RRepoGEO

REPOGEO REPORT · LITE

adithya-s-k/VARAG

Default branch main · commit 078010c7 · scanned 6/17/2026, 2:53:40 PM

GitHub: 497 stars · 48 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 adithya-s-k/VARAG, 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 and clarify the README's core purpose and domain

    Why:

    CURRENT
    VARAG (Vision-Augmented Retrieval and Generation) is a vision-first RAG engine that emphasizes vision-based retrieval techniques. It enhances traditional Retrieval-Augmented Generation (RAG) systems by integrating both visual and textual data through Vision-Language models.
    COPY-PASTE FIX
    VARAG is a **Vision-Augmented Retrieval and Generation (RAG) engine** designed for building advanced multimodal AI applications. It enhances traditional RAG systems by integrating both visual and textual data through Vision-Language models, enabling grounded responses from diverse document types including scanned PDFs and images.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that best suits the project's intent.
  • mediumtopics#3
    Expand and refine repository topics for better categorization

    Why:

    CURRENT
    colpali, multimodal-retrieval, rag
    COPY-PASTE FIX
    multimodal-rag, vision-language-models, ocr, document-ai, information-retrieval, generative-ai, large-language-models, computer-vision, multimodal-retrieval, rag

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 adithya-s-k/VARAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
github.com/run-llama/llama_index
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. github.com/run-llama/llama_index · recommended 1×
  2. OpenAI CLIP · recommended 1×
  3. GPT-4o · recommended 1×
  4. GPT-4V · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to implement a retrieval-augmented generation system using both visual and textual data?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (github.com/run-llama/llama_index)
    2. OpenAI CLIP
    3. GPT-4o
    4. GPT-4V
    5. Pinecone
    6. Weaviate (github.com/weaviate/weaviate)
    7. Qdrant (github.com/qdrant/qdrant)
    8. LangChain (github.com/langchain-ai/langchain)
    9. FAISS (github.com/facebookresearch/faiss)
    10. ChromaDB (github.com/chroma-core/chroma)
    11. Hugging Face Transformers (github.com/huggingface/transformers)
    12. BLIP-2
    13. PyTorch (github.com/pytorch/pytorch)
    14. TensorFlow (github.com/tensorflow/tensorflow)
    15. Pillow (github.com/python-pillow/Pillow)
    16. NLTK (github.com/nltk/nltk)
    17. SpaCy (github.com/explosion/spaCy)
    18. sentence-transformers (github.com/UKPLab/sentence-transformers)
    19. OpenAI text-embedding-ada-002
    20. OpenAI API
    21. gpt-4-vision-preview
    22. Haystack (github.com/deepset-ai/haystack)
    23. Elasticsearch (github.com/elastic/elasticsearch)
    24. LLaVA (github.com/haotian-liu/LLaVA)

    AI recommended 24 alternatives but never named adithya-s-k/VARAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for multimodal RAG, including OCR for scanned documents?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. GPT Index
    3. Tesseract
    4. Google Cloud Vision AI
    5. Azure AI Vision
    6. LangChain
    7. Google Cloud Document AI
    8. Azure Cognitive Services for Vision
    9. Amazon Textract
    10. Amazon Kendra
    11. OpenSearch
    12. Unstructured.io
    13. pytesseract

    AI recommended 13 alternatives but never named adithya-s-k/VARAG. 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 adithya-s-k/VARAG?
    pass
    AI named adithya-s-k/VARAG explicitly

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

  • If a team adopts adithya-s-k/VARAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named adithya-s-k/VARAG 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 adithya-s-k/VARAG solve, and who is the primary audience?
    pass
    AI named adithya-s-k/VARAG 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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adithya-s-k/VARAG — RepoGEO report