feat: add warning (#1591)

Co-authored-by: Wendong-Fan <[email protected]> Co-authored-by: Xiaotian Jin <[email protected]> Co-authored-by: Wendong <[email protected]>
camel-ai · Feb 18, 2025 · 69e4281 · 69e4281
1 parent 7821e6e
commit 69e4281
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diff --git a/camel/memories/agent_memories.py b/camel/memories/agent_memories.py
@@ -12,6 +12,7 @@
 # limitations under the License.
 # ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
 
+import warnings
 from typing import List, Optional
 
 from camel.memories.base import AgentMemory, BaseContextCreator
@@ -49,7 +50,17 @@ def __init__(
         self._chat_history_block = ChatHistoryBlock(storage=storage)
 
     def retrieve(self) -> List[ContextRecord]:
-        return self._chat_history_block.retrieve(self._window_size)
+        records = self._chat_history_block.retrieve(self._window_size)
+        if self._window_size is not None and len(records) == self._window_size:
+            warnings.warn(
+                f"Chat history window size limit ({self._window_size}) "
+                f"reached. Some earlier messages will not be included in "
+                f"the context. Consider increasing window_size if you need "
+                f"a longer context.",
+                UserWarning,
+                stacklevel=2,
+            )
+        return records
 
     def write_records(self, records: List[MemoryRecord]) -> None:
         self._chat_history_block.write_records(records)

diff --git a/camel/memories/context_creators/score_based.py b/camel/memories/context_creators/score_based.py
@@ -15,11 +15,14 @@
 
 from pydantic import BaseModel
 
+from camel.logger import get_logger
 from camel.memories.base import BaseContextCreator
 from camel.memories.records import ContextRecord
 from camel.messages import OpenAIMessage
 from camel.utils import BaseTokenCounter
 
+logger = get_logger(__name__)
+
 
 class _ContextUnit(BaseModel):
     idx: int
@@ -103,6 +106,12 @@ def create_context(
         if total_tokens <= self.token_limit:
             return self._create_output(context_units)
 
+        # Log warning about token limit being exceeded
+        logger.warning(
+            f"Token limit reached ({total_tokens} > {self.token_limit}). "
+            f"Some messages will be pruned from memory to meet the limit."
+        )
+
         # Sort by score
         context_units = sorted(
             context_units, key=lambda unit: unit.record.score