RRepoGEO

REPOGEO REPORT · LITE

haltakov/natural-language-image-search

Default branch main · commit 280f4736 · scanned 5/14/2026, 4:41:56 AM

GitHub: 1,038 stars · 105 forks

AI VISIBILITY SCORE
22 /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
1 / 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 haltakov/natural-language-image-search, 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 the README's opening paragraph to clarify project scope

    Why:

    CURRENT
    Search photos on Unsplash using natural language descriptions. The search is powered by OpenAI's CLIP model and the Unsplash Dataset.
    COPY-PASTE FIX
    This repository offers a self-contained, open-source demonstration of natural language image search, specifically designed to search the entire Unsplash Dataset using text descriptions. It leverages OpenAI's CLIP model to power semantic search over nearly 2 million Unsplash photos.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://colab.research.google.com/github/haltakov/natural-language-image-search/blob/main/colab/unsplash-image-search.ipynb
  • mediumtopics#3
    Add 'semantic-search' and 'demo' topics

    Why:

    CURRENT
    clip, computer-vision, image-search, machine-learning, photos, unsplash
    COPY-PASTE FIX
    clip, computer-vision, image-search, machine-learning, photos, unsplash, semantic-search, demo

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 haltakov/natural-language-image-search
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Weaviate
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Weaviate · recommended 2×
  2. Pinecone · recommended 2×
  3. Faiss · recommended 2×
  4. OpenAI CLIP · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How can I implement image search using natural language descriptions for visual content?
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. Hugging Face Transformers
    3. Weaviate
    4. Pinecone
    5. Faiss
    6. Google Cloud Vision AI
    7. Azure Cognitive Services
    8. AWS Rekognition

    AI recommended 8 alternatives but never named haltakov/natural-language-image-search. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools allow searching large photo datasets with text-based AI models?
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. OpenSearch
    3. Pinecone
    4. Weaviate
    5. Milvus
    6. Faiss
    7. Google Cloud Vertex AI Matching Engine

    AI recommended 7 alternatives but never named haltakov/natural-language-image-search. 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 haltakov/natural-language-image-search?
    pass
    AI named haltakov/natural-language-image-search explicitly

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

  • If a team adopts haltakov/natural-language-image-search in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name haltakov/natural-language-image-search — 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 haltakov/natural-language-image-search solve, and who is the primary audience?
    pass
    AI did not name haltakov/natural-language-image-search — 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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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