francwa
de02bdea06
- Refactor memory system (episodic/STM/LTM with components) - Implement complete subtitle domain (scanner, matcher, placer) - Add YAML workflow infrastructure - Externalize knowledge base (patterns, release groups) - Add comprehensive testing suite - Create manual testing CLIs"
91 lines
3.3 KiB
Python
91 lines
3.3 KiB
Python
"""Memory — unified manager for the 3 memory tiers."""
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import json
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import logging
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from pathlib import Path
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from .episodic import EpisodicMemory
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from .ltm import LongTermMemory
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from .stm import ShortTermMemory
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logger = logging.getLogger(__name__)
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class Memory:
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"""
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Unified manager for the 3 memory tiers.
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Usage:
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memory = Memory("data/memory")
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memory.ltm.workspace.download = "/downloads"
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memory.stm.add_message("user", "Hello")
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memory.episodic.store_search_results("query", results)
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memory.save()
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"""
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def __init__(self, storage_dir: str = "memory"):
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self.storage_dir = Path(storage_dir)
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self.storage_dir.mkdir(parents=True, exist_ok=True)
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self.ltm_file = self.storage_dir / "ltm.json"
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self.ltm = self._load_ltm()
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self.stm = ShortTermMemory()
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self.episodic = EpisodicMemory()
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logger.info(f"Memory initialized (storage: {storage_dir})")
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def _load_ltm(self) -> LongTermMemory:
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"""Load LTM from disk, or return a fresh instance."""
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if self.ltm_file.exists():
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try:
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data = json.loads(self.ltm_file.read_text(encoding="utf-8"))
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logger.info("LTM loaded from file")
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return LongTermMemory.from_dict(data)
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except (OSError, json.JSONDecodeError) as e:
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logger.warning(f"Could not load LTM: {e}")
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return LongTermMemory()
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def save(self) -> None:
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"""Persist LTM to disk (STM and Episodic are volatile)."""
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try:
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self.ltm_file.write_text(
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json.dumps(self.ltm.to_dict(), indent=2, ensure_ascii=False),
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encoding="utf-8",
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)
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logger.debug("LTM saved")
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except OSError as e:
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logger.error(f"Failed to save LTM: {e}")
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raise
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def get_context_for_prompt(self) -> dict:
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"""Snapshot of relevant memory for the system prompt."""
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return {
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"workspace": self.ltm.workspace.as_dict(),
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"library_paths": self.ltm.library_paths.to_dict(),
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"preferences": self.ltm.preferences.to_dict(),
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"current_workflow": self.stm.workflow.to_dict(),
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"current_topic": self.stm.entities.topic,
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"extracted_entities": self.stm.entities.data,
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"last_search": {
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"query": self.episodic.search_results.last.get("query") if self.episodic.search_results.last else None,
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"result_count": len(self.episodic.search_results.last.get("results", [])) if self.episodic.search_results.last else 0,
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},
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"active_downloads_count": len(self.episodic.downloads.active),
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"pending_question": self.episodic.pending_question is not None,
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"unread_events": len([e for e in self.episodic.events.items if not e.get("read")]),
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}
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def get_full_state(self) -> dict:
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"""Full state dump for debug/API."""
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return {
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"ltm": self.ltm.to_dict(),
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"stm": self.stm.to_dict(),
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"episodic": self.episodic.to_dict(),
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}
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def clear_session(self) -> None:
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"""Reset volatile memories (STM + Episodic)."""
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self.stm.clear()
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self.episodic.clear()
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logger.info("Session memories cleared")
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