more pydantic

PLAYBOOKS/Case_L3_SOC_Analyst_Agent.py

@@ -1,6 +1,6 @@
 import json
 from enum import Enum
-from typing import Optional, Union, Dict, Any
+from typing import Any
 
 from langchain_core.messages import HumanMessage
 from langgraph.graph import StateGraph
@@ -37,9 +37,9 @@ class AnalyzeResult(BaseModel):
     original_severity: Severity = Field(description="Original alert severity")
     new_severity: Severity = Field(description="Recommended new severity level")
     confidence: ConfidenceLevel = Field(description="Confidence score, only one of 'Low', 'Medium', or 'High'")
-    analysis_rationale: str = Field(description="Analysis process and reasons", default=None)
-    current_attack_stage: Optional[Union[str, Dict[str, Any]]] = Field(description="e.g., 'T1059 - Command and Control', 'Lateral Movement'", default=None)
-    recommended_actions: Optional[Union[str, Dict[str, Any]]] = Field(description="e.g., 'Isolate host 10.1.1.5'", default=None)
+    analysis_rationale: str | None = Field(description="Analysis process and reasons", default=None)
+    current_attack_stage: str | dict[str, Any] | None = Field(description="e.g., 'T1059 - Command and Control', 'Lateral Movement'", default=None)
+    recommended_actions: str | dict[str, Any] | None = Field(description="e.g., 'Isolate host 10.1.1.5'", default=None)
 
 
 class Playbook(LanggraphPlaybook):

PLAYBOOKS/Case_Threat_Hunting_Agent.py

@@ -1,6 +1,6 @@
 import json
 import operator
-from typing import Annotated, Dict, List, Union
+from typing import Annotated, Dict, List
 
 from langchain_core.messages import AnyMessage, ToolMessage, AIMessage
 from langgraph.graph import StateGraph, START, END
@@ -38,7 +38,7 @@ class PlanningRecord(BaseModel):
 
 class AnalystOutput(BaseModel):
     answer: str = Field(description="对调查问题的最终、简洁的回答")
-    reasoning: Union[str, List[str]] = Field(description="支持最终结论的详细推理过程和关键证据。")
+    reasoning: str | List[str] = Field(description="支持最终结论的详细推理过程和关键证据。")
 
 
 # 定义 Planner 的结构化输出
@@ -51,7 +51,7 @@ class HuntingPlan(BaseModel):
 class Finding(BaseModel):
     question: str = Field(description="需要调查的问题")
     answer: str = Field(description="调查得到的答案")
-    reasoning: Union[str, List[str]] = Field(description="调查的推理过程")
+    reasoning: str | List[str] = Field(description="调查的推理过程")
     tool_calls: List = Field(default_factory=list, description="工具调用记录")
 
     def to_markdown(self) -> str:
@@ -98,7 +98,7 @@ class AnalystState(BaseModel):
     )
 
     # 输出：推理过程
-    reasoning: Union[str, List[str]] = Field(
+    reasoning: str | List[str] = Field(
         default="",
         description="得出结论的详细推理步骤及证据。"
     )

PLUGINS/Mock/CMDB.py

@@ -182,7 +182,7 @@ class CMDB(object):
 
     def fuzzy_search_ci(self,
                         partial_hostname: Annotated[Optional[str], "部分主机名片段，例如 'prod-'。如果提供，将进行部分匹配。"] = None,
-                        regex_pattern: Annotated[Optional[str], "用于高级匹配的正则表达式，例如 'aws-\d+'。与 partial_hostname 二选一。"] = None
+                        regex_pattern: Annotated[Optional[str], "用于高级匹配的正则表达式"] = None
                         ) -> Annotated[List[Dict[str, Any]], "匹配到的CI列表，仅返回 CI ID、CI 类型、主机名和业务关键性。"]:
         """
         [B. 模糊/部分匹配检索]：根据部分主机名或正则表达式检索匹配的配置项（CI）列表。