add

KNOWLEDGE_GRAPH_EXAMPLE.md

@@ -1,142 +0,0 @@
-# AgentGraph Knowledge Graph 完美示例
-
-这是一个完整的、生产就绪的 Knowledge Graph 示例，展示了 AgentGraph 的所有核心功能。
-
-## 📊 **数据概览**
-
-- **实体数量**: 6 个（Agent, Task, Input, Output, Human, Tool）
-- **关系数量**: 6 个（涵盖所有标准关系类型）
-- **失败检测**: 3 个实际问题（拼写错误、系统缺陷、流程问题）
-- **优化建议**: 4 个可操作的改进方案
-- **质量分数**: 0.89/1.0 整体评分
-
-## 🎯 **核心特性展示**
-
-### ✅ **完整的实体模型**
-
-每个实体都包含：
-
-- 清晰的`raw_prompt`内容（无转义字符）
-- 精确的`ContentReference`引用（带置信度）
-- 正确的重要性级别分类
-- 有意义的实体名称
-
-### ✅ **丰富的关系描述**
-
-每个关系都具备：
-
-- 详细的`interaction_prompt`说明
-- 准确的源实体和目标实体映射
-- 正确的关系类型使用
-- 实际的交互场景描述
-
-### ✅ **智能失败检测**
-
-实现了 3 种失败类型：
-
-- **HALLUCINATION**: 用户输入拼写错误
-- **AGENT_ERROR**: 系统提示拼写错误
-- **PLANNING_ERROR**: 缺失验证步骤
-
-### ✅ **实用优化建议**
-
-提供了 4 类优化建议：
-
-- **PROMPT_REFINEMENT**: 提示词改进
-- **WORKFLOW_SIMPLIFICATION**: 流程优化
-- **TOOL_ENHANCEMENT**: 工具增强
-- **反馈机制**: 质量改进循环
-
-### ✅ **高质量元数据**
-
-包含完整的处理信息：
-
-- 内容解析统计
-- 窗口处理信息
-- 合并去重统计
-- 质量评估指标
-
-## 🚀 **使用方式**
-
-### 1. **作为初始数据**
-
-```bash
-# 将此文件用作系统初始化数据
-cp sample_knowledge_graph.json data/initial_knowledge_graph.json
-```
-
-### 2. **作为测试数据**
-
-```bash
-# 用于功能测试和展示
-curl -X POST /api/knowledge-graphs \
-  -H "Content-Type: application/json" \
-  -d @sample_knowledge_graph.json
-```
-
-### 3. **作为参考模板**
-
-开发者可以参考此结构创建新的 knowledge graphs
-
-## 🎨 **前端展示效果**
-
-此示例支持以下前端功能：
-
-### **实体可视化**
-
-- **颜色编码**: 基于类型和重要性
-- **大小调整**: 反映实体重要性
-- **工具提示**: 显示 raw_prompt 内容
-
-### **关系展示**
-
-- **箭头方向**: 明确的数据流向
-- **线条样式**: 区分不同关系类型
-- **交互说明**: 悬停显示 interaction_prompt
-
-### **问题突出显示**
-
-- **失败标记**: 红色边框或图标
-- **风险分类**: 不同颜色表示风险类型
-- **影响范围**: 突出显示受影响的实体
-
-### **优化建议面板**
-
-- **建议列表**: 右侧面板显示
-- **优先级排序**: 基于影响范围
-- **操作按钮**: 快速应用建议
-
-## 📋 **质量指标**
-
-| 指标       | 分数 | 说明                   |
-| ---------- | ---- | ---------------------- |
-| 整体质量   | 0.89 | 高质量 knowledge graph |
-| 实体质量   | 0.91 | 实体定义清晰准确       |
-| 关系质量   | 0.88 | 关系映射正确完整       |
-| 内容引用   | 0.87 | ContentReference 准确  |
-| 失败检测   | 0.92 | 问题识别全面           |
-| 优化相关性 | 0.86 | 建议实用可行           |
-
-## 🔄 **更新建议**
-
-定期使用此模板检查新生成的 knowledge graphs 是否具备：
-
-1. ✅ 所有必需字段
-2. ✅ 非空的 failures 和 optimizations 数组
-3. ✅ 正确的 ContentReference 格式
-4. ✅ 有意义的 system_summary
-5. ✅ 完整的元数据信息
-
-## 📝 **最佳实践**
-
-基于此示例，建议在 AgentGraph 中：
-
-1. **始终包含失败检测**，即使没有发现问题也要有空数组
-2. **提供优化建议**，展示系统的分析能力
-3. **使用置信度评分**，帮助用户评估结果质量
-4. **编写流畅的 system_summary**，包含指代词的自然叙述
-5. **确保 ContentReference 准确性**，支持内容溯源
-
----
-
-_此示例代表了 AgentGraph knowledge extraction 的最高质量标准，可直接用于生产环境。_

README_KNOWLEDGE_GRAPH.md

@@ -1,179 +0,0 @@
-# 🧠 AgentGraph Knowledge Graph 完美示例指南
-
-本目录包含了 AgentGraph 系统的完美 Knowledge Graph 示例，展示了所有核心功能和最佳实践。
-
-## 📁 **文件说明**
-
-### 1. `sample_knowledge_graph.json` - 完整功能示例
-
-- **用途**: 展示 AgentGraph 的完整功能集
-- **特色**: 包含所有字段、详细元数据、质量评估
-- **场景**: 功能演示、质量基准、开发参考
-
-### 2. `initial_data.json` - 简化初始化数据
-
-- **用途**: HF 环境初始化和快速部署
-- **特色**: 核心功能完整、元数据简化、易于理解
-- **场景**: 系统初始化、用户培训、快速展示
-
-## 🎯 **核心特性对比**
-
-| 特性         | sample_knowledge_graph.json | initial_data.json |
-| ------------ | --------------------------- | ----------------- |
-| **实体数量** | 6 个（完整示例）            | 6 个（核心示例）  |
-| **关系数量** | 6 个（全类型覆盖）          | 5 个（主要流程）  |
-| **失败检测** | 3 个详细案例                | 1 个简化案例      |
-| **优化建议** | 4 个全面建议                | 2 个重点建议      |
-| **元数据**   | 完整处理统计                | 简化核心信息      |
-| **复杂度**   | 生产级完整                  | 演示级简化        |
-
-## 🚀 **使用指南**
-
-### **方式 1: 快速体验（推荐新用户）**
-
-```bash
-# 使用简化版本进行初始化
-cp initial_data.json data/demo_knowledge_graph.json
-```
-
-### **方式 2: 完整功能展示（推荐开发者）**
-
-```bash
-# 使用完整版本展示所有功能
-cp sample_knowledge_graph.json data/full_demo_knowledge_graph.json
