mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-10-03 02:24:31 +00:00
819443813b
- Introduce BaseAsTokenCounter and EstimatedAsTokenCounter for token estimation - Add AsMsgStat and AsBlockStat schema for message statistics tracking - Implement FileIO class with read/write/append/edit operations - Create file utility functions for safe async file reading and truncation - Add MemorySearch component for semantic search in memory files - Register new component types in ComponentEnum and update imports - Add constants for default host, port, and truncation limits - Create BaseService abstract base class for service implementations - Implement BaseStep with component accessors and lifecycle management - Add proper __all__ exports for all new modules and components
134 lines
4.8 KiB
Python
134 lines
4.8 KiB
Python
"""ReMe CLI application entry point."""
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import asyncio
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import sys
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from pathlib import Path
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from agentscope.formatter import FormatterBase
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from agentscope.message import Msg
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from agentscope.model import ChatModelBase
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from agentscope.token import HuggingFaceTokenCounter, TokenCounterBase
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from agentscope.tool import Toolkit
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from .application import Application
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from .component import R
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from .component.runtime_context import RuntimeContext
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from .config import parse_args
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from .enumeration import ComponentEnum
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from .file_based.summarizer import Summarizer
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class ReMe(Application):
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"""ReMe memory management application."""
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async def summary_memory(
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self,
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messages: list[Msg],
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as_llm: str | ChatModelBase = "default",
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as_llm_formatter: str | FormatterBase = "default",
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as_token_counter: str | TokenCounterBase | HuggingFaceTokenCounter = "default",
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toolkit: Toolkit | None = None,
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language: str = "zh",
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max_input_length: float = 128 * 1024,
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compact_ratio: float = 0.7,
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timezone: str | None = None,
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add_thinking_block: bool = True,
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) -> str:
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"""Summarize and compact memory messages.
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Args:
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messages: List of AgentScope messages to summarize.
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as_llm: LLM model name or instance.
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as_llm_formatter: Formatter name or instance.
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as_token_counter: Token counter name or instance.
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toolkit: Optional toolkit for the summarizer agent.
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language: Language for prompts (zh or en).
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max_input_length: Maximum input token length.
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compact_ratio: Ratio of max_input_length to use as compact threshold.
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timezone: Optional timezone for date formatting.
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add_thinking_block: Whether to include thinking blocks.
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Returns:
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Summarized memory string.
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"""
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working_dir = Path(self.config.working_dir).absolute()
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memory_dir = working_dir / "memory"
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memory_compact_threshold = int(max_input_length * compact_ratio)
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# Resolve token counter - use provided instance or create default
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token_counter_instance = None
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if isinstance(as_token_counter, HuggingFaceTokenCounter):
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token_counter_instance = as_token_counter
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else:
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token_counter_instance = HuggingFaceTokenCounter()
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summarizer = Summarizer(
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working_dir=str(working_dir),
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memory_dir=str(memory_dir),
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memory_compact_threshold=memory_compact_threshold,
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toolkit=toolkit,
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timezone=timezone,
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add_thinking_block=add_thinking_block,
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as_token_counter=token_counter_instance,
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language=language,
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as_llm=as_llm if isinstance(as_llm, str) else "default",
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as_llm_formatter=as_llm_formatter if isinstance(as_llm_formatter, str) else "default",
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)
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context = RuntimeContext(
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messages=messages,
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application_context=self.context,
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)
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result = await summarizer(context=context)
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return result or ""
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async def memory_search(self, query: str, max_results: int = 5, min_score: float = 0.1) -> str:
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"""Search memory for relevant entries."""
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from .file_based.memory_search import MemorySearch
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try:
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search_step = MemorySearch()
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self.logger.info(f"Running memory search with {query} {max_results} {min_score}")
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return await search_step(query=query, max_results=max_results, min_score=min_score)
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except Exception as e:
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return str(e)
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async def dream(
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self,
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as_llm: str | ChatModelBase = "default",
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as_llm_formatter: str | FormatterBase = "default",
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as_token_counter: str | TokenCounterBase = "default",
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toolkit: Toolkit | None = None,
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language: str = "zh",
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timezone: str | None = None,
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) -> str:
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"""Process and consolidate memories in background."""
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async def proactive(
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self,
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as_llm: str | ChatModelBase = "default",
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as_llm_formatter: str | FormatterBase = "default",
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as_token_counter: str | TokenCounterBase = "default",
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toolkit: Toolkit | None = None,
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language: str = "zh",
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timezone: str | None = None,
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) -> str:
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"""Generate proactive memory insights."""
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def main():
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"""Entry point for ReMe CLI."""
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action, config = parse_args(sys.argv[1:])
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if action == "start":
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reme = ReMe(**config)
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reme.run_app()
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else:
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backend: str = config.pop("backend", "http")
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client_cls = R.get(ComponentEnum.CLIENT, backend)
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client = client_cls(action=action, **config)
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asyncio.run(client())
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if __name__ == "__main__":
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main()
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