File size: 6,724 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 | """Adapter for MemVerse — uses build_memory for storage + cosine retrieval."""
from __future__ import annotations
import json
import os
import sys
import shutil
import tempfile
from pathlib import Path
from typing import Any
import numpy as np
from dotenv import load_dotenv
load_dotenv(Path(__file__).resolve().parents[2] / ".env")
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/MemVerse")
class MemVerseAdapter(MemoryAdapter):
"""Adapter for MemVerse using build_memory + cosine retrieval.
Bypasses the async orchestrator/LightRAG and uses the core
memory building + embedding-based retrieval directly.
"""
def __init__(
self,
*,
source_root: str | os.PathLike[str] | None = None,
**kwargs: Any,
) -> None:
root = Path(source_root or _DEFAULT_SOURCE).resolve()
if str(root) not in sys.path:
sys.path.insert(0, str(root))
from openai import OpenAI
self._client = OpenAI(
api_key=os.getenv("OPENAI_API_KEY"),
base_url=os.getenv("OPENAI_BASE_URL"),
)
self._model = os.getenv("OPENAI_MODEL") or "gpt-4o"
# Working directory for memory files
self._work_dir = Path(tempfile.mkdtemp(prefix="memverse_eval_"))
self._root = root
self._session_id = ""
self._prev_snapshot_ids: set[str] = set()
self._memories: list[dict[str, Any]] = [] # {id, text, embedding, output}
self._conversation: list[dict[str, Any]] = []
self._turn_counter = 0
# Load system prompts for memory agents
self._prompts: dict[str, str] = {}
for name in ["core_memory_agent", "episodic_memory_agent", "semantic_memory_agent"]:
prompt_path = root / "MemoryKB" / "Long_Term_Memory" / "system" / f"{name}.txt"
if prompt_path.exists():
self._prompts[name] = prompt_path.read_text(encoding="utf-8").strip()
def _get_embedding(self, text: str) -> np.ndarray:
resp = self._client.embeddings.create(
model="text-embedding-3-small",
input=text,
)
return np.array(resp.data[0].embedding)
def _cosine_sim(self, a: np.ndarray, b: np.ndarray) -> float:
norm = np.linalg.norm(a) * np.linalg.norm(b)
if norm == 0:
return 0.0
return float(np.dot(a, b) / norm)
def reset(self) -> None:
self._memories = []
self._conversation = []
self._prev_snapshot_ids = set()
self._turn_counter = 0
if self._work_dir.exists():
shutil.rmtree(self._work_dir, ignore_errors=True)
self._work_dir = Path(tempfile.mkdtemp(prefix="memverse_eval_"))
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}"
entry_id = f"turn_{self._turn_counter}"
self._turn_counter += 1
entry = {
"id": entry_id,
"query": text,
"videocaption": None,
"audiocaption": None,
"imagecaption": None,
}
self._conversation.append(entry)
# Build memory: get embedding + LLM extraction for each memory type
embedding = self._get_embedding(text)
# Use the first available prompt (core memory agent) for extraction
prompt = next(iter(self._prompts.values()), "Extract key facts from this text.")
try:
resp = self._client.chat.completions.create(
model=self._model,
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": text},
],
temperature=0,
max_tokens=512,
)
output = resp.choices[0].message.content or ""
except Exception:
output = text
self._memories.append({
"id": entry_id,
"text": text,
"output": output,
"embedding": embedding,
"session_id": turn.session_id,
})
def end_session(self, session_id: str) -> None:
self._session_id = session_id
def snapshot_memories(self) -> list[MemorySnapshotRecord]:
return [
MemorySnapshotRecord(
memory_id=m["id"],
text=m["output"],
session_id=m.get("session_id", self._session_id),
status="active",
source="MemVerse",
raw_backend_id=m["id"],
raw_backend_type="memverse",
metadata={},
)
for m in self._memories
]
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": "MemVerse"},
)
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:
if not self._memories:
return RetrievalRecord(query=query, top_k=top_k, items=[], raw_trace={})
query_emb = self._get_embedding(query)
scored = []
for m in self._memories:
sim = self._cosine_sim(query_emb, m["embedding"])
scored.append((sim, m))
scored.sort(key=lambda x: x[0], reverse=True)
items = [
RetrievalItem(
rank=i,
memory_id=m["id"],
text=m["output"],
score=float(sim),
raw_backend_id=m["id"],
)
for i, (sim, m) in enumerate(scored[:top_k])
]
return RetrievalRecord(
query=query, top_k=top_k, items=items,
raw_trace={"baseline": "MemVerse"},
)
def get_capabilities(self) -> dict[str, Any]:
return {
"backend": "MemVerse",
"baseline": "MemVerse",
"available": True,
"delta_granularity": "per_turn",
"snapshot_mode": "full",
}
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