RRepoGEO

REPOGEO REPORT · LITE

HKUSTDial/NL2SQL_Handbook

Default branch main · commit 317f6c9c · scanned 5/20/2026, 4:53:30 PM

GitHub: 1,456 stars · 92 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
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 HKUSTDial/NL2SQL_Handbook, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository. For content-heavy projects like a handbook, consider a Creative Commons license such as `CC-BY-4.0`.
  • highreadme#2
    Reposition the README's opening to emphasize its role as a living handbook/survey

    Why:

    CURRENT
    This is the official repository for **[TKDE'25] A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?** and **[VLDB'24] The Dawn of Natural Language to SQL: Are We Fully Ready?**. From this repository, you can explore the [latest advancements](#-text-to-sql-survey--tutorial) in Text-to-SQL research (a.k.a NL2SQL). We provide a comprehensive survey, in-depth research papers, and benchmark evaluations.
    COPY-PASTE FIX
    This is the official, continuously updated **Text-to-SQL Handbook**, serving as a living companion to our **[TKDE'25] A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?** and **[VLDB'24] The Dawn of Natural Language to SQL: Are We Fully Ready?** papers. It provides a comprehensive, up-to-date overview of Text-to-SQL research, practical guidance, and benchmark evaluations for researchers and practitioners.
  • mediumtopics#3
    Add 'survey' and 'handbook' to the repository topics

    Why:

    CURRENT
    ai4db, awesome, awesome-agents, awesome-nl2sql, awesome-text-to-sql, awesome-text2sql, db, llms, nl-to-code, nl-to-sql, nl2sql, nlp, text-to-code, text-to-sql, text2sql
    COPY-PASTE FIX
    ai4db, awesome, awesome-agents, awesome-nl2sql, awesome-text-to-sql, awesome-text2sql, db, llms, nl-to-code, nl-to-sql, nl2sql, nlp, text-to-code, text-to-sql, text2sql, survey, handbook

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 HKUSTDial/NL2SQL_Handbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 2×
  2. Spider · recommended 2×
  3. WikiSQL · recommended 2×
  4. T5 · recommended 2×
  5. BART · recommended 2×
  • CATEGORY QUERY
    Where can I find a comprehensive overview of current text-to-SQL techniques with LLMs?
    you: not recommended
    AI recommended (in order):
    1. Awesome Text-to-SQL GitHub Repository
    2. arXiv
    3. Hugging Face Blog Posts and Tutorials
    4. GPT-3.5
    5. GPT-4
    6. Llama 2
    7. Papers with Code - Text-to-SQL
    8. Spider
    9. WikiSQL
    10. T5
    11. BART
    12. CodeLlama
    13. Databricks Blog Posts and Notebooks
    14. Databricks SQL
    15. MosaicML's MPT
    16. Google AI Blog
    17. Microsoft Research Blog
    18. PaLM 2
    19. Copilot

    AI recommended 19 alternatives but never named HKUSTDial/NL2SQL_Handbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest advancements and practical guidance for natural language to SQL systems?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3
    3. Llama 3
    4. Pinecone
    5. Weaviate
    6. ChromaDB
    7. T5
    8. BART
    9. Spider
    10. WikiSQL
    11. BIRD
    12. GPT-4o
    13. Claude 3 Opus/Sonnet
    14. GPT-3.5 Turbo
    15. Qdrant

    AI recommended 15 alternatives but never named HKUSTDial/NL2SQL_Handbook. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 HKUSTDial/NL2SQL_Handbook?
    pass
    AI did not name HKUSTDial/NL2SQL_Handbook — 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 HKUSTDial/NL2SQL_Handbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HKUSTDial/NL2SQL_Handbook 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 HKUSTDial/NL2SQL_Handbook solve, and who is the primary audience?
    pass
    AI named HKUSTDial/NL2SQL_Handbook explicitly

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

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HKUSTDial/NL2SQL_Handbook — 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