Dingo MCP 服务器

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

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:

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
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 →

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

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:

Summary Report (summary.json): Overall metrics and scores
Detailed Reports: Specific issues for each rule violation

Report Description:

score: num_good / total
type_ratio: The count of type / total, such as: QUALITY_BAD_COMPLETENESS / total
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 Data Quality Evaluation Tool for Large Models},
  author={Dingo Contributors},
  howpublished={\url{https://github.com/DataEval/dingo}},
  year={2024}
}