RRepoGEO

REPOGEO REPORT · LITE

dingodb/dingo

Default branch develop · commit 80145a85 · scanned 6/29/2026, 10:02:00 PM

GitHub: 1,703 stars · 265 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
20 /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
0 / 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 dingodb/dingo, 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 paragraph for clearer AI positioning

    Why:

    CURRENT
    # DingoDB DingoDB is an open-source distributed multi-modal vector database independently designed and developed by DataCanvas, which integrates real-time strong consistency, relational semantics, and vector semantics into a unified platform, DingoDB positioning itself as a distinctive multi-modal database solution.
    COPY-PASTE FIX
    **# DingoDB: The Distributed Multi-Modal Vector Database with Unified MySQL-Compatible SQL**

DingoDB is an open-source distributed multi-modal vector database designed to unify structured and unstructured data management with high-performance vector search, all accessible via a familiar MySQL-compatible SQL interface. It integrates real-time strong consistency, relational semantics, and vector semantics into a single platform, offering a distinctive solution for scalable, low-latency data-driven applications.
  • mediumabout#2
    Optimize 'About' description for AI recognition

    Why:

    CURRENT
    A multi-modal vector database that supports upserts and vector queries using unified SQL (MySQL-Compatible) on structured and unstructured data, while meeting the requirements of high concurrency and ultra-low latency.
    COPY-PASTE FIX
    DingoDB is a distributed multi-modal vector database for unified SQL (MySQL-Compatible) queries on structured and unstructured data, enabling high-concurrency, ultra-low-latency vector search and upserts for AI applications.
  • mediumreadme#3
    Add a 'Comparison with Alternatives' section to README

    Why:

    COPY-PASTE FIX
    ## Why DingoDB? (Comparison with Alternatives)

DingoDB stands out by uniquely combining a distributed multi-modal vector database with full MySQL-compatible SQL support for both structured and unstructured data. While solutions like Milvus, Weaviate, and Qdrant excel in vector search, they typically require separate systems for relational data. Similarly, databases like SingleStoreDB, MyScale, and TiDB offer strong SQL capabilities, but DingoDB provides a deeper, unified integration of vector semantics directly within a familiar SQL environment, simplifying development for hybrid data applications.

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 dingodb/dingo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Milvus
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Milvus · recommended 2×
  2. Weaviate · recommended 2×
  3. SingleStoreDB · recommended 1×
  4. MyScale · recommended 1×
  5. TiDB · recommended 1×
  • CATEGORY QUERY
    How to store structured and unstructured data with vector search using MySQL-compatible SQL?
    you: not recommended
    AI recommended (in order):
    1. SingleStoreDB
    2. MyScale
    3. TiDB
    4. Faiss
    5. Milvus
    6. Vitess
    7. HNSWlib
    8. Weaviate
    9. Amazon Aurora
    10. Amazon Sagemaker
    11. PlanetScale

    AI recommended 11 alternatives but never named dingodb/dingo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a scalable vector database for real-time hybrid search on diverse data types.
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Qdrant
    4. Milvus
    5. Elasticsearch
    6. Vald

    AI recommended 6 alternatives but never named dingodb/dingo. 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 dingodb/dingo?
    pass
    AI did not name dingodb/dingo — 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 dingodb/dingo in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name dingodb/dingo — 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 dingodb/dingo solve, and who is the primary audience?
    pass
    AI did not name dingodb/dingo — 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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dingodb/dingo — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite