File size: 17,234 Bytes
750b88d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 | """A-MEM adapter.
RobustAgenticMemorySystem with the evaluation's LLM and embedding clients injected. Each visit is added as a note
stamped with the visit date, with memory evolution. A failed LLM call leaves the note with heuristic keywords and no
evolution, and the visit is reported as not successful. Retrieval: one keyword-generation call, then the top
retrieve_num notes and the notes they link to.
"""
from __future__ import annotations
import importlib
import logging
import sys
import time
from pathlib import Path
from typing import Dict, List
import numpy as np
from .base import EMBED_ENC, BaseAgent, MemoryBuildResult, MemoryContext, as_list
from ..llm_client import OpenAIEmbedder, create_llm_client
logger = logging.getLogger(__name__)
AMEM_DIR = (Path(__file__).resolve().parents[2]
/ "vendor" / "amem" / "A-mem")
class _ControllerShim:
"""Drop-in for llm_controller.llm: callers use get_completion(prompt, temperature).
Counts the calls that raised in `failures`, with the last error in `last_error`."""
SYSTEM_MSG = ("Follow the format specified in the prompt exactly. "
"Do not add extra commentary.")
def __init__(self, client, temperature: float):
self._client = client
self._temperature = temperature
self.failures = 0
self.last_error = None
def get_completion(self, prompt: str, temperature: float | None = None) -> str:
try:
return self._client.chat([{"role": "system", "content": self.SYSTEM_MSG},
{"role": "user", "content": prompt}],
temperature=self._temperature if temperature is None else temperature).content
except Exception as e:
self.failures += 1
self.last_error = f"{type(e).__name__}: {e}"[:200]
raise
def _make_st_shim(embedder: OpenAIEmbedder, max_input_tokens: int):
"""Embedding class bound in place of memory_layer.SentenceTransformer."""
def _truncate(t: str) -> str:
ids = EMBED_ENC.encode(t, disallowed_special=())
return EMBED_ENC.decode(ids[:max_input_tokens]) if len(ids) > max_input_tokens else t
class _STShim:
def __init__(self, _model_name: str):
self._emb = embedder
def encode(self, texts):
single = isinstance(texts, str)
batch = [_truncate(texts)] if single else [_truncate(t) for t in texts]
vecs = np.asarray(self._emb.embed(batch), dtype="float32")
return vecs[0] if single else vecs
return _STShim
class AMemAgent(BaseAgent):
ANSWERS_INTERNALLY = False
MEMORIZE_WITH_LLM = True
def __init__(self, *, model: str | None = None, retrieve_num: int,
amem_evo_threshold: int, max_tokens: int, temperature: float,
memorize_chunk_tokens: int,
embedding_max_input_tokens: int,
embedding_model: str | None = None,
embedding_batch_size: int | None = None,
internal_base_url: str | None = None,
working_dir: str | None = None, **params):
super().__init__(**params)
self.model = model
self.retrieve_num = retrieve_num
self._vec_seen: set[str] = set()
self._kw_fallbacks = 0
self.memorize_chunk_tokens = memorize_chunk_tokens
self.embedding_max_input_tokens = embedding_max_input_tokens
if str(AMEM_DIR) not in sys.path:
sys.path.append(str(AMEM_DIR))
ml = importlib.import_module("memory_layer")
mlr = importlib.import_module("memory_layer_robust")
self._embedder = OpenAIEmbedder(model=embedding_model, batch_size=embedding_batch_size)
ml.SentenceTransformer = _make_st_shim(self._embedder, self.embedding_max_input_tokens)
self._system = mlr.RobustAgenticMemorySystem(
model_name="injected", llm_backend="openai", llm_model=model,
evo_threshold=amem_evo_threshold, api_key="injected", check_connection=False)
self._llm_shim = _ControllerShim(create_llm_client(
model=model, max_tokens=max_tokens, base_url=internal_base_url), temperature=temperature)
self._system.llm_controller.llm = self._llm_shim
self._note_visit: Dict[str, set] = {}
self._cur_visit: int | None = None
self._trace_evolution()
def _trace_evolution(self) -> None:
"""Record the neighbour lookup and the evolution decision."""
