Dingo MCP Server

# 文案一 [OC] Comprehensive AI Data Quality Metrics Documentation - 50+ Evaluation Metrics with Academic Sources We've just released what might be the most comprehensive documentation of AI data quality evaluation metrics available. This covers everything from pre-training data assessment to multimodal evaluation. **What's included:** - 50+ evaluation metrics across text, image, and multimodal data - Academic citations for every metric (RedPajama, CLIP, NIMA, etc.) - Rule-based and LLM-based evaluation approaches - Practical usage examples and API documentation **Key categories:** - Text Quality: Completeness, Fluency, Relevance, Effectiveness - Image Quality: Clarity, Similarity, Validity - Security: Political sensitivity, prohibited content, harmful information - Classification: Topic categorization, content classification This is particularly useful for: - Data scientists working on model training - Researchers needing standardized evaluation frameworks - Anyone dealing with large-scale data quality assessment The documentation includes detailed academic references and practical implementation examples. All open source and ready to use. **Link:** https://github.com/MigoXLab/dingo/blob/dev/docs/metrics.md Thoughts? What metrics do you find most valuable in your work? # 文案二 [P] Comprehensive AI Data Quality Metrics Documentation - 50+ Evaluation Metrics with Academic Sources for Dingo Framework Hey r/MachineLearning! We've just released comprehensive documentation for AI data quality evaluation metrics in our open-source framework **Dingo**. This covers everything from pre-training data assessment to multimodal evaluation. **Project Overview:** Dingo is an enterprise-grade data quality assessment tool designed for large-scale AI training pipelines. **What's included in the metrics documentation:** - 50+ evaluation metrics across text, image, and multimodal data - Academic citations for every metric (RedPajama, CLIP, NIMA, etc.) - Rule-based and LLM-based evaluation approaches - Practical usage examples and API documentation **Key metric categories:** - **Text Quality**: Completeness, Fluency, Relevance, Effectiveness - **Image Quality**: Clarity, Similarity, Validity - **Security Assessment**: Political sensitivity, prohibited content detection - **SFT Evaluation**: 3H principles (Honest, Helpful, Harmless) **Technical features:** - Multi-source support (HuggingFace, local files, S3) - Distributed processing with Spark integration - CLI/SDK/Web interfaces - Comprehensive evaluation reports This is particularly useful for: - Data scientists working on model training pipelines - Researchers needing standardized evaluation frameworks - MLOps teams implementing data quality gates - Anyone dealing with large-scale data preprocessing **Documentation:** https://github.com/MigoXLab/dingo/blob/dev/docs/metrics.md **Project repo:** https://github.com/MigoXLab/dingo Would love to get feedback from the community! What data quality metrics do you find most valuable in your work? # 文案三 ### For r/MachineLearning **Title**: [D] Dingo 1.9.0 released: Open-source data quality evaluation with enhanced hallucination detection Just released **Dingo 1.9.0** with major upgrades for RAG-era data quality assessment. ### Key Updates: **🔍 Enhanced Hallucination Detection** Dingo 1.9.0 integrates two powerful hallucination detection approaches: - **HHEM-2.1-Open local model** (recommended) - runs locally without API costs - **GPT-based cloud detection** - leverages OpenAI models for detailed analysis Both evaluate LLM-generated answers against provided context using consistency scoring (0.0-1.0 range, configurable thresholds). **⚙️ Configuration System Overhaul** Complete rebuild with modern DevOps practices: - Hierarchical inheritance (project → user → system levels) - Hot-reload capabilities for instant config changes - Schema validation with clear error messages - Template system for common scenarios **📚 DeepWiki Document Q&A** Transform static documentation into interactive knowledge bases: - Multi-language support (EN/CN/JP) - Context-aware multi-turn conversations - Visual document structure parsing - Semantic navigation and cross-references ### Why It Matters: Traditional hallucination detection relies on static rules. Our approach provides context-aware validation essential for production RAG systems, SFT data quality assessment, and real-time LLM output verification. Perfect for: - RAG system quality monitoring - Training data preprocessing - Enterprise knowledge management - Multi-modal data evaluation **GitHub**: https://github.com/MigoXLab/dingo **Docs**: https://deepwiki.com/MigoXLab/dingo What hallucination detection approaches are you currently using? Interested in your RAG quality challenges. --- ### For r/OpenSource **Title**: [Project] Dingo 1.9.0: Major update to our data quality evaluation toolkit The community response has been incredible! **Dingo 1.9.0** delivers features you've been requesting. ### Project Stats: - ⭐ 311 GitHub stars and growing - 🍴 32 active development forks - 📚 Comprehensive multi-language documentation - 🔄 Full CI/CD pipeline with automated testing ### What's New: **Hallucination Detection**: Integrated HHEM-2.1-Open model and GPT-based detection for comprehensive fact-checking against context. **Config System Redesign**: Hierarchical inheritance, hot-reload, and template-based setup replacing the previous complex configuration approach. **DeepWiki Integration**: Interactive documentation system that transforms static docs into conversational AI assistants. ### Community Impact: This release addresses community requests through extensive collaboration - issues resolved, PRs merged, and new contributors welcomed from around the world. ### Contributing Opportunities: - **Core Development**: Python/ML implementation - **Documentation**: Technical writing and tutorials - **Community**: Discord moderation and outreach - **Testing**: QA and automated testing **Getting Started:** 1. Star: https://github.com/MigoXLab/dingo 2. Check "good first issue" labels for beginner-friendly tasks 3. Join our community discussions **License**: Apache 2.0 - fully open-source, no vendor lock-in What data quality tools does your team currently use? Would love to hear about your experiences and challenges. --- ### For r/artificial **Title**: Dingo 1.9.0: Addressing AI hallucination through enhanced detection As AI systems become more prevalent, data quality and factual accuracy are paramount concerns. Sharing our latest release addressing these challenges. ### The Challenge: - LLM hallucinations in production systems - RAG systems losing factual accuracy when combining sources - Temporal inconsistency as information becomes outdated - Quality control across different data modalities ### Our Solution: **Dingo 1.9.0** provides comprehensive hallucination detection through two complementary approaches: **Local HHEM-2.1-Open Integration**: Runs Vectara's hallucination evaluation model locally, providing fast, cost-effective fact-checking without API dependencies. **Cloud-based GPT Detection**: Leverages advanced language models for detailed consistency analysis with comprehensive reasoning. **Smart Configuration Management**: Completely redesigned system enabling environment-aware inheritance, hot-reload capabilities, and template-based setups for rapid deployment. **Interactive Documentation**: DeepWiki transforms static documentation into conversational AI assistants, improving team knowledge sharing and reducing information silos. ### Real-World Applications: - **Production Monitoring**: Real-time quality control for customer-facing AI systems - **Training Pipeline**: Pre-processing validation for SFT datasets - **Enterprise Knowledge**: Quality assurance for internal AI applications - **Research**: Systematic evaluation across different model architectures ### Community Adoption: Growing adoption across organizations focused on AI safety and reliability, with particular interest from teams building production RAG systems and those requiring systematic data quality assessment. **Try it**: Available on GitHub under Apache 2.0 license **Resources**: https://github.com/MigoXLab/dingo What approaches does your team use for AI quality assurance? How do you currently handle hallucination detection in production systems?