mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-09-19 00:01:33 +00:00
5fc57e7f4e
- Add BaseClient, BaseFileStore, BaseFileWatcher, BaseJob, BaseService, and BaseStep classes - Move component initialization logic from ApplicationContext to Application class - Add logo printing and logging initialization in Application startup - Create client module with base client implementation - Add file store base class with embedding resolution and validation - Implement file watcher base class with watchfiles integration - Add job base class for sequential step execution orchestration - Create service base class for job exposure mechanisms - Refactor BaseStep with LLM workflow execution capabilities - Add case converter utility for naming convention transformations - Update import structure and module organization - Add proper type hints and docstrings across all components - Implement component registry integration for dynamic loading - Add error handling for missing backend configurations
77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
"""ReMe CLI application entry point."""
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import asyncio
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import sys
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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 TokenCounterBase
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from agentscope.tool import Toolkit, ToolResponse
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from .application import Application
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from .component import R
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from .config import parse_args
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from .enumeration import ComponentEnum
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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 = "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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async def memory_search(self, query: str, max_results: int = 5, min_score: float = 0.1) -> ToolResponse:
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"""Search memory for relevant entries."""
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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 == "app":
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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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