Dingo MCP Server

<div align="center" xmlns="http://www.w3.org/1999/html"> <!-- logo --> <p align="center"> <img src="docs/assets/dingo-logo.png" width="300px" style="vertical-align:middle;"> </p> <!-- badges --> <p align="center"> <a href="https://github.com/pre-commit/pre-commit"><img src="https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white" alt="pre-commit"></a> <a href="https://pypi.org/project/dingo-python/"><img src="https://img.shields.io/pypi/v/dingo-python.svg" alt="PyPI version"></a> <a href="https://pypi.org/project/dingo-python/"><img src="https://img.shields.io/pypi/pyversions/dingo-python.svg" alt="Python versions"></a> <a href="https://github.com/DataEval/dingo/blob/main/LICENSE"><img src="https://img.shields.io/github/license/DataEval/dingo" alt="License"></a> <a href="https://github.com/DataEval/dingo/stargazers"><img src="https://img.shields.io/github/stars/DataEval/dingo" alt="GitHub stars"></a> <a href="https://github.com/DataEval/dingo/network/members"><img src="https://img.shields.io/github/forks/DataEval/dingo" alt="GitHub forks"></a> <a href="https://github.com/DataEval/dingo/issues"><img src="https://img.shields.io/github/issues/DataEval/dingo" alt="GitHub issues"></a> <a href="https://mseep.ai/app/dataeval-dingo"><img src="https://mseep.net/pr/dataeval-dingo-badge.png" alt="MseeP.ai Security Assessment Badge" height="20"></a> <a href="https://deepwiki.com/MigoXLab/dingo"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a> [](https://archestra.ai/mcp-catalog/dataeval__dingo) </p> </div> <div align="center"> [English](README.md) · [简体中文](README_zh-CN.md) · [日本語](README_ja.md) </div> <!-- join us --> <p align="center"> 👋 join us on <a href="https://discord.gg/Jhgb2eKWh8" target="_blank">Discord</a> and <a href="./docs/assets/wechat.jpg" target="_blank">WeChat</a> </p> <p align="center"> If you like Dingo, please give us a ⭐ on GitHub! <br/> <a href="https://github.com/DataEval/dingo/stargazers" target="_blank"> <img src="docs/assets/clickstar_2.gif" alt="Click Star" width="480"> </a> </p> # 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](https://github.com/open-compass/opencompass). ## Architecture Diagram  # Quick Start ## Installation ```shell pip install dingo-python ``` ## Example Use Cases ### 1. Evaluate LLM chat data ```python 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 ```python 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 ```shell python -m dingo.run.cli --input test/env/local_plaintext.json ``` ### Evaluate with LLM (e.g., GPT-4o) ```shell 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: ```shell python -m dingo.run.vsl --input output_directory ``` Where `output_directory` contains the evaluation results with a `summary.json` file.  ## Online Demo Try Dingo on our online demo: [(Hugging Face)🤗](https://huggingface.co/spaces/DataEval/dingo) ## Local Demo Try Dingo in local: ```shell cd app_gradio python app.py ```  ## Google Colab Demo Experience Dingo interactively with Google Colab notebook: [](https://colab.research.google.com/github/DataEval/dingo/blob/dev/examples/colab/dingo_colab_demo.ipynb) # 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 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 →](docs/metrics.md)** 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: ```python 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 →](docs/hallucination_guide.md)** ### Factuality Assessment For comprehensive guidance on using Dingo's two-stage factuality evaluation system: 📖 **[View Factuality Assessment Guide →](docs/factcheck_guide.md)** # 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: ```python 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 ```python 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 ```python 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: - [Register Rules](examples/register/sdk_register_rule.py) - [Register Prompts](examples/register/sdk_register_prompt.py) - [Register Models](examples/register/sdk_register_llm.py) ## Execution Engines ### Local Execution ```python 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 ```python 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: ```json { "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 - [RedPajama-Data](https://github.com/togethercomputer/RedPajama-Data) - [mlflow](https://github.com/mlflow/mlflow) - [deepeval](https://github.com/confident-ai/deepeval) # Contribution We appreciate all the contributors for their efforts to improve and enhance `Dingo`. Please refer to the [Contribution Guide](docs/en/CONTRIBUTING.md) for guidance on contributing to the project. # License This project uses the [Apache 2.0 Open Source License](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} } ```