sysm = self._system
if getattr(sysm, "__traced__", False):
return
orig_find = sysm.find_related_memories
orig_proc = sysm.process_memory
state = {"stage": "query"}
def find(query, k=5):
out = orig_find(query, k=k)
try:
mem_str, indices = out if isinstance(out, tuple) else (out, [])
idx = [] if indices is None else list(indices)
ids = list(self._system.memories.keys())
if state["stage"] == "write":
mems = self._system.memories
state["nb"] = {}
for i in idx:
if int(i) < len(ids):
nb = mems.get(ids[int(i)])
state["nb"][ids[int(i)]] = (
str(getattr(nb, "tags", None)), str(getattr(nb, "context", None)))
self._trace("neighbour_lookup", stage=state["stage"], query=query, k=k,
n_found=len(idx),
neighbours=[{"index": int(i),
"visits": (sorted(self._note_visit.get(ids[int(i)], ()))
if int(i) < len(ids) else None)}
for i in idx],
neighbour_text=mem_str)
except Exception:
pass
return out
def proc(note):
state["stage"] = "write"
state["nb"] = {}
before = getattr(note, "content", "")
kw_before = as_list(getattr(note, "keywords", None))
try:
evolved, out_note = orig_proc(note)
finally:
state["stage"] = "query"
cur = self._cur_visit
merged: list = []
try:
if cur is not None:
mems = self._system.memories
for nid, sig in (state.get("nb") or {}).items():
nb = mems.get(nid)
if nb is None:
continue
if (str(getattr(nb, "tags", None)),
str(getattr(nb, "context", None))) != sig:
merged.append({"note_id": str(nid),
"visits_before": sorted(
self._note_visit.get(str(nid), ()))})
self._note_visit.setdefault(str(nid), set()).add(int(cur))
except Exception:
pass
try:
self._trace("evolution", note_id=str(getattr(note, "id", "")),
evolved=bool(evolved),
visit=cur,
neighbours_merged=merged,
content_before=before,
content_after=getattr(out_note, "content", ""),
keywords_before=kw_before,
keywords_after=as_list(getattr(out_note, "keywords", None)),
links_after=as_list(getattr(out_note, "links", None)))
except Exception:
pass
return evolved, out_note
retr = getattr(sysm, "retriever", None)
if retr is not None and not getattr(retr, "__traced__", False):
orig_search = retr.search
def search(query, k=5, *a, **kw):
out = orig_search(query, k, *a, **kw)
try:
ids = list(self._system.memories.keys())
res = [] if out is None else list(out)
self._trace("retrieval", stage=state["stage"], query=query, k=k,
n_found=len(res),
hits=[{"index": int(i),
"visits": sorted(self._note_visit.get(ids[int(i)], ()))
if int(i) < len(ids) else None,
"content": (self._system.memories[ids[int(i)]].content
if int(i) < len(ids) else "")[:300]}
for i in res])
except Exception:
pass
return out
retr.search = search
retr.__traced__ = True
sysm.find_related_memories = find
sysm.process_memory = proc
sysm.__traced__ = True
def _split_chunks(self, text: str) -> List[str]:
toks = self.count_tokens(text)
if toks <= self.memorize_chunk_tokens:
return [text] if text.strip() else []
from .base import ENC
ids = ENC.encode(text)
return [ENC.decode(ids[i:i + self.memorize_chunk_tokens])
for i in range(0, len(ids), self.memorize_chunk_tokens)]
def memorize(self, text: str, visit_number: int, date: str) -> MemoryBuildResult:
"""Add the visit as notes. Not successful when add_note raises (the visit is rolled back) or when
an LLM call of content analysis or evolution failed (the notes are kept as A-MEM stored them)."""
t0 = time.time()
failures_before = self._llm_shim.failures
evo_before = self._system.evo_cnt
pre_retr = self._system.retriever
pre_corpus = len(pre_retr.corpus)
pre_emb = (0 if getattr(pre_retr, "embeddings", None) is None
else int(pre_retr.embeddings.shape[0]))
pre_docids = dict(pre_retr.document_ids)
pre_notes = len(self._system.memories)
self._cur_visit = visit_number
added = []
try:
for chunk in self._split_chunks(text):
note_id = self._system.add_note(content=chunk, time=date)
self._note_visit.setdefault(str(note_id), set()).add(int(visit_number))
added.append(str(note_id))
except Exception as e:
logger.error(f"[A-MEM] add_note failed on v{visit_number}: {e}")
try:
self._system.retriever = pre_retr
del pre_retr.corpus[pre_corpus:]
if pre_emb == 0:
pre_retr.embeddings = None
elif pre_retr.embeddings is not None:
pre_retr.embeddings = pre_retr.embeddings[:pre_emb]
pre_retr.document_ids = pre_docids
mems = self._system.memories
while len(mems) > pre_notes:
orphan_id = next(reversed(mems))
mems.pop(orphan_id)
self._note_visit.pop(str(orphan_id), None)
self._system.evo_cnt = evo_before
except Exception as rb_err:
logger.error(f"[A-MEM] rollback failed, store may be inconsistent: "
f"{rb_err}")
return MemoryBuildResult(success=False, method="amem", action="add_note",
visit_number=visit_number,
chunk_count=len(self._system.memories),
time_cost=time.time() - t0,
extra={"error": str(e)[:200],
"rolled_back_to_notes": pre_notes})
extra = {"evolutions": self._system.evo_cnt - evo_before,
"evo_cnt_total": self._system.evo_cnt}
llm_failures = self._llm_shim.failures - failures_before
if llm_failures:
logger.warning("[A-MEM] v%s: %d failed LLM call(s); A-MEM stored the visit without their "
"result (%s)", visit_number, llm_failures, self._llm_shim.last_error)
extra.update(llm_failures=llm_failures, error=self._llm_shim.last_error)
return MemoryBuildResult(success=llm_failures == 0, method="amem", action="add_note",
visit_number=visit_number,
chunk_count=len(self._system.memories),
added_chunks=len(added),
time_cost=time.time() - t0,
extra=extra)
_KEYWORDS_PROMPT = ("Given the following question, generate several keywords "
"separated by commas.\n\nQuestion: {question}\n\nKeywords:")
def _keyword_retrieve(self, question: str):
"""Keyword query, then the top retrieve_num notes and the notes they link to."""