-```
-
-### **方式 3: API 导入**
-
-```javascript
-// 前端导入示例
-fetch("/api/knowledge-graphs/import", {
-  method: "POST",
-  headers: { "Content-Type": "application/json" },
-  body: JSON.stringify(initialData),
-});
-```
-
-## 🔍 **数据结构亮点**
-
-### ✅ **实体模型完善**
-
-```json
-{
-  "id": "agent_001",
-  "type": "Agent",
-  "name": "Oxford Economics Knowledge Agent",
-  "importance": "HIGH",
-  "raw_prompt": "清晰可读的提示内容",
-  "raw_prompt_ref": [{ "line_start": 31, "line_end": 32 }]
-}
-```
-
-### ✅ **关系描述丰富**
-
-```json
-{
-  "id": "rel_001",
-  "source": "input_001",
-  "target": "agent_001",
-  "type": "CONSUMED_BY",
-  "interaction_prompt": "具体的交互场景描述"
-}
-```
-
-### ✅ **失败检测智能**
-
-```json
-{
-  "id": "failure_001",
-  "risk_type": "HALLUCINATION",
-  "description": "具体问题描述和影响分析",
-  "affected_id": "input_001"
-}
-```
-
-### ✅ **优化建议实用**
-
-```json
-{
-  "id": "opt_001",
-  "recommendation_type": "PROMPT_REFINEMENT",
-  "description": "详细的改进建议和实施方案",
-  "affected_ids": ["agent_001"]
-}
-```
-
-## 🎨 **前端渲染支持**
-
-这些 Knowledge Graph 为前端提供了丰富的渲染数据：
-
-### **实体可视化**
-
-- 基于`type`和`importance`的颜色编码
-- 悬停显示`raw_prompt`内容
-- 点击查看`ContentReference`详情
-
-### **关系流向**
-
-- 清晰的箭头指向和数据流
-- `interaction_prompt`作为边标签
-- 不同`type`的线条样式区分
-
-### **问题突出显示**
-
-- `failures`用红色边框标记
-- 悬停显示风险类型和描述
-- 受影响实体的高亮显示
-
-### **优化建议面板**
-
-- 右侧面板显示建议列表
-- 按`recommendation_type`分组
-- 点击查看详细改进方案
-
-## 📊 **质量标准**
-
-基于这些示例，高质量 Knowledge Graph 应具备：
-
-1. **完整性** (✓ 所有必需字段存在)
-2. **准确性** (✓ ContentReference 指向正确)
-3. **可读性** (✓ 描述清晰易懂)
-4. **实用性** (✓ 失败和优化建议有价值)
-5. **一致性** (✓ 命名和格式统一)
-
-## 🔄 **自定义建议**
-
-在创建新的 Knowledge Graph 时：
-
-### **保持的元素**
-
-- 标准的实体类型 (Agent, Task, Tool, Input, Output, Human)
-- 预定义的关系类型 (CONSUMED_BY, PERFORMS, 等)
-- 结构化的`ContentReference`格式
-- 有意义的`system_summary`
-
-### **可调整的元素**
-
-- 具体的实体数量和名称
-- 失败类型和数量（基于实际问题）
-- 优化建议的具体内容
-- 元数据的详细程度
-
-## 🛠 **开发者提示**
-
-1. **总是包含 failures 和 optimizations**，即使是空数组
-2. **使用有意义的实体 ID**，便于调试和维护
-3. **确保 ContentReference 准确性**，支持内容溯源
-4. **编写流畅的 system_summary**，包含指代词
-5. **提供置信度信息**，帮助质量评估
-
-## 📝 **更新历史**
-
-- **v2.1.0**: 添加了置信度评分和优化建议
-- **v2.0.0**: 引入失败检测和内容引用
-- **v1.5.0**: 增强元数据和质量指标
-- **v1.0.0**: 基础 Knowledge Graph 结构
-
----
-
-_这些示例代表了 AgentGraph Knowledge Graph 的最高质量标准，可作为开发和评估的基准。_

agentgraph/shared/models/reference_based/relation.py

@@ -93,3 +93,5 @@ DEFAULT_RELATION_TYPES = [
         target_type="Task"
     )
 ]
+
+

example_template_hand_crafted.json

@@ -1,18 +0,0 @@
-{
-  "id": 58,
-  "subset": "Hand-Crafted",
-  "mistake_step": 1,
-  "question": "Your question here - what task is the agent trying to solve?",
-  "agent": "Primary_Agent_Name",
-  "agents": [
-    "Agent1",
-    "Agent2",
-    "Agent3"
-  ],
-  "trace": "[\n  {\n    \"content\": \"System prompt or initial instruction\",\n    \"name\": \"System\",\n    \"role\": \"system\"\n  },\n  {\n    \"content\": \"User's question or task description\",\n    \"name\": \"User\",\n    \"role\": \"user\"\n  },\n  {\n    \"content\": \"Agent's response or action\",\n    \"name\": \"Agent_Name\",\n    \"role\": \"assistant\"\n  },\n  {\n    \"content\": \"Follow-up interaction or error\",\n    \"name\": \"Agent_Name\",\n    \"role\": \"assistant\"\n  }\n]",
-  "is_correct": false,
-  "question_id": "84c5fae2-0bad-47f2-87f5-61bd66ab3a84",
-  "ground_truth": "The correct answer or expected result",
-  "mistake_agent": "Agent_Name",
-  "mistake_reason": "Specific reason why the agent failed - be descriptive"
-}

initial_data.json

@@ -1,210 +0,0 @@
-{
-  "system_name": "Oxford Economics AI Assistant",
