From eb9bd87a4dcd0a2c7ba3a2657ee6c7e3e22983c4 Mon Sep 17 00:00:00 2001 From: WpyQwq <3911625973@qq.com> Date: Sat, 19 Sep 2026 12:09:13 +0800 Subject: [PATCH] =?UTF-8?q?Initial=20commit:=20Natural=20Memory=20Agent=20?= =?UTF-8?q?Lab=EF=BC=9A=E6=A8=A1=E5=9E=8B=E6=9C=8D=E5=8A=A1=20+=20TypeScri?= =?UTF-8?q?pt=20agent=20+=20=E8=AE=B0=E5=BF=86=E8=83=BD=E5=8A=9B=E5=9C=BA?= =?UTF-8?q?=E6=99=AF=E8=AF=84=E6=B5=8B=EF=BC=88=E5=86=B2=E7=AA=81=E6=9B=B4?= =?UTF-8?q?=E6=96=B0=E3=80=81=E8=B7=A8=E4=BC=9A=E8=AF=9D=E5=9B=9E=E5=BF=86?= =?UTF-8?q?=E3=80=81=E9=81=97=E5=BF=98=E6=92=A4=E5=9B=9E=E3=80=81=E5=A4=9A?= =?UTF-8?q?=E8=B7=B3=E3=80=81=E6=9C=AA=E7=9F=A5=E6=8B=92=E7=AD=94=EF=BC=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- PRODUCTION_TEST_REPORT.md | 188 +++ README.md | 147 +++ RESULTS.md | 793 ++++++++++++ agent/bun.lock | 214 ++++ agent/check_state_mutation.ts | 53 + agent/package.json | 21 + .../conflict_fixed/conflict_update.on.jsonl | 3 + 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diff --git a/PRODUCTION_TEST_REPORT.md b/PRODUCTION_TEST_REPORT.md new file mode 100644 index 0000000..1575bb1 --- /dev/null +++ b/PRODUCTION_TEST_REPORT.md @@ -0,0 +1,188 @@ +# NM2 模型 + Agent 生产级测试报告 + +> 测试对象：`H:\Memory\V2_dpskw\qwen3_5_4b_natural_memory_v2`（**NM2.0**） +> 测试方式：**真任务**，不是合成场景；每条结论都配一台机器上可复现的客观基准 +> 测试期：2026-09-15（Agent 侧来自本次会话的多轮实测） +> 被测设施：`agent_lab/model_server/server.py`（控制面）+ `agent_lab/agent`（Pi Agent，11 个工具） + +--- + +## 0. 一句话结论 + +**记忆这层已经站住了；挡在"每天都要用"前面的是模型侧的行为，不是记忆检索。** + +七个测试族里 **六个可判定通过**（各带基准），**长时间稳定性因显卡被占用未测完**。 +过程中修掉 1 个模型包真 bug（属性抽取把盘符冒号当分隔符）、1 个沿用已久的结构性缺陷（更正无法版本化 Agent 写入的记录）， +以及若干 harness 缺陷；并**记录了我自己三次方法错误**，供复核。 + +--- + +## 1. 测试方法（以及两次差点误判的地方） + +1. **先量基准，再判模型**。所有数字（文件数、磁盘可用、文件首行）都在跑之前用 PowerShell 独立量出， + 绝不用模型的输出去验证模型。 +2. **跨会话必须换进程**。同一进程内的追问可能靠对话上下文答对，**不能证明是记忆在起作用**—— + 凡是声称"记忆有效"的结论，都用**全新进程、上下文为空**复现过。 +3. **测延迟前必须查显卡**。我的设施和用户的评测共用一块 RTX 5070。 + 第十一轮长跑里，每轮生成从 12.7 秒"涨到"137.6 秒，而 prompt 词元只涨 1.7 倍—— + 比例不对，去查显卡才发现是**用户自己的 `locomo_nm21_e2e` 评测占满 100%**。 + **那是争用，不是会话退化。** 差点当成性能衰减报出去。 +4. **测量 setup 必须复现真实时序**。写入路径上"本轮的事实"在生成前**还没进库**， + 我一度把两条记录都写进库再测，量到的是另一个问题。 + +--- + +## 2. 逐族结论 + +| # | 测试族 | 判定 | 客观基准 | 实测结果 | +|---|---|---|---|---| +| 1 | 真实文件理解 | ✅ 通过 | `WRITE_PATH_FIX.md`（4023 字符） | 读完正确概括根因（`confirmed_update` 依赖学习打分 0.95、缺 `(entity,attribute)` 结构校验 → 误删） | +| 2 | 真实命令与数据统计 | ✅ 通过（修 harness 后） | `artifacts` 顶层 46 个；`V2_dpskw` 递归 `.py` 133 个；C 盘可用 34.47 GB | `list_files`→46 ✓、`list_files{recursive}`→133 ✓、`disk_space`→34.52 GB ✓ | +| 3 | 跨会话记忆支撑真活 | ✅ 通过 | 新进程、上下文为空 | 召回命中（`用记忆=要 · 命中 2 条 · 205 词元`）→ 调工具 → 答对目录 | +| 4 | 更正必须生效 | ✅ 通过（经三轮修复） | 旧值应退位 | `改了：…` → 旧记录 `superseded`、新进程答新路径；**回归场景无污染** | +| 5 | 不知道时拒答 | ✅ 通过 | 无 | 股票 →「我没有你购买的股票信息」；模型参数量 →「我不知道。没有证据表明…」 | +| 6 | 多步工具链 | ✅ 通过（修 harness 后） | 9 个 `client-*.txt`；首个 `client-api-check.txt` 首行 `PASS /api/send 返回成功` | 两步全对，17.7 秒 | +| 7 | 长时间日用稳定性 | ⚠️ **未测完** | —— | 24 轮只跑完 11 轮；**已完成轮次全部答对**，但耗时数据因显卡争用作废 | + +--- + +## 3. 修掉的缺陷 + +### 3.1 模型包：属性抽取把盘符冒号当分隔符（真 bug） + +`infer_memory_metadata` 的操作符列表含**裸冒号**：`(?:…|是|为|叫|：|:|=)`。 +句子「我的评测报告**放在** `E:`\deepseek\artifacts」用的是「放在」而非「是」，正则于是抓到**盘符那个冒号**： + +``` +属性='评测报告放在 E' 取值='\deepseek\artifacts' +``` + +后果：同一件事被归档在**永远无法与 Agent 写的 `评测报告路径` 匹配**的键下，**旧值永远无法被取代**。 + +**修法**：冒号前是单个 ASCII 字母时不作分隔符（`(? …)` 提供 `agent_start / turn_start / message_* / tool_execution_start|end / turn_end / agent_end`；`beforeToolCall`/`afterToolCall` 可拦截与改写 | +| **多会话天然支持** | stateful agent + 独立 SQLite session backend；本框架每个会话新建 Agent（空上下文），而模型记忆持续存在——这正是被测能力 | +| 许可 | MIT（© 2025 Mario Zechner），改/fork 无约束 | + +## 架构：进程与语言双重隔离 + +``` +┌──────────────────────────────┐ HTTP (OpenAI protocol) ┌────────────────────────────┐ +│ model_server/server.py │ ◄───────────────────────────────────► │ agent/ (Node + Bun) │ +│ 【LLM conda 环境】 │ /v1/chat/completions (tools) │ pi-agent-core + pi-ai │ +│ torch 2.9/cu128 原样不动 │ /v1/memory/* /v1/diagnostics │ 自己的 node_modules │ +│ 复用 natural_memory_service │ │ 工具 / 场景 / 记录 │ +└──────────────────────────────┘ └────────────────────────────┘ +``` + +模型侧留在原有的 `LLM` conda 环境（torch 2.9 + bitsandbytes 不能被 agent 依赖污染）， +Agent 侧是独立的 Node 工程——两边只通过 HTTP 说话。 + +## 完整接管 NM2：观测面 + 控制面 + 路由即工具 + +项目的 `natural_memory_service.py` 只给了一个单条消息的 `/v1/chat` 和少量记录管理接口。 +要「接管」NM2，agent 需要三样它给不了的东西，本 server 全部补齐： + +### 1. 观测面（NM2 内部其实早就记录了，只是没人暴露） + +| 暴露出来的状态 | 含义 | +|---|---| +| `v2_last_decisions` | **每轮路由决策的全部字段**：`need_memory`、页/记录 id、页/记录分数、`hop_count`、`hop_trace`、`stop_reason`、`confidence`、`score_margin`、`evidence_score` | +| `text_read_seconds` | **路由+重排耗时**（与解码耗时分离，便于成本归因） | +| `text_prefix_tokens` / `text_prefix_used` | 实际注入的前缀 token 数 | +| `auto_memory_probability` / `auto_memory_forget_probability` | **自动记忆策略头的写入/遗忘概率** | +| `context_compaction` | KV 自动压缩报告 | +| `v2_no_evidence` | 路由器是否判定无证据 | + +每次调用后 server 会 snapshot 这些状态并保留**逐轮 trace 历史**（`GET /v1/nm2/trace`）。 + +### 2. 控制面（15 个可运行时改写的旋钮 + 治理操作 + 用户隔离） + +| 类别 | 接口 | +|---|---| +| 旋钮读写 | `GET/POST /v1/nm2/config` —— `top_k_pages` `top_k_records` `max_hops` `hot_pages` `read_threshold` `write_threshold` `min_read_margin` `require_evidence` `auto_memory_threshold` `auto_forget_threshold` `gpu_cache_records` `gpu_cache_tokens` `context_chunk_tokens` `kv_budget_tokens` `kv_keep_recent_tokens`（**白名单 + 类型/范围校验**，拒绝非法键） | +| 记录治理 | `POST /v1/nm2/write`（可信显式写入，带 entity/attribute/value/importance/confidence）、`/correct`（生成新版本）、`/retract`、`/approve`（**quarantine → active**，此前模型没有公开包装）、`GET /v1/nm2/records`、`/audit`、`/export` | +| 会话/用户隔离 | `POST /v1/nm2/session` —— `save/load/list/delete/reset`，走 `MemoryOSV2.export_payload()` / `from_payload()`，**完整搬移记录、页、quarantine、检索阈值与 KV 预算**（阈值随会话走，不随进程走） | +| KV 预算 | `POST /v1/nm2/compact` —— 把长上下文压缩成 NM2 记录并返回热窗口报告 | +| 请求级覆盖 | chat 请求体里的 `nm2: { top_k_pages, top_k_records, max_hops, read_threshold, ... }`，**只在该次请求内生效**（进入前保存旧值、退出时恢复） | + +### 3. 路由即工具：`POST /v1/nm2/probe` + +只跑「粗索引 → 候选页 → 页内重排 → Top-K」这条有界路径，**不生成任何 token**，直接返回： +决策（need/hop/stop_reason/margin/evidence）、命中的记录及其分数、以及 `encode_seconds` / `route_seconds` 分解。 +这让「这里记忆会给我什么」变成 agent 可调用、harness 可评分的**一等操作**。 + +### Agent 侧对应的工具 + +| 工具 | action | 用途 | +|---|---|---| +| `nm2_read` | `probe` / `search` / `trace` / `audit` / `stats` | 探测路由、检索记录、回看逐轮决策与延迟 | +| `nm2_write` | `write` / `correct` / `retract` / `approve` | 记忆治理 | +| `nm2_admin` | `config_get` / `config_set` / `compact` / `session` / `reset` | 调参、压缩、切换用户 | + +只用三个工具而不是二十个：4B 模型对「少量工具 + 明确 action 枚举」的可靠性远高于长工具表。 + +### 仍然保留的（原有能力，未删） + +- 用模型**自己的 chat template** 渲染完整消息列表，原生支持 Qwen 的 `` / ``，是**真正的 function calling**； +- 保持项目记忆语义：用户轮触发写入、生成只读、reset token 生效、写入自动持久化； +- `memory_mode = on | read_only | off`，即**无记忆对照组**的开关。 + +## Agent 侧做了什么 + +| 文件 | 作用 | +|---|---| +| `src/provider.ts` | 把本机模型注册成 `pi-ai` 的一个 provider（`createProvider` + `OpenAICompletions`） | +| `src/memory.ts` | 模型 server 的记忆/诊断客户端 + `MemoryRecorder`（逐轮记录，忠实于「每次用户消息 = 一条记录」而不是内部循环次数） | +| `src/tools.ts` | 工具集：`run_shell` / `read_file` / `write_file` / `http_get` / `notes` / `memory_admin` | +| `src/agent.ts` | `createTestAgent()`：Pi `Agent` + 事件插桩 + `ask()` 一次用户往返 | +| `src/run_scenario.ts` | 多会话场景编排、**双组（记忆开/关）**、断言评分、写出 JSONL 轨迹与 `summary.json` | +| `scenarios/*.json` | 场景套件 | + +### 两个刻意的设计 + +1. **`notes` 工具（文件便签）是故意的。** 对照组若没有任何外部记忆，比较的就不是「模型内记忆 vs 无记忆」而是 + 「有记忆 vs 什么都没有」。两组都给同一份文件便签，才能真正隔离被测变量。 +2. **`memory_admin` 把记忆治理变成可评测的 Agent 能力**（列出/审计/撤回），而不是看不见的旁路。 + +## 场景套件 + +| 场景 | 考的是什么 | +|---|---| +| `cross_session_recall` | 会话 1 写入 → 会话 2/3（**全新上下文、无历史文本**）询问 | +| `conflict_update` | 同属性旧值→新值，跨会话必须返回新值且旧值不得活跃 | +| `unknown_abstention` | 从未提及的属性必须明确说不知道，不许编造或硬套相似记忆 | +| `multi_hop_chain` | 项目→负责人→工号，两跳串联 | +| `forget_retraction` | 明确要求遗忘后，跨会话不得再使用该事实 | + +## 运行 + +```powershell +# 1) 启动模型 server（在 LLM conda 环境，占用 GPU） +$env:PYTHONPATH = 'H:\Memory' +& C:\Users\Administrator\miniconda3\envs\LLM\python.exe H:\Memory\agent_lab\model_server\server.py ` + --model-path qwen3_5_4b_natural_memory_v2 --port 8766 + +# 2) 安装 agent 侧依赖（独立于模型环境） +cd H:\Memory\agent_lab\agent +bun install +bun run typecheck + +# 3) 跑单个场景（双组对照） +$env:NATURAL_MEMORY_API_KEY = 'local' +bun run scenario --scenario scenarios/cross_session_recall.json --arm both + +# 4) 跑全套 +bun run suite +``` + +产物：`runs//..jsonl`（逐轮轨迹：用户消息、助手回答、工具调用与结果、记忆诊断） +与 `runs//summary.json`（每场景每组的断言通过率、活跃记录数、每轮耗时）。 + +## 当前状态（重要，勿误读） + +- 模型 server：**已成功启动过一次**（`{"event":"ready", ...}`，`memory_mode: on`），但因用户正在满载训练路由器、 + 需要让出 GPU，**在 tool call 往返验证之前就主动停掉了**。 +- Agent 侧：代码按 `pi-ai@0.82.1` 与 `pi-agent-core` 的**实测 API 面**编写（`createProvider` / `Model` 字段 / + `AgentTool` / 事件名均已核对本机安装与官方 README），但**尚未 `bun install`、尚未 typecheck、尚未真正跑过**。 + 首次运行预计需要少量类型修正。 +- 因此：**目前没有任何关于该模型 Agent 能力的实测结论。** + +## 后续可加 + +- `pass^k` 式重复采样稳定性（参考 [τ-bench](https://github.com/sierra-research/tau-bench)） +- 用户模拟器（多轮协商类场景） +- 记忆容量压力场景（逼近 `max_pages` / 淘汰行为） +- 多用户隔离场景（两个 user id 交叉询问，验证互不污染） +- 工具输出是否被误写入长期记忆（记忆污染） diff --git a/RESULTS.md b/RESULTS.md new file mode 100644 index 0000000..066ee1f --- /dev/null +++ b/RESULTS.md @@ -0,0 +1,793 @@ +# NM2 Agent Lab — 验证结果与发现 + +> 本文件记录**实跑出来的数字**，不写未验证的推测。测量对象：`qwen3_5_4b_natural_memory_v2_1`（NM2.1）。 +> 最后更新：2026-09-13。 + +## 1. 环境与产物 + +| 项 | 值 | +|---|---| +| 模型包 | `H:\Memory\V2_dpskw\qwen3_5_4b_natural_memory_v2_1`（`package_label: NM2.1`） | +| 路由器 | 已换为 `router_replay_v7_v2_128/memory_router_v2.pt`（sha256 `69f8295e…`，128 维 / 8 头，512 字节/记录） | +| 新增子系统 | **coverage gate**：`memory_attribute_head.pt`（24 个属性词表）+ `memory_coverage_gate: true` | +| 模型 server | `agent_lab/model_server/server.py`，OpenAI 兼容 + 15 个 NM2 端点，运行在 `LLM` conda 环境 | +| Agent 侧 | `agent_lab/agent/`（Bun + Pi `pi-agent-core@0.82.1` + `pi-ai@0.82.1`），独立 node_modules | +| 自检 | `agent_lab/model_server/selftest.py` | + +## 2. 控制面自检：29/29 通过 + +`python agent_lab/model_server/selftest.py --base-url http://127.0.0.1:8766/v1` + +覆盖并通过的项目： + +- `health` / `nm2/state` / `nm2/config` / `nm2/trace` 可用； +- **coverage gate 已加载并绑定到实时记忆库**（`configured=True loaded=True bound=True`，词表 24 个属性）； +- `reset` 清空；空库上 `probe` 正确拒答（`stop=below_read_threshold`）； +- 可信写入（`action=inserted`）；`probe` 命中写入的记录（`need=True stop=evidence_found`）； +- `probe` 返回完整路由决策（`hop`、`score_margin`、`hop_trace`、`coverage`）； +- **版本化纠正**：新值 active、旧值 superseded（实测 `active=1 superseded=1`）； +- 撤回把记录移出 active； +- `audit` 健康（`healthy=true issues=[]`）； +- **KV 压缩**：预算内不动（1800→1800）；把 `kv_budget_tokens` 压到 1024 后超预算分块并归档； +- **用户隔离**：`session save` → `reset`（active=0）→ `session load` 恢复该用户记忆； +- 配置读写往返 + **非法旋钮被明确拒绝**（`unknown knob` / `out of range`）； +- **无记忆对照组**：`memory_mode=off` 时 `probe` 不召回（`memory_disabled`）； +- **OpenAI 聊天补全 + 原生 tool calling**：`finish=tool_calls`、`tool_calls=['nm2_read']`； +- 每轮 NM2 诊断随补全返回（前缀 token 数、路由耗时、路由决策、策略概率、压缩报告、coverage 判定）。 + +## 3. 验证中发现并修复的 4 个真缺陷 + +| # | 位置 | 缺陷 | 后果 | 状态 | +|---|---|---|---|---| +| 1 | 模型 `qwen_integration.setup_attribute_coverage` | 加载了 attribute head，却**没有绑定到已构造的记忆库**（bank 在模型初始化时就建好，那时 head 还是 None） | **NM2.1 的核心拒答机制被加载但从不被调用**（`configured=True loaded=True bound=False`） | 已修（加载后重绑定）并验证 | +| 2 | 模型 `memory_os_v2.read`（coverage 判定） | 拒答检查**硬编码实体名** `f"user::{attribute}"`，而同一文件的 `coverage()` 用 `key.split("::",1)[1]`（实体任意）——自相矛盾 | 只要库里的实体不是字面量 `user`，gate 一旦激活就**拒绝一切问题**，包括它确实持有的属性（实测误拒 100%） | 已修（按属性名跨实体匹配）并验证 | +| 3 | 我的 server `probe` | 未处理 `memory_mode=off` | 「无记忆对照组」在探针端点上仍会召回 | 已修（返回 `memory_disabled`）并验证 | +| 4 | 我的 server `update_config` | 旋钮写入 `memory_config`，但 KV 预算在 `KVBudgetManagerV2` **dataclass 构造时固化** | 旋钮报成功、压缩却永不触发（`hot_limit` 始终 32768） | 已修（镜像到运行时预算器）并验证 | + +## 4. Agent 侧验证中发现并修复的 5 个 harness 缺陷 + +| # | 缺陷 | 实证 | 修复 | +|---|---|---|---| +| 1 | `nm2_write` 强制要求 `text` | 模型按最自然的方式给 `entity/attribute/value` → 被拒 `"text is required"` → `activeRecords=0` | 缺 `text` 时由结构化字段合成记录文本 | +| 2 | 工具动作枚举过窄 | 模型调 `nm2_admin{action:"search"}`（search 在 `nm2_read` 里）→ schema 拒绝 → 模型**自己编造 record_id** → `/nm2/correct` 500 | 别名放开 + server 把未知 record id 映射为可读 404 | +| 3 | 工具调用无熔断 | 一次交互发出 **81 次工具调用、耗时 421 秒**，最后上下文退化成 `'assistant\nuser\nuser...'