RRepoGEO

REPOGEO REPORT · LITE

llm-lab-org/Multimodal-RAG-Survey

Default branch main · commit 656c8113 · scanned 6/10/2026, 8:48:08 AM

GitHub: 520 stars · 27 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
15 /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
0 / 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 llm-lab-org/Multimodal-RAG-Survey, 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 'survey' and 'literature-review' to repository topics.

    Why:

    CURRENT
    multimodal-learning, rag, retrieval-augmented-generation
    COPY-PASTE FIX
    multimodal-learning, rag, retrieval-augmented-generation, survey, literature-review
  • highlicense#2
    Add a LICENSE file and reference it in the README.

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the chosen license (e.g., MIT, Apache-2.0, or a custom license). Then, add a line to the README, for example: "This project is released under the [Your Chosen License Name] license. See the [LICENSE file](LICENSE) for details."
  • mediumreadme#3
    Add a clear disclaimer in the README that this is a survey, not an implementation.

    Why:

    CURRENT
    This repository is designed to collect and categorize papers related to Multimodal Retrieval-Augmented Generation (RAG) according to our survey paper: Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation.
    COPY-PASTE FIX
    This repository is designed to collect and categorize papers related to Multimodal Retrieval-Augmented Generation (RAG) according to our survey paper: Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation. **Please note: This repository is a comprehensive literature survey and resource collection, not an implementation or a deployable system.** Given the rapid growth in this field, we will continuously update both the paper and this repository to serve as a resource for researchers working on future projects.

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 llm-lab-org/Multimodal-RAG-Survey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Retrieval-Augmented Generation for Large Language Models: A Survey
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Retrieval-Augmented Generation for Large Language Models: A Survey · recommended 1×
  2. A Survey of Retrieval-Augmented Generation for LLMs · recommended 1×
  3. A Survey on Multimodal Large Language Models · recommended 1×
  4. Multimodal Foundation Models: A Survey · recommended 1×
  5. Deep Cross-Modal Hashing: A Survey · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive survey on techniques for multimodal retrieval-augmented generation?
    you: not recommended
    AI recommended (in order):
    1. Retrieval-Augmented Generation for Large Language Models: A Survey
    2. A Survey of Retrieval-Augmented Generation for LLMs
    3. A Survey on Multimodal Large Language Models
    4. Multimodal Foundation Models: A Survey
    5. Deep Cross-Modal Hashing: A Survey
    6. A Survey on Cross-Modal Retrieval

    AI recommended 6 alternatives but never named llm-lab-org/Multimodal-RAG-Survey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for integrating diverse data modalities into RAG systems?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate (weaviate/weaviate)
    3. Qdrant (qdrant/qdrant)
    4. Chroma (chroma-core/chroma)
    5. OpenAI CLIP
    6. Google LaMDA/PaLM 2/Gemini
    7. Hugging Face Transformers (huggingface/transformers)
    8. Neo4j (neo4j/neo4j)
    9. Amazon Neptune
    10. Grakn (vaticle/typedb)
    11. LangChain (langchain-ai/langchain)
    12. LlamaIndex (run-llama/llama_index)
    13. Haystack (deepset-ai/haystack)

    AI recommended 13 alternatives but never named llm-lab-org/Multimodal-RAG-Survey. 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 llm-lab-org/Multimodal-RAG-Survey?
    pass
    AI did not name llm-lab-org/Multimodal-RAG-Survey — likely talking about a different project

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

  • If a team adopts llm-lab-org/Multimodal-RAG-Survey in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name llm-lab-org/Multimodal-RAG-Survey — likely talking about a different project

    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 llm-lab-org/Multimodal-RAG-Survey solve, and who is the primary audience?
    pass
    AI did not name llm-lab-org/Multimodal-RAG-Survey — likely talking about a different project

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

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  • Brand-free category queries5 vs 2 in Lite
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