-  "system_summary": "This intelligent assistant processes user inquiries about Oxford Economics through a streamlined workflow. When users submit questions, the knowledgeable agent analyzes them using advanced language models and delivers accurate, contextual responses about economic analysis and forecasting services.",
-  "entities": [
-    {
-      "id": "agent_001",
-      "type": "Agent",
-      "name": "Oxford Economics Knowledge Agent",
-      "importance": "HIGH",
-      "raw_prompt": "You are a knowledgeable assistant on Oxford Economics designed to answer questions accurately based on the provided context. Use the information in the documents below to respond concisely and correctly.",
-      "raw_prompt_ref": [
-        {
-          "line_start": 31,
-          "line_end": 32
-        }
-      ]
-    },
-    {
-      "id": "task_001",
-      "type": "Task",
-      "name": "Economic Inquiry Processing",
-      "importance": "HIGH",
-      "raw_prompt": "Process user inquiry about Oxford Economics and generate an accurate, contextual response based on available information and company expertise.",
-      "raw_prompt_ref": [
-        {
-          "line_start": 26,
-          "line_end": 28
-        }
-      ]
-    },
-    {
-      "id": "input_001",
-      "type": "Input",
-      "name": "User Economic Query",
-      "importance": "HIGH",
-      "raw_prompt": "what does oxford economics present?",
-      "raw_prompt_ref": [
-        {
-          "line_start": 19,
-          "line_end": 19
-        }
-      ]
-    },
-    {
-      "id": "output_001",
-      "type": "Output",
-      "name": "Economic Services Overview",
-      "importance": "HIGH",
-      "raw_prompt": "Oxford Economics provides economic analysis, forecasting, and consultancy services.",
-      "raw_prompt_ref": [
-        {
-          "line_start": 20,
-          "line_end": 20
-        }
-      ]
-    },
-    {
-      "id": "human_001",
-      "type": "Human",
-      "name": "Business User",
-      "importance": "MEDIUM",
-      "raw_prompt": "Professional seeking economic insights and analysis",
-      "raw_prompt_ref": [
-        {
-          "line_start": 31,
-          "line_end": 31
-        }
-      ]
-    },
-    {
-      "id": "tool_001",
-      "type": "Tool",
-      "name": "GPT-4o Language Model",
-      "importance": "HIGH",
-      "raw_prompt": "Advanced AI language model with economic domain knowledge and structured response capabilities.",
-      "raw_prompt_ref": [
-        {
-          "line_start": 49,
-          "line_end": 49
-        }
-      ]
-    }
-  ],
-  "relations": [
-    {
-      "id": "rel_001",
-      "source": "input_001",
-      "target": "agent_001",
-      "type": "CONSUMED_BY",
-      "importance": "HIGH",
-      "interaction_prompt": "User query received and processed by the economic knowledge agent",
-      "interaction_prompt_ref": [
-        {
-          "line_start": 19,
-          "line_end": 19
-        }
-      ]
-    },
-    {
-      "id": "rel_002",
-      "source": "agent_001",
-      "target": "task_001",
-      "type": "PERFORMS",
-      "importance": "HIGH",
-      "interaction_prompt": "Agent executes economic inquiry processing task",
-      "interaction_prompt_ref": [
-        {
-          "line_start": 26,
-          "line_end": 28
-        }
-      ]
-    },
-    {
-      "id": "rel_003",
-      "source": "task_001",
-      "target": "output_001",
-      "type": "PRODUCES",
-      "importance": "HIGH",
-      "interaction_prompt": "Processing task generates comprehensive economic services response",
-      "interaction_prompt_ref": [
-        {
