115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
"""File-based memory store — persistent facts with confidence ranking."""
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from __future__ import annotations
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import json
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import logging
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import uuid
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from datetime import datetime
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from pathlib import Path
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from typing import Any
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from pydantic import BaseModel, Field
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logger = logging.getLogger(__name__)
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MEMORY_DIR = Path(__file__).resolve().parent.parent.parent / "memory"
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class Fact(BaseModel):
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id: str = Field(default_factory=lambda: uuid.uuid4().hex[:8])
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content: str
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category: str = "context" # preference | knowledge | context | behavior | goal
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confidence: float = 0.7
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source: str = "" # which report/session created this
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created_at: str = Field(default_factory=lambda: datetime.now().isoformat())
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class MemoryFile(BaseModel):
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"""One memory file per client (or global)."""
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client_id: str = "global"
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preferences: dict[str, str] = Field(default_factory=dict)
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facts: list[Fact] = Field(default_factory=list)
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class MemoryStore:
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"""Read/write persistent memory as JSON files.
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Storage layout:
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memory/
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├── global.json — system-wide facts
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└── client_<id>.json — per-client facts
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"""
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def __init__(self):
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MEMORY_DIR.mkdir(parents=True, exist_ok=True)
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def _path(self, client_id: str = "global") -> Path:
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safe_name = client_id.replace("/", "_").replace("..", "_")
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return MEMORY_DIR / f"{safe_name}.json"
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def load(self, client_id: str = "global") -> MemoryFile:
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path = self._path(client_id)
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if not path.exists():
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return MemoryFile(client_id=client_id)
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try:
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data = json.loads(path.read_text(encoding="utf-8"))
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return MemoryFile(**data)
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except Exception as e:
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logger.warning(f"[memory] failed to load {path}: {e}")
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return MemoryFile(client_id=client_id)
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def save(self, mem: MemoryFile):
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path = self._path(mem.client_id)
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path.write_text(
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json.dumps(mem.model_dump(), ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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logger.info(f"[memory] saved {len(mem.facts)} facts to {path}")
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def add_fact(
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self,
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content: str,
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client_id: str = "global",
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category: str = "context",
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confidence: float = 0.7,
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source: str = "",
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) -> Fact:
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mem = self.load(client_id)
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# Deduplicate by content (normalized)
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normalized = content.strip().lower()
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for existing in mem.facts:
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if existing.content.strip().lower() == normalized:
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# Update confidence if higher
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if confidence > existing.confidence:
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existing.confidence = confidence
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self.save(mem)
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return existing
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fact = Fact(
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content=content,
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category=category,
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confidence=confidence,
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source=source,
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)
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mem.facts.append(fact)
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self.save(mem)
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return fact
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def get_top_facts(
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self, client_id: str = "global", limit: int = 15
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) -> list[str]:
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"""Get top N facts sorted by confidence, formatted for prompt injection."""
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mem = self.load(client_id)
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sorted_facts = sorted(mem.facts, key=lambda f: f.confidence, reverse=True)
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return [f.content for f in sorted_facts[:limit]]
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def set_preference(self, key: str, value: str, client_id: str = "global"):
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mem = self.load(client_id)
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mem.preferences[key] = value
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self.save(mem)
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def get_preferences(self, client_id: str = "global") -> dict[str, str]:
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return self.load(client_id).preferences
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