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https://github.com/agentscope-ai/ReMe.git
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e16b52e68b
Reframe digest as the abstract memory layer (details stay in the daily/
resource material; digest holds principles, patterns, precedents reachable
via derived_from provenance edges). Replaces the old digester with a
2-phase ReAct workflow + a daily-tick wrapper:
- Phase 1 (Dreamer extract): clusters material into orthogonal memory
sub-units; each sub-unit maps 1:1 to a digest node (no inner atom
enumeration). Biases toward fewer / richer sub-units.
- Phase 2 (Dreamer integrate per sub-unit): cross-bucket recall +
exactly one write decision (CREATE / UPDATE / SKIP); UPDATE shapes
surfaced explicitly (corroborate / refine / correct).
- CronDreamer: scans <daily_dir>/<today>.md + <daily_dir>/<today>/**
+ <resource_dir>/<today>/** and runs dream_one per file.
Write tools are proper subclasses of the canonical file_io WriteStep /
EditStep with only path-shape + bucket + E-1 edge-conservation rules
layered on top:
- DigestWriteStep(WriteStep): path = <digest_dir>/<bucket>/<slug>.md,
must-not-exist, schema mirrors `write` (path / name / description /
content) so frontmatter lands automatically.
- DigestEditStep(EditStep): body-only find-and-replace + must-exist +
E-1 conservation preflight (refuses if any outbound wikilink would
be dropped).
Configuration:
- Bucket vocabulary structured in code (tuple[{name, description}]);
prompt renders the heuristic block at runtime via {buckets}.
- digest_dir / daily_dir / resource_dir come from app config (not tool
params); prompts use {digest_dir} placeholder.
- BaseStep walks class MRO when loading prompts, so subclasses inherit
parent yaml without duplication.
Tooling: agentscope register_tool_function schemas now wrap in the
proper {"type":"function","function":{...}} envelope. OpenAIAsLLM
routes base_url through client_kwargs so non-default endpoints work.
Smoke: tests4/smoke/{_dreamer_fixture.py,test_dreamer_inproc.py,
test_dreamer_cli.sh} drive the end-to-end pipeline.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
"""AgentScope LLM model wrappers."""
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from agentscope.model import AnthropicChatModel, ChatModelBase, OpenAIChatModel
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from ..base_component import BaseComponent
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from ..component_registry import R
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from ...enumeration import ComponentEnum
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class BaseAsLLM(BaseComponent):
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"""Base wrapper for AgentScope chat models. Builds ``self.model`` in ``_start``."""
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component_type = ComponentEnum.AS_LLM
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def __init__(self, **kwargs) -> None:
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super().__init__(**kwargs)
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self.model: ChatModelBase | None = None
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async def _close(self) -> None:
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self.model = None
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@R.register("openai")
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class OpenAIAsLLM(BaseAsLLM):
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"""OpenAI chat model wrapper."""
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async def _start(self) -> None:
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kwargs = dict(self.kwargs)
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base_url = kwargs.pop("base_url", None)
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if base_url:
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client_kwargs = dict(kwargs.pop("client_kwargs", None) or {})
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client_kwargs.setdefault("base_url", base_url)
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kwargs["client_kwargs"] = client_kwargs
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self.model = OpenAIChatModel(**kwargs)
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async def _close(self) -> None:
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if self.model is not None:
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assert isinstance(self.model, OpenAIChatModel)
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await self.model.client.close()
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@R.register("anthropic")
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class AnthropicAsLLM(BaseAsLLM):
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"""Anthropic chat model wrapper."""
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async def _start(self) -> None:
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self.model = AnthropicChatModel(**self.kwargs)
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async def _close(self) -> None:
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if self.model is not None:
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assert isinstance(self.model, AnthropicChatModel)
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await self.model.client.close()
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__all__ = [
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"BaseAsLLM",
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"OpenAIAsLLM",
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"AnthropicAsLLM",
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]
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