-          "line_start": 20,
-          "line_end": 20
-        }
-      ]
-    },
-    {
-      "id": "rel_004",
-      "source": "output_001",
-      "target": "human_001",
-      "type": "DELIVERS_TO",
-      "importance": "HIGH",
-      "interaction_prompt": "Economic analysis delivered to requesting business user",
-      "interaction_prompt_ref": [
-        {
-          "line_start": 20,
-          "line_end": 20
-        }
-      ]
-    },
-    {
-      "id": "rel_005",
-      "source": "agent_001",
-      "target": "tool_001",
-      "type": "USES",
-      "importance": "HIGH",
-      "interaction_prompt": "Agent leverages language model for natural language understanding and generation",
-      "interaction_prompt_ref": [
-        {
-          "line_start": 49,
-          "line_end": 49
-        }
-      ]
-    }
-  ],
-  "failures": [
-    {
-      "id": "failure_001",
-      "risk_type": "HALLUCINATION",
-      "description": "Minor spelling inconsistency in user query may affect search precision.",
-      "raw_text": "what does oxford economics present?",
-      "raw_text_ref": [
-        {
-          "line_start": 19,
-          "line_end": 19
-        }
-      ],
-      "affected_id": "input_001"
-    }
-  ],
-  "optimizations": [
-    {
-      "id": "opt_001",
-      "recommendation_type": "PROMPT_REFINEMENT",
-      "description": "Enhance the agent prompt to include spell-checking and query normalization capabilities for improved accuracy and user experience.",
-      "affected_ids": ["agent_001"],
-      "raw_text_ref": [
-        {
-          "line_start": 31,
-          "line_end": 32
-        }
-      ]
-    },
-    {
-      "id": "opt_002",
-      "recommendation_type": "TOOL_ENHANCEMENT",
-      "description": "Integrate Oxford Economics knowledge base with the language model to provide more specific and detailed responses about services and capabilities.",
-      "affected_ids": ["tool_001"],
-      "raw_text_ref": [
-        {
-          "line_start": 49,
-          "line_end": 49
-        }
-      ]
-    }
-  ],
-  "metadata": {
-    "creation_timestamp": "2025-01-27T12:00:00Z",
-    "schema_version": "2.1.0",
-    "quality_score": 0.89,
-    "entity_count": 6,
-    "relation_count": 5,
-    "failure_count": 1,
-    "optimization_count": 2,
-    "processing_method": "production_enhanced",
-    "content_source": "aif_inference_trace",
-    "language": "en",
-    "domain": "economics_consulting"
-  }
-}

parse_kg_output.py

@@ -1 +0,0 @@
- 

sample_knowledge_graph.json

@@ -1,390 +0,0 @@
-{
-    "system_name": "Oxford Economics Inquiry and Response System",
-    "system_summary": "This system manages Oxford Economics inquiries through a streamlined workflow. The process begins when `Mateusz Urban` (human_001) submits their `User Inquiry Prompt` (input_001), which gets consumed by the `Knowledgeable Assistant on Oxford Economics` (agent_001). The assistant then performs the `Inquiry Response Task` (task_001) using the `GPT-4o Model` (tool_001) to generate the `Oxford Economics Definition Response` (output_001), which is ultimately delivered back to the user, completing the information flow cycle.",
-    "entities": [
-        {
-            "id": "agent_001",
-            "type": "Agent",
-            "name": "Knowledgeable Assistant on Oxford Economics",
-            "importance": "HIGH",
-            "raw_prompt": "You are a knowledgeable assistant on Oxford Economics designed to answer questions accurately based on the provided context. Use the information in the documents below to respond concisely and correctly.",
-            "raw_prompt_ref": [
-                {
-                    "line_start": 31,
-                    "line_end": 32,
-                    "confidence": 0.95
-                }
-            ]
-        },
-        {
-            "id": "task_001",
-            "type": "Task", 
-            "name": "Inquiry Response Task",
-            "importance": "HIGH",
-            "raw_prompt": "Process user inquiry about Oxford Economics and generate an accurate, contextual response based on available information and company background.",
-            "raw_prompt_ref": [
-                {
-                    "line_start": 26,
-                    "line_end": 28,
-                    "confidence": 0.9
-                }
-            ]
-        },
-        {
-            "id": "output_001",
-            "type": "Output",
-            "name": "Oxford Economics Definition Response",
-            "importance": "HIGH",
-            "raw_prompt": "Oxford Economics provides economic analysis, forecasting, and consultancy services.",
-            "raw_prompt_ref": [
-                {
-                    "line_start": 20,
-                    "line_end": 20,
-                    "confidence": 1.0
-                }
-            ]
-        },
-        {
-            "id": "input_001",
-            "type": "Input",
-            "name": "User Inquiry Prompt",
-            "importance": "HIGH",
-            "raw_prompt": "what does oxford eonomics present?",
-            "raw_prompt_ref": [
-                {
-                    "line_start": 19,
-                    "line_end": 19,
-                    "confidence": 1.0
-                }
-            ]
-        },
-        {
-            "id": "human_001",
-            "type": "Human",
-            "name": "Mateusz Urban",
-            "importance": "MEDIUM",
-            "raw_prompt": "User interaction pattern: submits inquiry and receives response",
-            "raw_prompt_ref": [
-                {
-                    "line_start": 31,
-                    "line_end": 31,
-                    "confidence": 0.8
-                }
-            ]
-        },
-        {
-            "id": "tool_001",
-            "type": "Tool",
-            "name": "GPT-4o Model (2024-11-20)",
-            "importance": "HIGH",
-            "raw_prompt": "AI language model configured for Oxford Economics domain knowledge with structured response capabilities and context-aware processing.",
-            "raw_prompt_ref": [
-                {
-                    "line_start": 49,
-                    "line_end": 49,
-                    "confidence": 1.0
-                }
-            ]
-        }
-    ],
-    "relations": [
-        {
-            "id": "relation_001",
-            "source": "input_001",
-            "target": "agent_001",
-            "type": "CONSUMED_BY",
-            "importance": "HIGH",
-            "interaction_prompt": "User inquiry processed by assistant: 'what does oxford eonomics present?'",
-            "interaction_prompt_ref": [
-                {
-                    "line_start": 19,
-                    "line_end": 19,
-                    "confidence": 1.0
-                }
-            ]
-        },
-        {
-            "id": "relation_002",
-            "source": "agent_001",
-            "target": "task_001",
-            "type": "PERFORMS",
-            "importance": "HIGH",
-            "interaction_prompt": "Assistant actively processes the Oxford Economics inquiry using domain knowledge",
-            "interaction_prompt_ref": [
-                {
-                    "line_start": 31,
-                    "line_end": 32,
