REPOGEO REPORT · LITE

eosphoros-ai/DB-GPT-Hub

Default branch main · commit 3ed19c1f · scanned 5/17/2026, 2:06:59 PM

GitHub: 1,981 stars · 246 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 clear introductory sentence to the README emphasizing its 'hub' nature

Why:

COPY-PASTE FIX

DB-GPT-Hub serves as a dedicated hub for Text-to-SQL models, datasets, and fine-tuning techniques, specifically designed to enhance LLM performance for natural language to SQL conversion and related database interaction tasks.

mediumhomepage#2
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://github.com/eosphoros-ai/DB-GPT
```

lowtopics#3

Expand repository topics to include 'hub' and specific content types

Why:

CURRENT

database, datasets, fine-tuning, gpt, hacktoberfest, llm, nl2sql, sql, text-to-sql, text2sql

COPY-PASTE FIX

database, datasets, fine-tuning, gpt, hacktoberfest, llm, llm-hub, nl2sql, sql, text-to-sql, text-to-sql-models, text2sql, fine-tuning-datasets

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

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 1×
peft · recommended 1×
LangChain · recommended 1×
LlamaIndex · recommended 1×
OpenAI GPT-4 · recommended 1×

CATEGORY QUERY
How can I enhance large language model performance for accurate natural language to SQL conversion?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. peft
3. LangChain
4. LlamaIndex
5. OpenAI GPT-4
6. Anthropic Claude
7. Google Gemini
8. psycopg2
9. mysql-connector-python
10. sqlglot
11. Spider dataset-trained models
12. T5-based models
13. Pinecone
14. Weaviate
15. ChromaDB
AI recommended 15 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 fine-tuning techniques to optimize LLMs for text-to-SQL tasks?
you: not recommended
AI recommended (in order):
1. Spider Dataset
2. WikiSQL Dataset
3. BIRD (Big Bench for LLM-based Text-to-SQL)
4. Hugging Face Transformers Library
5. PEFT (Parameter-Efficient Fine-Tuning)
6. LoRA (Low-Rank Adaptation)
7. Hugging Face `peft` library
8. T5
9. BART
10. LLaMA
11. CodeLlama
12. LLaMA 2
13. Awesome-Text-to-SQL GitHub Repository
14. Microsoft's Text-to-SQL Resources
AI recommended 14 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?

Embed your GEO score

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/eosphoros-ai/DB-GPT-Hub.svg)](https://repogeo.com/en/r/eosphoros-ai/DB-GPT-Hub)

HTML

<a href="https://repogeo.com/en/r/eosphoros-ai/DB-GPT-Hub"><img src="https://repogeo.com/badge/eosphoros-ai/DB-GPT-Hub.svg" alt="RepoGEO" /></a>

Pro

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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