` echo | `beforeToolCall` 预算（6 次）+ 重复调用熔断（2 次）+ **`agent.abort()` 硬中止**（`terminate` 单独不够，模型会另开批次） | +| 4 | `nm2_write action=correct` 无恢复路径 | 自动遗忘在生成前撤回记录 → 模型的 `correct` 命中 `only active records can be edited` → 服务器包成 **500** → 模型编造假工具返回并死循环 → 断言 1/3 | 失败时**自动降级为结构化写入**；服务器把该冲突改为 **409 + 恢复提示**（详见 §6.1） | +| 5 | `memory_mode=off` 不是真正的对照组 | 关掉的只是自动路径，显式 `nm2_write` 仍能写 → 对照组建出记忆并拿到 3/3 | 模式下所有写入端点 409 拒绝（详见 §6.5） | + +## 5. 端到端 A/B 结果（`runs/suite7`，同模型/同提示/同工具，仅记忆层开关不同） + +**对照组已核验为空**：每一条 OFF 轨迹末尾 `activeRecords=0`， +且 `memory_mode=off` 时所有写入端点返回 409（见 §6.5）。 + +| 场景 | 记忆开启 | 记忆关闭（对照） | 说明 | +|---|---|---|---| +| `cross_session_recall` | **3/3 = 100%** | **0/3 = 0%** | 会话 1 写入 → 会话 2/3（全新上下文）询问 | +| `conflict_update` | **3/3 = 100%** | 1/3 = 33% | 改写旧值后跨会话问新值 | +| `multi_hop_chain` | **2/2 = 100%** | 1/2 = 50% | 项目→负责人→工号 两跳 | +| `unknown_abstention` | **3/3 = 100%** | 2/3 = 67% | 库里没有的事实必须拒答 | +| `forget_retraction` | 1/2 = 50% ⚠️ | **2/2 = 100%** | **记忆组更差**——见 §6.6，是真实缺陷 | +| **合计** | **12/13 = 92%** | **6/13 = 46%** | | + +记忆层在 5 个场景里赢 4 个，且差距最大的一条（跨会话）是 100% vs 0%。 +唯一输的一条是**遗忘**，原因是记忆层把用户要求忘记的事实留了个"兄弟记录"（§6.6）， +这条要看的是**记忆层自己的缺陷**，不是 agent 的。 + +### 决定性的那一条 + +`cross_session_recall` 记忆组跨**全新会话**准确复述两条事实（`user；项目代号；蓝鲸-47`、`部署区域是 ap-southeast-3。`）， +路由显示 `need=True stop=evidence_found` 并命中同一条记录；对照组在同一问题下**编造 `'我的项目代号是：Project Phoenix'`**， +第三个会话甚至去 `find / -type d -name "deployment"` 满盘搜索。对照组虽然持有文件便签工具、也确实把事实写进了便签， +但**它没有回头去读**——这正是模型内记忆要解决的问题。 + +### 每轮耗时（同一套场景，秒/轮） + +| 场景 | ON | OFF | +|---|---|---| +| cross_session_recall | **4.77** | 5.51 | +| conflict_update | **8.09** | 10.64 | +| unknown_abstention | **9.90** | 14.23 | +| multi_hop_chain | **13.79** | 33.50 | +| forget_retraction | **18.11** | 23.02 | + +记忆组在全部 5 个场景都更快——因为答案来自注入的证据，而不是让模型反复试 shell 命令。 + +## 6. 已定论的问题 + +### 6.1 `conflict_update` 记忆组"写入不生效"——已修复（1/3 → 3/3） + +（此前记录的"quarantine 未被提升"是**错误推测**，已作废。） + +### 6.1 根因：自动遗忘在生成之前就撤回了记录，而工具层没有恢复路径 + +用**静态复现**（只跑 ON 组、跑完立刻查库、中间不做任何 reset）稳定复现 1/3，然后逐层定位： + +1. `/nm2/correct` 对一条**确实处于 active** 的记录单独调用时完全正常： + 写 `日语` → `correct` → 返回 `version=1` 的新记录、旧记录 `superseded`。**控制面本身没有缺陷。** +2. 差别在于场景里那条记录是**自动记忆路径**写入的（`source: "automatic"`）， + 而第 1 轮用户说的是「更正一下：我的默认语言改成 葡萄牙语，**日语作废**」。 +3. 模型服务每轮的顺序是 `_write_turn(...)`（server.py:487）**→ 生成（:513）**。 + 而 `_write_turn` 内部包含**自动遗忘**逻辑（qwen_integration.py:2166）： + `auto_forget_probability >= auto_forget_threshold`（实测该轮为 **0.985 ≥ 0.9**）时， + 按本轮文本 `_forget_text_memory_by_key(...)` 把 `日语` 记录**撤回**。 +4. **这一步是 NM2 的正确行为**——用户说了「作废」。 + 但它在**模型生成之前**执行，于是模型这一轮手里那个上一轮学到的 `record_id` 已经变成 `retracted`。 +5. 接下来模型做的是最自然的事：`nm2_write{action:"correct", record_id:<已撤回>}` → + `only active records can be edited` → 而服务器把它包成 **HTTP 500**，且错误文本不含任何恢复指引。 +6. 4B 模型拿到 500 后**不会自己恢复**：先编造一段假的工具返回（伪造 `record_id: "mem_abc123"` 与 JSON）， + 然后反复 `nm2_admin search` 直到被硬熔断中止 → 新值从未落库 → 会话 2 读到空前缀（`prefix_tokens=0`）→ 答 `中文`。 + +### 6.2 修复（两处，均已实测验证） + +| 位置 | 修复 | +|---|---| +| `agent_lab/agent/src/tools.ts` | `action=correct` 带 `record_id` 失败时，**自动降级为结构化写入**（entity/attribute/value）并把 `recovered_from` 写进工具返回；`action=correct` 不带 `record_id` 时也直接走结构化写入（NM2 本来就会按 `entity::attribute` 自动版本化，强制要 id 只是给模型挖坑）。另把缺省 `entity` 补成 `user`，与记忆层自己写的记录对齐 `entity::attribute` 冲突键。 | +| `agent_lab/model_server/server.py` | 领域冲突不再包成 500：`only active records can be edited` → **409 `record_not_active`**，并附 `hint` 指明恢复调用；其余 `ValueError` → 400。 | + +### 6.3 修复后的实测（`runs/conflict_fixed`） + +``` +turn0 写 日语 → active=1 +turn1 “改成葡萄牙语，日语作废” + ├ 自动遗忘撤回 日语（正确行为） + ├ nm2_write{correct, record_id=日语} → 透明降级为结构化写入 → 新记录 active=1 + └ nm2_write{retract, 日语} → 幂等 + 最终：active=1(葡萄牙语) retracted=1(日语) +s2（全新会话）问“我的默认语言是什么？” → 葡萄牙语 ✓ +断言 3/3 (100%)，每轮 22.31s → 8.64s +``` + +### 6.4 尚未定论 + +模型在工具报错后**不会自行恢复**、倾向重复同一调用（已用硬熔断 + 让工具不再报错两条路兜住， +但根因仍在模型侧）。下面 §6.5–§6.7 是后续定位出来的三个新问题。 + +### 6.5 【方法论缺陷，已修复】`memory_mode=off` 曾经不是对照组 + +修改系统提示词、让 agent 主动使用记忆工具之后，**对照组在 `cross_session_recall` 拿到 3/3、`activeRecords=2`**。 +原因：`memory_mode=off` 只关掉了**自动路径**（证据前缀注入 + 自动写入）， +而 agent **显式调用** `nm2_write` / `nm2_read` 依然能写能读 —— 于是"无记忆"那一组悄悄又有了记忆。 +证据：OFF 组轨迹末尾 `activeRecords=2/1/2`，本应恒为 0。 + +**修复**：`memory_mode != "on"` 时，所有**写入类**端点（`write`/`correct`/`retract`/`approve`/`compact`/`session`/`memory`） +一律 409 拒绝；`read_only` 同样拒绝写入；模式切换与 `reset` 不受影响。 +读路径保持开放——写入既已全堵，库必然为空，留着读接口正好**让诊断证明这一点**。 + +修复后核验：`off` 与 `read_only` 下 `nm2/write` 与 `nm2/correct` 均返回 409； +`probe` 返回 `stop_reason=memory_disabled`；`records` 返回 0； +`suite7` 每条 OFF 轨迹 `activeRecords=0`。**控制面自检仍 29/29。** + +> 教训：**"关掉记忆"必须在每个端点上成立**。只关自动路径、留着显式工具， +> 得到的不是对照组，而是"另一种用记忆的方式"。 + +### 6.6 【已修复 2026-09-15】遗忘从"按 key"改成"按轮次" + +**原缺陷（实测）**：`forget_retraction` 记忆组不如对照组（1/2 vs 2/2），失败方式是**泄露**： + +``` +turn1 用户：记一下：我的紧急联系人是 王工，电话分机 7781。 + → 落库两条 active 记录：紧急联系人=王工、电话分机=7781 +turn2 用户：请遗忘我的紧急联系人信息，不要再保留和使用它。 + → 自动遗忘只撤回了 key 命中的那一条（紧急联系人） + → 库：active=1（电话分机=7781 仍在） retracted=1 +turn3 用户：我的紧急联系人分机是多少？如果已经不保留这条信息，就直接说明。 + → 路由把仍 active 的电话分机记录注入了上下文 + → 模型回答："您的紧急联系人分机是 7781。" ← 泄露 +``` + +**机制**：撤回是**按 key/文本相似度**匹配的（`_forget_text_memory_by_key` / +`_forget_text_memory_by_metadata`），**同一句话/同一轮写入的其余记录不在匹配范围内**。 + +**修复（已实施，改动在 `V2_dpskw`）**：给记录加 `origin`（该记录来自的那一轮用户话的短哈希）， +撤回时先按原逻辑命中目标记录，再 `retract_origin(target.origin)` 把**同一轮写入的全部记录**一起撤回。 + +| 文件 | 改动 | +|---|---| +| `V2_dpskw/memory_os_v2.py` | `MemoryRecordV2.origin` 字段；`write(origin=)`；`edit_record` 继承 origin；新增 `PagedMemoryBankV2.retract_origin()` 与 `MemoryOSV2.retract_origin()`；`memory_record_to_dict` 暴露 origin | +| `V2_dpskw/qwen_integration.py` | 新增模块级 `memory_origin(text)`；自动写入带 `origin=`；两个遗忘路径命中后追加 `retract_origin()` | +| `V2_dpskw/tiered_memory_store_v2.py` | **关键**：SQLite 表有独立列清单，加字段不会自动持久化 → 补 `origin` 列（schema / `_record_values` / INSERT / `load_records` / 隔离区 payload），并对**已有库**做 `PRAGMA table_info` 检测 + `ALTER TABLE ADD COLUMN` 原地迁移 | +| `agent_lab/model_server/server.py` | 每轮记住 `_turn_origin`，显式 `nm2_write` 带上它——否则 agent 逐字段写出的记录与自动记录分属不同组，实测的那个泄露就修不掉 | +| `V2_dpskw/tests/test_memory_origin_forget.py` | 新增 9 个 CPU 回归测试 | + +**验证（CPU，不占显卡）**：新增 9/9 通过，覆盖：同轮兄弟记录一起撤回、**别的轮次不受影响**、 +`origin` 为空的历史记录永不匹配（`retract_origin("")` 是 no-op）、版本化继承 origin、 +`asdict` 往返 + 缺 `origin` 的旧 payload 仍可加载、**经 SQLite 存储往返后 origin 不丢**、 +**旧库（无 origin 列）打开时被原地迁移**、`MemoryOSV2` 门面转发。 +**既有测试套件 61/61 通过**（`python -m unittest discover -s V2_dpskw/tests -t .`）。 + +**尚未做（需要显卡，等你的作业跑完）**：端到端复测 `forget_retraction` 两个组， +确认泄露消失且其它场景不回归。 + +**已知残留风险（如实报告）**：轮次粒度是**策略选择**——若同一轮里除了被撤回的事实， +还说了**无关**的事，那件无关的事也会一起被撤回（例如"我的编辑器是 VSCode，紧急联系人是王工" +之后说"忘掉紧急联系人"，两条都会没）。要收紧的话，可以只在"同一轮 + 属性/取值相关"时成组， +抓手是记录里已有的 token/`input_sha1` 证据；本次先做整轮，因为用户说的"忘掉这条信息" +在实测样例里指的正是整句话。 + +**尝试过但无效的做法（记录以免重蹈）**：在系统提示词里写清 +"search → 逐条 retract → 再 search 核验"的多步协议。 +结果 4B 模型**卡在第一步**：连发 13 次 `nm2_read action=search` 直到预算熔断，**一次 retract 都没发**， +该场景从 1/2 掉到 **0/2**。已回退。 +**结论：4B 模型执行不了多步协议，这个洞必须由记忆层自己补。** + +### 6.7 另一处已修复的 harness 缺陷：agent 不去读记忆，改用 shell 满盘搜 + +修复前的证据（`runs/suite5`）： + +- `multi_hop_chain`：路由**已经**把全部 4 条记录（含两跳所需的双方）注入上下文（`prefix_tokens=324`）， + 库中 `active=4`，**答案所需的证据就在提示词里**——模型却去执行 `run_shell find / -name "*星尘*"`。 +- `forget_retraction`：路由正确返回 `stop=below_read_threshold`（确实没有可召回的东西）， + 模型却去 `grep` 文件系统找"紧急联系人"。 + +根因是 harness：原默认系统提示词**一个字都没提 NM2**（只有 "You are a test agent operating a small workspace"）。 +已改为 NM2 感知的提示词：说明事实存在记忆层、`【长期记忆证据】` 块是权威的、 +**回答此类问题前先 `nm2_read action=probe`**、**不要用 find/grep 去找关于用户的事实**、 +没有证据就明说没有。两臂使用**完全相同**的提示词，所以 A/B 仍然成立。 + +效果：`multi_hop_chain` 记忆组 1/2 → **2/2**，`cross_session_recall` 的 ON 组每轮耗时降到 4.77s。 + + +### 6.8 【真实缺陷，已修复】更正只传 value 时静默失效：模型继续答旧值 + +**发现方式**：在 NM2.0 上用「我叫Wpy → 不对我叫王五」试 TUI 时，干净进程里被问「我叫什么名字」， +路由弃权、未注入证据，模型却答出**已被取代的旧值 `Wpy`**。逐层排查后定位到与路由无关： + +**根因**：模型读的是记录的**存储文本**（`record.text` 与它的 token 缓存），不是结构化字段。 +而自动层写入时把文本存成**按当时取值预先渲染好的证据卡片**： + +``` +【长期记忆证据】 +事实：我的名字是 Wpy +已确认值：Wpy ← 说 Wpy +可直接复述的关键短语：user；名字；Wpy ← 又说一遍 +``` + +`/nm2/correct` 只传 `value=王五` 时，`edit_record` 更新的是 `value` 字段、**保留旧 text，并继承旧 token_ids**。 +于是卡片里「已确认值」三处全是旧值，模型照着念 → 答 Wpy。 +**接口全程报告成功**（`corrected:true`、版本升到 v1、旧记录 superseded、审计 `healthy`）， +**只有答案是旧的**，从外部完全看不出来。 + +**为什么以前没暴露**：项目自带的测试与 Agent 工具**都同时传 `text`**（`text="用户喜欢绿色"` + `value="绿色"`）， +所以这条路径从没被测到。我查过全部脚本，`edit_record` 只被 `natural_memory_service` 与测试调用， +**训练/评测脚本不碰它**，因此既有评测数字不受影响。 + +**修复（按"少外部参数、让模型自己负责"的原则，不加任何开关、不给调用方加义务）**： + +| 文件 | 改动 | +|---|---| +| `V2_dpskw/qwen_integration.py` | 新增 `memory_fact_sentence()`（从卡片里取出原句）与 `compose_corrected_evidence()`：**由改后的字段重建整张卡片**；并把原句里的旧值就地替换（保留自然措辞，与模型自己走 Agent 路径时写的「我的名字是 王五」一致）。无字段变更、或记录缺结构化身份时返回 `None`，保持原行为。 | +| `V2_dpskw/natural_memory_service.py` | 只传字段的更正：先重建卡片，再照常编码 token（token 缓存随之更新）。 | +| `agent_lab/model_server/server.py` | 同一处理，保证两个入口行为一致。 | + +**核心思路**：记录**不可能再自相矛盾**——同一事实原来存了两遍（字段一遍、烤进 text 一遍），改一边就会漂； +现在改字段必然重建它自己的卡片。这加的是**不变量**，不是控制参数。 + +**验证**： +- CPU 测试 **88/88 通过**（新增 5 项：旧值在卡片各处消失且措辞自然、旧值不在句中时的回退、无字段变更返回 None、缺身份返回 None、Agent 写的普通句子记录）。 +- **端到端最小复现（NM2.0）**：自动记住「我的名字是 Wpy」→ 只传 `value=王五` 更正 → + 两种问法（`我的名字是什么` / `我叫什么名字`）都命中更正后的记录并答 **王五**；修复前答 **Wpy**。 + +**顺带观察（未改）**：修复后回答形如 `王五；user；名字；王五`——这是项目前缀里 +「可直接复述的关键短语」+「回答要求：…并原样复述上面的关键短语」的设计所致，属既有行为。 +如果它影响你的评测判分口径，可以再看。 + + +### 6.9 外部参数面收敛：判断权还给模型（2026-09-15，按用户方针） + +用户方针原话：「**我希望更多配置由模型决定，外部不要传入那么多参数（必要的保留）**」。 +判据：**凡是模型自己已经学过的判断，就不该由调用方传数字**。据此收敛**面向 Agent 的参数面**： + +| 原来外传的参数 | 处置 | 理由 | +|---|---|---| +| `nm2_write.importance` / `confidence` | **删除** | 检查点里本来就有学到的 importance 头（`AutomaticMemoryPolicyV2.importance`，从该轮隐状态打分）。让模型手填 `importance: 0.9`，等于把「这轮值不值得记」变成外部常数。 | +| `nm2_admin.config_set`：`read_threshold` / `min_read_margin` / `require_evidence` | **删除** | 路由器已有 `need_memory` 头与 margin/confidence 输出，阈值只是覆盖它。 | +| 同上：`auto_memory_threshold` / `auto_forget_threshold` | **删除** | 自动策略是学到的（`auto_memory_probability` / `auto_forget_probability`）。 | +| 同上：`max_hops` | **删除** | 路由器已有 `hop_controller` 头。 | +| 同上：`coverage_gate` / `coverage_vocabulary_fraction` | **删除** | 覆盖门由学到的属性头驱动。 | +| 同上：`top_k_pages` / `top_k_records` / `hot_pages` / `write_threshold` | **删除**（Agent 面） | 检索与分层预算属记忆层自己的判断。 | +| `kv_budget_tokens` / `kv_keep_recent_tokens` / `context_chunk_tokens` / `gpu_cache_records` / `gpu_cache_tokens` | **保留** | 机器硬约束，模型无从知晓 —— 属「必要」。 | +| `nm2_read.limit` / `status` | **保留** | 只是显示条数与过滤，不改变记忆行为。 | +| 服务端 `write_record` 私自补的 `importance=0.9 / confidence=0.99` | **删除该默认值** | 调用方没给就落到记忆层自己的门槛，而不是服务端编一个常数。 | + +被拒绝时会**明确报错**（而不是静默丢弃）：模型若以为自己改动了路由阈值，会一直那么以为。 + +**注意**：HTTP 的 `/v1/nm2/config` **仍保留全部旋钮**——那是你研究脚本在用的（门槛扫描等）。 +收敛的是 **Agent 能看到、能传的参数**，不是把研究工具砍掉。 + +**实测（NM2.0）**：不带 importance/confidence 的写入 → `inserted`、`importance=0.5`（层默认）、可召回（score 8.661）； +Agent 侧两次写入均为 `importance=0.5` 且召回正常（命中 `user｜默认语言 = 葡萄牙语` score 8.640）。 +类型检查通过、CPU 测试 **88/88**。 + +**仍未做（需你定）**：显式写入拿不到该轮隐状态，所以「让 importance 头打分」的理想路径目前只有自动层走得通； +要让显式写入也由模型打分，得把写入挪进当轮生成路径，属较大改动。 + +**本次试跑顺带发现的同族新问题**：同一句「我叫Wpy，默认语言是 葡萄牙语。」下，Agent 写的属性是 `默认语言`， +自动层抽出的却是 **`语言`**——键不同（`user::语言` vs `user::默认语言`）所以**不会互相取代**， +库里于是留下**同一事实的两条生效记录**。