RRepoGEO

REPOGEO REPORT · LITE

rom1504/clip-retrieval

Default branch main · commit 06352ae3 · scanned 5/10/2026, 4:26:57 PM

GitHub: 2,760 stars · 239 forks

AI VISIBILITY SCORE
33 /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
2 / 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 README opening to emphasize "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 end-to-end framework for building scalable semantic search systems using CLIP embeddings. It provides a complete toolkit for efficient embedding computation, indexing, and retrieval of multimodal data.
  • mediumtopics#2
    Add vector search and vector database related topics

    Why:

    CURRENT
    ai, clip, deep-learning, knn, multimodal, semantic-search
    COPY-PASTE FIX
    ai, clip, deep-learning, knn, multimodal, semantic-search, vector-search, vector-database
  • mediumreadme#3
    Add a "Why clip-retrieval?" section

    Why:

    COPY-PASTE FIX
    ## Why clip-retrieval?
- **Integrated Workflow:** Go from raw data to a live semantic search system with a single, cohesive toolkit.
- **Scalability Focus:** Built to handle and index billions of multimodal embeddings efficiently.
- **Simplified Deployment:** Reduces complexity by packaging inference, indexing, and serving into easy-to-use components.

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
0%
Of all named tools, what % are you?
Top rival
Faiss
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Faiss · recommended 2×
  2. Milvus · recommended 2×
  3. Weaviate · recommended 2×
  4. Elasticsearch · recommended 2×
  5. Pinecone · recommended 2×
  • CATEGORY QUERY
    How to build a scalable semantic search system for image and text data?
    you: not recommended
    AI recommended (in order):
    1. Faiss
    2. Milvus
    3. Weaviate
    4. Elasticsearch
    5. Pinecone
    6. Hugging Face Transformers
    7. Apache Kafka

    AI recommended 7 alternatives but never named rom1504/clip-retrieval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for efficiently generating and indexing multimodal embeddings for fast retrieval?
    you: not recommended
    AI recommended (in order):
    1. Weaviate
    2. Pinecone
    3. Qdrant
    4. Milvus
    5. Elasticsearch
    6. Elastic Learned Sparse Encoder (ELSER)
    7. Faiss
    8. Vald

    AI recommended 8 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 did not name rom1504/clip-retrieval — 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 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

Drop this badge into the README of rom1504/clip-retrieval. 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/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>
Pro

Subscribe to Pro for deep diagnoses

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