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Introduction

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.

🚀 Enterprise Dingo SaaS Version

Need a production-grade data quality platform? Try Dingo SaaS Enterprise Edition!

✨ Compared to the open-source version, SaaS provides:

• 🌐 Web UI - Visual evaluation interface, no coding required
• 🔐 Access Control - JWT + Google OAuth 2.0
• 📊 Visual Reports - Interactive charts, trend analysis, export features
• 🔌 RESTful API - Seamless integration with existing systems

📝 How to Get Free SaaS Code

👉 Apply for Dingo SaaS Repository Access

Review time: 1-5 business days | Suitable for enterprise data governance, team collaboration

Why Dingo?

🎯 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 & Agent 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

Architecture Diagram

Quick Start

Installation

# Core package (includes rule evaluation, LLM evaluation, MCP server, datasource support)
pip install dingo-python

# With HHEM hallucination detection model (requires transformers + torch)
pip install "dingo-python[hhem]"

# With all features (HHEM + Agent)
pip install "dingo-python[all]"

Example Use Cases of Dingo

1. Evaluate LLM chat data

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 RuleSpecialCharacter

data = Data(
    data_id='123',
    prompt="hello, introduce the world",
    content="�I am 8 years old. ^I love apple because:"
)


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 = RuleSpecialCharacter().eval(data)
    print(res)

rule()

2. Evaluate Dataset

from dingo.config import InputArgs
from dingo.exec import Executor

# Evaluate a dataset from Hugging Face
if __name__ == '__main__':
    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)

Command Line Interface

Evaluate with Rule Sets

dingo eval --input .github/env/local_plaintext.json

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

dingo eval --input .github/env/local_json.json

MCP Server

Dingo includes a built-in Model Context Protocol (MCP) server for AI agent integration:

# Start MCP server (SSE transport, default port 8000)
dingo serve

# Custom port
dingo serve --port 9000

# stdio transport (for Claude Desktop)
dingo serve --transport stdio

For detailed setup and Cursor/Claude Desktop integration, 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:

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

📚 Data Quality Metrics

Dingo provides 100+ evaluation metrics across multiple dimensions, combining rule-based speed with LLM-based depth.

Metric Categories

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

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

Quick Metric Usage

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.

🎓 Key Concepts for Practitioners

What Makes Dingo Production-Ready?

1. Multi-Field Evaluation Pipeline

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.

2. Stream Processing for Large Datasets

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.

3. Field Isolation in Memory

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.

4. Hybrid Rule-LLM Strategy

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.

5. Extensibility Through Registration

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.

🌟 Feature Highlights

📊 Multi-Source Data Integration

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

🤖 RAG System Evaluation

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 →

🧠 Hybrid Evaluation System

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

Agent-Based - Multi-step reasoning with tools
✅ Web search integration (Tavily)
✅ Adaptive context gathering
✅ Multi-source fact verification
✅ Custom agent & tool registration

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

🚀 Flexible Execution & Integration

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)

📈 Rich Reporting & Visualization

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

GUI Visualization (via Dingo SaaS)
✅ Web UI with interactive data exploration
✅ Visual reports with trend analysis
✅ Anomaly tracking

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

📖 User Guide

🔧 Extensibility

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

Custom Rule Registration

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..."]
        )

Custom LLM/Prompt Registration

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

Agent-Based Evaluation with Tools

Dingo supports agent-based evaluators that can use external tools for multi-step reasoning and adaptive context gathering. Two implementation patterns are available:

Pattern 1: LangChain-Based (e.g., AgentFactCheck)

• Framework-driven with autonomous multi-step reasoning
• Uses LangChain 1.0's create_agent with ReAct pattern
• Best for: Complex reasoning tasks, rapid prototyping
• Less code, more declarative

Pattern 2: Custom Workflow (e.g., AgentHallucination)

• Developer-driven with explicit workflow control
• Manual tool calls and LLM interactions
• Best for: Composing existing evaluators, domain-specific workflows
• Full control, explicit behavior

Both patterns share the same configuration interface and are transparent to users.

Built-in Agents:

• AgentFactCheck: LangChain-based fact-checking with autonomous search control
• AgentHallucination: Custom workflow hallucination detection with adaptive context gathering
• ArticleFactChecker: Two-phase article fact-checking — extracts verifiable claims then verifies each in parallel using web search and Arxiv, with configurable concurrency control

Quick Example:

from dingo.io import Data
from dingo.io.output.eval_detail import EvalDetail
from dingo.model import Model
from dingo.model.llm.agent.base_agent import BaseAgent

@Model.llm_register('MyAgent')
class MyAgent(BaseAgent):
    """Custom agent with tool support"""

    available_tools = ["tavily_search", "my_custom_tool"]
    max_iterations = 5

    @classmethod
    def eval(cls, input_data: Data) -> EvalDetail:
        # Use tools for fact-checking
        search_result = cls.execute_tool('tavily_search', query=input_data.content)

        # Multi-step reasoning with LLM
        result = cls.send_messages([...])

        return EvalDetail(...)

For detailed guidance on choosing and implementing agent patterns, see Agent Development Guide.

Configuration Example:

{
  "evaluator": [{
    "evals": [{
      "name": "AgentHallucination",
      "config": {
        "key": "openai-api-key",
        "model": "gpt-4",
        "parameters": {
          "agent_config": {
            "max_iterations": 5,
            "tools": {
              "tavily_search": {"api_key": "tavily-key"}
            }
          }
        }
      }
    }]
  }]
}

Learn More:

• Agent Development Guide - Comprehensive guide for creating custom agents and tools
• AgentHallucination Example - Production agent example
• AgentFactCheck Example - LangChain agent example
• ArticleFactChecker Example - Article-scale two-phase fact verification

⚙️ Execution Modes

Local Executor (Development & Small-Scale)

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

Spark Executor (Production & Large-Scale)

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

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

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
        }
    }
}

🚀 Roadmap & Contributions

Future Plans

• 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

Limitations

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.

Acknowledgments

• RedPajama-Data
• mlflow
• deepeval
• ragas

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}
}