REPOGEO REPORT · LITE

rom1504/clip-retrieval

Default branch main · commit 06352ae3 · scanned 6/20/2026, 3:32:10 PM

GitHub: 2,773 stars · 239 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

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 rom1504/clip-retrieval, 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 statement to highlight end-to-end system

Why:

CURRENT

Easily compute clip embeddings and build a clip retrieval system with them. 100M text+image embeddings can be processed in 20h using a 3080.

COPY-PASTE FIX

clip-retrieval is an open-source, end-to-end system for building scalable multimodal semantic search with CLIP embeddings. It provides tools for efficient embedding computation, indexing, filtering, and hosting, enabling you to deploy a full text-to-image/video retrieval service.

mediumtopics#2

Add more specific topics to reflect its system nature

Why:

CURRENT

ai, clip, deep-learning, knn, multimodal, semantic-search

COPY-PASTE FIX

ai, clip, deep-learning, knn, multimodal, semantic-search, semantic-retrieval, multimodal-search, image-search, text-to-image, open-source-system, vector-search-engine

lowreadme#3

Add a 'Comparison to Alternatives' section in the README

Why:

COPY-PASTE FIX

Add a new section to the README, perhaps titled 'Comparison to Alternatives' or 'Why clip-retrieval?', explaining how it differs from or integrates with tools like FAISS, Hugging Face Transformers, or commercial vector databases. For example: 'While tools like FAISS provide efficient indexing and Hugging Face Transformers offer models, clip-retrieval integrates these components into a complete, self-hostable system for multimodal semantic search, offering a full stack from data processing to UI.'

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 rom1504/clip-retrieval

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 2×
FAISS · recommended 2×
Weaviate · recommended 2×
Pinecone · recommended 2×
Milvus · recommended 2×

CATEGORY QUERY
How to build an efficient multimodal semantic search system for images and text?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. FAISS
3. PyTorch
4. TensorFlow
5. CLIP
6. OpenAI CLIP
7. Weaviate
8. Elasticsearch
9. Pinecone
10. Milvus
11. Jina AI
AI recommended 11 alternatives but never named rom1504/clip-retrieval. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools can I use to generate CLIP embeddings and create a retrieval index?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. FAISS
3. OpenAI API
4. Pinecone
5. sentence-transformers
6. Annoy
7. Weaviate
8. Milvus
9. Qdrant
AI recommended 9 alternatives but never named rom1504/clip-retrieval. 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 rom1504/clip-retrieval?
pass
AI named rom1504/clip-retrieval explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts rom1504/clip-retrieval in production, what risks or prerequisites should they evaluate first?
pass
AI named rom1504/clip-retrieval 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 rom1504/clip-retrieval solve, and who is the primary audience?
pass
AI named rom1504/clip-retrieval 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)

[![RepoGEO](https://repogeo.com/badge/rom1504/clip-retrieval.svg)](https://repogeo.com/en/r/rom1504/clip-retrieval)

HTML

<a href="https://repogeo.com/en/r/rom1504/clip-retrieval"><img src="https://repogeo.com/badge/rom1504/clip-retrieval.svg" alt="RepoGEO" /></a>

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rom1504/clip-retrieval — 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