fix bugs truncate_memory_with_tokenizer

swarms/structs/conversation.py

@@ -184,6 +184,7 @@ class Conversation:
         system_prompt: Optional[str] = None,
         time_enabled: bool = False,
         autosave: bool = False,  # Changed default to False
+        save_enabled: bool = False,  # New parameter to control if saving is enabled
         save_filepath: str = None,
         load_filepath: str = None,  # New parameter to specify which file to load from
         context_length: int = 8192,
@@ -222,6 +223,7 @@ class Conversation:
         self.system_prompt = system_prompt
         self.time_enabled = time_enabled
         self.autosave = autosave
+        self.save_enabled = save_enabled
         self.conversations_dir = conversations_dir
         self.tokenizer_model_name = tokenizer_model_name
         self.message_id_on = message_id_on
@@ -1019,6 +1021,13 @@ class Conversation:
                 )
                 return
 
+            # Don't save if saving is disabled (你的PR代码)
+            if not self.save_enabled:
+                logger.warning(
+                    "An attempt to save the conversation failed: save_enabled is False."
+                    "Please set save_enabled=True when creating a Conversation object to enable saving."
+                )
+                return
             # Get the full data including metadata and conversation history
             data = self.get_init_params()
 
@@ -1267,39 +1276,121 @@ class Conversation:
 
     def truncate_memory_with_tokenizer(self):
         """
-        Truncates the conversation history based on the total number of tokens using a tokenizer.
-
+        Truncate conversation history based on the total token count using tokenizer.
+        
+        This version is more generic, not dependent on a specific LLM model, and can work with any model that provides a counter.
+        Uses count_tokens function to calculate and truncate by message, ensuring the result is still valid content.
+        
         Returns:
             None
         """
+        from swarms.utils.litellm_tokenizer import count_tokens
+        
         total_tokens = 0
         truncated_history = []
-
+        
         for message in self.conversation_history:
             role = message.get("role")
             content = message.get("content")
-            tokens = count_tokens(content, self.tokenizer_model_name)
-            count = tokens  # Assign the token count
-            total_tokens += count
-
-            if total_tokens <= self.context_length:
+            
+            # Convert content to string if it's not already a string
+            if not isinstance(content, str):
+                content = str(content)
+                
+            # Calculate token count for this message
+            token_count = count_tokens(content, self.tokenizer_model_name)
+            
+            # Check if adding this message would exceed the limit
+            if total_tokens + token_count <= self.context_length:
+                # If not exceeding limit, add the full message
                 truncated_history.append(message)
+                total_tokens += token_count
             else:
-                remaining_tokens = self.context_length - (
-                    total_tokens - count
+                # Calculate remaining tokens we can include
+                remaining_tokens = self.context_length - total_tokens
+                
+                # If no token space left, break the loop
+                if remaining_tokens <= 0:
+                    break
+                    
+                # If we have space left, we need to truncate this message
+                # Use binary search to find content length that fits remaining token space
+                truncated_content = self._binary_search_truncate(
+                    content, 
+                    remaining_tokens,
+                    self.tokenizer_model_name
                 )
-                truncated_content = content[
-                    :remaining_tokens
-                ]  # Truncate the content based on the remaining tokens
+                
+                # Create the truncated message
                 truncated_message = {
                     "role": role,
                     "content": truncated_content,
                 }
+                
+                # Add any other fields from the original message
+                for key, value in message.items():
+                    if key not in ["role", "content"]:
+                        truncated_message[key] = value
+                
                 truncated_history.append(truncated_message)
                 break
-
+        
+        # Update conversation history
         self.conversation_history = truncated_history
 
+    def _binary_search_truncate(self, text, target_tokens, model_name):
+        """
+        Use binary search to find the maximum text substring that fits the target token count.
+        
+        Parameters:
+            text (str): Original text to truncate
+            target_tokens (int): Target token count
+            model_name (str): Model name for token counting
+        
+        Returns:
+            str: Truncated text with token count not exceeding target_tokens
+        """
+        from swarms.utils.litellm_tokenizer import count_tokens
+        
+        # If text is empty or target tokens is 0, return empty string
+        if not text or target_tokens <= 0:
+            return ""
+        
+        # If original text token count is already less than or equal to target, return as is
+        original_tokens = count_tokens(text, model_name)
+        if original_tokens <= target_tokens:
+            return text
+        
+        # Binary search
+        left, right = 0, len(text)
+        best_length = 0
+        best_text = ""
+        
+        while left <= right:
+            mid = (left + right) // 2
+            truncated = text[:mid]
+            tokens = count_tokens(truncated, model_name)
+            
+            if tokens <= target_tokens:
+                # If current truncated text token count is less than or equal to target, try longer text
+                best_length = mid
+                best_text = truncated
+                left = mid + 1
+            else:
+                # Otherwise try shorter text
+                right = mid - 1
+        
+        # Try to truncate at sentence boundaries if possible
+        sentence_delimiters = ['.', '!', '?', '\n']
+        for delimiter in sentence_delimiters:
+            last_pos = best_text.rfind(delimiter)
+            if last_pos > len(best_text) * 0.75:  # Only truncate at sentence boundary if we don't lose too much content
+                truncated_at_sentence = best_text[:last_pos+1]
+                if count_tokens(truncated_at_sentence, model_name) <= target_tokens:
+                    return truncated_at_sentence
+        
+        return best_text
+
     def clear(self):
         """Clear the conversation history."""
         if self.backend_instance: