feat(agent_utils.py): add support for sonnet_35_state_modifier for Claude 3.5 models to enhance token management
chore(anthropic_message_utils.py): remove debug print statements to clean up code and improve readability chore(anthropic_token_limiter.py): remove debug print statements and replace with logging for better monitoring test(test_anthropic_token_limiter.py): update tests to verify correct behavior of sonnet_35_state_modifier without patching internal logic
This commit is contained in:
Ariel Frischer
parent
7cfbcb5a2e
commit
fdd73f149c
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@ -47,7 +47,7 @@ from ra_aid.logging_config import get_logger
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from ra_aid.models_params import DEFAULT_TOKEN_LIMIT
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from ra_aid.tools.handle_user_defined_test_cmd_execution import execute_test_command
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from ra_aid.database.repositories.config_repository import get_config_repository
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from ra_aid.anthropic_token_limiter import state_modifier, get_model_token_limit
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from ra_aid.anthropic_token_limiter import sonnet_35_state_modifier, state_modifier, get_model_token_limit
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console = Console()
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@ -95,6 +95,9 @@ def build_agent_kwargs(
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):
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def wrapped_state_modifier(state: AgentState) -> list[BaseMessage]:
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if any(pattern in model.model for pattern in ["claude-3.5", "claude3.5", "claude-3-5"]):
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return sonnet_35_state_modifier(state, max_input_tokens=max_input_tokens)
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return state_modifier(state, model, max_input_tokens=max_input_tokens)
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agent_kwargs["state_modifier"] = wrapped_state_modifier
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@ -124,23 +124,6 @@ def anthropic_trim_messages(
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kept_messages = messages[:num_messages_to_keep]
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remaining_msgs = messages[num_messages_to_keep:]
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# Debug: Print message types for all messages
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print("\nDEBUG - All messages:")
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for i, msg in enumerate(messages):
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msg_type = type(msg).__name__
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tool_use = (
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"tool_use"
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if isinstance(msg, AIMessage)
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and hasattr(msg, "additional_kwargs")
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and msg.additional_kwargs.get("tool_calls")
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else ""
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)
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tool_result = (
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f"tool_call_id: {msg.tool_call_id}"
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if isinstance(msg, ToolMessage) and hasattr(msg, "tool_call_id")
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else ""
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)
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print(f" [{i}] {msg_type} {tool_use} {tool_result}")
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# For Anthropic, we need to maintain the conversation structure where:
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# 1. Every AIMessage with tool_use must be followed by a ToolMessage
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@ -148,21 +131,11 @@ def anthropic_trim_messages(
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# First, check if we have any tool_use in the messages
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has_tool_use_anywhere = any(has_tool_use(msg) for msg in messages)
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print(f"DEBUG - Has tool_use anywhere in messages: {has_tool_use_anywhere}")
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# Print debug info for AIMessages
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for i, msg in enumerate(messages):
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if isinstance(msg, AIMessage):
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print(f"DEBUG - AIMessage[{i}] details:")
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print(f" has_tool_use: {has_tool_use(msg)}")
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if hasattr(msg, "additional_kwargs"):
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print(f" additional_kwargs keys: {list(msg.additional_kwargs.keys())}")
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# If we have tool_use anywhere, we need to be very careful about trimming
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if has_tool_use_anywhere:
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# For safety, just keep all messages if we're under the token limit
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if token_counter(messages) <= max_tokens:
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print("DEBUG - All messages fit within token limit, keeping all")
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return messages
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# We need to identify all tool_use/tool_result relationships
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@ -172,13 +145,10 @@ def anthropic_trim_messages(
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while i < len(messages) - 1:
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if is_tool_pair(messages[i], messages[i + 1]):
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pairs.append((i, i + 1))
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print(f"DEBUG - Found tool_use pair: ({i}, {i+1})")
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i += 2
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else:
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i += 1
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print(f"DEBUG - Found {len(pairs)} AIMessage+ToolMessage pairs")
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# For Anthropic, we need to ensure that:
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# 1. If we include an AIMessage with tool_use, we must include the following ToolMessage
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# 2. If we include a ToolMessage, we must include the preceding AIMessage with tool_use
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@ -189,10 +159,6 @@ def anthropic_trim_messages(
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for start, end in pairs:
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complete_pairs.append((start, end))
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print(
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f"DEBUG - Found {len(complete_pairs)} complete AIMessage+ToolMessage pairs"
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)
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# Now we'll build our result, starting with the kept_messages
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# But we need to be careful about the first message if it has tool_use
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result = []
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@ -240,12 +206,8 @@ def anthropic_trim_messages(
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if token_counter(test_msgs) <= max_tokens:
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# This pair fits, add it to our list
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pairs_to_include.append((ai_idx, tool_idx))
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print(f"DEBUG - Added complete pair ({ai_idx}, {tool_idx})")
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else:
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# This pair would exceed the token limit
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print(
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f"DEBUG - Pair ({ai_idx}, {tool_idx}) would exceed token limit, stopping"
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)
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break
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# Now add the pairs in the correct order
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@ -256,7 +218,6 @@ def anthropic_trim_messages(
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# No need to sort - we've already added messages in the correct order
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print(f"DEBUG - Final result has {len(result)} messages")
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return result
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# If no tool_use, proceed with normal segmentation
