fix summarizer

memgpt/agent.py

@@ -10,9 +10,9 @@ import openai
 from .system import get_heartbeat, get_login_event, package_function_response, package_summarize_message, get_initial_boot_messages
 from .memory import CoreMemory as Memory, summarize_messages
 from .openai_tools import acompletions_with_backoff as acreate
-from .utils import get_local_time, parse_json, united_diff, printd
+from .utils import get_local_time, parse_json, united_diff, printd, count_tokens
 from .constants import \
-    FIRST_MESSAGE_ATTEMPTS, MESSAGE_SUMMARY_CUTOFF_FRAC, MAX_PAUSE_HEARTBEATS, \
+    FIRST_MESSAGE_ATTEMPTS, MAX_PAUSE_HEARTBEATS, \
     MESSAGE_CHATGPT_FUNCTION_MODEL, MESSAGE_CHATGPT_FUNCTION_SYSTEM_MESSAGE, MESSAGE_SUMMARY_WARNING_TOKENS, \
     CORE_MEMORY_HUMAN_CHAR_LIMIT, CORE_MEMORY_PERSONA_CHAR_LIMIT
 
@@ -539,7 +539,14 @@ class AgentAsync(object):
 
     async def summarize_messages_inplace(self, cutoff=None):
         if cutoff is None:
-            cutoff = round((len(self.messages) - 1) * MESSAGE_SUMMARY_CUTOFF_FRAC)  # by default, trim the first 50% of messages
+            tokens_so_far = 0   # Smart cutoff -- just below the max.
+            cutoff = len(self.messages) - 1
+            for m in reversed(self.messages):
+                tokens_so_far += count_tokens(str(m), self.model)
+                if tokens_so_far >= MESSAGE_SUMMARY_WARNING_TOKENS*0.2:
+                    break
+                cutoff -= 1
+            cutoff = min(len(self.messages) - 3, cutoff) # Always keep the last two messages too
 
         # Try to make an assistant message come after the cutoff
         try:

memgpt/constants.py

@@ -12,7 +12,6 @@ STARTUP_QUOTES = [
 INITIAL_BOOT_MESSAGE_SEND_MESSAGE_FIRST_MSG = STARTUP_QUOTES[2]
 
 # Constants to do with summarization / conversation length window
-MESSAGE_SUMMARY_CUTOFF_FRAC = 0.5
 MESSAGE_SUMMARY_WARNING_TOKENS = 7000 # the number of tokens consumed in a call before a system warning goes to the agent
 MESSAGE_SUMMARY_WARNING_STR = f"Warning: the conversation history will soon reach its maximum length and be trimmed. Make sure to save any important information from the conversation to your memory before it is removed."
 

memgpt/memory.py

@@ -4,7 +4,8 @@ import re
 import faiss
 import numpy as np
 
-from .utils import cosine_similarity, get_local_time, printd
+from .constants import MESSAGE_SUMMARY_WARNING_TOKENS
+from .utils import cosine_similarity, get_local_time, printd, count_tokens
 from .prompts.gpt_summarize import SYSTEM as SUMMARY_PROMPT_SYSTEM
 from .openai_tools import acompletions_with_backoff as acreate, async_get_embedding_with_backoff
 
@@ -105,6 +106,11 @@ async def summarize_messages(
 
     summary_prompt = SUMMARY_PROMPT_SYSTEM
     summary_input = str(message_sequence_to_summarize)
+    summary_input_tkns = count_tokens(summary_input, model)
+    if summary_input_tkns > MESSAGE_SUMMARY_WARNING_TOKENS:
+        trunc_ratio = (MESSAGE_SUMMARY_WARNING_TOKENS / summary_input_tkns) * 0.8   # For good measure...
+        cutoff = int(len(message_sequence_to_summarize) * trunc_ratio)
+        summary_input = str([await summarize_messages(model, message_sequence_to_summarize[:cutoff])] + message_sequence_to_summarize[cutoff:])
     message_sequence = [
         {"role": "system", "content": summary_prompt},
         {"role": "user", "content": summary_input},

memgpt/persistence_manager.py

@@ -54,7 +54,7 @@ class InMemoryStateManager(PersistenceManager):
 
     def trim_messages(self, num):
         # printd(f"InMemoryStateManager.trim_messages")
-        self.messages = self.messages[num:]
+        self.messages = [self.messages[0]] + self.messages[num:]
 
     def prepend_to_messages(self, added_messages):
         # first tag with timestamps

memgpt/utils.py

@@ -6,7 +6,11 @@ import json
 import pytz
 import os
 import faiss
+import tiktoken
 
+def count_tokens(s: str, model: str = "gpt-4") -> int:
+    encoding = tiktoken.encoding_for_model(model)
+    return len(encoding.encode(s))
 
 # DEBUG = True
 DEBUG = False