2aa6e9240f
- 新增 context_rewrite 工具：允许 AI 在单轮内压缩工具调用上下文 - 清理 persona_query_max 和 task_query_max 死配置（commit 9be60ba 引入） - 同步全栈：后端/前端/文档/测试 18 个文件 - 测试：70/70 通过
330 lines
11 KiB
Python
330 lines
11 KiB
Python
"""
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工具执行器
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"""
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import json
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from typing import Any, Dict
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def execute_tool(graph: Any, tool_name: str, arguments: dict) -> str:
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"""执行工具调用"""
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print(f"\n[工具调用] {tool_name}")
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print(f"[参数] {json.dumps(arguments, ensure_ascii=False, indent=2)}")
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try:
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# 基础记忆工具
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if tool_name == "memory_recall":
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result = graph.recall(
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query_intent=arguments.get("query_intent", ""),
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seed_entities=arguments.get("seed_entities"),
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depth=arguments.get("depth", 2),
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time_range=arguments.get("time_range"),
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session_filter=arguments.get("session_filter")
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)
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return format_recall_result(result)
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elif tool_name == "memory_commit":
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result = graph.commit(
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triplets=arguments.get("triplets", []),
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entity_types=arguments.get("entity_types"),
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temporal_tag=arguments.get("temporal_tag")
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)
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return json.dumps(result, ensure_ascii=False, default=str)
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elif tool_name == "memory_purge":
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result = graph.purge(
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criteria=arguments.get("criteria", {}),
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mode=arguments.get("mode", "soft"),
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new_relation=arguments.get("new_relation")
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)
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return json.dumps(result, ensure_ascii=False, default=str)
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elif tool_name == "memory_introspect":
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result = graph.introspect(session_id=arguments.get("session_id"))
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return json.dumps(result, ensure_ascii=False, default=str)
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elif tool_name == "memory_archive":
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result = graph.archive(days=arguments.get("days", 30))
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return json.dumps(result, ensure_ascii=False, default=str)
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elif tool_name == "memory_cleanup":
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result = graph.cleanup(dry_run=arguments.get("dry_run", True))
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return json.dumps(result, ensure_ascii=False, default=str)
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elif tool_name == "context_rewrite":
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result = execute_context_rewrite(graph, arguments)
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return json.dumps(result, ensure_ascii=False, default=str)
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# 人设图管理工具
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elif tool_name == "persona_update":
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result = execute_persona_update(graph, arguments)
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return json.dumps(result, ensure_ascii=False, default=str)
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elif tool_name == "persona_clear":
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result = execute_persona_clear(graph, arguments)
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return json.dumps(result, ensure_ascii=False, default=str)
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# 工作记忆链管理工具
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elif tool_name == "task_create":
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result = execute_task_create(graph, arguments)
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return json.dumps(result, ensure_ascii=False, default=str)
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elif tool_name == "task_set_state":
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result = execute_task_set_state(graph, arguments)
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return json.dumps(result, ensure_ascii=False, default=str)
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elif tool_name == "task_delete":
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result = execute_task_delete(graph, arguments)
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return json.dumps(result, ensure_ascii=False, default=str)
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elif tool_name == "task_link_info":
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result = execute_task_link_info(graph, arguments)
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return json.dumps(result, ensure_ascii=False, default=str)
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return f"未知工具: {tool_name}"
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except Exception as e:
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return f"工具执行错误: {str(e)}"
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def format_recall_result(result: dict) -> str:
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"""格式化检索结果"""
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lines = ["===== 记忆检索结果 ====="]
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if result.get("entities"):
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lines.append(f"\n实体 ({len(result['entities'])} 个):")
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for e in result["entities"]:
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if e and isinstance(e, dict):
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lines.append(f" - {e.get('name', 'N/A')} (类型: {e.get('type', 'unknown')}, 提及: {e.get('mention_count', 1)}次)")
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if result.get("relations"):
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lines.append(f"\n关系 ({len(result['relations'])} 条):")
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for r in result["relations"]:
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if r and isinstance(r, dict):
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lines.append(f" - {r.get('source', 'N/A')} --[{r.get('type', 'N/A')}]--> {r.get('target', 'N/A')}")
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created = r.get("created_at", "N/A")
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if created and created != "N/A":
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created = created[:19] if "T" in str(created) else str(created)
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session_id = r.get('session_id', 'N/A')
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session_display = session_id[:20] if session_id and session_id != 'N/A' else 'N/A'
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lines.append(f" 时间: {created}, 会话: {session_display}, 轮次: {r.get('turn_id', 0)}, 置信度: {r.get('confidence', 1.0)}")
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if not result.get("entities") and not result.get("relations"):
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lines.append("\n(未找到相关记忆)")
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lines.append("=" * 30)
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return "\n".join(lines)
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def execute_context_rewrite(graph: Any, arguments: dict) -> dict:
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"""压缩工具调用上下文"""
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summary = arguments.get("summary", "")
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# 验证格式：必须包含工具调用标记
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if "[工具调用总结" not in summary:
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return {
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"status": "error",
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"message": "总结格式错误：必须包含 [工具调用总结: 本次总结了 N 次工具调用 | 调用工具: ...] 标记"
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}
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return {
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"status": "success",
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"message": "上下文已压缩",
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"summary": summary
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}
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# 人设图管理工具实现
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def execute_persona_update(graph: Any, arguments: dict) -> dict:
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"""更新人设"""
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attributes = arguments.get("attributes", [])
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mode = arguments.get("mode", "merge")
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if mode == "replace":
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# 先清除旧人设
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graph.purge(
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criteria={"subject_contains": "AI", "relation_type": "扮演角色"},
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mode="soft"
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)
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graph.purge(
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criteria={"subject_contains": "AI", "relation_type": "说话风格"},
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mode="soft"
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)
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graph.purge(
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criteria={"subject_contains": "AI", "relation_type": "性格特点"},
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mode="soft"
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)
