fix : memory summarization issue

sources/memory.py

@@ -15,7 +15,7 @@ from sources.utility import timer_decorator, pretty_print
 class Memory():
     """
     Memory is a class for managing the conversation memory
-    It provides a method to compress the memory (experimental, use with caution).
+    It provides a method to compress the memory using summarization model.
     """
     def __init__(self, system_prompt: str,
                  recover_last_session: bool = False,
@@ -38,6 +38,7 @@ class Memory():
         self.model = AutoModelForSeq2SeqLM.from_pretrained(self.model)
     
     def get_filename(self) -> str:
+        """Get the filename for the save file."""
         return f"memory_{self.session_time.strftime('%Y-%m-%d_%H-%M-%S')}.txt"
     
     def save_memory(self, agent_type: str = "casual_agent") -> None:
@@ -103,6 +104,7 @@ class Memory():
         self.memory = []
     
     def clear_section(self, start: int, end: int) -> None:
+        """Clear a section of the memory."""
         self.memory = self.memory[:start] + self.memory[end:]
     
     def get(self) -> list:
@@ -134,23 +136,23 @@ class Memory():
         inputs = self.tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
         summary_ids = self.model.generate(
             inputs['input_ids'],
-            max_length=max_length,  # Maximum length of the summary
-            min_length=min_length,  # Minimum length of the summary
-            length_penalty=1.0,  # Adjusts length preference
-            num_beams=4,  # Beam search for better quality
-            early_stopping=True  # Stop when all beams finish
+            max_length=max_length,
+            min_length=min_length,
+            length_penalty=1.0,
+            num_beams=4,
+            early_stopping=True
         )
         summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
         summary.replace('summary:', '')
         return summary
     
-    @timer_decorator
+    #@timer_decorator
     def compress(self) -> str:
         """
         Compress the memory using the AI model.
         """
         for i in range(len(self.memory)):
-            if i < 3:
+            if i < 2:
                 continue
             if self.memory[i]['role'] == 'system':
                 continue
@@ -179,10 +181,12 @@ Use the -I flag to specify the directory containing helper_functions.h.
 Ensure the file exists in the specified location.
     """
     
-    memory.push('user', "why do i get this error?")
+    memory.push('user', "hello")
+    memory.push('assistant', "how can i help you?")
+    memory.push('user', "why do i get this cuda error?")
     memory.push('assistant', sample_text)
     print("\n---\nmemory before:", memory.get())
     memory.compress()
     print("\n---\nmemory after:", memory.get())
-    memory.save_memory()
+    #memory.save_memory()