v1.11.0 #235
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v1.11.0 #235
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…shu (no text changes)
docs: fix artimuse example link; polish Tuiwen.md
docs: update readme with seo meta
docs: update readme with Trust Score badage
feat: new config
fix: fix Hallucination eval
fix: fix Hallucination docs
feat：Add Document Parse Quality Prompt for MinerU training dataset
Add Doc Parse Doc
add mineru OCR prompt
add layout prompt & docs
- Add 15 rule-based resume quality detection rules covering 7 metric types - Add Chinese and English prompt templates for LLM-based evaluation - Support privacy, contact, format, structure, professionalism, date, and completeness checks - Follow Dingo code style and automatic registration mechanism Metric Types: - RESUME_QUALITY_BAD_PRIVACY (2 rules) - RESUME_QUALITY_BAD_CONTACT (3 rules) - RESUME_QUALITY_BAD_FORMAT (2 rules) - RESUME_QUALITY_BAD_STRUCTURE (2 rules) - RESUME_QUALITY_BAD_PROFESSIONALISM (2 rules) - RESUME_QUALITY_BAD_DATE (1 rule) - RESUME_QUALITY_BAD_COMPLETENESS (2 rules)
- Add LLM layer: llm_resume_quality.py with LLMResumeQuality class - Add Response model: response_resume.py with ResponseResumeQuality schema - Update Prompt registration to link with LLMResumeQuality - Fix rule_resume.py slice index type issue (use int instead of dynamic config) Complete three-layer architecture: - Rule layer: 15 rule-based detection rules - Prompt layer: Chinese and English LLM prompts - LLM layer: Response processing and metric type mapping All layers properly registered and integrated through Dingo's auto-discovery mechanism.
feat: fix RuleUnsafeWords
feat: Add resume quality detection with complete three-layer architecture
…ng==24.1, use packaging>=24.1
fix: resolve packaging dependency conflict
feat: v1.11
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