backend/database/samples/samples_config.json

{
  "samples": [
    {
      "id": "python_documentation_demo",
      "name": "Python Documentation Assistant Demo",
      "description": "Comprehensive example showing RAG-powered AI assistant handling multi-turn programming inquiry with knowledge search, detailed explanations, code examples, performance analysis, and interactive learning",
      "trace_file": "traces/python_documentation_inquiry.json",
      "knowledge_graph_file": "knowledge_graphs/kg_python_documentation_enhanced.json",
      "tags": [
        "programming",
        "rag_assistant",
        "documentation",
        "failure_detection",
        "optimization"
      ],
      "complexity": "enhanced",
      "trace_type": "documentation_search",
      "trace_source": "sample_data",
      "features": [
        "rag_search",
        "failure_detection",
        "optimization_recommendations",
        "content_references",
        "quality_scoring"
      ]
    },
    {
      "id": "algorithm_sample_1",
      "name": "Location-Based Restaurant Discovery System",
      "description": "Complex location-based services example showcasing geographic analysis, time-based filtering, and data verification failures. Features Location-Based Services Expert, Eateries Expert, Data Verification Expert, and Computer Terminal collaborating on restaurant discovery near Harkness Memorial State Park with specific time constraints.",
      "trace_file": "traces/algorithm_sample_1.json",
      "knowledge_graph_file": "knowledge_graphs/kg_algorithm_sample_1.json",
      "tags": [
        "multi_agent",
        "algorithm_generated",
        "location_services",
        "data_verification",
        "real_failure",
        "geographic_analysis",
        "time_constraints",
        "api_integration"
      ],
      "complexity": "expert",
      "trace_type": "location_based_services",
      "trace_source": "algorithm_generated",
      "features": [
        "geographic_proximity_analysis",
        "time_based_filtering",
        "data_verification_failures",
        "api_integration_challenges",
        "multi_source_validation",
        "real_world_constraints",
        "execution_error_analysis",
        "tool_enhancement_recommendations",
        "temporal_replay",
        "progressive_knowledge_evolution",
        "window_based_visualization",
        "independent_window_analysis",
        "stage_specific_processing",
        "non_cumulative_visualization"
      ],
      "supports_replay": true,
      "window_info": {
        "window_count": 3,
        "processing_run_id": "sample_replay_002",
        "window_files": [
          "knowledge_graphs/kg_algorithm_sample_1_window_0.json",
          "knowledge_graphs/kg_algorithm_sample_1_window_1.json",
          "knowledge_graphs/kg_algorithm_sample_1_window_2.json"
        ],
        "progression_stages": [
          "User Query & Geographic Analysis",
          "Restaurant Data Collection",
          "Data Verification & Delivery"
        ],
        "stage_descriptions": [
          "用户输入餐厅查询，位置专家进行地理分析，计算工具提供支持",
          "餐厅专家独立收集和处理餐厅数据信息",
          "数据验证专家验证信息质量并向用户交付最终推荐结果"
        ],
        "window_mode": "independent",
        "window_description": "每个窗口代表餐厅发现流程中的独立处理阶段，展示不同专家和工具的专门职责"
      }
    },
    {
      "id": "algorithm_sample_3",
      "name": "Probability Game Theory Analysis System",
      "description": "Cross-disciplinary collaboration between probability and theoretical chemistry experts solving complex riddle scenarios. Demonstrates interdisciplinary problem-solving with game theory, statistical modeling, and chemical process analysis.",
      "trace_file": "traces/algorithm_sample_3.json",
      "knowledge_graph_file": "knowledge_graphs/kg_algorithm_sample_3.json",
      "tags": [
        "multi_agent",
        "algorithm_generated",
        "probability",
        "theoretical_chemistry",
        "game_theory",
        "simulation",
        "cross_disciplinary",
        "complex_riddles"
      ],
      "complexity": "expert",
      "trace_type": "probability_game_theory",
      "trace_source": "algorithm_generated",
      "features": [
        "cross_disciplinary_collaboration",
        "probability_calculations",
        "chemical_modeling",
        "game_theory_analysis",
        "simulation_implementation",
        "interdisciplinary_validation",
        "execution_error_analysis",
        "tool_enhancement_recommendations",
        "temporal_replay",
        "independent_window_analysis",
        "multi_disciplinary_analysis"
      ],
      "supports_replay": true,
      "window_info": {
        "window_count": 3,
        "processing_run_id": "sample_replay_algo3",
        "window_files": [
          "knowledge_graphs/kg_algorithm_sample_3_window_0.json",
          "knowledge_graphs/kg_algorithm_sample_3_window_1.json",
