File size: 9,914 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 | """Adapter for the external A-Mem baseline."""
from __future__ import annotations
import importlib
import os
import sys
from pathlib import Path
from typing import Any, Callable
from eval_framework.datasets.schemas import (
MemoryDeltaRecord,
MemorySnapshotRecord,
NormalizedTurn,
RetrievalItem,
RetrievalRecord,
)
from eval_framework.memory_adapters.base import MemoryAdapter
_BACKEND_ID = "A-Mem"
INTEGRATION_ERROR = (
f"{_BACKEND_ID} backend unavailable."
)
class AMemAdapter(MemoryAdapter):
"""Thin wrapper around A-Mem's robust memory system."""
def __init__(
self,
*,
backend: Any | None = None,
backend_factory: Callable[[], Any] | None = None,
source_root: str | os.PathLike[str] | None = None,
model_name: str = "all-MiniLM-L6-v2",
llm_backend: str = "openai",
llm_model: str | None = None,
api_key: str | None = None,
api_base: str | None = None,
sglang_host: str = "http://localhost",
sglang_port: int = 30000,
) -> None:
self._source_root = Path(source_root).resolve() if source_root else self._default_source_root()
resolved_llm_model = llm_model or os.getenv("OPENAI_MODEL") or "gpt-5.1"
self._backend: Any | None = None
self._backend_factory = backend_factory
self._integration_error: str | None = None
self._session_id = ""
self._prev_snapshot_ids: set[str] = set()
self._note_session_map: dict[str, str] = {}
if backend is not None:
self._backend = backend
else:
try:
if self._backend_factory is None:
self._backend_factory = self._build_backend_factory(
model_name=model_name,
llm_backend=llm_backend,
llm_model=resolved_llm_model,
api_key=api_key,
api_base=api_base,
sglang_host=sglang_host,
sglang_port=sglang_port,
)
self._backend = self._backend_factory()
except Exception as exc:
self._integration_error = str(exc)
@staticmethod
def _default_source_root() -> Path:
here = Path(__file__).resolve()
# memory_adapters/ -> eval_framework/ -> our/ -> Benchmark/
return (here.parents[2].parent / "data_pipline" / "A-mem").resolve()
def _build_backend_factory(
self,
*,
model_name: str,
llm_backend: str,
llm_model: str,
api_key: str | None,
api_base: str | None,
sglang_host: str,
sglang_port: int,
) -> Callable[[], Any]:
if not self._source_root.is_dir():
raise RuntimeError(
f"{_BACKEND_ID}: source root not found at {self._source_root}"
)
src = str(self._source_root)
if src not in sys.path:
sys.path.insert(0, src)
mod = importlib.import_module("memory_layer_robust")
backend_cls = getattr(mod, "RobustAgenticMemorySystem")
return lambda: backend_cls(
model_name=model_name,
llm_backend=llm_backend,
llm_model=llm_model,
api_key=api_key or os.getenv("OPENAI_API_KEY"),
api_base=api_base or os.getenv("OPENAI_BASE_URL"),
sglang_host=sglang_host,
sglang_port=sglang_port,
)
def _runtime_error(self) -> RuntimeError:
detail = self._integration_error or INTEGRATION_ERROR
return RuntimeError(
f"{_BACKEND_ID}: backend unavailable — {detail}"
)
def reset(self) -> None:
if self._backend_factory is None and self._backend is None:
raise self._runtime_error()
if self._backend_factory is not None:
self._backend = self._backend_factory()
self._prev_snapshot_ids = set()
self._note_session_map = {}
self._session_id = ""
def ingest_turn(self, turn: NormalizedTurn) -> None:
backend = self._require_backend()
self._session_id = turn.session_id
text = self._turn_text(turn)
note_id = backend.add_note(text, time=turn.timestamp)
self._note_session_map[str(note_id)] = turn.session_id
def end_session(self, session_id: str) -> None:
self._require_backend()
self._session_id = session_id
def snapshot_memories(self) -> list[MemorySnapshotRecord]:
backend = self._require_backend()
rows: list[MemorySnapshotRecord] = []
for note_id, note in getattr(backend, "memories", {}).items():
sid = self._note_session_map.get(str(note_id), self._session_id)
content = str(getattr(note, "content", ""))
context = getattr(note, "context", "")
keywords = list(getattr(note, "keywords", []) or [])
tags = list(getattr(note, "tags", []) or [])
