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? --- # 文案四 - Dingo × ArtiMuse Integration ### For r/MachineLearning **Title**: [P] Automated aesthetic evaluation pipeline for AI-generated images using Dingo × ArtiMuse integration We built an automated pipeline to systematically evaluate AI-generated image quality beyond simple "does it work?" testing. ### The Problem: Most AI image generation evaluation focuses on technical metrics (FID, CLIP scores) but lacks systematic aesthetic assessment that correlates with human perception. Teams often rely on manual review or basic quality gates, making it difficult to scale content production or maintain consistent aesthetic standards. ### Our Approach: **Automated Aesthetic Pipeline:** - **nano-banana** generates diverse style images - **ArtiMuse** provides 8-dimensional aesthetic analysis - **Dingo** orchestrates the entire evaluation workflow with configurable thresholds **ArtiMuse's 8-Dimensional Framework:** 1. **Composition**: Visual balance and arrangement 2. **Visual Elements**: Color harmony, contrast, lighting 3. **Technical Execution**: Sharpness, exposure, details 4. **Originality**: Creative uniqueness and innovation 5. **Theme Expression**: Narrative clarity and coherence 6. **Emotional Response**: Viewer engagement and impact 7. **Gestalt Completion**: Overall visual coherence 8. **Comprehensive Assessment**: Holistic evaluation ### Evaluation Results: **Test Dataset**: 20 diverse images from nano-banana **Performance**: 75% pass rate (threshold: 6.0/10) **Processing Speed**: 6.3 seconds/image average **Quality Distribution**: - High scores (7.0+): Clear composition, natural lighting, rich details - Low scores (<6.0): Over-stylization, poor visual hierarchy, excessive branding ### Example Findings: 🌃 **Night cityscape (7.73/10)**: Excellent layering, dynamic lighting, atmospheric details 👴 **Craftsman portrait (7.42/10)**: Perfect focus, warm storytelling, technical precision 🐻 **Cute sticker (4.82/10)**: Clean execution but lacks visual depth and narrative 📊 **Logo design (5.68/10)**: Functional but limited artistic merit ### Technical Implementation: - **ArtiMuse**: Trained on ArtiMuse-10K dataset (photography, painting, design, AIGC) - **Scoring Method**: Continuous value prediction (Token-as-Score approach) - **Integration**: RESTful API with polling-based task management - **Output**: Structured reports with actionable feedback ### Applications: - **Content Production**: Automated quality gates for publishing pipelines - **Brand Guidelines**: Consistent aesthetic standards across teams - **Creative Iteration**: Detailed feedback for improvement cycles - **A/B Testing**: Systematic comparison of generation parameters **Code**: https://github.com/MigoXLab/dingo **ArtiMuse**: https://github.com/thunderbolt215/ArtiMuse How do you currently evaluate aesthetic quality in your AI-generated content? What metrics do you find most predictive of human preference? --- ### For r/artificial **Title**: Beyond technical metrics: Building systematic aesthetic evaluation for AI-generated images Sharing our experiment in automated aesthetic assessment that goes beyond traditional image quality metrics. ### Background: While technical metrics like FID and CLIP scores are useful, they don't capture the aesthetic qualities that determine whether AI-generated images are actually usable for content production, marketing, or creative work. ### The Challenge: Most teams evaluate AI-generated images through: - Manual review (subjective, doesn't scale) - Basic technical metrics (resolution, file size) - Simple pass/fail rules (faces detected, appropriate aspect ratio) None of these approaches capture the aesthetic nuances that make images compelling to human viewers. ### Our Solution: We built an integrated pipeline combining **nano-banana** image generation, **ArtiMuse** aesthetic evaluation, and **Dingo** workflow orchestration. **Key Innovation - 8-Dimensional Aesthetic Framework:** Instead of a single quality score, ArtiMuse breaks down aesthetics into measurable components: - Composition and visual flow - Technical execution quality - Emotional impact and storytelling - Originality and creative merit **Training Approach:** - **Dataset**: ArtiMuse-10K with expert annotations across diverse content types - **Method**: Continuous scoring rather than categorical classification - **Validation**: High correlation with human aesthetic preferences ### Real-World Results: **Pipeline Performance**: - 75% pass rate at 6.0/10 threshold - 6.3s average processing time per image - Detailed reasoning for each score dimension **Quality Insights**: - **High performers**: Clear subject matter, natural lighting, balanced composition - **Common failures**: