[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-1697":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":14,"subscribersCount":14,"size":14,"stars1d":15,"stars7d":15,"stars30d":15,"stars90d":14,"forks30d":14,"starsTrendScore":16,"compositeScore":17,"rankGlobal":9,"rankLanguage":9,"license":18,"archived":19,"fork":19,"defaultBranch":20,"hasWiki":21,"hasPages":19,"topics":22,"createdAt":9,"pushedAt":9,"updatedAt":23,"readmeContent":24,"aiSummary":25,"trendingCount":14,"starSnapshotCount":14,"syncStatus":26,"lastSyncTime":27,"discoverSource":28},1697,"llm-production-toolkit","frckeepit\u002Fllm-production-toolkit","frckeepit","Production-ready toolkit for evaluating, monitoring, and ensuring safety of LLM deployments. Hallucination detection, bias evaluation, feedback loops, and production readiness assessment.",null,"Python",148,9,8,0,50,150,3,"MIT License",false,"main",true,[],"2026-06-12 02:00:31","# LLM Production Toolkit\n\nA Python toolkit for evaluating, monitoring, and ensuring the safety of LLM deployments in production.\n\n**The problem:** 95% of enterprise AI pilots fail to deliver value — not because the models are bad, but because organizations lack the production engineering to deploy them reliably.\n\n**The solution:** Concrete, runnable tools that address the most common failure modes: hallucination, bias, lack of feedback loops, and operational unreadiness.\n\n## Quick Start\n\n```bash\npip install llm-production-toolkit\n```\n\nFor ML-powered modules (hallucination detection):\n```bash\npip install llm-production-toolkit[hallucination]\n```\n\nFor everything:\n```bash\npip install llm-production-toolkit[all]\n```\n\n## Modules\n\n### Hallucination Grounding Check\n\nEvaluate whether LLM output is grounded in source documents. Uses embedding similarity + NLI entailment for robust detection.\n\n```bash\nllm-toolkit hallucination check \\\n  --output \"The Eiffel Tower was built in 1820\" \\\n  --source \"The Eiffel Tower was constructed from 1887 to 1889 in Paris, France\"\n```\n\n```python\nfrom llm_production_toolkit.hallucination import GroundingEvaluator\n\nevaluator = GroundingEvaluator()\nresult = evaluator.evaluate(\n    llm_output=\"The Eiffel Tower was built in 1820.\",\n    source_context=\"The Eiffel Tower was constructed from 1887 to 1889.\",\n)\nprint(f\"Grounding score: {result.overall_score:.2f}\")\nprint(f\"Flagged claims: {len(result.flagged_claims)}\")\n```\n\n### Bias Evaluation\n\nTest any LLM for demographic bias across gender, race, and age using controlled prompt variations.\n\n```python\nfrom llm_production_toolkit.bias import BiasEvaluator\n\ndef my_llm(prompt: str) -> str:\n    # Wrap any LLM — OpenAI, Anthropic, local model, etc.\n    return call_your_llm(prompt)\n\nevaluator = BiasEvaluator(llm_callable=my_llm, categories=[\"gender\", \"race\"])\nreport = evaluator.evaluate(num_runs=3)\nprint(f\"Overall bias score: {report.overall_bias_score:.2f}\")\n```\n\n### Production Feedback Loop\n\nCollect and analyze user feedback on LLM outputs. Python API or REST server.\n\n```bash\n# Start the feedback server\nllm-toolkit feedback start --port 8100\n\n# Check metrics\nllm-toolkit feedback metrics --window 24\n```\n\n```python\nfrom llm_production_toolkit.feedback import FeedbackCollector, FeedbackEntry\n\ncollector = FeedbackCollector(\"feedback.db\")\ncollector.record(FeedbackEntry(\n    session_id=\"sess-123\",\n    prompt_hash=\"abc\",\n    feedback_type=\"thumbs\",\n    thumbs_value=True,\n))\nmetrics = collector.get_metrics(window_hours=24)\nprint(f\"Satisfaction: {metrics.satisfaction_rate:.1%}\")\n```\n\n### Production Readiness Assessment\n\nInteractive CLI that scores your LLM deployment's operational maturity across 9 categories.\n\n```bash\nllm-toolkit readiness assess\n```\n\nProduces a readiness score (0-100) with category breakdowns and prioritized recommendations.\n\n### Compliance Mapper\n\nMap evaluation results to AI best-practice requirements. Works with any subset of module outputs.\n\n```bash\nllm-toolkit compliance report \\\n  --readiness readiness.json \\\n  --hallucination grounding.json\n```\n\n## Architecture\n\nEach module is independently usable with its own CLI and Python API. Optional dependencies keep the core installation small (~5MB). ML-powered modules (hallucination, bias) add heavier dependencies only when needed.\n\n```\nllm-production-toolkit\n├── hallucination    # Grounding evaluation (requires torch)\n├── bias             # Demographic bias testing (requires textblob)\n├── feedback         # Feedback collection (requires fastapi)\n├── readiness        # Readiness assessment (core deps only)\n└── compliance       # Compliance mapping (core deps only)\n```\n\n## Development\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Ffrckeepit\u002Fllm-production-toolkit.git\ncd llm-production-toolkit\npip install -e \".[dev,all]\"\npytest tests\u002F -m \"not slow\"\nruff check src\u002F tests\u002F\n```\n\n## License\n\nMIT\n","LLM Production Toolkit 是一个用于评估、监控和保障大型语言模型（LLM）在生产环境中部署安全性的Python工具包。它提供了一系列具体可运行的工具，包括幻觉检测、偏见评估、反馈循环机制以及生产就绪性评估等功能，旨在解决如幻觉生成、数据偏见、缺乏用户反馈等常见问题。该工具包特别适合于需要确保AI系统稳定性和可靠性的企业级应用场景中使用，帮助组织克服从试点到实际部署过程中的挑战。",2,"2026-06-11 02:45:29","CREATED_QUERY"]