RRepoGEO

REPOGEO REPORT · LITE

eosphoros-ai/DB-GPT-Hub

Default branch main · commit 3ed19c1f · scanned 6/28/2026, 6:42:06 PM

GitHub: 1,995 stars · 249 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 eosphoros-ai/DB-GPT-Hub, 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
    Add a clarifying sentence under the main heading to emphasize its role as a specialized hub.

    Why:

    CURRENT
    # DB-GPT-Hub: Text-to-SQL parsing with LLMs
    COPY-PASTE FIX
    # DB-GPT-Hub: Text-to-SQL parsing with LLMs

This repository serves as a central hub for models, datasets, and fine-tuning techniques specifically designed to enhance Text-to-SQL parsing performance for DB-GPT.
  • highabout#2
    Expand the repository description to include broader capabilities like Text2NLU and Text2GQL.

    Why:

    CURRENT
    A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL
    COPY-PASTE FIX
    A comprehensive hub for DB-GPT, offering models, datasets, and fine-tuning techniques to enhance performance in Text-to-SQL, Text-to-NLU, and Text-to-GQL parsing.
  • mediumhomepage#3
    Add the repository URL as the homepage.

    Why:

    COPY-PASTE FIX
    https://github.com/eosphoros-ai/DB-GPT-Hub

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 eosphoros-ai/DB-GPT-Hub
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
T5
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. T5 · recommended 2×
  2. BART · recommended 2×
  3. LoRA · recommended 1×
  4. QLoRA · recommended 1×
  5. huggingface/peft · recommended 1×
  • CATEGORY QUERY
    How to fine-tune large language models for better Text-to-SQL parsing accuracy?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. QLoRA
    3. PEFT Library (huggingface/peft)
    4. Hugging Face Transformers (huggingface/transformers)
    5. T5
    6. BART
    7. FAISS (facebookresearch/faiss)
    8. Weaviate (weaviate/weaviate)

    AI recommended 8 alternatives but never named eosphoros-ai/DB-GPT-Hub. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find datasets and models to improve LLM Text-to-SQL capabilities?
    you: not recommended
    AI recommended (in order):
    1. Spider
    2. WikiSQL
    3. CoSQL
    4. SParC
    5. BIRD
    6. Picard
    7. RAT-SQL
    8. T5
    9. BART
    10. Code Llama
    11. Llama 2
    12. SQLCoder

    AI recommended 12 alternatives but never named eosphoros-ai/DB-GPT-Hub. 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 eosphoros-ai/DB-GPT-Hub?
    pass
    AI did not name eosphoros-ai/DB-GPT-Hub — 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 eosphoros-ai/DB-GPT-Hub in production, what risks or prerequisites should they evaluate first?
    pass
    AI named eosphoros-ai/DB-GPT-Hub 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 eosphoros-ai/DB-GPT-Hub solve, and who is the primary audience?
    pass
    AI named eosphoros-ai/DB-GPT-Hub explicitly

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

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eosphoros-ai/DB-GPT-Hub — 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