from llm_text_parsers import parse_keywords_response
keywords = parse_keywords_response(
self._system.llm_controller.llm.get_completion(
self._KEYWORDS_PROMPT.format(question=question)))
query = str(keywords).strip()
if not query:
self._kw_fallbacks += 1
query = question
if not self._system.memories:
return "", [], query
indices = self._system.retriever.search(query, self.retrieve_num)
all_memories = list(self._system.memories.values())
note_ids = list(self._system.memories.keys())
memory_str, hit_ids = "", []
for i in indices:
j = 0
m = all_memories[i]
memory_str += ("talk start time:" + m.timestamp + "memory content: "
+ m.content + "memory context: " + m.context
+ "memory keywords: " + str(m.keywords)
+ "memory tags: " + str(m.tags) + "\n")
hit_ids.append(note_ids[i])
for neighbor in m.links:
n = all_memories[neighbor]
memory_str += ("talk start time:" + n.timestamp + "memory content: "
+ n.content + "memory context: " + n.context
+ "memory keywords: " + str(n.keywords)
+ "memory tags: " + str(n.tags) + "\n")
hit_ids.append(note_ids[neighbor])
if j >= self.retrieve_num:
break
j += 1
return memory_str, hit_ids, query
def build_memory_context(self, question: str, as_of: str = "",
display_question: str | None = None) -> MemoryContext:
try:
memory_str, hit_ids, keywords = self._keyword_retrieve(question)
except Exception as e:
logger.error(f"[A-MEM] retrieval failed: {e}")
return MemoryContext(mem="", n_tok=0, retrieved_count=0, error=True,
extra={"method": "amem", "error": str(e)[:200]})
visits = sorted({v for h in hit_ids
for v in self._note_visit.get(h, ())})
return MemoryContext(
mem=memory_str, n_tok=self.count_tokens(memory_str),
retrieved_count=len(hit_ids), retrieved_visits=visits,
retrieved_memories=[{"visit": None, "memory": memory_str[:300]}],
extra={"method": "amem",
"query_keywords": str(keywords)[:200],
"keyword_fallbacks": self._kw_fallbacks,
"notes_total": len(self._system.memories),
"evo_cnt": self._system.evo_cnt})
def dump_memory(self, out_dir):
from .base import snapshot_jsonl_gz, snapshot_vectors_delta
rows = []
for nid, note in self._system.memories.items():
d = {k: v for k, v in vars(note).items()
if k not in ("embedding",) and not k.startswith("_")}
d["note_id"] = str(nid)
d["visits"] = sorted(self._note_visit.get(str(nid), ()))
rows.append(d)
n = snapshot_jsonl_gz(out_dir / "notes.jsonl.gz", rows)
nv = 0
try:
r = self._system.retriever
corpus = list(getattr(r, "corpus", []) or [])
embs = getattr(r, "embeddings", None)
if embs is not None and len(corpus) == len(embs):
nv = snapshot_vectors_delta(
out_dir, ((f"pos{i}", embs[i]) for i in range(len(corpus))),
self._vec_seen)
snapshot_jsonl_gz(out_dir / "corpus_order.jsonl.gz",
({"pos": i, "text": t} for i, t in enumerate(corpus)))
except Exception as e:
logger.error("[A-MEM] vector snapshot failed (run continues): %s", e)
return {"notes": n, "evo_cnt": self._system.evo_cnt,
"corpus": len(getattr(self._system.retriever, "corpus", [])),
"vectors_new": nv}
|