RRepoGEO

REPOGEO REPORT · LITE

dataease/SQLBot

Default branch main · commit 582893ab · scanned 6/27/2026, 12:56:49 AM

GitHub: 6,318 stars · 765 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 dataease/SQLBot, 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
    Emphasize SQLBot as a complete conversational data analysis system in the README's opening

    Why:

    CURRENT
    SQLBot 是一款基于大语言模型和 RAG 的智能问数系统，由 DataEase 开源项目组匠心出品。借助 SQLBot，用户可以实现对话式数据分析（ChatBI），快速提炼获取所需的数据信息及可视化图表，并且支持进一步开展智能分析。
    COPY-PASTE FIX
    SQLBot is an out-of-the-box, intelligent conversational data analysis system (ChatBI) powered by large language models and RAG. Developed by the DataEase open-source team, SQLBot enables users to quickly extract data insights and visualizations through natural language queries, supporting advanced intelligent analysis.
  • mediumtopics#2
    Expand topics to include application-level keywords for conversational data analysis

    Why:

    CURRENT
    chatbi, deepseek, llm, nl2sql, rag, sqlbot, text-to-sql, text2sql
    COPY-PASTE FIX
    chatbi, deepseek, llm, nl2sql, rag, sqlbot, text-to-sql, text2sql, conversational-ai, data-analysis, business-intelligence, ai-assistant, data-querying, natural-language-interface
  • lowreadme#3
    Add a clear statement about the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under [Specify License Name(s) here, e.g., 'a custom license' or 'Apache-2.0 and MIT']. Please refer to the LICENSE file for full details.

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 dataease/SQLBot
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. OpenAI API · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. Microsoft Text-to-SQL · recommended 1×
  • CATEGORY QUERY
    How can I build a system for users to query databases using natural language?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. LangChain
    3. LlamaIndex
    4. Hugging Face Transformers
    5. Microsoft Text-to-SQL
    6. Rasa NLU
    7. Stanford CoreNLP
    8. spaCy

    AI recommended 8 alternatives but never named dataease/SQLBot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source tools provide conversational data analysis with LLMs and RAG?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack (deepset/Haystack)
    4. Rasa
    5. OpenAI Evals

    AI recommended 5 alternatives but never named dataease/SQLBot. 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 dataease/SQLBot?
    pass
    AI named dataease/SQLBot explicitly

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

  • If a team adopts dataease/SQLBot in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dataease/SQLBot 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 dataease/SQLBot solve, and who is the primary audience?
    pass
    AI did not name dataease/SQLBot — 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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dataease/SQLBot — 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