👋 join us on Discord and WeChat

If you like Dingo, please give us a ⭐ on GitHub!

Dingo is A Comprehensive AI Data, Model and Application Quality Evaluation Tool, designed for ML practitioners, data engineers, and AI researchers. It helps you systematically assess and improve the quality of training data, fine-tuning datasets, and production AI systems.

🎯 Production-Grade Quality Checks - From pre-training datasets to RAG systems, ensure your AI gets high-quality data

🗄️ Multi-Source Data Integration - Seamlessly connect to Local files, SQL databases (PostgreSQL/MySQL/SQLite), HuggingFace datasets, and S3 storage

🔍 Multi-Field Evaluation - Apply different quality rules to different fields in parallel (e.g., ISBN validation for isbn, text quality for title)

🤖 RAG System Assessment - Comprehensive evaluation of retrieval and generation quality with 5 academic-backed metrics

🧠 LLM & Rule Hybrid - Combine fast heuristic rules (30+ built-in) with LLM-based deep assessment

🚀 Flexible Execution - Run locally for rapid iteration or scale with Spark for billion-scale datasets

📊 Rich Reporting - Detailed quality reports with GUI visualization and field-level insights

pip install dingo-python

from dingo.config.input_args import EvaluatorLLMArgs
from dingo.io.input import Data
from dingo.model.llm.text_quality.llm_text_quality_v4 import LLMTextQualityV4
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():
    LLMTextQualityV4.dynamic_config = EvaluatorLLMArgs(
        key='YOUR_API_KEY',
        api_url='https://api.openai.com/v1/chat/completions',
        model='gpt-4o',
    )
    res = LLMTextQualityV4.eval(data)
    print(res)


def rule():
    res = RuleEnterAndSpace().eval(data)
    print(res)

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": {
        "result_save": {
            "bad": True  # Save evaluation results
        }
    },
    "evaluator": [
        {
            "evals": [
                {"name": "RuleColonEnd"},
                {"name": "RuleSpecialCharacter"}
            ]
        }
    ]
}

input_args = InputArgs(**input_data)
executor = Executor.exec_map["local"](input_args)
result = executor.execute()
print(result)

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

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

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.

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

Try Dingo in local:

cd app_gradio
python app.py

Experience Dingo interactively with Google Colab notebook:

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

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

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

Apply different quality checks to different fields in a single pass:

"evaluator": [
    {"fields": {"content": "isbn"}, "evals": [{"name": "RuleIsbn"}]},
    {"fields": {"content": "title"}, "evals": [{"name": "RuleAbnormalChar"}]},
    {"fields": {"content": "description"}, "evals": [{"name": "LLMTextQualityV5"}]}
]

Why It Matters: Evaluate structured data (like database tables) without writing separate scripts for each field.

SQL datasources use SQLAlchemy's server-side cursors:

# Handles billions of rows without OOM
for data in dataset.get_data():  # Yields one row at a time
    result = evaluator.eval(data)

Why It Matters: Process production databases without exporting to intermediate files.

RAG evaluations prevent context bleeding across different field combinations:

outputs/
├── user_input,response,retrieved_contexts/  # Faithfulness group
└── user_input,response/                     # Answer Relevancy group

Why It Matters: Accurate metric calculations when evaluating multiple field combinations.

Combine fast rules (100% coverage) with sampled LLM checks (10% coverage):

"evals": [
    {"name": "RuleAbnormalChar"},        # Fast, runs on all data
    {"name": "LLMTextQualityV5"}         # Expensive, sample if needed
]

Why It Matters: Balance cost and coverage for production-scale evaluation.

Clean plugin architecture for custom rules, prompts, and models:

@Model.rule_register('QUALITY_BAD_CUSTOM', ['default'])
class MyCustomRule(BaseRule):
    @classmethod
    def eval(cls, input_data: Data) -> EvalDetail:
        # Example: check if content is empty
        if not input_data.content:
            return EvalDetail(
                metric=cls.__name__,
                status=True,  # Found an issue
                label=[f'{cls.metric_type}.{cls.__name__}'],
                reason=["Content is empty"]
            )
        return EvalDetail(
            metric=cls.__name__,
            status=False,  # No issue found
            label=['QUALITY_GOOD']
        )

Why It Matters: Adapt to domain-specific requirements without forking the codebase.

Dingo provides 70+ evaluation metrics across multiple dimensions, combining rule-based speed with LLM-based depth.

📊 View Complete Metrics Documentation →
📖 RAG Evaluation Guide → | 中文版
🔍 Hallucination Detection Guide → | 中文版
✅ Factuality Assessment Guide → | 中文版

Most metrics are backed by academic research to ensure scientific rigor.

llm_config = {
    "model": "gpt-4o",
    "key": "YOUR_API_KEY",
    "api_url": "https://api.openai.com/v1/chat/completions"
}

input_data = {
    "evaluator": [
        {
            "fields": {"content": "content"},
            "evals": [
                {"name": "RuleAbnormalChar"},           # Rule-based (fast)
                {"name": "LLMTextQualityV5", "config": llm_config}  # LLM-based (deep)
            ]
        }
    ]
}

Customization: All prompts are defined in dingo/model/llm/ directory (organized by category: text_quality/, rag/, hhh/, etc.). Extend or modify them for domain-specific requirements.