          "knowledge_graphs/kg_algorithm_sample_3_window_2.json"
        ],
        "progression_stages": [
          "Probability Analysis & Statistics",
          "Chemical Process Modeling",
          "Verification & Solution Delivery"
        ],
        "stage_descriptions": [
          "概率专家接收博弈理论查询，进行统计分析和概率计算",
          "理论化学专家独立进行化学过程建模分析",
          "验证专家进行解决方案验证并向游戏参与者交付最终结果"
        ],
        "window_mode": "independent",
        "window_description": "展示概率博弈理论的多专业分析流程，每个窗口聚焦不同专业领域"
      }
    },
    {
      "id": "algorithm_sample_14",
      "name": "Academic Literature Research System",
      "description": "Scholarly research system combining literary analysis and Norse mythology expertise for academic paper investigation. Features specialized academic database research and interdisciplinary scholarly analysis.",
      "trace_file": "traces/algorithm_sample_14.json",
      "knowledge_graph_file": "knowledge_graphs/kg_algorithm_sample_14.json",
      "tags": [
        "multi_agent",
        "algorithm_generated",
        "academic_research",
        "literature_analysis",
        "norse_mythology",
        "scholarly_work",
        "database_research",
        "interdisciplinary_studies"
      ],
      "complexity": "advanced",
      "trace_type": "academic_literature_analysis",
      "trace_source": "algorithm_generated",
      "features": [
        "academic_database_research",
        "literary_analysis_methods",
        "mythological_expertise",
        "scholarly_validation",
        "interdisciplinary_coordination",
        "retrieval_error_analysis",
        "database_tool_enhancement",
        "workflow_integration",
        "temporal_replay",
        "independent_window_analysis",
        "specialized_expert_workflow"
      ],
      "supports_replay": true,
      "window_info": {
        "window_count": 3,
        "processing_run_id": "sample_replay_algo14",
        "window_files": [
          "knowledge_graphs/kg_algorithm_sample_14_window_0.json",
          "knowledge_graphs/kg_algorithm_sample_14_window_1.json",
          "knowledge_graphs/kg_algorithm_sample_14_window_2.json"
        ],
        "progression_stages": [
          "Literature Investigation",
          "Mythological Analysis",
          "Academic Validation"
        ],
        "stage_descriptions": [
          "文学分析专家接收查询并进行学术文章调研",
          "北欧神话专家进行神话参考文献分析",
          "验证专家进行学术来源验证并交付研究结果"
        ],
        "window_mode": "independent",
        "window_description": "展示学术文献研究的多专业协作流程，每个窗口聚焦不同的研究领域"
      }
    },
    {
      "id": "algorithm_sample_16",
      "name": "Wildlife Data Analysis and Ecological Monitoring System",
      "description": "Comprehensive ecological data analysis system specializing in government wildlife datasets and invasive species monitoring. Demonstrates data science applications in conservation and ecological research.",
      "trace_file": "traces/algorithm_sample_16.json",
      "knowledge_graph_file": "knowledge_graphs/kg_algorithm_sample_16.json",
      "tags": [
        "multi_agent",
        "algorithm_generated",
        "data_analysis",
        "wildlife_research",
        "statistical_analysis",
        "ecological_data",
        "government_datasets",
        "conservation_science"
      ],
      "complexity": "advanced",
      "trace_type": "wildlife_data_analysis",
      "trace_source": "algorithm_generated",
      "features": [
        "government_dataset_processing",
        "statistical_population_analysis",
        "ecological_data_validation",
        "temporal_trend_analysis",
        "invasive_species_monitoring",
        "data_retrieval_challenges",
        "pipeline_optimization",
        "conservation_applications",
        "temporal_replay",
        "independent_window_analysis",
        "specialized_expert_workflow"
      ],
      "supports_replay": true,
      "window_info": {
        "window_count": 3,
        "processing_run_id": "sample_replay_algo16",
        "window_files": [
          "knowledge_graphs/kg_algorithm_sample_16_window_0.json",
          "knowledge_graphs/kg_algorithm_sample_16_window_1.json",
          "knowledge_graphs/kg_algorithm_sample_16_window_2.json"
        ],
        "progression_stages": [
          "Data Processing",
          "Statistical Analysis",
          "Ecological Validation"
        ],
        "stage_descriptions": [
          "数据分析专家接收查询并处理政府数据集",
          "统计分析专家进行种群统计计算",
          "数据验证专家进行生态数据验证并交付结果"
        ],
        "window_mode": "independent",
        "window_description": "展示野生动物数据分析的专业化处理流程，每个窗口代表不同的分析阶段"
      }
    }
  ],
  "metadata": {
    "version": "2.4.0",
    "created": "2025-01-27",
    "updated": "2025-01-27",
    "description": "Comprehensive AgentGraph sample data showcasing diverse multi-agent interactions across multiple domains including location services, probability theory, academic research, and ecological data analysis"
  }
}