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MigoXLab

Dingo MCP Server

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Introduction

Dingo is a data quality evaluation tool that helps you automatically detect data quality issues in your datasets. Dingo provides a variety of built-in rules and model evaluation methods, and also supports custom evaluation methods. Dingo supports commonly used text datasets and multimodal datasets, including pre-training datasets, fine-tuning datasets, and evaluation datasets. In addition, Dingo supports multiple usage methods, including local CLI and SDK, making it easy to integrate into various evaluation platforms, such as OpenCompass.

Architecture Diagram

Architecture of dingo

Quick Start

Related MCP server: Tigris MCP Server

Installation

pip install dingo-python

Example Use Cases

1. Evaluate LLM chat data

from dingo.config.input_args import EvaluatorLLMArgs from dingo.io.input import Data from dingo.model.llm.llm_text_quality_model_base import LLMTextQualityModelBase from dingo.model.rule.rule_common import RuleEnterAndSpace data = Data( data_id='123', prompt="hello, introduce the world", content="Hello! The world is a vast and diverse place, full of wonders, cultures, and incredible natural beauty." ) def llm(): LLMTextQualityModelBase.dynamic_config = EvaluatorLLMArgs( key='YOUR_API_KEY', api_url='https://api.openai.com/v1/chat/completions', model='gpt-4o', ) res = LLMTextQualityModelBase.eval(data) print(res) def rule(): res = RuleEnterAndSpace().eval(data) print(res)

2. Evaluate Dataset

from dingo.config import InputArgs from dingo.exec import Executor # Evaluate a dataset from Hugging Face input_data = { "input_path": "tatsu-lab/alpaca", # Dataset from Hugging Face "dataset": { "source": "hugging_face", "format": "plaintext" # Format: plaintext }, "executor": { "eval_group": "sft", # Rule set for SFT data "result_save": { "bad": True # Save evaluation results } } } input_args = InputArgs(**input_data) executor = Executor.exec_map["local"](input_args) result = executor.execute() print(result)

Command Line Interface

Evaluate with Rule Sets

python -m dingo.run.cli --input test/env/local_plaintext.json

Evaluate with LLM (e.g., GPT-4o)

python -m dingo.run.cli --input test/env/local_json.json

GUI Visualization

After evaluation (with result_save.bad=True), a frontend page will be automatically generated. To manually start the frontend:

python -m dingo.run.vsl --input output_directory

Where output_directory contains the evaluation results with a summary.json file.

GUI output

Online Demo

Try Dingo on our online demo: (Hugging Face)🤗

Local Demo

Try Dingo in local:

cd app_gradio python app.py

Gradio demo

Google Colab Demo

Experience Dingo interactively with Google Colab notebook: Open In Colab

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 · 简体中文 · 日本語

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 provides comprehensive data quality assessment through both rule-based and prompt-based evaluation metrics. These metrics cover multiple quality dimensions including effectiveness, completeness, similarity, security, and more.

📊 View Complete Metrics Documentation →

Our evaluation system includes:

  • Text Quality Assessment Metrics: Pre-training data quality evaluation using DataMan methodology and enhanced multi-dimensional assessment

  • SFT Data Assessment Metrics: Honest, Helpful, Harmless evaluation for supervised fine-tuning data

  • Classification Metrics: Topic categorization and content classification

  • Multimodality Assessment Metrics: Image classification and relevance evaluation

  • Rule-Based Quality Metrics: Automated quality checks using heuristic rules for effectiveness and similarity detection

  • Factuality Assessment Metrics: Two-stage factuality evaluation based on GPT-5 System Card

  • etc

Most metrics are backed by academic sources to ensure objectivity and scientific rigor.

Using LLM Assessment in Evaluation

To use these assessment prompts in your evaluations, specify them in your configuration:

input_data = { # Other parameters... "executor": { "prompt_list": ["QUALITY_BAD_SIMILARITY"], # Specific prompt to use }, "evaluator": { "llm_config": { "LLMTextQualityPromptBase": { # LLM model to use "model": "gpt-4o", "key": "YOUR_API_KEY", "api_url": "https://api.openai.com/v1/chat/completions" } } } }

You can customize these prompts to focus on specific quality dimensions or to adapt to particular domain requirements. When combined with appropriate LLM models, these prompts enable comprehensive evaluation of data quality across multiple dimensions.

Hallucination Detection & RAG System Evaluation

For detailed guidance on using Dingo's hallucination detection capabilities, including HHEM-2.1-Open local inference and LLM-based evaluation:

📖 View Hallucination Detection Guide →

Factuality Assessment

For comprehensive guidance on using Dingo's two-stage factuality evaluation system:

📖 View Factuality Assessment Guide →

Rule Groups

Dingo provides pre-configured rule groups for different types of datasets:

Group

Use Case

Example Rules

default

General text quality

RuleColonEnd

,

RuleContentNull

,

RuleDocRepeat

, etc.

sft

Fine-tuning datasets

Rules from

default

plus

RuleHallucinationHHEM

for hallucination detection

rag

RAG system evaluation

RuleHallucinationHHEM

,

PromptHallucination

for response consistency

hallucination

Hallucination detection

PromptHallucination

with LLM-based evaluation

pretrain

Pre-training datasets

Comprehensive set of 20+ rules including

RuleAlphaWords

,

RuleCapitalWords

, etc.

