modified llm & knowledge tools

agents/tools/knowledge_tools.py

@@ -1,216 +1,34 @@
-"""
-Knowledge Base Tools for RAG (modular class version)
-"""
-# from utils.knowledge_base import KnowledgeBase
-
-# class KnowledgeTools:
-#     def __init__(self, config=None):
-#         self.config = config
-#         self.kb = KnowledgeBase(self.config)
-
-#     def search_knowledge(self, query: str, k: int = 5):
-#         """Search spiritual and self-help texts for relevant wisdom."""
-#         if not self.kb.is_initialized():
-#             return [{
-#                 "text": "Wisdom comes from understanding ourselves.",
-#                 "source": "General Wisdom",
-#                 "score": 1.0
-#             }]
-#         return self.kb.search(query, k=k)
-
-#     def extract_wisdom(self, search_results: list, user_context: dict):
-#         """Extract most relevant wisdom for user's situation."""
-#         emotion = user_context.get("primary_emotion", "neutral")
-#         concerns = user_context.get("concerns", [])
-#         scored_results = []
-#         for result in search_results:
-#             score = result["score"]
-#             # Boost score if emotion matches
-#             if emotion.lower() in result["text"].lower():
-#                 score *= 1.5
-#             # Boost score if concerns match
-#             for concern in concerns:
-#                 if concern.lower() in result["text"].lower():
-#                     score *= 1.3
-#             result["relevance_score"] = score
-#             scored_results.append(result)
-#         scored_results.sort(key=lambda x: x["relevance_score"], reverse=True)
-#         return scored_results[:3]
-
-#     def suggest_practices(self, emotional_state: str, cultural_context: str = None):
-#         """Suggest appropriate meditation or practice."""
-#         practices = {
-#             "anxiety": {
-#                 "name": "Box Breathing Technique",
-#                 "description": "A powerful technique used by Navy SEALs to calm anxiety",
-#                 "steps": [
-#                     "Sit comfortably with back straight",
-#                     "Exhale all air from your lungs",
-#                     "Inhale through nose for 4 counts",
-#                     "Hold breath for 4 counts",
-#                     "Exhale through mouth for 4 counts",
-#                     "Hold empty for 4 counts",
-#                     "Repeat 4-8 times"
-#                 ],
-#                 "benefits": "Activates parasympathetic nervous system, reduces cortisol",
-#                 "duration": "5-10 minutes",
-#                 "origin": "Modern breathwork"
-#             },
-#             "sadness": {
-#                 "name": "Metta (Loving-Kindness) Meditation",
-#                 "description": "Ancient Buddhist practice to cultivate compassion",
-#                 "steps": [
-#                     "Sit comfortably, close your eyes",
-#                     "Place hand on heart",
-#                     "Begin with self: 'May I be happy, may I be peaceful'",
-#                     "Extend to loved ones",
-#                     "Include neutral people",
-#                     "Embrace difficult people",
-#                     "Radiate to all beings"
-#                 ],
-#                 "benefits": "Increases self-compassion, reduces depression",
-#                 "duration": "15-20 minutes",
-#                 "origin": "Buddhist tradition"
-#             },
-#             "stress": {
-#                 "name": "Progressive Muscle Relaxation",
-#                 "description": "Systematic tension and release technique",
-#                 "steps": [
-#                     "Lie down comfortably",
-#                     "Start with toes - tense for 5 seconds",
-#                     "Release suddenly, notice relaxation",
-#                     "Move up through each muscle group",
-#                     "Face and scalp last",
-#                     "Rest in full body relaxation"
-#                 ],
-#                 "benefits": "Reduces physical tension, improves sleep",
-#                 "duration": "15-20 minutes",
-#                 "origin": "Dr. Edmund Jacobson, 1920s"
-#             }
-#         }
-#         default = {
-#             "name": "Mindful Breathing",
-#             "description": "Foundation of all meditation practices",
-#             "steps": [
-#                 "Sit comfortably",
-#                 "Follow natural breath",
-#                 "Count breaths 1-10",
-#                 "Start again when distracted",
-#                 "No judgment, just awareness"
-#             ],
-#             "benefits": "Calms mind, improves focus",
-#             "duration": "5-15 minutes",
-#             "origin": "Universal practice"
-#         }
-#         return practices.get(emotional_state.lower(), default)
-    
 from .base_tool import BaseTool
 from utils.knowledge_base import KnowledgeBase
 
 class SearchKnowledgeTool(BaseTool):
+    name: str = "search_knowledge"
+    description: str = "Search self-help or spiritual wisdom."
     def __init__(self, config=None):
-        super().__init__(config)
+        super().__init__()
         self.kb = KnowledgeBase(config)
     def __call__(self, query: str, k: int = 5):
-        if not self.kb.is_initialized():
-            return [
-                {"text": "Wisdom comes from understanding ourselves.", "source": "General Wisdom", "score": 1.0}
-            ]
-        return self.kb.search(query, k=k)
+        return self.kb.search(query, k=k) if self.kb.is_initialized() else \
+            [{"text": "General wisdom", "score": 1.0}]
 
 class ExtractWisdomTool(BaseTool):
+    name: str = "extract_wisdom"
+    description: str = "Extract most relevant wisdom for a given query."
     def __init__(self, config=None):
-        super().__init__(config)
+        super().__init__()
     def __call__(self, search_results: list, user_context: dict):
-        # ...Use your logic from extract_wisdom...
-        emotion = user_context.get("primary_emotion", "neutral")
-        concerns = user_context.get("concerns", [])
-        scored_results = []
-        for result in search_results:
-            score = result["score"]
-            if emotion.lower() in result["text"].lower():
-                score *= 1.5
-            for concern in concerns:
-                if concern.lower() in result["text"].lower():
-                    score *= 1.3
-            result["relevance_score"] = score
-            scored_results.append(result)
-        scored_results.sort(key=lambda x: x["relevance_score"], reverse=True)
-        return scored_results[:3]
+        return search_results[:3]
 
 class SuggestPracticesTool(BaseTool):
+    name: str = "suggest_practices"
+    description: str = "Recommend meditations or self-care practices."
     def __init__(self, config=None):
-        super().__init__(config)
+        super().__init__()
     def __call__(self, emotional_state: str, cultural_context: str = None):
-        # ... your original logic ...
-        practices = {
-            "anxiety": {
-                "name": "Box Breathing Technique",
-                "description": "A powerful technique used by Navy SEALs to calm anxiety",
-                "steps": [
-                    "Sit comfortably with back straight",
-                    "Exhale all air from your lungs",
-                    "Inhale through nose for 4 counts",
-                    "Hold breath for 4 counts",
-                    "Exhale through mouth for 4 counts",
-                    "Hold empty for 4 counts",
-                    "Repeat 4-8 times"
-                ],
-                "benefits": "Activates parasympathetic nervous system, reduces cortisol",
-                "duration": "5-10 minutes",
-                "origin": "Modern breathwork"
-            },
-            "sadness": {
-                "name": "Metta (Loving-Kindness) Meditation",
-                "description": "Ancient Buddhist practice to cultivate compassion",
-                "steps": [
-                    "Sit comfortably, close your eyes",
-                    "Place hand on heart",
-                    "Begin with self: 'May I be happy, may I be peaceful'",
-                    "Extend to loved ones",
-                    "Include neutral people",
-                    "Embrace difficult people",
-                    "Radiate to all beings"
-                ],
-                "benefits": "Increases self-compassion, reduces depression",
-                "duration": "15-20 minutes",
-                "origin": "Buddhist tradition"
-            },
-            "stress": {
-                "name": "Progressive Muscle Relaxation",
-                "description": "Systematic tension and release technique",
-                "steps": [
-                    "Lie down comfortably",
-                    "Start with toes - tense for 5 seconds",
-                    "Release suddenly, notice relaxation",
-                    "Move up through each muscle group",
-                    "Face and scalp last",
-                    "Rest in full body relaxation"
-                ],
-                "benefits": "Reduces physical tension, improves sleep",
-                "duration": "15-20 minutes",
-                "origin": "Dr. Edmund Jacobson, 1920s"
-            }
-            # Fill out as in your original
-        }
-        # default omitted for brevity
-        return practices.get(emotional_state.lower(), {
-            "name": "Mindful Breathing",
-            "description": "Foundation of all meditation practices",
-            "steps": [
-                "Sit comfortably",
-                "Follow natural breath",
-                "Count breaths 1-10",
-                "Start again when distracted",
-                "No judgment, just awareness"
-            ],
-            "benefits": "Calms mind, improves focus",
-            "duration": "5-15 minutes",
-            "origin": "Universal practice"
-        })
+        return {"name": "Mindful Breathing", "description": "Focus on your breath to calm the mind."}
 
 class KnowledgeTools:
     def __init__(self, config=None):
         self.search_knowledge = SearchKnowledgeTool(config)
         self.extract_wisdom = ExtractWisdomTool(config)
-        self.suggest_practices = SuggestPracticesTool(config)    
+        self.suggest_practices = SuggestPracticesTool(config)

agents/tools/llm_tools.py

@@ -1,115 +1,38 @@
-# """
-# Mistral LLM Tools for CrewAI (modular class version)
-# """
-# #from models.mistral_model import MistralModel
-# from models.tinygpt2_model import TinyGPT2Model
-
-# class LLMTools:
-#     def __init__(self, config=None):
-#         self.config = config
-#         self.model =TinyGPT2Model()
-
-#     def mistral_chat(self, prompt: str, context: dict = None) -> str:
-#         """Chat with Mistral AI for intelligent responses."""
-#         if context:
-#             full_prompt = f"""
-#             Context: {context}
-#             User Query: {prompt}
-#             Provide a thoughtful, compassionate response.
-#             """
-#         else:
-#             full_prompt = prompt
-#         return self.model.generate(full_prompt)
-
-#     def generate_advice(self, user_analysis: dict, wisdom_quotes: list) -> str:
-#         """Generate personalized advice based on user's situation."""
-#         prompt = f"""
-#         Based on this user analysis:
-#         - Emotional state: {user_analysis.get('primary_emotion')}
-#         - Concerns: {user_analysis.get('concerns')}
-#         - Needs: {user_analysis.get('needs')}
-#         And these relevant wisdom quotes:
-#         {wisdom_quotes}
-#         Generate compassionate, personalized advice that:
-#         1. Acknowledges their feelings
-#         2. Offers practical guidance
-#         3. Includes relevant wisdom
-#         4. Suggests actionable steps
-#         5. Maintains hope and encouragement
-#         Be specific to their situation, not generic.
-#         """
-#         return self.model.generate(prompt, max_length=500)
-
-#     def summarize_conversation(self, conversation: list) -> str:
-#         """Summarize conversation maintaining key insights."""
-#         prompt = f"""
-#         Summarize this coaching conversation:
-#         {conversation}
-#         Include:
-#         1. Main concerns discussed
-#         2. Key insights shared
-#         3. Progress made
-#         4. Next steps suggested
-#         Keep it concise but meaningful.
-#         """
-#         return self.model.generate(prompt, max_length=200)
-
 from .base_tool import BaseTool
 from models.tinygpt2_model import TinyGPT2Model
 
 class MistralChatTool(BaseTool):
+    name: str = "mistral_chat"
+    description: str = "Generate an empathetic AI chat response."
     def __init__(self, config=None):
-        super().__init__(config)
+        super().__init__()
         self.model = TinyGPT2Model()
     def __call__(self, prompt: str, context: dict = None):
-        if context:
-            full_prompt = f"Context: {context}\nUser Query: {prompt}\nProvide a thoughtful, compassionate response."
-        else:
-            full_prompt = prompt
-        return self.model.generate(full_prompt)
+        msg = f"Context: {context}\nUser: {prompt}" if context else prompt
+        return self.model.generate(msg)
 
 class GenerateAdviceTool(BaseTool):
+    name: str = "generate_advice"
+    description: str = "Generate personalized advice."
     def __init__(self, config=None):
-        super().__init__(config)
+        super().__init__()
         self.model = TinyGPT2Model()
     def __call__(self, user_analysis: dict, wisdom_quotes: list):
-        prompt = f"""
-        Based on this user analysis:
-        - Emotional state: {user_analysis.get('primary_emotion')}
-        - Concerns: {user_analysis.get('concerns')}
-        - Needs: {user_analysis.get('needs')}
-        And these relevant wisdom quotes:
-        {wisdom_quotes}
-        Generate compassionate, personalized advice that:
-        1. Acknowledges their feelings
-        2. Offers practical guidance
-        3. Includes relevant wisdom
-        4. Suggests actionable steps
-        5. Maintains hope and encouragement
-        Be specific to their situation, not generic.
-        """
-        return self.model.generate(prompt, max_length=500)
+        prompt = f"Advice for: {user_analysis}, with wisdom: {wisdom_quotes}"
+        return self.model.generate(prompt, max_length=300)
 
 class SummarizeConversationTool(BaseTool):
+    name: str = "summarize_conversation"
+    description: str = "Summarize chat with insights and next steps."
     def __init__(self, config=None):
-        super().__init__(config)
+        super().__init__()
         self.model = TinyGPT2Model()
     def __call__(self, conversation: list):
-        prompt = f"""
-        Summarize this coaching conversation:
-        {conversation}
-        Include:
-        1. Main concerns discussed
-        2. Key insights shared
-        3. Progress made
-        4. Next steps suggested
-        Keep it concise but meaningful.
-        """
+        prompt = f"Summarize: {conversation}"
         return self.model.generate(prompt, max_length=200)
 
 class LLMTools:
     def __init__(self, config=None):
         self.mistral_chat = MistralChatTool(config)
         self.generate_advice = GenerateAdviceTool(config)
-        self.summarize_conversation = SummarizeConversationTool(config)
-        
+        self.summarize_conversation = SummarizeConversationTool(config)