mirror of
https://github.com/agentscope-ai/ReMe.git
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dd2de16481
feat(file_watcher): add directory deletion support with descendant indexing Add support for deleting entire directories and their indexed descendants in the file watcher. Previously only individual file deletions were handled properly. Now when a directory is deleted, the system finds all indexed files beneath that directory path and removes them along with their metadata and chunks. The implementation includes: - New `_descendant_indexed_paths` method to find all indexed files under a given directory path - Updated `_on_deleted` method to process both the target path and all its indexed descendants - Proper handling of symlinks and path resolution differences - Enhanced logging to show directory deletion with child count Also adds necessary os import for path operations. refactor(config): restructure configuration profiles for clarity Rename curated.yaml to remove outdated configuration file and rename full.yaml to expert.yaml with updated documentation. Add new service.yaml configuration profile that provides a service-aligned MCP surface with three main tools: - retrieve: graph-aware hybrid retrieval - remember: single write entry point with log/distill modes - maintain: vault hygiene sweep The expert configuration now excludes the ingest tool since cold-path operations are handled by external agents, and adds memory_lint tool for structural issue detection. Updated documentation to clarify the different configuration profiles and their intended usage patterns. ```
70 lines
2.7 KiB
Python
70 lines
2.7 KiB
Python
"""memory_lint — read-only projection of the Maintainer's lint findings.
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Tier A surface for the Maintainer service. The host agent calls this
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to discover what's wrong with the vault (broken wikilinks, schema
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violations, stem collisions) and decides what to do with each finding
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using the existing memory_* write primitives.
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Equivalent to invoking `Maintainer.execute(ops=["lint"], dry_run=True)`
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but with a focused response shape and a tighter parameter surface — the
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agent doesn't need to know about decay/merge/split knobs.
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"""
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from __future__ import annotations
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from ...component import R
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from ...component.base_step import BaseStep
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from ...component.runtime_response import _set_answer
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from ...enumeration import ComponentEnum
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from ...memory.maintainer import Maintainer
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@R.register("memory_lint")
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class MemoryLint(BaseStep):
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"""Run the Maintainer's lint pass and surface findings only.
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Inputs (RuntimeContext, all optional):
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target_prefix (str): restrict scan to relpaths starting with
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this prefix (e.g. "events/2026-05-09/").
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Output (context.response.answer):
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{
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"scanned": int, # files inspected
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"findings": [LintFinding, ...], # each {path, kind, detail}
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"target_prefix": str,
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"ran_at": iso,
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}
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"""
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async def execute(self):
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assert self.context is not None
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target_prefix = str(self.context.get("target_prefix") or "")
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# Delegate to a Maintainer step. Force ops=["lint"] + dry_run so
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# we never mutate. The Maintainer reads these from the context
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# and produces a full audit; we narrow the response shape below.
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if getattr(self, "_maintainer", None) is None:
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self._maintainer = R.get(ComponentEnum.STEP, "maintainer")(
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app_context=self.app_context,
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)
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self.context["ops"] = ["lint"]
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self.context["dry_run"] = True
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self.context["target_prefix"] = target_prefix
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await self._maintainer(self.context)
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# The Maintainer wrote a full audit to context.response.answer
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# (proposed/plan/applied/skipped/failed/...). For lint, all the
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# action lives in `proposed` (LintFindings never get applied or
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# dropped). Reshape into a focused response.
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import json
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raw = self.context.response.answer
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audit = json.loads(raw) if isinstance(raw, str) else (raw or {})
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findings = audit.get("proposed") or []
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_set_answer(self.context, {
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"scanned": audit.get("scanned", 0),
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"findings": findings,
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"target_prefix": target_prefix,
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"ran_at": audit.get("ran_at", ""),
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})
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self.context.response.success = True
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