To use a specific rule group:

input_data = { "executor": { "eval_group": "sft", # Use "default", "sft", "rag", "hallucination", or "pretrain" } # other parameters... }

Feature Highlights

Multi-source & Multi-modal Support

  • Data Sources: Local files, Hugging Face datasets, S3 storage

  • Data Types: Pre-training, fine-tuning, and evaluation datasets

  • Data Modalities: Text and image

Rule-based & Model-based Evaluation

  • Built-in Rules: 20+ general heuristic evaluation rules

  • LLM Integration: OpenAI, Kimi, and local models (e.g., Llama3)

  • Hallucination Detection: HHEM-2.1-Open local model and GPT-based evaluation

  • RAG System Evaluation: Response consistency and context alignment assessment

  • Custom Rules: Easily extend with your own rules and models

  • Security Evaluation: Perspective API integration

Flexible Usage

  • Interfaces: CLI and SDK options

  • Integration: Easy integration with other platforms

  • Execution Engines: Local and Spark

Comprehensive Reporting

  • Quality Metrics: 7-dimensional quality assessment

  • Traceability: Detailed reports for anomaly tracking

User Guide

Custom Rules, Prompts, and Models

If the built-in rules don't meet your requirements, you can create custom ones:

Custom Rule Example

from dingo.model import Model from dingo.model.rule.base import BaseRule from dingo.config.input_args import EvaluatorRuleArgs from dingo.io import Data from dingo.model.modelres import ModelRes @Model.rule_register('QUALITY_BAD_RELEVANCE', ['default']) class MyCustomRule(BaseRule): """Check for custom pattern in text""" dynamic_config = EvaluatorRuleArgs(pattern=r'your_pattern_here') @classmethod def eval(cls, input_data: Data) -> ModelRes: res = ModelRes() # Your rule implementation here return res

Custom LLM Integration

from dingo.model import Model from dingo.model.llm.base_openai import BaseOpenAI @Model.llm_register('my_custom_model') class MyCustomModel(BaseOpenAI): # Custom implementation here pass

See more examples in:

Execution Engines

Local Execution

from dingo.config import InputArgs from dingo.exec import Executor input_args = InputArgs(**input_data) executor = Executor.exec_map["local"](input_args) result = executor.execute() # Get results summary = executor.get_summary() # Overall evaluation summary bad_data = executor.get_bad_info_list() # List of problematic data good_data = executor.get_good_info_list() # List of high-quality data

Spark Execution

from dingo.config import InputArgs from dingo.exec import Executor from pyspark.sql import SparkSession # Initialize Spark spark = SparkSession.builder.appName("Dingo").getOrCreate() spark_rdd = spark.sparkContext.parallelize([...]) # Your data as Data objects input_data = { "executor": { "eval_group": "default", "result_save": {"bad": True} } } input_args = InputArgs(**input_data) executor = Executor.exec_map["spark"](input_args, spark_session=spark, spark_rdd=spark_rdd) result = executor.execute()

Evaluation Reports

After evaluation, Dingo generates:

  1. Summary Report (summary.json): Overall metrics and scores

  2. Detailed Reports: Specific issues for each rule violation

Report Description:

  1. score: num_good / total

  2. type_ratio: The count of type / total, such as: QUALITY_BAD_COMPLETENESS / total

  3. name_ratio: The count of name / total, such as: QUALITY_BAD_COMPLETENESS-RuleColonEnd / total

Example summary:

{ "task_id": "d6c922ec-981c-11ef-b723-7c10c9512fac", "task_name": "dingo", "eval_group": "default", "input_path": "test/data/test_local_jsonl.jsonl", "output_path": "outputs/d6c921ac-981c-11ef-b723-7c10c9512fac", "create_time": "20241101_144510", "score": 50.0, "num_good": 1, "num_bad": 1, "total": 2, "type_ratio": { "QUALITY_BAD_COMPLETENESS": 0.5, "QUALITY_BAD_RELEVANCE": 0.5 }, "name_ratio": { "QUALITY_BAD_COMPLETENESS-RuleColonEnd": 0.5, "QUALITY_BAD_RELEVANCE-RuleSpecialCharacter": 0.5 } }

Future Plans

  • Richer graphic and text evaluation indicators

  • Audio and video data modality evaluation

  • Small model evaluation (fasttext, Qurating)

  • Data diversity evaluation

Limitations

The current built-in detection rules and model methods focus on common data quality problems. For specialized evaluation needs, we recommend customizing detection rules.

Acknowledgments

Contribution

We appreciate all the contributors for their efforts to improve and enhance Dingo. Please refer to the Contribution Guide for guidance on contributing to the project.

License

This project uses the Apache 2.0 Open Source License.

This project uses fasttext for some functionality including language detection. fasttext is licensed under the MIT License, which is compatible with our Apache 2.0 license and provides flexibility for various usage scenarios.

Citation

If you find this project useful, please consider citing our tool:

@misc{dingo, title={Dingo: A Comprehensive AI Data Quality Evaluation Tool for Large Models}, author={Dingo Contributors}, howpublished={\url{https://github.com/MigoXLab/dingo}}, year={2024} }

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