-                    "confidence": 0.9
-                }
-            ]
-        },
-        {
-            "id": "relation_003",
-            "source": "task_001",
-            "target": "output_001",
-            "type": "PRODUCES",
-            "importance": "HIGH",
-            "interaction_prompt": "Task generates structured response about Oxford Economics services",
-            "interaction_prompt_ref": [
-                {
-                    "line_start": 20,
-                    "line_end": 20,
-                    "confidence": 1.0
-                }
-            ]
-        },
-        {
-            "id": "relation_004",
-            "source": "output_001",
-            "target": "human_001",
-            "type": "DELIVERS_TO",
-            "importance": "HIGH",
-            "interaction_prompt": "Response delivered to Mateusz Urban with Oxford Economics definition",
-            "interaction_prompt_ref": [
-                {
-                    "line_start": 20,
-                    "line_end": 20,
-                    "confidence": 1.0
-                }
-            ]
-        },
-        {
-            "id": "relation_005",
-            "source": "agent_001",
-            "target": "tool_001",
-            "type": "USES",
-            "importance": "HIGH",
-            "interaction_prompt": "Assistant leverages GPT-4o model capabilities for processing and response generation",
-            "interaction_prompt_ref": [
-                {
-                    "line_start": 49,
-                    "line_end": 49,
-                    "confidence": 0.9
-                }
-            ]
-        },
-        {
-            "id": "relation_006",
-            "source": "task_001",
-            "target": "tool_001",
-            "type": "REQUIRED_BY",
-            "importance": "HIGH",
-            "interaction_prompt": "Task execution requires GPT-4o model for natural language processing",
-            "interaction_prompt_ref": [
-                {
-                    "line_start": 49,
-                    "line_end": 49,
-                    "confidence": 0.8
-                }
-            ]
-        }
-    ],
-    "failures": [
-        {
-            "id": "failure_001",
-            "risk_type": "HALLUCINATION",
-            "description": "User input contains spelling error 'eonomics' instead of 'economics' which may lead to misinterpretation or processing errors.",
-            "raw_text": "what does oxford eonomics present?",
-            "raw_text_ref": [
-                {
-                    "line_start": 19,
-                    "line_end": 19,
-                    "confidence": 1.0
-                }
-            ],
-            "affected_id": "input_001"
-        },
-        {
-            "id": "failure_002", 
-            "risk_type": "AGENT_ERROR",
-            "description": "System prompt contains spelling error 'knowledgable' instead of 'knowledgeable' which may affect professional credibility.",
-            "raw_text": "You are a knowledgable assitant on Oxford Economics",
-            "raw_text_ref": [
-                {
-                    "line_start": 31,
-                    "line_end": 31,
-                    "confidence": 0.9
-                }
-            ],
-            "affected_id": "agent_001"
-        },
-        {
-            "id": "failure_003",
-            "risk_type": "PLANNING_ERROR",
-            "description": "Missing validation step for user input quality and spell-checking before processing, leading to potential propagation of errors.",
-            "raw_text": "",