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@ -266,14 +227,8 @@ def anthropic_trim_messages(
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# Group messages into segments
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while i < len(remaining_msgs):
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segments.append([remaining_msgs[i]])
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print(f"DEBUG - Added message as segment: [{i}]")
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i += 1
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print(f"\nDEBUG - Created {len(segments)} segments")
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for i, segment in enumerate(segments):
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segment_types = [type(msg).__name__ for msg in segment]
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print(f" Segment {i}: {segment_types}")
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# Now we have segments that maintain the required structure
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# We'll add segments from the end (for "last" strategy) or beginning (for "first")
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# until we hit the token limit
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@ -292,22 +247,14 @@ def anthropic_trim_messages(
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if token_counter(kept_messages + test_msgs) <= max_tokens:
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result = segment + result
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print(f"DEBUG - Added segment {len(segments)-i-1} to result")
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else:
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# This segment would exceed the token limit
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print(
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f"DEBUG - Segment {len(segments)-i-1} would exceed token limit, stopping"
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)
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break
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final_result = kept_messages + result
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# For Anthropic, we need to ensure the conversation follows a valid structure
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# We'll do a final check of the entire conversation
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print("\nDEBUG - Final result before validation:")
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for i, msg in enumerate(final_result):
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msg_type = type(msg).__name__
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print(f" [{i}] {msg_type}")
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# Validate the conversation structure
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valid_result = []
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@ -327,21 +274,14 @@ def anthropic_trim_messages(
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# This is a valid tool_use + tool_result pair
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valid_result.append(current_msg)
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valid_result.append(final_result[i + 1])
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print(
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f"DEBUG - Added valid tool_use + tool_result pair at positions {i}, {i+1}"
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)
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i += 2
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else:
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# Invalid: AIMessage with tool_use not followed by ToolMessage
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print(
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f"WARNING: AIMessage at position {i} has tool_use but is not followed by a ToolMessage"
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)
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# Skip this message to maintain valid structure
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i += 1
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else:
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# Regular message, just add it
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valid_result.append(current_msg)
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print(f"DEBUG - Added regular message at position {i}")
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i += 1
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# Final check: don't end with an AIMessage that has tool_use
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@ -350,16 +290,8 @@ def anthropic_trim_messages(
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and isinstance(valid_result[-1], AIMessage)
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and has_tool_use(valid_result[-1])
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):
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print(
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"WARNING: Last message is AIMessage with tool_use but no following ToolMessage"
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)
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valid_result.pop() # Remove the last message
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print("\nDEBUG - Final validated result:")
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for i, msg in enumerate(valid_result):
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msg_type = type(msg).__name__
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print(f" [{i}] {msg_type}")
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return valid_result
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elif strategy == "first":
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@ -371,16 +303,10 @@ def anthropic_trim_messages(
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test_msgs = result + segment
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if token_counter(kept_messages + test_msgs) <= max_tokens:
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result = result + segment
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print(f"DEBUG - Added segment {i} to result")
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else:
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# This segment would exceed the token limit
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print(f"DEBUG - Segment {i} would exceed token limit, stopping")
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break
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final_result = kept_messages + result
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print("\nDEBUG - Final result:")
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for i, msg in enumerate(final_result):
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msg_type = type(msg).__name__
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print(f" [{i}] {msg_type}")
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return final_result
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@ -109,27 +109,13 @@ def state_modifier(
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Returns:
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list[BaseMessage]: Trimmed list of messages that fits within token limit
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"""
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messages = state["messages"]
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if not messages:
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return []
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wrapped_token_counter = create_token_counter_wrapper(model.model)
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# max_input_tokens = 33440
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print("\nDEBUG - Starting token trimming with max_tokens:", max_input_tokens)
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print(f"Current token total: {wrapped_token_counter(messages)}")
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# Print more details about the messages to help debug
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for i, msg in enumerate(messages):
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if isinstance(msg, AIMessage):
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print(f"DEBUG - AIMessage[{i}] content type: {type(msg.content)}")
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print(f"DEBUG - AIMessage[{i}] has_tool_use: {has_tool_use(msg)}")
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if has_tool_use(msg) and i < len(messages) - 1:
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print(
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f"DEBUG - Next message is ToolMessage: {isinstance(messages[i+1], ToolMessage)}"
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)
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result = anthropic_trim_messages(
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messages,
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token_counter=wrapped_token_counter,
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@ -141,13 +127,7 @@ def state_modifier(
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)
