feat: add return type hints to LLM class methods (#5173)

openhands/llm/llm.py

@@ -369,16 +369,16 @@ class LLM(RetryMixin, DebugMixin):
                 ):
                     self.config.max_output_tokens = self.model_info['max_tokens']
 
-    def vision_is_active(self):
+    def vision_is_active(self) -> bool:
         with warnings.catch_warnings():
             warnings.simplefilter('ignore')
             return not self.config.disable_vision and self._supports_vision()
 
-    def _supports_vision(self):
+    def _supports_vision(self) -> bool:
         """Acquire from litellm if model is vision capable.
 
         Returns:
-            bool: True if model is vision capable. If model is not supported by litellm, it will return False.
+            bool: True if model is vision capable. Return False if model not supported by litellm.
         """
         # litellm.supports_vision currently returns False for 'openai/gpt-...' or 'anthropic/claude-...' (with prefixes)
         # but model_info will have the correct value for some reason.
@@ -476,7 +476,7 @@ class LLM(RetryMixin, DebugMixin):
         if stats:
             logger.debug(stats)
 
-    def get_token_count(self, messages):
+    def get_token_count(self, messages) -> int:
         """Get the number of tokens in a list of messages.
 
         Args:
@@ -491,7 +491,7 @@ class LLM(RetryMixin, DebugMixin):
             # TODO: this is to limit logspam in case token count is not supported
             return 0
 
-    def _is_local(self):
+    def _is_local(self) -> bool:
         """Determines if the system is using a locally running LLM.
 
         Returns:
@@ -506,7 +506,7 @@ class LLM(RetryMixin, DebugMixin):
                 return True
         return False
 
-    def _completion_cost(self, response):
+    def _completion_cost(self, response) -> float:
         """Calculate the cost of a completion response based on the model.  Local models are treated as free.
         Add the current cost into total cost in metrics.
 
@@ -555,7 +555,7 @@ class LLM(RetryMixin, DebugMixin):
     def __repr__(self):
         return str(self)
 
-    def reset(self):
+    def reset(self) -> None:
         self.metrics.reset()
 
     def format_messages_for_llm(self, messages: Message | list[Message]) -> list[dict]: