dingodb/dingo

• highreadme#1

  Strengthen README's opening paragraph for clearer AI positioning

  原因：

  当前

  # 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.

  复制粘贴的修复

  复制

  **# 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.

  待办完成忽略
• mediumabout#2

  Optimize 'About' description for AI recognition

  原因：

  当前

  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.

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  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.

  待办完成忽略
• mediumreadme#3

  Add a 'Comparison with Alternatives' section to README

  原因：

  复制粘贴的修复

  复制

  ## 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.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How to store structured and unstructured data with vector search using MySQL-compatible SQL?

  你：未被推荐
  AI 推荐顺序：

  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 推荐了 11 个替代方案，却始终没点名 dingodb/dingo。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Need a scalable vector database for real-time hybrid search on diverse data types.

  你：未被推荐
  AI 推荐顺序：

  1. Pinecone
  2. Weaviate
  3. Qdrant
  4. Milvus
  5. Elasticsearch
  6. Vald

  AI 推荐了 6 个替代方案，却始终没点名 dingodb/dingo。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of dingodb/dingo?

  pass
  ⚠AI 未点名 dingodb/dingo —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts dingodb/dingo in production, what risks or prerequisites should they evaluate first?

  pass
  ⚠AI 未点名 dingodb/dingo —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo dingodb/dingo solve, and who is the primary audience?

  pass
  ⚠AI 未点名 dingodb/dingo —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？