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 refactor(config): restructure configuration profiles for clarity Rename curated.yaml to remove outdated configuration file and rename full.yaml to expert.yaml with updated documentation.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. ```
134 lines
3.9 KiB
Python
134 lines
3.9 KiB
Python
"""Memory presets — common axis combinations for hot-write paths.
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Hot-write tools (`sync`) and cold-write services (Ingestor R-M-W) start
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with a preset, layer the agent's input on top, then validate the merged
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dict against `Memory`. Adding a new memory shape = adding one preset
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entry here + (optionally) a `LEGACY_AXES_FROM_CATEGORY` row in memory.py
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for migration.
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Presets carry only the 4 axes + optional default `status` for streaming
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memories. Identity fields (title, description, tags, created, updated)
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come from the caller.
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Values are stored as **plain strings** (not StrEnum members) so they
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flow cleanly through `frontmatter.dumps` → `yaml.dump`, which doesn't
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know how to represent enum subclasses. Pydantic still coerces them
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back into StrEnum members when `Memory.model_validate` runs.
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"""
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from __future__ import annotations
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from .memory import Lifecycle, Role, Scope, Source, Status
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EVENT_PRESET: dict = {
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"lifecycle": Lifecycle.STREAMING.value,
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"scope": Scope.INSTANCE.value,
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"source": Source.AUTO.value,
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"role": Role.OBSERVATION.value,
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"status": Status.ACTIVE.value,
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# Legacy compat: the `category` field is preserved by extra="allow",
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# but emit it explicitly so old tooling that still reads `category`
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# (hooks, scripts, downstream consumers) keeps working.
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"category": "event",
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}
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PROFILE_PRESET: dict = {
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"lifecycle": Lifecycle.EVOLVING.value,
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"scope": Scope.CLASS.value,
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"source": Source.CURATED.value,
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"role": Role.PROFILE.value,
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"category": "profile",
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}
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CONCEPT_PRESET: dict = {
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"lifecycle": Lifecycle.EVOLVING.value,
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"scope": Scope.CLASS.value,
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"source": Source.CURATED.value,
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"role": Role.CONCEPT.value,
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"category": "concept",
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}
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THESIS_PRESET: dict = {
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"lifecycle": Lifecycle.EVOLVING.value,
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"scope": Scope.CLASS.value,
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"source": Source.CURATED.value,
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"role": Role.CLAIM.value,
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"category": "thesis",
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}
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MODEL_PRESET: dict = {
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"lifecycle": Lifecycle.EVOLVING.value,
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"scope": Scope.CLASS.value,
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"source": Source.CURATED.value,
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"role": Role.CLAIM.value,
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"category": "model",
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}
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QUESTIONS_PRESET: dict = {
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"lifecycle": Lifecycle.EVOLVING.value,
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"scope": Scope.CLASS.value,
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"source": Source.CURATED.value,
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"role": Role.QUESTION.value,
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"category": "questions",
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}
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METHOD_PRESET: dict = {
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"lifecycle": Lifecycle.EVOLVING.value,
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"scope": Scope.CLASS.value,
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"source": Source.CURATED.value,
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"role": Role.METHOD.value,
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"category": "method",
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}
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TOOL_PRESET: dict = {
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"lifecycle": Lifecycle.EVOLVING.value,
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"scope": Scope.CLASS.value,
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"source": Source.CURATED.value,
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"role": Role.REFERENCE.value,
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"category": "tool",
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}
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FUNDAMENTALS_PRESET: dict = {
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"lifecycle": Lifecycle.EVOLVING.value,
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"scope": Scope.CLASS.value,
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"source": Source.CURATED.value,
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"role": Role.FUNDAMENTALS.value,
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"category": "fundamentals",
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}
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MATERIAL_PRESET: dict = {
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"lifecycle": Lifecycle.FROZEN.value,
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"scope": Scope.INSTANCE.value,
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"source": Source.AUTO.value,
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"role": Role.REFERENCE.value,
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"category": "material",
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}
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# Old `category` → preset, for migration / lookup use.
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PRESETS_BY_CATEGORY: dict[str, dict] = {
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"event": EVENT_PRESET,
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"profile": PROFILE_PRESET,
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"company": CONCEPT_PRESET,
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"sector": CONCEPT_PRESET,
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"concept": CONCEPT_PRESET,
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"thesis": THESIS_PRESET,
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"model": MODEL_PRESET,
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"questions": QUESTIONS_PRESET,
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"method": METHOD_PRESET,
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"tool": TOOL_PRESET,
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"fundamentals": FUNDAMENTALS_PRESET,
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"material": MATERIAL_PRESET,
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}
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def preset_for_category(category: str) -> dict | None:
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"""Look up the preset bound to a legacy category name.
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Returns a fresh dict each call (callers may mutate it). Returns
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None for unknown categories — caller decides whether to refuse or
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fall through to a generic shape.
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"""
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p = PRESETS_BY_CATEGORY.get(category)
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return dict(p) if p is not None else None
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