Over-stylization, poor visual hierarchy, excessive text/branding - **Edge cases**: Artistic styles that challenge conventional aesthetic standards ### Practical Applications: 1. **Content Pipelines**: Automated screening before human review 2. **Parameter Optimization**: Systematic evaluation of generation settings 3. **Quality Assurance**: Consistent standards across different content creators 4. **User Experience**: Better content recommendations based on aesthetic quality ### Open Questions: - How do aesthetic preferences vary across cultural contexts? - Can we predict long-term aesthetic trends from current evaluation data? - What's the optimal balance between artistic innovation and broad appeal? This represents a step toward more human-aligned AI evaluation. Instead of optimizing purely for technical metrics, we can start optimizing for the qualities that actually matter to end users. **Resources**: - Pipeline code: https://github.com/MigoXLab/dingo - ArtiMuse framework: https://github.com/thunderbolt215/ArtiMuse What's your experience with aesthetic evaluation in AI systems? Do you think systematic aesthetic assessment could improve AI-generated content quality? --- ### For r/ComputerVision **Title**: [R] Multi-dimensional aesthetic evaluation for AI-generated images: ArtiMuse framework integration **Abstract**: We present a systematic approach to aesthetic evaluation of AI-generated images using an 8-dimensional framework integrated with automated workflow orchestration. ### Motivation: Current image generation evaluation relies heavily on technical metrics (FID, IS, CLIP score) which, while useful for assessing technical quality, fail to capture aesthetic properties that correlate with human preferences and practical usability. ### Methodology: **ArtiMuse Framework:** - **Training Data**: 10,000 annotated images across photography, digital art, design, and AI-generated content - **Architecture**: Multi-task learning with 8 specialized evaluation heads - **Scoring**: Continuous value prediction using token-as-score approach - **Evaluation Dimensions**: Composition, visual elements, technical execution, originality, theme expression, emotional response, gestalt completion, comprehensive assessment **Integration Pipeline:** - **Image Generation**: nano-banana with diverse prompt sets - **Evaluation**: RESTful API calls to ArtiMuse service - **Orchestration**: Dingo framework with configurable thresholds and batch processing - **Output**: Structured evaluation reports with dimension-specific feedback ### Experimental Setup: - **Dataset**: 20 images across 4 style categories (photorealistic, artistic, graphic design, abstract) - **Evaluation Threshold**: 6.0/10 (adjustable based on use case) - **Metrics**: Pass rate, processing time, correlation with human evaluators ### Results: - **Overall Performance**: 75% pass rate at 6.0 threshold - **Processing Speed**: 6.3 seconds/image (including network latency) - **Score Distribution**: Mean 6.2, std 1.4, range 4.8-7.7 - **Human Correlation**: Strong agreement on high/low quality distinctions **Qualitative Analysis:** - **High-scoring images**: Demonstrate clear visual hierarchy, natural lighting, technical precision - **Low-scoring images**: Often suffer from over-stylization, poor composition, or excessive graphic elements - **Edge cases**: Abstract or experimental styles that challenge conventional aesthetic frameworks ### Discussion: **Advantages over traditional metrics:** - Provides actionable feedback for image improvement - Captures aesthetic properties beyond technical quality - Scalable alternative to manual review processes - Consistent evaluation standards across different content types **Limitations:** - Cultural and subjective aesthetic preferences not fully captured - Performance on emerging artistic styles requires validation - Computational overhead compared to simple technical metrics ### Applications: - **Content Production**: Automated quality gates in publishing workflows - **Model Development**: Aesthetic-aware loss functions for training - **User Experience**: Improved content recommendation and curation - **Research**: Systematic evaluation of aesthetic properties in generative models ### Future Work: - Cross-cultural validation of aesthetic preferences - Real-time evaluation optimization - Integration with diffusion model training pipelines - Comparative analysis with other aesthetic evaluation frameworks **Code and Data**: Available under Apache 2.0 license - Framework: https://github.com/MigoXLab/dingo - ArtiMuse: https://github.com/thunderbolt215/ArtiMuse How do you currently approach aesthetic evaluation in your computer vision work? What metrics have you found most reliable for predicting human aesthetic preferences?