fix: Replace deprecated typing imports with built-in types

- Replace Dict, List, Set, Tuple with dict, list, set, tuple throughout codebase
- Add missing type annotations to crew_events.py methods
- Add proper type annotations to test_crew_cancellation.py
- Use type: ignore[method-assign] comments for mock assignments
- Maintain backward compatibility while modernizing type hints

This resolves lint and type-checker failures in CI while preserving
the cancellation functionality.

Co-Authored-By: João <joao@crewai.com>

src/crewai/knowledge/knowledge.py

@@ -1,5 +1,5 @@
 import os
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from pydantic import BaseModel, ConfigDict, Field
 
@@ -13,22 +13,22 @@ class Knowledge(BaseModel):
     """
     Knowledge is a collection of sources and setup for the vector store to save and query relevant context.
     Args:
-        sources: List[BaseKnowledgeSource] = Field(default_factory=list)
+        sources: list[BaseKnowledgeSource] = Field(default_factory=list)
         storage: Optional[KnowledgeStorage] = Field(default=None)
-        embedder: Optional[Dict[str, Any]] = None
+        embedder: Optional[dict[str, Any]] = None
     """
 
-    sources: List[BaseKnowledgeSource] = Field(default_factory=list)
+    sources: list[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: Optional[KnowledgeStorage] = Field(default=None)
-    embedder: Optional[Dict[str, Any]] = None
+    embedder: Optional[dict[str, Any]] = None
     collection_name: Optional[str] = None
 
     def __init__(
         self,
         collection_name: str,
-        sources: List[BaseKnowledgeSource],
-        embedder: Optional[Dict[str, Any]] = None,
+        sources: list[BaseKnowledgeSource],
+        embedder: Optional[dict[str, Any]] = None,
         storage: Optional[KnowledgeStorage] = None,
         **data,
     ):
@@ -43,8 +43,8 @@ class Knowledge(BaseModel):
         self.storage.initialize_knowledge_storage()
 
     def query(
-        self, query: List[str], results_limit: int = 3, score_threshold: float = 0.35
-    ) -> List[Dict[str, Any]]:
+        self, query: list[str], results_limit: int = 3, score_threshold: float = 0.35
+    ) -> list[dict[str, Any]]:
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.