modified llm & knowledge tools

agents/tools/knowledge_tools.py

@@ -1,12 +1,14 @@
 from .base_tool import BaseTool
 from utils.knowledge_base import KnowledgeBase
+from pydantic import BaseModel, PrivateAttr
 
 class SearchKnowledgeTool(BaseTool):
     name: str = "search_knowledge"
     description: str = "Search self-help or spiritual wisdom."
+    _kp: KnowledgeBase = PrivateAttr()
     def __init__(self, config=None):
         super().__init__()
-        self.kb = KnowledgeBase(config)
+        self._kb = KnowledgeBase(config)
     def __call__(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}]

agents/tools/llm_tools.py

@@ -1,12 +1,14 @@
 from .base_tool import BaseTool
 from models.tinygpt2_model import TinyGPT2Model
+from pydantic import BaseModel, PrivateAttr
 
 class MistralChatTool(BaseTool):
     name: str = "mistral_chat"
     description: str = "Generate an empathetic AI chat response."
+    _model: TinyGPT2Model = PrivateAttr()
     def __init__(self, config=None):
         super().__init__()
-        self.model = TinyGPT2Model()
+        self._model = TinyGPT2Model()
     def __call__(self, prompt: str, context: dict = None):
         msg = f"Context: {context}\nUser: {prompt}" if context else prompt
         return self.model.generate(msg)
@@ -14,9 +16,10 @@ class MistralChatTool(BaseTool):
 class GenerateAdviceTool(BaseTool):
     name: str = "generate_advice"
     description: str = "Generate personalized advice."
+    _model: TinyGPT2Model = PrivateAttr()
     def __init__(self, config=None):
         super().__init__()
-        self.model = TinyGPT2Model()
+        self._model = TinyGPT2Model()
     def __call__(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)
@@ -24,9 +27,10 @@ class GenerateAdviceTool(BaseTool):
 class SummarizeConversationTool(BaseTool):
     name: str = "summarize_conversation"
     description: str = "Summarize chat with insights and next steps."
+    _model: TinyGPT2Model = PrivateAttr()
     def __init__(self, config=None):
         super().__init__()
-        self.model = TinyGPT2Model()
+        self._model = TinyGPT2Model()
     def __call__(self, conversation: list):
         prompt = f"Summarize: {conversation}"
         return self.model.generate(prompt, max_length=200)