AI named OpenBMB/VisRAG explicitly

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

  • If a team adopts OpenBMB/VisRAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenBMB/VisRAG 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 OpenBMB/VisRAG solve, and who is the primary audience?
    pass
    AI named OpenBMB/VisRAG 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 OpenBMB/VisRAG. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/OpenBMB/VisRAG.svg)](https://repogeo.com/en/r/OpenBMB/VisRAG)
HTML
<a href="https://repogeo.com/en/r/OpenBMB/VisRAG"><img src="https://repogeo.com/badge/OpenBMB/VisRAG.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

OpenBMB/VisRAG — 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