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https://github.com/tkenaz/breathe-memory.git
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4e671a0a83
Context optimization and associative memory for LLM applications. Two-phase system: SYNAPSE (pre-generation memory injection) + GraphCompactor (structured context compression). - Interface-based, storage-agnostic, LLM-agnostic - Memory Nexus: PostgreSQL + pgvector reference backend - Zero mandatory dependencies beyond stdlib - 28 tests passing, clean install verified Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
"""Data models for Memory Nexus."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from datetime import datetime
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from typing import Any, Optional
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@dataclass
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class MemoryMetadata:
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"""Structured metadata attached to a memory."""
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tags: list[str] = field(default_factory=list)
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source: str = ""
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importance: float = 0.5 # 0–1
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extra: dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> dict:
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return {
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"tags": self.tags,
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"source": self.source,
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"importance": self.importance,
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**self.extra,
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}
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@classmethod
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def from_dict(cls, data: dict) -> "MemoryMetadata":
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return cls(
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tags=data.get("tags", []),
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source=data.get("source", ""),
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importance=data.get("importance", 0.5),
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extra={k: v for k, v in data.items() if k not in ("tags", "source", "importance")},
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)
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@dataclass
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class Memory:
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"""A single memory entry."""
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id: str
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content: str
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metadata: MemoryMetadata = field(default_factory=MemoryMetadata)
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created_at: datetime = field(default_factory=datetime.utcnow)
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similarity: Optional[float] = None # set during search results
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def to_dict(self) -> dict:
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return {
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"id": self.id,
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"content": self.content,
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"metadata": self.metadata.to_dict(),
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"created_at": self.created_at.isoformat(),
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"similarity": self.similarity,
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}
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@classmethod
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def from_dict(cls, data: dict) -> "Memory":
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meta = data.get("metadata", {})
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if isinstance(meta, str):
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import json
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try:
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meta = json.loads(meta)
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except Exception:
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meta = {}
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return cls(
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id=data["id"],
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content=data["content"],
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metadata=MemoryMetadata.from_dict(meta),
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created_at=datetime.fromisoformat(data["created_at"])
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if "created_at" in data
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else datetime.utcnow(),
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similarity=data.get("similarity"),
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)
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