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# 写入新人设
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triplets = []
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for attr in attributes:
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triplets.append({
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"subject": "AI",
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"relation": attr["attribute"],
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"object": attr["value"],
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"confidence": 1.0
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})
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result = graph.commit(triplets=triplets)
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return {
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"status": "success",
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"mode": mode,
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"updated_attributes": len(attributes),
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"details": result
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}
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def execute_persona_clear(graph: Any, arguments: dict) -> dict:
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"""清除人设"""
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if not arguments.get("confirm", True):
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return {"status": "cancelled", "message": "需要确认才能清除人设"}
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# 删除所有人设相关关系
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result1 = graph.purge(
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criteria={"subject_contains": "AI", "relation_type": "扮演角色"},
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mode="soft"
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)
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result2 = graph.purge(
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criteria={"subject_contains": "AI", "relation_type": "说话风格"},
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mode="soft"
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)
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result3 = graph.purge(
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criteria={"subject_contains": "AI", "relation_type": "性格特点"},
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mode="soft"
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)
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result4 = graph.purge(
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criteria={"subject_contains": "AI", "relation_type": "语气特征"},
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mode="soft"
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)
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total_deleted = (
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result1.get("deleted_count", 0) +
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result2.get("deleted_count", 0) +
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result3.get("deleted_count", 0) +
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result4.get("deleted_count", 0)
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)
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return {
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"status": "success",
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"deleted_count": total_deleted,
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"message": "人设已清除，恢复默认身份"
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}
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# 工作记忆链管理工具实现
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def execute_task_create(graph: Any, arguments: dict) -> dict:
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"""创建任务节点"""
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task_id = arguments.get("task_id")
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description = arguments.get("description")
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info_nodes = arguments.get("info_nodes", [])
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# 创建任务节点
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triplets = [
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{"subject": task_id, "relation": "is_type", "object": "TaskNode"},
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{"subject": task_id, "relation": "has_description", "object": description},
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{"subject": task_id, "relation": "HAS_STATE", "object": "State_进行中"}
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]
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result = graph.commit(triplets=triplets)
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# 关联信息节点
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if info_nodes:
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link_triplets = []
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for node_name in info_nodes:
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link_triplets.append({
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"subject": task_id,
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"relation": "CONTAINS_INFO",
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"object": node_name
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})
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graph.commit(triplets=link_triplets)
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return {
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"status": "success",
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"task_id": task_id,
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"description": description,
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"info_nodes": info_nodes,
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"details": result
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}
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def execute_task_set_state(graph: Any, arguments: dict) -> dict:
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"""设置任务状态"""
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task_id = arguments.get("task_id")
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state = arguments.get("state")
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# 删除旧状态
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graph.purge(
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criteria={"subject_contains": task_id, "relation_type": "HAS_STATE"},
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mode="soft"
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)
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# 设置新状态
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state_node = f"State_{state}"
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result = graph.commit(
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triplets=[{"subject": task_id, "relation": "HAS_STATE", "object": state_node}]
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)
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return {
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"status": "success",
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"task_id": task_id,
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"new_state": state,
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"details": result
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}
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def execute_task_delete(graph: Any, arguments: dict) -> dict:
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"""删除任务节点"""
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task_id = arguments.get("task_id")
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delete_info_nodes = arguments.get("delete_info_nodes", True)
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# 查询关联的信息节点
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if delete_info_nodes:
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recall_result = graph.recall(
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query_intent=f"{task_id},CONTAINS_INFO",
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depth=1
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)
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# 删除信息节点
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for relation in recall_result.get("relations", []):
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if relation.get("type") == "CONTAINS_INFO" and relation.get("source") == task_id:
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info_node = relation.get("target")
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graph.purge(
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criteria={"subject_contains": info_node},
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mode="soft"
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)
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# 删除任务节点
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result = graph.purge(
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criteria={"subject_contains": task_id},
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mode="soft"
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)
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return {
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"status": "success",
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"task_id": task_id,
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"deleted_info_nodes": delete_info_nodes,
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"details": result
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}
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def execute_task_link_info(graph: Any, arguments: dict) -> dict:
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"""关联信息节点"""
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task_id = arguments.get("task_id")
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info_node_names = arguments.get("info_node_names", [])
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triplets = []
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for node_name in info_node_names:
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triplets.append({
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"subject": task_id,
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"relation": "CONTAINS_INFO",
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"object": node_name
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})
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result = graph.commit(triplets=triplets)
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return {
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"status": "success",
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"task_id": task_id,
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"linked_nodes": info_node_names,
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"details": result
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}
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