calling run instead of call for basetool import

agents/tools/knowledge_tools.py

@@ -12,7 +12,7 @@ class SearchKnowledgeTool(BaseTool):
     def __init__(self, config=None):
         super().__init__()
         self.kb = KnowledgeBase(config)
-    def __call__(self, query: str, k: int = 5):
+    def _run(self, query: str, k: int = 5):
         return self.kb.search(query, k=k) if self.kb.is_initialized() else \
             [{"text": "General wisdom", "score": 1.0}]
 
@@ -21,7 +21,7 @@ class ExtractWisdomTool(BaseTool):
     description: str = "Extract most relevant wisdom for a given query."
     def __init__(self, config=None):
         super().__init__()
-    def __call__(self, search_results: list, user_context: dict):
+    def _run(self, search_results: list, user_context: dict):
         return search_results[:3]
 
 class SuggestPracticesTool(BaseTool):
@@ -29,7 +29,7 @@ class SuggestPracticesTool(BaseTool):
     description: str = "Recommend meditations or self-care practices."
     def __init__(self, config=None):
         super().__init__()
-    def __call__(self, emotional_state: str, cultural_context: str = None):
+    def _run(self, emotional_state: str, cultural_context: str = None):
         return {"name": "Mindful Breathing", "description": "Focus on your breath to calm the mind."}
 
 class KnowledgeTools:

agents/tools/llm_tools.py

@@ -12,7 +12,7 @@ class MistralChatTool(BaseTool):
     def __init__(self, config=None):
         super().__init__()
         self.model = TinyGPT2Model()
-    def __call__(self, prompt: str, context: dict = None):
+    def _run(self, prompt: str, context: dict = None):
         msg = f"Context: {context}\nUser: {prompt}" if context else prompt
         return self.model.generate(msg)
 
@@ -23,7 +23,7 @@ class GenerateAdviceTool(BaseTool):
     def __init__(self, config=None):
         super().__init__()
         self.model = TinyGPT2Model()
-    def __call__(self, user_analysis: dict, wisdom_quotes: list):
+    def _run(self, user_analysis: dict, wisdom_quotes: list):
         prompt = f"Advice for: {user_analysis}, with wisdom: {wisdom_quotes}"
         return self.model.generate(prompt, max_length=300)
 
@@ -34,7 +34,7 @@ class SummarizeConversationTool(BaseTool):
     def __init__(self, config=None):
         super().__init__()
         self.model = TinyGPT2Model()
-    def __call__(self, conversation: list):
+    def _run(self, conversation: list):
         prompt = f"Summarize: {conversation}"
         return self.model.generate(prompt, max_length=200)
 

agents/tools/validation_tools.py

@@ -412,7 +412,7 @@ class ValidateResponseTool(BaseTool):
         super().__init__(**data)
         self.config = config
         # ... any required initialization ...
-    def __call__(self, response: str, context: dict = None):
+    def _run(self, response: str, context: dict = None):
         # Place your actual validation logic here, include dummy for illustration
         # For full validation logic, use your own code! 
         #     """Result of validation check"""

agents/tools/voice_tools.py

@@ -56,7 +56,7 @@ class TranscribeAudioTool(BaseTool):
     def __init__(self, config=None):
         super().__init__()
         self.vp = MultilingualVoiceProcessor()
-    def  __call__(self, audio_data: np.ndarray, language=None):
+    def  _run(self, audio_data: np.ndarray, language=None):
         text, detected_lang = asyncio.run(self.vp.transcribe(audio_data, language))
         return {"text": text, "language": detected_lang}
 
@@ -65,7 +65,7 @@ class DetectEmotionTool(BaseTool):
     description: str = "Detect the emotional state from text."
     def __init__(self, config=None):
         super().__init__()
-    def  __call__(self, text: str):
+    def  _run(self, text: str):
         model = TinyGPT2Model()
         prompt = f'Analyse emotions in: "{text}". Format: JSON with primary_emotion, intensity, feelings, concerns.'
         response = model.generate(prompt)
@@ -79,7 +79,7 @@ class GenerateReflectiveQuestionsTool(BaseTool):
     description: str = "Generate reflective questions."
     def __init__(self, config=None):
         super().__init__()
-    def  __call__(self, context: dict):
+    def  _run(self, context: dict):
         emotion = context.get("primary_emotion", "neutral")
         questions_map = {
             "anxiety": ["What triggers your anxiety?", "How do you cope?"],