Merge pull request #40 from Sathvika-891/main

updated reasoning

Author: Sathvika-891

maslibpy/agent/agent.py

@@ -6,7 +6,6 @@
 from maslibpy.prompts.cot.cot_prompts import CoT
 from maslibpy.agent.baseagent import BaseAgent
 
-
 class Agent(BaseAgent):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
@@ -26,12 +25,12 @@ def select_prompt(self,kwargs):
             self.prompt_pattern=kwargs.get("prompt_pattern","cot")
             cot_instance=CoT(**{self.prompt_pattern: True})
             self.system_prompt = cot_instance.fetch_prompt()
-            print("cot instane",cot_instance)   
+              
         else:
             self.prompt_pattern=kwargs.get("prompt_pattern","react")
             react_instance = ReAct(**{self.prompt_pattern: True})
             self.system_prompt = react_instance.fetch_prompt()
-            print("react instane",react_instance)
+            
 
     def invoke(self, query: Union[str, List[Dict[str, str]]]):
         """Default Scoring function is prompt based"""
@@ -40,19 +39,4 @@ def invoke(self, query: Union[str, List[Dict[str, str]]]):
             generated_response=Scorer().mathematical(agent=self,query=query)
         else:
             generated_response=Scorer().prompt_based(agent=self,query=query)
-        return generated_response
-if __name__=="__main__":
-    agent = Agent(
-        name="TestAgent-1",
-        role="AI Assistant",
-        goal="Assist users effectively",
-        backstory="An advanced AI designed to provide helpful insights.",
-        prompt_type="react",
-        prompt_pattern="react",
-        max_iterations=3,
-        critique_llm=LLM(provider="together", model_name="together_ai/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo"),
-        generator_llm=LLM(provider="together", model_name="together_ai/mistralai/Mistral-7B-Instruct-v0.1"),
-        # score_type="mathematical"
-    )
-
-    print(agent.invoke([{"role":"user","content":"what are AI Agents"}]))
+        return generated_response

maslibpy/reasoning/mathematical.py

@@ -119,20 +119,18 @@ def invoke(self,agent,query: Union[str, List[Dict[str, str]]]) -> str:
         """Evaluate and refine model responses based on entropy and coherence."""
         best_score, best_response = float('-inf'), None
         plateau_count = 0
-        res=""
-        start_time=time.time()
+        
         for i in tqdm(range(agent.max_iterations), desc="Iterations"):
 
             initial_response = self.generate(agent,agent.generator_llm,query)
-            res+=f"Initial Response:\n{initial_response}\n\n"
+            
             critique_comments = self.critique(agent,agent.critique_llm,initial_response, query)
-            res+=f"Critique_comments:\n{critique_comments}\n\n"
+            
             revised_response = self.refine_response(agent,agent.generator_llm,initial_response, critique_comments)
-            res+=f"Revised Response:\n{critique_comments}\n\n"
+            
             metrics = self.calculate_metrics(revised_response)
             current_score = self._calculate_composite_score(agent,metrics)
-            res+=f"Metrics:\n\n{metrics}\n\n"
-            res+=f"Score:\n\n{current_score}\n\n"
+            
             logger.info(f"Epoch {i+1} - Metrics: {metrics}")
 
             if current_score > best_score + agent.entropy_threshold:
@@ -161,11 +159,11 @@ def update_chat_history(self,agent,query:Union[str, List[Dict[str, str]]]):
                     user_msg=UserMessage(content=agent.system_prompt.format(query=query[-1]["content"]))
                     query[-1]=user_msg
                 agent.messages.extend(query)
-        # return agent.messages
+        
 
     def generate(self, agent,llm,query: Union[str, List[Dict[str, str]]]) -> str:
         """Generate response for a given query using the provided LLM"""
-        # agent.messages=self.update_chat_history(agent,query)
+        
         self.update_chat_history(agent,query)
         messages = [{"role": msg.role, "content": msg.content} for msg in agent.messages]
         res = llm.invoke(messages)
@@ -179,7 +177,7 @@ def critique(self, agent,llm,response: str, original_query: Union[str, List[Dict
         # mathematical critique and prompt critique are two different
         """Generate critique comments for the response balancing completeness and conciseness."""
         if isinstance(original_query,UserMessage):
-            print("user msg")
+            
             original_query=original_query[-1].content
         elif isinstance(original_query, list) and all(isinstance(msg, dict) for msg in original_query):
             original_query=original_query[-1]["content"]

maslibpy/reasoning/prompt_based.py

@@ -11,13 +11,11 @@ class PromptBased():
 
     def invoke(self,agent,query: Union[str, List[Dict[str, str]]]) -> str:
         actual_query = query
-        res=""
-        start_time=time.time()
+        
         for i in tqdm(range(agent.max_iterations),desc="Iterations"):
             try:
                 generated_response = self.generate(agent,agent.generator_llm,actual_query)
-                res += f"===== Epoch {i+1} =====\n\n"
-                res += f"**Generated Response**:\n\n{generated_response}\n\n"
+                
                 if generated_response is not None:
                     ind=generated_response.rfind("Final Answer")
                     if ind>=0:
@@ -28,19 +26,16 @@ def invoke(self,agent,query: Union[str, List[Dict[str, str]]]) -> str:
 
                 critiqued_response = self.critique(agent,agent.critique_llm,generated_response,original_query=query)
 
-                res += f"\n\n**Critiqued Response**:\n\n{critiqued_response}\n\n"
+                
                 grade_output=self.grade(agent,agent.critique_llm,query,generated_response,critiqued_response)
-                res+=f"\n\n**Grade node output**\n\n{grade_output}"
+                
                 if grade_output:
                     print("breaking the loop")
                     break
-                os.makedirs("results",exist_ok=True)  
-                res += "=" * 100 + "\n\n"
-            except Exception as e:
 
-                res += f"\n\n**Error occurred {e} in iteration {i+1}**\n\n"
+            except Exception as e:
                 raise e
-                break
+                
 
         return generated_response
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "maslibpy"
-version = "0.1.1"
+version = "0.0.2"
 description = "Multiagent Framework"
 authors = ["Bayes Labs", "contact@bayeslabs.co"]
 readme = "README.md"