Diverse Data Sources - Connect to where your data lives
✅ Local Files: JSONL, CSV, TXT, Parquet
✅ SQL Databases: PostgreSQL, MySQL, SQLite, Oracle, SQL Server (with stream processing)
✅ Cloud Storage: S3 and S3-compatible storage
✅ ML Platforms: Direct HuggingFace datasets integration

Enterprise-Ready SQL Support - Production database integration
✅ Memory-efficient streaming for billion-scale datasets
✅ Connection pooling and automatic resource cleanup
✅ Complex SQL queries (JOIN, WHERE, aggregations)
✅ Multiple dialect support with SQLAlchemy

Multi-Field Quality Checks - Different rules for different fields
✅ Parallel evaluation pipelines (e.g., ISBN validation + text quality simultaneously)
✅ Field aliasing and nested field extraction (user.profile.name)
✅ Independent result reports per field
✅ ETL pipeline architecture for flexible data transformation

5 Academic-Backed Metrics - Based on RAGAS, DeepEval, TruLens research
✅ Faithfulness: Answer-context consistency (hallucination detection)
✅ Answer Relevancy: Answer-query alignment
✅ Context Precision: Retrieval precision
✅ Context Recall: Retrieval recall
✅ Context Relevancy: Context-query relevance

Comprehensive Reporting - Auto-aggregated statistics
✅ Average, min, max, standard deviation for each metric
✅ Field-grouped results
✅ Batch and single evaluation modes

📖 View RAG Evaluation Guide →

Rule-Based - Fast, deterministic, cost-effective
✅ 30+ built-in rules (text quality, format, PII detection)
✅ Regex, heuristics, statistical checks
✅ Custom rule registration

LLM-Based - Deep semantic understanding
✅ OpenAI (GPT-4o, GPT-3.5), DeepSeek, Kimi
✅ Local models (Llama3, Qwen)
✅ Vision-Language Models (InternVL, Gemini)
✅ Custom prompt registration

Extensible Architecture
✅ Plugin-based rule/prompt/model registration
✅ Clean separation of concerns (agents, tools, orchestration)
✅ Domain-specific customization

Multiple Interfaces
✅ CLI for quick checks
✅ Python SDK for integration
✅ MCP (Model Context Protocol) server for IDEs (Cursor, etc.)

Scalable Execution
✅ Local executor for rapid iteration
✅ Spark executor for distributed processing
✅ Configurable concurrency and batching

Data Sources
✅ Local Files: JSONL, CSV, TXT, Parquet formats
✅ Hugging Face: Direct integration with HF datasets hub
✅ S3 Storage: AWS S3 and S3-compatible storage
✅ SQL Databases: PostgreSQL, MySQL, SQLite, Oracle, SQL Server (stream processing for large-scale data)

Modalities
✅ Text (chat, documents, code)
✅ Images (with VLM support)
✅ Multimodal (text + image consistency)

Multi-Level Reports
✅ Summary JSON with overall scores
✅ Field-level breakdown
✅ Per-rule violation details
✅ Type and name distribution

GUI Visualization
✅ Built-in web interface
✅ Interactive data exploration
✅ Anomaly tracking

Metric Aggregation
✅ Automatic statistics (avg, min, max, std_dev)
✅ Field-grouped metrics
✅ Overall quality score

Dingo uses a clean plugin architecture for domain-specific customization:

from dingo.model import Model
from dingo.model.rule.base import BaseRule
from dingo.io import Data
from dingo.io.output.eval_detail import EvalDetail

@Model.rule_register('QUALITY_BAD_CUSTOM', ['default'])
class DomainSpecificRule(BaseRule):
    """Check domain-specific patterns"""

    @classmethod
    def eval(cls, input_data: Data) -> EvalDetail:
        text = input_data.content

        # Your custom logic
        is_valid = your_validation_logic(text)

        return EvalDetail(
            metric=cls.__name__,
            status=not is_valid,  # False = good, True = bad
            label=['QUALITY_GOOD' if is_valid else 'QUALITY_BAD_CUSTOM'],
            reason=["Validation details..."]
        )

from dingo.model import Model
from dingo.model.llm.base_openai import BaseOpenAI

@Model.llm_register('custom_evaluator')
class CustomEvaluator(BaseOpenAI):
    """Custom LLM evaluator with specialized prompts"""

    _metric_info = {
        "metric_name": "CustomEvaluator",
        "metric_type": "LLM-Based Quality",
        "category": "Custom Category"
    }

    prompt = """Your custom prompt here..."""

Examples:

• Custom Rules
• Custom Models

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()

# Access results
summary = executor.get_summary()           # Overall metrics
bad_data = executor.get_bad_info_list()    # Quality issues
good_data = executor.get_good_info_list()  # High-quality data

Best For: Rapid iteration, debugging, datasets < 100K rows

from pyspark.sql import SparkSession
from dingo.exec import Executor

spark = SparkSession.builder.appName("Dingo").getOrCreate()
spark_rdd = spark.sparkContext.parallelize(your_data)

executor = Executor.exec_map["spark"](
    input_args,
    spark_session=spark,
    spark_rdd=spark_rdd
)
result = executor.execute()

Best For: Production pipelines, distributed processing, datasets > 1M rows

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

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": {
        "content": {
            "QUALITY_BAD_COMPLETENESS.RuleColonEnd": 0.5,
            "QUALITY_BAD_RELEVANCE.RuleSpecialCharacter": 0.5
        }
    }
}

• Agent-as-a-Judge - Multi-agent debate patterns for bias reduction and complex reasoning
• SaaS Platform - Hosted evaluation service with API access and dashboard
• Audio & Video Modalities - Extend beyond text/image
• Diversity Metrics - Statistical diversity assessment
• Real-time Monitoring - Continuous quality checks in production pipelines

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

• RedPajama-Data
• mlflow
• deepeval
• ragas

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.

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.

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}
}