diff --git a/ARCHITECTURE.md b/ARCHITECTURE.md index 8e4485d5..dc0897d4 100644 --- a/ARCHITECTURE.md +++ b/ARCHITECTURE.md @@ -180,7 +180,7 @@ memory_ops 的人工正文编辑与删除都核对预期修订号，并在同一 ## M2 生命周期与确定性维护 -迁移 008 添加 maintenance_runs（阶段游标与固定大小的汇总计数）／maintenance_items（仅实际变更与失败）、memory_dependency_losses（每对来源的一次性扣减）、maintenance_batch_retries（每批一次）、batch_message_refs（冻结 JSON 段的引用索引），扩展 admin_operations 的对象字段，并在 memories 保存中等重要度的衰减余数和 purged_at。升级回填批次引用、当前失效依据及人工修订操作标识；在事务内延迟外键检查并原样重建 messages 为 AUTOINCREMENT，防止已清理消息的 ID 被复用。原始记忆与检索排序保持原样。 +迁移 008 添加 maintenance_runs（阶段游标与固定大小的汇总计数）／maintenance_items（仅实际变更与失败）、memory_dependency_losses（每对来源的一次性扣减）、maintenance_batch_retries（迁移 036 起每批在第 1、3、7 天各一次）、batch_message_refs（冻结 JSON 段的引用索引），扩展 admin_operations 的对象字段，并在 memories 保存中等重要度的衰减余数和 purged_at。升级回填批次引用、当前失效依据及人工修订操作标识；在事务内延迟外键检查并原样重建 messages 为 AUTOINCREMENT，防止已清理消息的 ID 被复用。原始记忆与检索排序保持原样。 `adjust_retention(store, id, delta=0, *, value=None, floor=None, expected_revision=None, current=None, settings=None, _conn=None)` 接受原子增量、绝对值或下限；有 _conn 时加入调用者的短事务，否则自行开启 Store.write。缺失／已删除／修订不符返回 None；成功返回最新行。截断、双阈值与 forgotten_at 同事务，不加修订号，不写日常修订。`confirm_retention(..., _conn=conn)` 读取 confirmation_increment 并更新 last_confirmed_at；学习已在 confirm_once 中调用，confirmed_once 保证每批一次，默认 +5；显式确认与写入去重共用同一事务和集合。在途记忆仅转为遗忘且修订号不变时仍可确认，修正／反驳继续要求有效状态。反馈、管理员强度、维护衰减及 M11 已共用此接口。 diff --git a/src/iris/learning.py b/src/iris/learning.py index e5eed21b..9797bbee 100644 --- a/src/iris/learning.py +++ b/src/iris/learning.py @@ -58,6 +58,12 @@ def _validate_output_shape(output: Any, raw: str) -> None: raise ValueError("invalid learning output shape (" + "; ".join(errors) + ")") +def _dropped_memory(item: dict[str, Any], reason: str) -> dict[str, Any]: + # The batch result is JSON; the runtime vector attached before _apply is not. + return {"section": "memories", "reason": reason, + "item": {key: value for key, value in item.items() if key != "_embedding"}} + + def _score(value: Any, default: int) -> int: try: return max(0, min(100, round(float(value)))) @@ -781,7 +787,7 @@ def self_output_only(memory: Any, evidence: list[int]) -> bool: "SELECT 1 FROM memories WHERE id=? AND revision=? AND lifecycle='active'", (m['id'],m['revision'] + (m['id'] in updated))).fetchone() for m in derived) if stale: - dropped.append({'section':'memories','item':item,'reason':'derived memory changed or invisible'}) + dropped.append(_dropped_memory(item, 'derived memory changed or invisible')) continue incoming_scope = visibility.evidence(item['evidence'], [m['id'] for m in derived]) speaker_id = canonical_subject(conn, item["speaker_id"]) @@ -818,8 +824,7 @@ def same_meaning(memory: Any) -> bool: if duplicate: if duplicate["id"] not in created: if self_output_only(duplicate, item["evidence"]): - dropped.append({"section": "memories", "reason": SELF_ONLY_CONFIRMATION, - "item": {k: v for k, v in item.items() if k != "_embedding"}}) + dropped.append(_dropped_memory(item, SELF_ONLY_CONFIRMATION)) else: confirm_once(duplicate["id"]) for message_id in item["evidence"]: diff --git a/tests/test_learning_dropped_serialization.py b/tests/test_learning_dropped_serialization.py new file mode 100644 index 00000000..2b0258c4 --- /dev/null +++ b/tests/test_learning_dropped_serialization.py @@ -0,0 +1,62 @@ +"""Dropped items are saved in the batch result, so runtime vectors must stay out of them.""" +import json + +from conftest import FakeGateway, msg +from iris.learning import LearningEngine, PROMPT_VERSION +from iris.models import ModelConfig +from iris.queue import form_batch +from test_batches import memory + + +def run(store, output, hook=None): + formed = form_batch(store, "A", PROMPT_VERSION, target_count=1, future_count=2, history_count=2) + gateway = FakeGateway(output, hook=hook) + gateway.configs["embedding"] = ModelConfig("fake", "", "fake-embedding") + return formed, LearningEngine(store, gateway).run_batch(formed.id, force=True) + + +def stored_result(store, batch_id): + with store.read() as conn: + row = conn.execute("SELECT state,result_json FROM batches WHERE id=?", (batch_id,)).fetchone() + return row["state"], json.loads(row["result_json"]) + + +def assert_no_runtime_fields(result): + assert all("_embedding" not in item["item"] for item in result["dropped"] if isinstance(item["item"], dict)) + + +def test_stale_derived_memory_drop_does_not_fail_the_batch(store): + msg(store, 1, "小林养了一只猫") + _, seeded = run(store, {"memories": [memory()]}) + source = seeded["created"][0] + msg(store, 2, "小林最近常去宠物店") + + def forget_source(_): + with store.write() as conn: + conn.execute("UPDATE memories SET lifecycle='forgotten' WHERE id=?", (source,)) + + output = {"memories": [{**memory("小林常去宠物店", [2]), "derived_from": ["M1"]}]} + # The material must offer M1 for the derived reference to pass validation. + formed, result = run(store, output, hook=forget_source) + + assert result["created"] == [] + assert [item["reason"] for item in result["dropped"]] == ["derived memory changed or invisible"] + assert_no_runtime_fields(result) + state, stored = stored_result(store, formed.id) + assert state == "succeeded" + assert stored["dropped"][0]["item"]["content"] == "小林常去宠物店" + assert_no_runtime_fields(stored) + + +def test_batch_limit_drops_stay_serializable_with_embeddings(store): + msg(store, 1, "小林列了很多爱好") + output = {"memories": [memory(f"小林喜欢第{i}种爱好") for i in range(22)]} + + formed, result = run(store, output) + + assert len(result["created"]) == 20 + assert [item["reason"] for item in result["dropped"]] == ["batch memory limit 20"] * 2 + assert_no_runtime_fields(result) + state, stored = stored_result(store, formed.id) + assert state == "succeeded" + assert_no_runtime_fields(stored)