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https://github.com/agentscope-ai/ReMe.git
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7d86658f33
* chore(logging): change info logs to debug level for data loading operations - Changed stopwords loading log from info to debug level - Changed file catalog nodes loading log from info to debug level - Changed file graph nodes loading log from info to debug level * feat(dream): add dream schema definitions and enum for auto-dream functionality - Add DreamBucketEnum with procedure, personal, and wiki values - Create comprehensive dream-related Pydantic models including DreamUnit, DreamTopic, DreamExtractOutput, IntegrateOutcome, TopicSelectionOutput, ProactiveResult, and DreamState - Move schema definitions from local step module to shared schema package - Update dream extraction and integration steps to use new enum-based bucket validation - Initialize digest directories for each dream bucket type - Enhance embedding store health check with workspace directory logging * refactor(tests): update DreamState import path in test_auto_dream.py - Move DreamState import from reme.steps.evolve.dream.schema to reme.schema - Maintain same functionality with updated module reference - Align import with new schema location in project structure
94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
"""Auto-dream schemas."""
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from typing import Literal
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from pydantic import BaseModel, Field
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from ..enumeration import DreamBucketEnum
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class DreamUnit(BaseModel):
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"""One cross-file memory unit emitted by global extract."""
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name: str = Field(description="Short kebab-case handle for the abstraction.")
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bucket: DreamBucketEnum = Field(description="Digest bucket; unknown raw values route to wiki before validation.")
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summary: str = Field(description="Grounded abstraction summary with evidence pointers.")
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paths: list[str] = Field(default_factory=list, description="Workspace-relative source paths.")
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class DreamTopic(BaseModel):
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"""One topic candidate emitted by global extract."""
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title: str = Field(description="Specific user-interest topic title.")
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reason: str = Field(description="Why this topic may interest the user.")
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evidence: str = Field(description="Grounded evidence pointer.")
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keywords: list[str] = Field(default_factory=list, description="Keywords for de-duplication.")
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paths: list[str] = Field(default_factory=list, description="Workspace-relative source paths.")
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class DreamExtractOutput(BaseModel):
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"""Structured output for ``dream_extract_step``."""
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units: list[DreamUnit] = Field(default_factory=list)
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topics: list[DreamTopic] = Field(default_factory=list)
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class IntegrateOutcome(BaseModel):
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"""Structured output for one unit integration."""
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action: Literal["CREATE", "CORROBORATE", "REFINE", "CORRECT"] = Field(description="Write decision.")
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target_path: str = Field(description="Digest path written or edited.")
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note: str = Field(default="", description="Short summary of what landed.")
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class TopicSelectionOutput(BaseModel):
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"""Structured output for daily topic selection."""
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topics: list[DreamTopic] = Field(default_factory=list)
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class ProactiveResult(BaseModel):
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"""Result of reading daily interest topics."""
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date: str = ""
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path: str = ""
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topics: list[dict] = Field(default_factory=list)
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content: str = ""
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skipped: bool = False
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error: str = ""
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summary: str = ""
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class DreamState(BaseModel):
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"""Shared state passed across the dream steps."""
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date: str = ""
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dates: list[str] = Field(default_factory=list)
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scan_days: int = 2
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hint: str = ""
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daily_dir: str = ""
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workspace: str = ""
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files_scanned: int = 0
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files_unchanged: int = 0
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files_changed: int = 0
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files_deleted: int = 0
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changed_paths: list[str] = Field(default_factory=list)
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unchanged_paths: list[str] = Field(default_factory=list)
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deleted_paths: list[str] = Field(default_factory=list)
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existing: dict[str, float] = Field(default_factory=dict)
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indexed: dict[str, float] = Field(default_factory=dict)
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units: list[dict] = Field(default_factory=list)
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topics: list[dict] = Field(default_factory=list)
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extract_summary: str = ""
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integrate_results: list[dict] = Field(default_factory=list)
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nodes_created: list[str] = Field(default_factory=list)
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nodes_updated: list[str] = Field(default_factory=list)
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failed_units: list[dict] = Field(default_factory=list)
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failed_paths: list[str] = Field(default_factory=list)
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interests_path: str = ""
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interests_paths: list[str] = Field(default_factory=list)
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topics_written: int = 0
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topic_error: str = ""
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checkpoint_paths: list[str] = Field(default_factory=list)
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errors: list[str] = Field(default_factory=list)
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summary: str = ""
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