File size: 5,601 Bytes
85b19cf | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | """Adapter for SimpleMem and Omni-SimpleMem baselines."""
from __future__ import annotations
import os
import sys
from pathlib import Path
from typing import Any
from eval_framework.datasets.schemas import (
MemoryDeltaRecord,
MemorySnapshotRecord,
NormalizedTurn,
RetrievalItem,
RetrievalRecord,
)
from eval_framework.memory_adapters.base import MemoryAdapter
_DEFAULT_SOURCE = Path("/data1/toby/nips26/baselines/SimpleMem")
class SimpleMemAdapter(MemoryAdapter):
"""Adapter for SimpleMem (text mode) or Omni-SimpleMem (omni mode)."""
def __init__(
self,
*,
mode: str = "text",
source_root: str | os.PathLike[str] | None = None,
**kwargs: Any,
) -> None:
self._mode = mode # "text" or "omni"
root = Path(source_root or _DEFAULT_SOURCE).resolve()
if str(root) not in sys.path:
sys.path.insert(0, str(root))
import simplemem_router as simplemem
self._simplemem = simplemem
self._mem: Any = None
self._session_id = ""
self._prev_snapshot_ids: set[str] = set()
self._stored_texts: list[dict[str, str]] = []
self._init_mem()
def _init_mem(self) -> None:
self._mem = self._simplemem.create(mode=self._mode, clear_db=True)
self._stored_texts = []
def reset(self) -> None:
if self._mem is not None:
try:
self._mem.close()
except Exception:
pass
self._init_mem()
self._prev_snapshot_ids = set()
def ingest_turn(self, turn: NormalizedTurn) -> None:
self._session_id = turn.session_id
text = f"{turn.role}: {turn.text}"
for att in turn.attachments:
text += f"\n[{att.type}] {att.caption}"
mid = str(len(self._stored_texts))
if self._mode == "omni":
self._mem.add_text(text, tags=[f"session:{turn.session_id}"])
else:
speaker = "User" if turn.role == "user" else "Assistant"
ts = turn.timestamp or ""
self._mem.add_dialogue(speaker, text, ts)
self._stored_texts.append({"id": mid, "text": text, "session_id": turn.session_id})
def end_session(self, session_id: str) -> None:
self._session_id = session_id
if self._mode == "text":
try:
self._mem.finalize()
except Exception:
pass
def snapshot_memories(self) -> list[MemorySnapshotRecord]:
return [
MemorySnapshotRecord(
memory_id=t["id"], text=t["text"],
session_id=t["session_id"], status="active",
source=f"SimpleMem-{self._mode}",
raw_backend_id=t["id"], raw_backend_type="simplemem",
metadata={},
)
for t in self._stored_texts
]
def export_memory_delta(self, session_id: str) -> list[MemoryDeltaRecord]:
current = self.snapshot_memories()
current_ids = {s.memory_id for s in current}
deltas = [
MemoryDeltaRecord(
session_id=session_id, op="add", text=s.text,
linked_previous=(), raw_backend_id=s.raw_backend_id,
metadata={"baseline": f"SimpleMem-{self._mode}"},
)
for s in current if s.memory_id not in self._prev_snapshot_ids
]
self._prev_snapshot_ids = current_ids
return deltas
def retrieve(self, query: str, top_k: int) -> RetrievalRecord:
items: list[RetrievalItem] = []
try:
if self._mode == "omni":
result = self._mem.query(query, top_k=top_k)
if isinstance(result, list):
for i, r in enumerate(result[:top_k]):
text = r.get("text", str(r)) if isinstance(r, dict) else str(r)
items.append(RetrievalItem(
rank=i, memory_id=str(i), text=text,
score=1.0 / (i + 1), raw_backend_id=None,
))
else:
answer = self._mem.ask(query)
if answer:
items.append(RetrievalItem(
rank=0, memory_id="answer", text=str(answer),
score=1.0, raw_backend_id=None,
))
except Exception:
pass
if not items:
# Fallback: simple text search over stored memories
query_lower = query.lower()
scored = []
for t in self._stored_texts:
overlap = len(set(query_lower.split()) & set(t["text"].lower().split()))
scored.append((overlap, t))
scored.sort(key=lambda x: x[0], reverse=True)
for i, (sc, t) in enumerate(scored[:top_k]):
items.append(RetrievalItem(
rank=i, memory_id=t["id"], text=t["text"],
score=float(sc) / max(len(query.split()), 1),
raw_backend_id=t["id"],
))
return RetrievalRecord(
query=query, top_k=top_k, items=items[:top_k],
raw_trace={"baseline": f"SimpleMem-{self._mode}"},
)
def get_capabilities(self) -> dict[str, Any]:
name = "Omni-SimpleMem" if self._mode == "omni" else "SimpleMem"
return {
"backend": name, "baseline": name,
"available": self._mem is not None,
"delta_granularity": "per_turn",
"snapshot_mode": "full",
}
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