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86a0172 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | """Data models for the Memory System."""
from __future__ import annotations
import uuid
from dataclasses import dataclass, field, asdict
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Optional
class MemoryTier(str, Enum):
"""Which memory layer an entry belongs to."""
SESSION = "session" # short-term / conversation context
EPISODIC = "episodic" # mid-term / past tasks & events
SEMANTIC = "semantic" # long-term / vector-backed knowledge
@dataclass
class MemoryEntry:
"""A single memory record stored across tiers."""
id: str = field(default_factory=lambda: uuid.uuid4().hex[:12])
content: str = ""
title: str = ""
tier: MemoryTier = MemoryTier.SESSION
tags: List[str] = field(default_factory=list)
metadata: Dict[str, Any] = field(default_factory=dict)
importance: float = 0.5 # 0.0 – 1.0
access_count: int = 0
created_at: str = field(default_factory=lambda: datetime.utcnow().isoformat())
updated_at: str = field(default_factory=lambda: datetime.utcnow().isoformat())
session_id: Optional[str] = None # groups session memories
source: str = "" # origin of the memory
# ── helpers ──────────────────────────────────────────────
def to_dict(self) -> Dict[str, Any]:
d = asdict(self)
d["tier"] = self.tier.value
return d
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "MemoryEntry":
data = dict(data) # shallow copy
if "tier" in data and isinstance(data["tier"], str):
data["tier"] = MemoryTier(data["tier"])
return cls(**{k: v for k, v in data.items() if k in cls.__dataclass_fields__})
def to_markdown(self) -> str:
"""Render as a Markdown document with YAML front-matter."""
lines = [
"---",
f"id: {self.id}",
f"title: \"{self.title}\"",
f"tier: {self.tier.value}",
f"tags: [{', '.join(self.tags)}]",
f"importance: {self.importance}",
f"access_count: {self.access_count}",
f"created_at: {self.created_at}",
f"updated_at: {self.updated_at}",
]
if self.session_id:
lines.append(f"session_id: {self.session_id}")
if self.source:
lines.append(f"source: \"{self.source}\"")
if self.metadata:
import json
lines.append(f"metadata: {json.dumps(self.metadata)}")
lines.append("---")
lines.append("")
lines.append(self.content)
return "\n".join(lines)
@classmethod
def from_markdown(cls, text: str) -> "MemoryEntry":
"""Parse a Markdown document with YAML front-matter."""
import re, json as _json
fm_match = re.match(r"^---\n(.*?)\n---\n?(.*)", text, re.DOTALL)
if not fm_match:
return cls(content=text)
front, body = fm_match.group(1), fm_match.group(2).strip()
data: Dict[str, Any] = {"content": body}
for line in front.splitlines():
line = line.strip()
if not line or ":" not in line:
continue
key, _, val = line.partition(":")
key = key.strip()
val = val.strip().strip('"')
if key == "tags":
# parse [tag1, tag2]
inner = val.strip("[]")
data["tags"] = [t.strip() for t in inner.split(",") if t.strip()]
elif key == "importance":
data["importance"] = float(val)
elif key == "access_count":
data["access_count"] = int(val)
elif key == "metadata":
try:
data["metadata"] = _json.loads(val)
except _json.JSONDecodeError:
data["metadata"] = {}
else:
data[key] = val
return cls.from_dict(data)
@dataclass
class SearchResult:
"""Wrapper returned by semantic search."""
entry: MemoryEntry
score: float = 0.0 # similarity / relevance
distance: float = 0.0 # raw distance from vector DB
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