# Include A-Mem enrichments in the snapshot text so that the
# eval captures what the system actually processed, not just
# the raw input.
enriched_parts = [content]
if context:
enriched_parts.append(f"[context] {context}")
if keywords:
enriched_parts.append(f"[keywords] {', '.join(keywords)}")
if tags:
enriched_parts.append(f"[tags] {', '.join(tags)}")
rows.append(
MemorySnapshotRecord(
memory_id=str(getattr(note, "id", note_id)),
text="\n".join(enriched_parts),
session_id=sid,
status="active",
source=_BACKEND_ID,
raw_backend_id=str(getattr(note, "id", note_id)),
raw_backend_type="a_mem_note",
metadata={
"timestamp": getattr(note, "timestamp", None),
"context": context,
"keywords": keywords,
"tags": tags,
"links": list(getattr(note, "links", []) or []),
},
)
)
return rows
def export_memory_delta(self, session_id: str) -> list[MemoryDeltaRecord]:
"""Export delta by diffing current snapshot against previous snapshot."""
self._require_backend()
current_snapshot = self.snapshot_memories()
deltas: list[MemoryDeltaRecord] = []
current_ids: set[str] = set()
for snap in current_snapshot:
current_ids.add(snap.memory_id)
if snap.memory_id not in self._prev_snapshot_ids:
deltas.append(
MemoryDeltaRecord(
session_id=session_id,
op="add",
text=snap.text,
linked_previous=(),
raw_backend_id=snap.raw_backend_id,
metadata={
"baseline": _BACKEND_ID,
"backend_type": snap.raw_backend_type,
},
)
)
self._prev_snapshot_ids = current_ids
return deltas
def retrieve(self, query: str, top_k: int) -> RetrievalRecord:
backend = self._require_backend()
items: list[RetrievalItem] = []
memories = list(getattr(backend, "memories", {}).values())
retriever = getattr(backend, "retriever", None)
if retriever is not None and hasattr(retriever, "search"):
for rank, idx in enumerate(retriever.search(query, top_k)):
if 0 <= int(idx) < len(memories):
note = memories[int(idx)]
items.append(
RetrievalItem(
rank=rank,
memory_id=str(getattr(note, "id", idx)),
text=str(getattr(note, "content", "")),
score=1.0 / float(rank + 1),
raw_backend_id=str(getattr(note, "id", idx)),
)
)
if not items and hasattr(backend, "find_related_memories_raw"):
raw = backend.find_related_memories_raw(query, k=top_k)
if raw:
items.append(
RetrievalItem(
rank=0,
memory_id="a_mem:bundle",
text=str(raw),
score=1.0,
raw_backend_id=None,
)
)
return RetrievalRecord(
query=query,
top_k=top_k,
items=items[:top_k],
raw_trace={"baseline": _BACKEND_ID},
)
def get_capabilities(self) -> dict[str, Any]:
available = self._backend is not None or self._backend_factory is not None
return {
"backend": _BACKEND_ID,
"baseline": _BACKEND_ID,
"available": available and self._integration_error is None,
"integration_status": "integrated" if available and self._integration_error is None else "unavailable",
"integration_error": self._integration_error or INTEGRATION_ERROR,
"delta_granularity": "ingest_turn_only",
"snapshot_mode": "full_store",
}
def _require_backend(self) -> Any:
if self._backend is None:
raise self._runtime_error()
return self._backend
@staticmethod
def _turn_text(turn: NormalizedTurn) -> str:
parts = [f"{turn.role}: {turn.text}"]
for att in turn.attachments:
parts.append(f"[{att.type}] {att.caption}")
return "\n".join(parts)
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