RRepoGEO

REPOGEO REPORT · LITE

XGenerationLab/XiYan-SQL

Default branch main · commit 603dedac · scanned 5/20/2026, 11:03:44 PM

GitHub: 1,001 stars · 50 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
35 /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
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 XGenerationLab/XiYan-SQL, 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
  • hightopics#1
    Add specific topics for better categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    text-to-sql, natural-language-processing, llm, sql-generation, ensemble-framework, deep-learning, nlp-framework, bird-critic
  • highreadme#2
    Reposition README introduction to clarify core function

    Why:

    CURRENT
    The README immediately follows the H1 with "News🔥" and then detailed news items.
    COPY-PASTE FIX
    Move the "News🔥" section further down the README. Immediately after the H1, add a concise paragraph like: "XiYan-SQL is a state-of-the-art multi-generator ensemble framework designed to accurately convert natural language questions into complex SQL queries. It achieves top performance on challenging benchmarks like BIRD-CRITIC by leveraging an innovative ensemble approach, making it ideal for NLP researchers and developers building advanced text-to-SQL solutions."
  • mediumreadme#3
    Add a "Key Features" section early in the README

    Why:

    CURRENT
    The README excerpt does not show a dedicated "Key Features" section near the top.
    COPY-PASTE FIX
    Add a "Key Features" section after the introductory paragraph, listing bullet points such as: "- Multi-Generator Ensemble: Combines multiple LLM generators for enhanced accuracy and robustness. - State-of-the-Art Performance: Achieves SOTA results on complex text-to-SQL benchmarks like BIRD-CRITIC. - XiYan-SQLTraining Framework: Provides a comprehensive framework for training and evaluating SQL/general LLMs. - Multi-Dialect Support: Designed to handle diverse SQL dialects."

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 XGenerationLab/XiYan-SQL
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. OpenAI GPT-4 · recommended 1×
  3. Microsoft Text-to-SQL · recommended 1×
  4. Google Cloud Vertex AI · recommended 1×
  5. Dataherald · recommended 1×
  • CATEGORY QUERY
    How to accurately convert natural language questions into complex SQL queries?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. Hugging Face Transformers
    3. Microsoft Text-to-SQL
    4. Google Cloud Vertex AI
    5. Dataherald
    6. SQLFlow

    AI recommended 6 alternatives but never named XGenerationLab/XiYan-SQL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks help train large language models for text-to-SQL generation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. TensorFlow
    4. Keras
    5. OpenNMT
    6. Fairseq
    7. DeepSpeed

    AI recommended 7 alternatives but never named XGenerationLab/XiYan-SQL. 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 XGenerationLab/XiYan-SQL?
    pass
    AI named XGenerationLab/XiYan-SQL explicitly

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

  • If a team adopts XGenerationLab/XiYan-SQL in production, what risks or prerequisites should they evaluate first?
    pass
    AI named XGenerationLab/XiYan-SQL 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 XGenerationLab/XiYan-SQL solve, and who is the primary audience?
    pass
    AI named XGenerationLab/XiYan-SQL explicitly

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

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XGenerationLab/XiYan-SQL — 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