-            "raw_text_ref": [
-                {
-                    "line_start": 19,
-                    "line_end": 32,
-                    "confidence": 0.7
-                }
-            ],
-            "affected_id": "task_001"
-        }
-    ],
-    "optimizations": [
-        {
-            "id": "opt_001",
-            "recommendation_type": "PROMPT_REFINEMENT",
-            "description": "Enhance the system prompt to include explicit spell-checking and error correction capabilities. The current prompt should be refined to handle common misspellings and provide clarification when ambiguous terms are encountered. This would improve robustness and user experience.",
-            "affected_ids": ["agent_001"],
-            "raw_text_ref": [
-                {
-                    "line_start": 31,
-                    "line_end": 32,
-                    "confidence": 0.9
-                }
-            ]
-        },
-        {
-            "id": "opt_002",
-            "recommendation_type": "WORKFLOW_SIMPLIFICATION",
-            "description": "Add an input validation and preprocessing step before the main task execution. This would include spell-checking, query normalization, and intent clarification to improve overall system reliability and reduce downstream errors.",
-            "affected_ids": ["task_001", "input_001"],
-            "raw_text_ref": [
-                {
-                    "line_start": 19,
-                    "line_end": 19,
-                    "confidence": 0.8
-                }
-            ]
-        },
-        {
-            "id": "opt_003",
-            "recommendation_type": "TOOL_ENHANCEMENT",
-            "description": "Configure the GPT-4o model with specific Oxford Economics domain knowledge and terminology database to provide more accurate and detailed responses. Consider implementing RAG (Retrieval-Augmented Generation) with Oxford Economics documentation.",
-            "affected_ids": ["tool_001"],
-            "raw_text_ref": [
-                {
-                    "line_start": 49,
-                    "line_end": 49,
-                    "confidence": 0.8
-                }
-            ]
-        },
-        {
-            "id": "opt_004",
-            "recommendation_type": "PROMPT_REFINEMENT",
-            "description": "Implement response quality metrics and feedback collection from users to continuously improve the system's knowledge base and response accuracy. This would enable iterative enhancement of the Oxford Economics information repository.",
-            "affected_ids": ["output_001", "human_001"],
-            "raw_text_ref": [
-                {
-                    "line_start": 20,
-                    "line_end": 20,
-                    "confidence": 0.7
-                }
-            ]
-        }
-    ],
-    "metadata": {
-        "content_resolution": {
-            "resolved_at": "2025-01-27T11:35:54.766346",
-            "original_trace_length": 4203,
-            "resolution_method": "enhanced_content_reference_resolver",
-            "confidence_scoring": true
-        },
-        "window_info": {
-            "window_index": 1,
-            "window_start_char": 1914,
-            "window_end_char": 4202,
-            "chunk_size": 2288,
-            "window_size": 800000,
-            "overlap_size": 1144,
-            "splitter_type": "agent_semantic",
-            "log_type": "structured_json",
-            "boundary_used": "json_object_end",
-            "boundary_confidence": 0.8,
-            "contains_agent_markers": false,
-            "contains_tool_patterns": true,
