mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-08-28 05:25:04 +00:00
940a2f47a9
Use user-specified timezone instead of system local time when generating daily note filenames and timestamps in memory summarization and CLI. Changes: - Summarizer: accept 'timezone' param in __init__; use datetime.now(zoneinfo.ZoneInfo(tz)) instead of naive datetime.now() - ReMeLight.summary_memory(): accept 'timezone' param and pass to Summarizer - CliAgent: accept 'timezone' param in __init__; pass to Summarizer and use for current_time timestamp in system prompts - Fallback to system local time if timezone is None (preserves original behavior) Fixes timezone mismatch when system timezone differs from user's actual location (e.g., server in UTC+8 but user in America/Chicago).
326 lines
12 KiB
Python
326 lines
12 KiB
Python
"""CLI component for interactive chat using agentscope-based memory tools."""
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import asyncio
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from datetime import datetime
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from pathlib import Path
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import zoneinfo
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from agentscope.agent import ReActAgent
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from agentscope.message import Msg, TextBlock
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from agentscope.pipeline import stream_printing_messages
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from agentscope.tool import Toolkit, ToolResponse
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from .compactor import Compactor
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from .context_checker import ContextChecker
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from .summarizer import Summarizer
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from ..tools import FileIO, MemorySearch
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from ....core.op import BaseOp
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from ....core.utils import format_messages
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from ....core.utils import get_logger
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logger = get_logger()
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# name + desc + "{working_dir}/skills/{skill_name}/SKILL.md"
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_DEFAULT_AGENT_SKILL_INSTRUCTION = (
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"# Agent Skills\n"
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"The agent skills are a collection of folds of instructions, scripts, "
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"and resources that you can load dynamically to improve performance "
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"on specialized tasks. Each agent skill has a `SKILL.md` file in its "
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"folder that describes how to use the skill. If you want to use a "
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"skill, you MUST read its `SKILL.md` file carefully."
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)
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_DEFAULT_AGENT_SKILL_TEMPLATE = """## {name}
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{description}
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Check "{dir}/SKILL.md" for how to use this skill"""
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class CliAgent(BaseOp):
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"""CLI agent for interactive chat with memory management."""
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def __init__(
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self,
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working_dir: str,
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vector_weight: float = 0.7,
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candidate_multiplier: float = 3.0,
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context_window_tokens: int = 128000,
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reserve_tokens: int = 36000,
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keep_recent_tokens: int = 20000,
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language: str = "zh",
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timezone: str | None = None,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.working_dir: str = working_dir
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Path(self.working_dir).mkdir(parents=True, exist_ok=True)
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self.vector_weight: float = vector_weight
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self.candidate_multiplier: float = candidate_multiplier
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self.context_window_tokens: int = context_window_tokens
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self.reserve_tokens: int = reserve_tokens
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self.keep_recent_tokens: int = keep_recent_tokens
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self.language: str = language
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self.timezone: str | None = timezone
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# Initialize message history
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self.messages: list[Msg] = []
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self.previous_summary: str = ""
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self.summary_tasks: list[asyncio.Task] = []
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def add_summary_task(self, messages: list[Msg]):
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"""Add summary task to queue."""
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remaining_tasks = []
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for task in self.summary_tasks:
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if task.done():
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exc = task.exception()
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if exc is not None:
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logger.exception(f"Summary task failed: {exc}")
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else:
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result = task.result()
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logger.info(f"Summary task completed: {result}")
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else:
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remaining_tasks.append(task)
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self.summary_tasks = remaining_tasks
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# Create a toolkit for the summarizer
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toolkit = self._create_file_toolkit()
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# Create summarizer instance
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memory_path = Path(self.working_dir) / "memory"
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summarizer = Summarizer(
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working_dir=self.working_dir,
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memory_dir=str(memory_path),
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memory_compact_threshold=int(self.context_window_tokens * 0.7),
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token_counter=self.as_token_counter,
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toolkit=toolkit,
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as_llm=self.as_llm,
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as_llm_formatter=self.as_llm_formatter,
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language=self.language if self.language == "zh" else "",
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console_enabled=False, # We disable the terminal printing to avoid messy outputs
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timezone=self.timezone,
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)
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# Create summary task
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summary_task = asyncio.create_task(
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summarizer.call(
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messages=messages,
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service_context=self.service_context,
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),
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)
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self.summary_tasks.append(summary_task)
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def _create_file_toolkit(self):
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"""Create a toolkit with file operations."""
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toolkit = Toolkit()
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file_io = FileIO(working_dir=self.working_dir)
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toolkit.register_tool_function(file_io.read)
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toolkit.register_tool_function(file_io.write)
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toolkit.register_tool_function(file_io.edit)
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return toolkit
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async def new(self) -> str:
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"""Reset conversation history using summary."""
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if not self.messages:
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self.messages.clear()
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self.previous_summary = ""
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return "No history to reset."
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self.add_summary_task(self.messages)
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self.messages.clear()
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self.previous_summary = ""
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return "History saved to memory files and reset."
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async def context_check(self) -> dict:
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"""Check if messages exceed token limits."""
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# Create context checker
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checker = ContextChecker(
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memory_compact_threshold=self.context_window_tokens - self.reserve_tokens,
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memory_compact_reserve=self.keep_recent_tokens,
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token_counter=self.as_token_counter,
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)
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return await checker.call(
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messages=self.messages,
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service_context=self.service_context,
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)
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async def compact(self, force_compact: bool = False) -> str:
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"""Compact history then reset."""
