fixing type issues

just_agents/llm_session.py

@@ -19,9 +19,9 @@
 
 @dataclass(kw_only=True)
 class LLMSession:
-    llm_options: Dict[str, Any] = field(default_factory=lambda: LLAMA3)
-    tools: List[Callable] = field(default_factory=list)
-    available_tools: Dict[str, Callable] = field(default_factory=lambda: {})
+    llm_options: dict[str, Any] = field(default_factory=lambda: LLAMA3)
+    tools: list[Callable] = field(default_factory=list)
+    available_tools: dict[str, Callable] = field(default_factory=lambda: {})
 
     on_response: list[OnCompletion] = field(default_factory=list)
     memory: Memory = field(default_factory=lambda: Memory())
@@ -87,7 +87,7 @@ def stream_all(self, messages: list, run_callbacks: bool = True): # -> ContentSt
         self.memory.add_messages(messages, run_callbacks)
         return self._stream()
 
-    async def stream_async(self, prompt: str, run_callbacks: bool = True, output: Optional[Path] = None) -> List[Any]:
+    async def stream_async(self, prompt: str, run_callbacks: bool = True, output: Optional[Path] = None) -> list[Any]:
         """temporary function that allows testing the stream function which Alex wrote but I do not fully understand"""
         collected_data = []
         async for item in self.stream(prompt, run_callbacks, output):
@@ -176,7 +176,7 @@ def _process_function_calls(self, response: ModelResponse) -> Optional[ModelResp
             return rotate_completion(messages=self.memory.messages, stream=False, options=self.llm_options)
         return None
 
-    def _prepare_tools(self, functions: List[Any]):
+    def _prepare_tools(self, functions: list[Any]):
         """
         Prepares functions as tools that LLM can call.
         Note, the functions should have comments explaining LLM how to use them

just_agents/streaming/openai_streaming.py

@@ -11,8 +11,8 @@
 class AsyncSession(AbstractStreaming):
 
     async def resp_async_generator(self, memory: Memory,
-                                   options: Dict,
-                                   available_tools: Dict[str, Callable]
+                                   options: dict,
+                                   available_tools: dict[str, Callable]
                                    ) -> AsyncGenerator[str, None]:
 
         response: ModelResponse = rotate_completion(messages=memory.messages, stream=True, options=options)