mirror of
https://github.com/agentscope-ai/ReMe.git
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c4ca617992
* refactor(evolve): consolidate auto memory planner and writer into single step - Removed separate AutoMemoryPlannerStep and AutoMemoryWriterStep classes - Combined functionality into new AutoMemoryStep class in auto_memory.py - Migrated prompt templates from separate YAML files to unified auto_memory.yaml - Updated module imports to reference new consolidated step - Simplified memory recording process using single ReAct agent instead of two-stage planning/writing - Maintained same input/output contract with messages, session_id, and memory_hint parameters - Preserved all original functionality for creating/updating daily notes with conversation facts * fix(daily): update empty session_id handling to create day-level file - Changed test to verify empty session_id creates day-level file daily/<date>.md - Updated assertion to check response success instead of rejection - Modified metadata verification to include path, session_id and created status - Added file existence check for the generated daily markdown file - Updated test name and print statement to reflect new behavior - Fixed test registration to use updated function name
88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
"""``auto_memory`` — record conversation facts into a daily note.
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Calls ``daily_create`` as a system call to provision the note path,
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then hands off to a ReAct agent that reads existing content (if any),
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decides what to preserve, and writes the note via ``read`` / ``edit``
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/ ``frontmatter_update`` / ``write`` tools.
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Inputs (from RuntimeContext):
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messages (list[Msg], required): conversation slice to inspect.
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session_id (str, optional): passed to daily_create to determine
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the note path.
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memory_hint (str, optional): caller-supplied hint for the agent.
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timezone (str, optional): IANA timezone for date resolution.
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Output (written to context.response):
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answer: one-line summary from the agent.
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metadata: {path, created}.
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"""
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from agentscope.agent import ReActAgent
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from agentscope.message import Msg
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from agentscope.tool import Toolkit
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from ._evolve import format_history, now
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from ..base_step import BaseStep
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from ...components import R
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@R.register("auto_memory_step")
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class AutoMemoryStep(BaseStep):
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"""Record conversation facts into a daily note via a ReAct agent."""
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def __init__(self, console_enabled: bool = False, **kwargs):
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super().__init__(**kwargs)
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self.console_enabled = console_enabled
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self.agent_tools: list[str] = ["read", "edit", "frontmatter_update", "write"]
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async def execute(self):
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assert self.context is not None
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messages: list[Msg] = [
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item if isinstance(item, Msg) else Msg.from_dict(item) for item in self.context.get("messages", [])
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]
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session_id: str = self.context.get("session_id", "")
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memory_hint: str = self.context.get("memory_hint", "")
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current = now(self.context.get("timezone"))
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if not messages:
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self.context.response.success = True
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self.context.response.answer = "Skipped: no messages supplied"
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return
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create_response = await self.run_job("daily_create", session_id=session_id)
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if not create_response.success:
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self.context.response.success = False
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self.context.response.answer = f"daily_create failed: {create_response.answer}"
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return
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note_path: str = create_response.metadata["path"]
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created: bool = create_response.metadata["created"]
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toolkit = Toolkit()
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for job_name in self.agent_tools:
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self.add_as_tool(toolkit, job_name)
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agent = ReActAgent(
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name="auto_memory",
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model=self.as_llm,
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sys_prompt=self.prompt_format("system_prompt"),
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formatter=self.as_llm_formatter,
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toolkit=toolkit,
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)
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agent.set_console_output_enabled(self.console_enabled)
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template_key = "user_message_create" if created else "user_message_update"
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user_message: str = self.prompt_format(
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template_key,
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today=current.strftime("%Y-%m-%d"),
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vault_dir=str(self.file_store.vault_path),
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note=memory_hint or "(none)",
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note_path=note_path,
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history=format_history(messages),
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)
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final_msg: Msg = await agent.reply(Msg(name="reme", role="user", content=user_message))
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self.context.response.success = True
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self.context.response.answer = (final_msg.get_text_content() or "").strip()
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self.context.response.metadata.update({"path": note_path, "created": created})
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