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if not self.messages:
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return "No history to compact."
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# Check and find cut point
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messages_to_compact, messages_to_keep, _ = await self.context_check()
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tokens_before = len(self.messages)
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if force_compact:
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messages_to_summarize = self.messages
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left_messages = []
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elif not messages_to_compact:
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return "History is within token limits, no compaction needed."
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else:
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messages_to_summarize = messages_to_compact
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left_messages = messages_to_keep
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# Create compactor
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compactor = Compactor(
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memory_compact_threshold=self.context_window_tokens - self.reserve_tokens,
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token_counter=self.as_token_counter,
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as_llm=self.as_llm,
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as_llm_formatter=self.as_llm_formatter,
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language=self.language if self.language == "zh" else "",
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console_enabled=False, # We disable the terminal printing to avoid messy outputs
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timezone=self.timezone,
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)
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summary_content = await compactor.call(
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messages=messages_to_summarize,
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previous_summary=self.previous_summary,
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service_context=self.service_context,
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)
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self.add_summary_task(messages=messages_to_summarize)
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# Assemble final messages
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self.messages = left_messages
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self.previous_summary = summary_content
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return f"History compacted from {tokens_before} messages."
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def format_history(self) -> str:
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"""Format history messages."""
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return format_messages(
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messages=self.messages,
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add_index=False,
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add_reasoning=False,
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strip_markdown_headers=False,
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)
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async def _build_messages(self, query: str) -> list[Msg]:
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"""Build system prompt message."""
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tz = zoneinfo.ZoneInfo(self.timezone) if self.timezone else None
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current_time = datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S %A")
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# Create system prompt
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system_prompt = self.prompt_format(
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"system_prompt",
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workspace_dir=self.working_dir,
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current_time=current_time,
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has_previous_summary=bool(self.previous_summary),
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previous_summary=self.previous_summary or "",
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)
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logger.info(f"[{self.__class__.__name__}] system_prompt: {system_prompt}")
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# Build message list
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messages = [Msg(name="system", role="system", content=system_prompt)]
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messages.extend(self.messages)
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messages.append(Msg(name="user", role="user", content=query))
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return messages
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async def memory_search(self, query: str, max_results: int = 5, min_score: float = 0.1) -> ToolResponse:
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"""
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Mandatory recall step: semantically search MEMORY.md + memory/*.md (and optional session transcripts)
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before answering questions about prior work, decisions, dates, people, preferences, or todos;
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returns top snippets with path + lines.
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Args:
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query: The semantic search query to find relevant memory snippets
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max_results: Maximum number of search results to return (optional), default is 5
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min_score: Minimum similarity score threshold for results (optional), default is 0.1
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Returns:
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Search results as formatted string
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"""
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search_tool = MemorySearch(
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vector_weight=self.vector_weight,
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candidate_multiplier=self.candidate_multiplier,
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)
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search_result = await search_tool.call(
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query=query,
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max_results=max_results,
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min_score=min_score,
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service_context=self.service_context,
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)
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return ToolResponse(
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content=[
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TextBlock(
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type="text",
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text=search_result,
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),
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],
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)
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async def execute(self):
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"""Execute the agent."""
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_ = await self.compact(force_compact=False)
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# Build messages for the agent
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query = self.context.query
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messages = await self._build_messages(query)
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toolkit = self._create_file_toolkit()
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# Register memory search tool
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toolkit.register_tool_function(self.memory_search)
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# Create the ReAct agent
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agent = ReActAgent(
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name="reme_cli_agent",
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model=self.as_llm,
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sys_prompt=messages[0].content, # 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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# We disable the terminal printing to avoid messy outputs
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agent.set_console_output_enabled(False)
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self.messages = messages[1:] # remove the first SYSTEM message
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agent.memory.content.clear()
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# Stream processing state
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in_thinking = False
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in_answer = False
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# obtain the printing messages from the agent in a streaming way
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last_text_content = ""
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last_think_content = ""
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async for msg, last in stream_printing_messages(
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agents=[agent],
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coroutine_task=agent(self.messages),
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):
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# print(msg, last)
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content_blocks = msg.get_content_blocks()
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for block in content_blocks:
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if block["type"] == "thinking":
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if not in_thinking and len(block["thinking"]) > len(last_think_content):
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print("\033[90m\nThinking: ", end="", flush=True)
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in_thinking = True
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print(block["thinking"][len(last_think_content) :], end="", flush=True)
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last_think_content = block["thinking"]
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elif block["type"] == "text":
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if in_thinking:
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print("\033[0m") # reset color after thinking
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in_thinking = False
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if not in_answer:
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print("\nRemy: ", end="", flush=True)
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in_answer = True
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print(block["text"][len(last_text_content) :], end="", flush=True)
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last_text_content = block["text"]
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elif block["type"] == "tool_use":
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if in_thinking:
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print("\033[0m") # reset color after thinking
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in_thinking = False
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if last:
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print(f"\033[36m -> Executing Tool: name={block['name']}, input={block['input']}\033[0m")
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elif block["type"] == "tool_result":
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if last:
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last_think_content = "" # reset for further thinking
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print(f"\033[36m -> Tool Result for `{block['name']}`: {block['output'][0]['text']}\033[0m")
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else:
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print(f"Unknown block type: {block['type']}")
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if last:
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self.messages.append(msg)
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