-            "overlap_with_previous": true,
-            "global_line_start": 1,
-            "global_line_end": 53,
-            "processed_at": "2025-01-27T11:35:22.437186",
-            "line_mapping_created": true,
-            "window_total": 4,
-            "trace_id": "1dca1078-8505-4263-998a-740e7794a94c",
-            "processing_run_id": "eac673d4"
-        },
-        "merge_info": {
-            "source_graphs": 2,
-            "merge_timestamp": "2025-01-27T11:35:54.762510",
-            "window_count": 2,
-            "merged_entity_count": 6,
-            "merged_relation_count": 6,
-            "deduplication_applied": true,
-            "quality_score": 0.92
-        },
-        "processing_info": {
-            "entity_deduplication": {
-                "original_count": 8,
-                "deduplicated_count": 6,
-                "duplicates_removed": 2
-            },
-            "relationship_deduplication": {
-                "original_count": 7,
-                "deduplicated_count": 6,
-                "duplicates_removed": 1
-            },
-            "failure_detection": {
-                "total_failures_detected": 3,
-                "failure_types": ["HALLUCINATION", "AGENT_ERROR", "PLANNING_ERROR"],
-                "confidence_threshold": 0.7
-            },
-            "optimization_generation": {
-                "total_optimizations": 4,
-                "recommendation_types": ["PROMPT_REFINEMENT", "WORKFLOW_SIMPLIFICATION", "TOOL_ENHANCEMENT"],
-                "priority_scoring": true
-            }
-        },
-        "hierarchical_merge_info": {
-            "source_graphs": 4,
-            "batch_size": 3,
-            "max_parallel": 3,
-            "merge_timestamp": "2025-01-27T11:35:54.763664",
-            "total_window_count": 4,
-            "final_entity_count": 6,
-            "final_relation_count": 6,
-            "skip_layers_threshold": 3,
-            "optimization_applied": true,
-            "failure_detection_enabled": true
-        },
-        "trace_info": {
-            "trace_id": "1dca1078-8505-4263-998a-740e7794a94c",
-            "window_count": 4,
-            "processed_at": "2025-01-27T11:35:54.764137",
-            "source_trace_id": "1dca1078-8505-4263-998a-740e7794a94c",
-            "processing_run_id": "eac673d4",
-            "quality_assessment": {
-                "overall_score": 0.89,
-                "entity_quality": 0.91,
-                "relation_quality": 0.88,
-                "content_reference_quality": 0.87,
-                "failure_detection_coverage": 0.92,
-                "optimization_relevance": 0.86
-            }
-        },
-        "processing_params": {
-            "method_name": "production_enhanced",
-            "batch_size": 3,
-            "parallel_processing": true,
-            "merge_method": "hierarchical_batch_with_quality_control",
-            "optimization_applied": true,
-            "failure_detection_enabled": true,
-            "confidence_scoring_enabled": true,
-            "window_size": 800000,
-            "overlap_size": 2288,
-            "splitter_type": "agent_semantic",
-            "enhancement_features": [
-                "spell_checking",
-                "content_quality_assessment",
-                "automatic_optimization_generation",
-                "comprehensive_failure_detection"
-            ]
-        },
-        "schema_version": "2.1.0",
-        "generation_timestamp": "2025-01-27T12:00:00.000000Z",
-        "model_used": "gpt-4o-mini-enhanced",
-        "processing_duration_seconds": 168.4
-    }
-}