RRepoGEO

REPOGEO REPORT · LITE

epsilla-cloud/vectordb

Default branch main · commit df5a5f5a · scanned 6/8/2026, 12:36:44 AM

GitHub: 874 stars · 45 forks

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 epsilla-cloud/vectordb, 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
    Strengthen README's opening statement for AI recall

    Why:

    CURRENT
    **A 10x faster, cheaper, and better vector database**

Epsilla is an open-source vector database. Our focus is on ensuring scalability, high performance, and cost-effectiveness of vector search. EpsillaDB bridges the gap between information retrieval and memory retention in Large Language Models.
    COPY-PASTE FIX
    Epsilla is a high-performance, scalable, and cost-effective open-source vector database designed for efficient storage, indexing, and querying of high-dimensional vector embeddings, especially for AI applications and Large Language Models.
  • mediumreadme#2
    Add a dedicated 'Key Features' or 'Why Epsilla?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why Epsilla?
*   **High Performance:** Engineered for speed and efficiency in vector search.
*   **Scalable & Cost-Effective:** Designed to handle large datasets with minimal resource consumption.
*   **Lightweight & Easy to Deploy:** Simple to get started with Docker and integrate into your AI applications.
*   **LLM Integration:** Bridges information retrieval and memory retention for Large Language Models.
  • lowabout#3
    Refine the repository description to include key differentiators

    Why:

    CURRENT
    Epsilla is a high performance Vector Database Management System
    COPY-PASTE FIX
    Epsilla is a high-performance, scalable, and cost-effective Vector Database Management System for AI applications and LLMs.

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 epsilla-cloud/vectordb
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. Weaviate · recommended 2×
  3. Qdrant · recommended 2×
  4. Milvus · recommended 2×
  5. Faiss · recommended 2×
  • CATEGORY QUERY
    What's a performant vector database for integrating with large language models?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Qdrant
    4. Milvus
    5. Chroma
    6. Faiss
    7. LanceDB

    AI recommended 7 alternatives but never named epsilla-cloud/vectordb. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a scalable and cost-effective vector database for AI application embeddings.
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Qdrant
    4. Milvus
    5. Vald
    6. Faiss

    AI recommended 6 alternatives but never named epsilla-cloud/vectordb. 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 epsilla-cloud/vectordb?
    pass
    AI named epsilla-cloud/vectordb explicitly

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

  • If a team adopts epsilla-cloud/vectordb in production, what risks or prerequisites should they evaluate first?
    pass
    AI named epsilla-cloud/vectordb 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 epsilla-cloud/vectordb solve, and who is the primary audience?
    pass
    AI named epsilla-cloud/vectordb 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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epsilla-cloud/vectordb — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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