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52 changes: 50 additions & 2 deletions .gitignore
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@@ -1,2 +1,50 @@
__pycache__/
*.egg-info/
*.tar
*.tar.gz
*.zip
venv*/
envs/
slurm_logs/
local_tests/

__pycache__
*.log
*.pyc
.vscode
debug/
*.ipynb
.idea
.python-version

# vscode history
.history

.DS_Store
.env

bad_words/
bak/

app/tests/*
temp/
tmp/
tmp
.vscode
.vscode/
ocr_demo
.coveragerc


# sphinx docs
_build/


output/
**/temp.py

# coverage file
.coverage*
coverage.xml

llm_web_kit.egg-info/*
.llm-web-kit.jsonc
.llm-web-kit-pageclassify.jsonc
25 changes: 17 additions & 8 deletions README.md
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<div align="center">

[English](README.md) · [简体中文](README_zh-CN.md)
[English](README.md) · [简体中文](README_zh-CN.md) · [日本語](README_ja.md)

</div>

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## Online Demo
Try Dingo on our online demo: [(Hugging Face)🤗](https://huggingface.co/spaces/DataEval/dingo)


# MCP Server

Dingo includes an experimental Model Context Protocol (MCP) server. For details on running the server and integrating it with clients like Cursor, please see the dedicated documentation:

[English](README_mcp.md) · [简体中文](README_mcp_zh-CN.md) · [日本語](README_mcp_ja.md)

## Video Demonstration

To help you get started quickly with Dingo MCP, we've created a video walkthrough:

https://github.com/user-attachments/assets/aca26f4c-3f2e-445e-9ef9-9331c4d7a37b

This video demonstrates step-by-step how to use Dingo MCP server with Cursor.


# Data Quality Metrics

Dingo classifies data quality issues into 7 dimensions of Quality Metrics. Each dimension can be evaluated using both rule-based methods and LLM-based prompts:
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```


# MCP Server (Experimental)

Dingo includes an experimental Model Context Protocol (MCP) server. For details on running the server and integrating it with clients like Cursor, please see the dedicated documentation:

[**Dingo MCP Server Documentation (README_mcp.md)**](README_mcp.md)


# Research & Publications

## Research Powered by Dingo
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