mirror of
https://github.com/agentscope-ai/ReMe.git
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9a6cf2b994
* update * refactor(memory): remove unnecessary type check and update error logging * refactor(core): standardize logger import and update agentscope dependency * fix(memory): disable console output and add logging for summarizer component * feat(core): replace OpenAI token counter with custom ReMe token counter - Replace OpenAITokenCounter with ReMeTokenCounter implementation - Add support for HuggingFace mirror and configurable tokenizer - Register ReMeTokenCounter as default token counter in registry - Update config to use hf backend with Qwen2.5-7B-Instruct model refactor(memory): convert token counting methods to async in message handlers - Change count_str_token, stat_message, count_msgs_token to async methods - Update format_msgs_to_str and context_check to use async token counting - Modify _format_tool_result_output to support async token counting - Adjust all dependent methods to await async token counting calls feat(memory): add dialog persistence to in-memory storage - Implement _append_messages_to_dialog for saving messages to JSONL files - Add dialog_path parameter to ReMeInMemoryMemory constructor - Persist messages to daily JSONL files based on timestamp grouping - Update mark_messages_compressed to save and remove compressed messages - Modify clear_content to persist all messages before clearing memory refactor(ops): update token counter type hints and initialization - Change BaseOp to use HuggingFaceTokenCounter instead of TokenCounterBase - Update type annotations for as_token_counter property and parameters - Remove direct token counter injection from Compactor and ContextChecker - Pass as_token_counter parameter through service context mechanism style(logging): improve error logging with exception details - Replace logger.error with logger.exception in browser control tool - Change logger.error to logger.exception in memory get tool error handling - Add proper exception logging with stack trace information chore(config): add token counter configuration to light YAML - Add as_token_counters section with default hf backend configuration - Configure Qwen/Qwen2.5-7B-Instruct model with mirror support enabled - Set up pretrained_model_name_or_path and use_mirror parameters test(context): update context check tests to async implementation - Convert verify_context_check_invariants to async function - Update context check test methods to use async calls - Change stat_message calls to await async implementation - Modify test_empty_messages and test_below_threshold_returns_all to async * feat(core): implement context checking and memory management features * refactor(core): replace direct loguru import with logger utility function * refactor(reme): remove RuntimeContext dependency and simplify context checking * feat(docs): add raw conversation persistence to ReMe framework
79 lines
2.8 KiB
Python
79 lines
2.8 KiB
Python
"""Compactor module for memory compaction operations."""
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from agentscope.agent import ReActAgent
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from agentscope.message import Msg
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from ..utils import AsMsgHandler
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from ....core.op import BaseOp
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from ....core.utils import get_logger
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logger = get_logger()
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class Compactor(BaseOp):
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"""Compactor class for compacting memory messages."""
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def __init__(
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self,
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memory_compact_threshold: int,
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console_enabled: bool = False,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.memory_compact_threshold: int = memory_compact_threshold
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self.console_enabled: bool = console_enabled
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async def execute(self):
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messages: list[Msg] = self.context.get("messages", [])
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previous_summary: str = self.context.get("previous_summary", "")
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if not messages:
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return ""
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msg_handler = AsMsgHandler(self.as_token_counter)
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before_token_count = await msg_handler.count_msgs_token(messages)
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history_formatted_str: str = await msg_handler.format_msgs_to_str(
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messages=messages,
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memory_compact_threshold=self.memory_compact_threshold,
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)
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after_token_count = await msg_handler.count_str_token(history_formatted_str)
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logger.info(f"Compactor before_token_count={before_token_count} after_token_count={after_token_count}")
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if not history_formatted_str:
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logger.warning(f"No history to compact. messages={messages}")
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return ""
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agent = ReActAgent(
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name="reme_compactor",
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model=self.as_llm,
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sys_prompt=self.get_prompt("system_prompt"),
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formatter=self.as_llm_formatter,
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)
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agent.set_console_output_enabled(self.console_enabled)
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if previous_summary:
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prefix: str = self.get_prompt("update_user_message_prefix")
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suffix: str = self.get_prompt("update_user_message_suffix")
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user_message: str = (
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f"<conversation>\n{history_formatted_str}\n</conversation>\n\n"
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f"{prefix}\n\n"
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f"<previous-summary>\n{previous_summary}\n</previous-summary>\n\n"
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f"{suffix}"
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)
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else:
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user_message: str = f"<conversation>\n{history_formatted_str}\n</conversation>\n\n" + self.get_prompt(
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"initial_user_message",
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)
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logger.info(f"Compactor sys_prompt={agent.sys_prompt} user_message={user_message}")
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compact_msg: Msg = await agent.reply(
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Msg(
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name="reme",
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role="user",
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content=user_message,
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),
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)
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history_compact: str = compact_msg.get_text_content()
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logger.info(f"Compactor Result:\n{history_compact}")
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return history_compact
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