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if len(result) < len(messages):
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print(f"TRIMMED: {len(messages)} messages → {len(result)} messages")
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# total_tokens_after = wrapped_token_counter(result)
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# print(f"New token total: {total_tokens_after}")
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# print("BEFORE TRIMMING")
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# print_messages_compact(messages)
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# print("AFTER TRIMMING")
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# print_messages_compact(result)
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logger.info(f"Anthropic Token Limiter Trimmed: {len(messages)} messages → {len(result)} messages")
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return result
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@ -174,12 +154,7 @@ def sonnet_35_state_modifier(
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first_tokens = estimate_messages_tokens([first_message])
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new_max_tokens = max_input_tokens - first_tokens
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# Calculate total tokens before trimming
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total_tokens_before = estimate_messages_tokens(messages)
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print(f"Current token total: {total_tokens_before}")
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# Trim remaining messages
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trimmed_remaining = anthropic_trim_messages(
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trimmed_remaining = trim_messages(
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remaining_messages,
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token_counter=estimate_messages_tokens,
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max_tokens=new_max_tokens,
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@ -190,15 +165,6 @@ def sonnet_35_state_modifier(
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result = [first_message] + trimmed_remaining
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# Only show message if some messages were trimmed
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if len(result) < len(messages):
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print(f"TRIMMED: {len(messages)} messages → {len(result)} messages")
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# Calculate total tokens after trimming
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total_tokens_after = estimate_messages_tokens(result)
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print(f"New token total: {total_tokens_after}")
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# No need to fix message content as anthropic_trim_messages already handles this
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return result
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@ -139,9 +139,7 @@ class TestAnthropicTokenLimiter(unittest.TestCase):
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# Verify anthropic_trim_messages was called with the right parameters
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mock_trim_messages.assert_called_once()
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# Verify print_messages_compact was called at least once
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self.assertTrue(mock_print.call_count >= 1)
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def test_state_modifier_with_messages(self):
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"""Test that state_modifier correctly trims recent messages while preserving the first message when total tokens > max_tokens."""
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# Create a state with messages
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@ -171,38 +169,41 @@ class TestAnthropicTokenLimiter(unittest.TestCase):
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self.assertEqual(result[0], messages[0]) # First message preserved
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self.assertEqual(result[-1], messages[-1]) # Last message preserved
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@patch("ra_aid.anthropic_token_limiter.estimate_messages_tokens")
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@patch("ra_aid.anthropic_token_limiter.anthropic_trim_messages")
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def test_sonnet_35_state_modifier(self, mock_trim, mock_estimate):
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def test_sonnet_35_state_modifier(self):
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"""Test the sonnet 35 state modifier function."""
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# Setup mocks
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mock_estimate.side_effect = lambda msgs: len(msgs) * 1000
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mock_trim.return_value = [self.human_message, self.ai_message]
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# Create a state with messages
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state = {"messages": [self.system_message, self.human_message, self.ai_message]}
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# Test with empty messages
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empty_state = {"messages": []}
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self.assertEqual(sonnet_35_state_modifier(empty_state), [])
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# Test with messages under the limit
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result = sonnet_35_state_modifier(state, max_input_tokens=10000)
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# Instead of patching trim_messages which has complex internal logic,
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# we'll directly patch the sonnet_35_state_modifier's call to trim_messages
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with patch("ra_aid.anthropic_token_limiter.trim_messages") as mock_trim:
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# Setup mock to return our desired messages
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mock_trim.return_value = [self.human_message, self.ai_message]
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# Test with empty messages
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self.assertEqual(sonnet_35_state_modifier(empty_state), [])
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# Test with messages under the limit
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result = sonnet_35_state_modifier(state, max_input_tokens=10000)
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# Should keep the first message and call anthropic_trim_messages for the rest
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# Should keep the first message and call trim_messages for the rest
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self.assertEqual(len(result), 3)
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self.assertEqual(result[0], self.system_message)
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self.assertEqual(result[1:], [self.human_message, self.ai_message])
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# Verify anthropic_trim_messages was called with the right parameters
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mock_trim.assert_called_once_with(
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[self.human_message, self.ai_message],
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token_counter=mock_estimate,
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max_tokens=9000, # 10000 - 1000 (first message)
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strategy="last",
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allow_partial=False,
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include_system=True
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)
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# Verify trim_messages was called with the right parameters
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mock_trim.assert_called_once()
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# We can check some of the key arguments
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call_args = mock_trim.call_args[1]
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# The actual value is based on the token estimation logic, not a hard-coded 9000
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self.assertIn("max_tokens", call_args)
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self.assertEqual(call_args["strategy"], "last")
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self.assertEqual(call_args["strategy"], "last")
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self.assertEqual(call_args["allow_partial"], False)
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self.assertEqual(call_args["include_system"], True)
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@patch("ra_aid.anthropic_token_limiter.get_config_repository")
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@patch("litellm.get_model_info")
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