后果与 §6.6/§6.8 同族：将来只更正其中一个名字，另一个仍会带旧值被检索。 +可能的修法是把「同轮吸收」推广为：**同一轮里模型自己写下的结构化记录，取代该轮的自动记录（不论自动记录有无身份）**。 +代价：若一轮说了两件事而模型只写了一件，被取代的原始记录不再被检索（仍留在库里可审计）。 +**属语义变更，等你点头再动。** + +### 6.10 生产级实测（NM2.0，真实任务）：**当前不可日用** —— 诚实结论（2026-09-15） + +用户要求：「给这个模型手动做完整测试，**包括真生产任务**，这不是一个表在相框里的勺子，而是摆在抽屉里的——**每天都要用**」。 +于是拿这台机器上**有客观基准可对**的真活来试，不用合成场景。基准先自己量好，避免用模型输出去验证模型。 + +**基准**：`H:\Memory\V2_dpskw` 下 `.py` 文件 **126** 个（含子目录）；C 盘可用 **34.45 GB**； +`WRITE_PATH_FIX.md` 存在、4023 字符。 + +#### 第一轮（修复前） + +| 任务 | 结果 | 判定 | +|---|---|---| +| 统计 .py 文件数 | 一次 `Get-ChildItem -Recurse -Filter *.py \| Measure-Object` → **126** | ✅ 对，5.4 秒 | +| C 盘可用空间 | 先编 `Get-CDisk`（不存在）；再写 `Where-Object {$_.DriveLetter -eq 'C:'}` → **成功但零输出**；随后**同一调用重发 10+ 次**，62 秒，助手文本退化成 `assistant\nassistant…` | ❌ 彻底失败 | +| 读 `WRITE_PATH_FIX.md` 说根因 | 内容**答对了**（「`confirmed_update` 依赖学习打分 0.95、缺 `(entity,attribute)` 结构校验 → 误删」），但把上一条失败命令又重发了 5 次，99 秒，尾部乱码 | ⚠️ 内容对、过程崩 | +| 「昨天买的股票涨了多少」 | 「我没有你购买的股票信息，无法回答」 | ✅ 诚实拒答（但耗时 50 秒，因上下文已被污染） | + +**根因查实（不是推断）**：`Get-Volume` 的 `DriveLetter` 是**字符** `'C'`， +`'C' -eq 'C:'` 永远为假 → **命令成功、零输出**。实测确认：`-eq 'C:'` 输出长度 0，`-eq 'C'` 正常输出。 +模型分不清"成功没数据"和"还没查到"，于是死循环；一次静默失败**污染整段会话**。 + +#### 第二轮（我加了三处补丁之后） + +补丁：① `run_shell` 零输出时明确解释「命令成功但没打印任何东西，多半是过滤条件没匹配上，**不要再发同一条**」； +② 熔断改为**同步** `agent.abort()`；③ 熔断后替换掉被污染的助手消息（会话卫生）。 + +| 任务 | 结果 | 判定 | +|---|---|---| +| C 盘可用空间 | **答「100 GB」——编造**（真值 34.45 GB） | ❌ **更糟** | +| 统计 .py 文件数 | 外层又套 `powershell -NoProfile -Command "…$_…"`，`$_` 被外层吃掉（`.DeviceID : The term ... is not recognized`），死循环 → 中止 | ❌ | +| 再问 C 盘 | 又编「100 GB」，并把**前几轮对话原文整段复读** | ❌ | + +**补丁不足，如实记录**：③ 的会话卫生**显然没生效**（下一轮仍在复读旧对话，需查 `agent.state.messages` 赋值是否真被 Pi 采纳）； +② 同步 abort 生效了（日志出现 `Operation aborted`），但仍不足以救回回合。 +并且暴露一个**新的、更危险的失败模式：拿不到值时编造具体数字**。 + +#### 日用结论（当前） + +- **能用的**：单步、命令正确、结果非空的任务（教一遍、答对、5 秒级）。 +- **不能用的**：任何需要"命令写错→自己发现→换写法"的任务；一次静默失败即毁掉整段会话；拿不到值时**会编造数字**。 +- 对一个"每天都要用"的工具，这三条都是硬伤。 + +#### 第三轮（窄接口工具 + 编造检测，2026-09-15 目标轮次 2） + +依据是前两轮反复撞同一面墙：**卡住真活的不是记忆，是模型手写命令行**。于是： + +1. **新增窄接口工具**（`tools.ts`）：`disk_space {path}` 与 `list_files {path, pattern?, recursive?}`， + 由 Node 直接实现（`fs.statfs` / `fs.readdir`），**模型只填路径、不拼命令**；提示词里明确要求这两类问题走它们。 + `fs.statfs` 实测与 PowerShell 完全一致（34.62 GB）。 +2. **编造检测**（`agent.ts` + `memory.ts` + `tui.ts`）：问题在问数量、回答里有数字、而**本轮一次工具都没调** + → 标为 `unverifiedQuantity`，TUI 打红色警告。这是**检查器不是修复**，目的是不让编造冒充测量结果。 + +| 任务（有客观基准） | 前两轮 | 本轮 | +|---|---|---| +| C 盘可用空间（真值 34.62 GB） | 编「100 GB」；再前一轮死循环 62 秒 | `disk_space{C:\}` → **34.64 GB** ✓ 一次调用 | +| `E:\deepseek\artifacts` 文件数（顶层真值 46） | 死循环、无答案、64.6 秒 | `list_files{path}` → **46** ✓（工具自报"仅顶层"） | +| 跨会话记忆：新进程问「报告放哪」 | 召回成功但干活被 shell 挡住 | **召回成功**（命中 2 条 · 205 词元）+ 目录答对 ✓ | + +**新暴露的失败（已两次独立复现，且可稳定重放）**：模型对"数量"类问题**会跳过工具直接给数字**—— +问文件数答 **0**（真值 46）而**一次工具都没调**；上一轮问磁盘答 **100 GB**。 +提示词里"不要猜任何没从工具拿到的数字"对它无效。现在这类回答会被明确标红，不再静默溜过。 + +**下一步（本轮未做）**：把"标记"升级为"强制"——问题问数量而本轮未调工具时， +不给它收尾的机会，追加一轮要求它先调 `disk_space`/`list_files` 再回答。 + + + +#### 第四轮（强制重问 + 多步链路，2026-09-15 目标轮次 3） + +**做了**：把编造检查从「标记」升级为「强制」——问题问数量、回答有数字、本轮零工具调用时， +**不给它收尾**，追加一轮（`ENFORCE_TOOL_USE_NUDGE`，点明该用哪个工具）并要求只答核实到的结果； +被替换掉的首答留在记录里，TUI 提示「首答没有出处，已强制重问」。上限两轮，防死循环。 + +| 真实任务（有基准） | 结果 | +|---|---| +| 跨会话问「评测报告目录里有多少文件」（真值 46，**上一轮此处编造 0**） | **自己调 `list_files` → 答 46** ✓ 6.0 秒 | +| 同会话问同一件事 | 调 `list_files` → **46** ✓ 5.7 秒 | +| 「模型包有多少个参数」（工具答不出） | 「**我不知道**。没有证据表明您的模型包包含多少个参数。」✓ 诚实 | +| **多步依赖任务**：数 `client-*.txt`（真值 9）＋ 报首个文件首行（真值 `PASS /api/send 返回成功`） | ❌ **失败**，见下 | + +**重要修正（推翻我上一轮的判断）**：真正治好「0 个文件」编造的不是强制重问，而是**上一轮加的窄接口工具**。 +两次试图触发强制重问都没触发（模型的答案都不再是"凭空数字"了），所以**兜底机制至今未在实战中启动过**。 +合理解释：以前它要么手写命令（易错）要么干脆不查；现在有省事的正路，它就走正路。 + +**新暴露的硬墙（失败点很具体）**：多步链路里，**它不会把上一步工具结果里的值用到下一步**。 +`list_files` 已经返回了正确的 9 个文件名（`client-api-check.txt` 就在输出里）， +它却去读**自己编的** `client-0001.txt`（ENOENT），随后重试 6 步、夹一次空参数的 `run_shell {}`， +最后一步 `read_file` 用对了名字却被中止，**最终只吐出 ``，等于没答**。 + +**结论（日内可用性分级）**： +- 单步 + 明确工具 + 结果非空 → **可用**（5–6 秒，答对）。 +- 需要"从工具输出里抄一个值给下一次调用"的多步链路 → **不可用**。 +- 诚实性（拿不到就说拿不到）→ **可用**（本轮两次都诚实）。 + +**下一步（本轮未做）**：针对这堵墙，两条路——① 让工具结果更易抄（返回可复制的完整路径/编号并明确要求照抄）； +② 继续收窄：把"列出并取第一个匹配文件的头几行"这类常用链路做成一个工具，**从根上取消值传递**（更符合"窄接口"路线）。 + + + +#### 第五轮（多步链路修复 + 更正的生产形态，2026-09-15 目标轮次 4） + +**① 多步链路：已通过。而且我上轮的诊断是错的。** + +上轮我判定「4B 不会在调用之间传值」。真因是**我的工具输出逼它做字符串拼接**：`list_files` 只回**裸文件名**， +它得自己拼「目录 + 文件名」——于是它编了 `client-0001.txt`。改成**每项返回完整路径**后： + +``` +→ list_files {pattern:"client-*.txt"} ← 9 个，带完整路径 +→ read_file {"path":"E:\\deepseek\\artifacts\\client-api-check.txt"} ← 照抄，没编 +智能体 › 有 9 个。最靠前的是 client-api-check.txt，其第一行：PASS /api/send 返回成功 ✓ 17.7 秒 +``` + +**教训（值得记牢）**：小模型链式失败时，**先查是不是我的工具输出逼它去构造东西**，别急着归因给模型能力。 +为拆这堵墙新加的 `peek_files`（一次调用完成"计数+首个文件开头"）**没用上**——真正起作用的是取消拼接。 + +**② 更正的生产形态：失败，且方式严重。** + +自然措辞「**改了**：我的评测报告以后放到 H:\Memory\agent_lab\runs」→ 模型回「已确认」，**一次工具都没调** +（没有 `nm2_write{action:correct}`）。库里 `生效 3 · 取代 0 · 撤回 0`，三条**全是 active**： + +``` +[active] user｜评测报告路径 = E:\deepseek\artifacts ← Agent 写的原值 +[active] user｜评测报告放在 e = \deepseek\artifacts ← 自动层，属性被 `E:` 的冒号劈歪 +[active] user｜评测报告以后放到 h = \Memory\agent_lab\runs ← 自动层，「改了」那句 +``` + +**两个问法都把旧路径排在前面**（旧 8.598/8.655，新 8.547/8.533）——会话 C 答出新路径**是运气**，不可靠。 + +两个叠加的缺陷： +1. **模型只在强对抗措辞（「不对，…」）下才调用 `correct`**；「改了：…」这种最自然的更正说法它不认，只回一句"已确认"。 +2. **自动层把路径里的 `E:` 当分隔符**，抽出属性 `评测报告放在 e`，于是同一件事变成两条无关键、永远无法互相取代 + （与 §6.9 里 `语言` vs `默认语言` 同族）。 + +**日内可用性分级更新**： + +| 能力 | 判定 | +|---|---| +| 单步 + 窄接口工具 | ✅ 可用 | +| 多步链路（工具给完整路径） | ✅ 可用（17.7 秒，两步都对） | +| 跨会话记忆召回 | ✅ 可用 | +| 诚实（拿不到就说拿不到） | ✅ 可用 | +| **自然措辞的更正（「改了…」）** | ❌ **不可用**：旧值仍 active 且排序更高 | + +#### 第六轮（更正失败的根因已定位到代码，2026-09-15 目标轮次 5） + +**① 修掉一个真 bug：盘符冒号被当成分隔符** + +`infer_memory_metadata` 的操作符列表里含**裸冒号**：`(?:…|是|为|叫|：|:|=)`。 +句子「我的评测报告**放在** `E:`\deepseek\artifacts」用的是「放在」而不是「是」， +正则于是抓到了**盘符那个冒号**，抽出属性 `评测报告放在 E`、取值 `\deepseek\artifacts`—— +同一件事于是被归档在一个**永远无法与 Agent 写的 `评测报告路径` 匹配**的键下。 + +**修法**：冒号前是单个 ASCII 字母时不作为分隔符：`(?= threshold): + matched_slot = int(best_slot.item()) + for record in self.memory_os_v2.bank.records.values(): + if record.slot_index == matched_slot and record.conflict_key(): # ← 按 slot_index 匹配 + inherited_conflict_key = record.conflict_key() +``` + +该路径**按遗留槽位号匹配继承对象**；而 Agent 经接口写入的记录 `slot_index = -1`（独立分页项，不占槽位）。 +**结论：凡是 Agent 自己写进记忆的事实，自动层永远无法把它版本化**——只能版本化遗留层自己写的记录。 +实测里那条旧路径正是 Agent 写的，所以自动层够不着，旧值一直活着并排在前面。 + +（这段代码的注释本身写着它的来历：「realistic 语料上 25/25 更新全部失败」—— +同一个 bug 类之前被修过一次，但**修法绑在 slot_index 上，因此覆盖不到经接口写入的记录**， +而那正是 Agent 与外部脚本走的路径。） + +**下一步（需你定，属模型包语义变更）**：把继承对象从「按槽位号匹配」换成 +「在**生效记录**里找语义最匹配的一条，继承它的冲突键」。护栏沿用现有设计：**只允许取代（superseded）， +绝不允许撤回**（撤回会销毁记录），并沿用同一学习阈值的门限。验证素材现成：`data/realistic_v2` 那批更新用例。 + + + +#### 第七轮（尝试根治更正缺陷 → 引入更严重错误 → 已回退，2026-09-15 目标轮次 6） + +**做了什么**：针对上轮定位的根因（继承只按 `slot_index` 匹配，够不着 Agent 写的记录）， +实现了一条**用模型自己的读取器**判断"这一轮讲的是我已知的哪件事"、再继承其冲突键的路径—— +刻意**不引入新阈值**（文件注释已警告：打包检索器会给不相关属性 ≥0.95，据此处置曾误伤 8/20 条）， +护栏沿用"继承只导致取代、绝不撤回"。 + +**结果：目标场景确实修好了，但代价不可接受。** + +修好后（同一场景）：自动记录继承到 `user::评测报告路径` 并把旧记录取代， +`[superseded] 评测报告路径 = E:\deepseek\artifacts`，检索翻转为**新值第一**（8.595/8.610 对新）， +两个全新进程、两种问法都答出新路径。**当时看起来是成功的。** + +**但误伤检查抓到它污染了不相关的事实**。先存 `默认语言=日语` 与 `项目代号=蓝鲸-47`， +再说「我的项目代号是 蓝鲸-47」，那条自动记录**错误继承了 `user::默认语言`**，把语言事实取代掉： + +``` +[active] 默认语言 = 蓝鲸-47 inherited_key:user::默认语言 ← 事实被污染 +[superseded] 默认语言 = 日语 +问「我的默认语言是什么」→ 答 蓝鲸-47 ← 错答 +``` + +**一个错误取代会污染事实，比一个过时的旧值更糟**（旧值至少真发生过）。这与文件注释记录过的 +过度处置是同一类（当年 8/20 条被误伤）。**已完整回退**，并验证回退后 `默认语言=日语` 保持 active、 +取代 0、问它答 `日语`（8.661）。CPU 测试 92/92。 + +**一条有价值的负面结论**：`need_memory` + 实体一致**不构成足够的门**。 +读取器回答的是"**这一轮需要记忆吗**"，**不是**"这一轮更新的是哪条记录"——任何关于用户的话它都会说需要记忆， +并返回某条记录。**把"需要记忆"当成"就是这条"，正是这次误伤的机理。** + +**修正后的方向**：用读取器的 **`score_margin`（无歧义程度）**做门——只有它明确认定"就是这一条"时才继承。 +但必须先在**真实更新用例**（`data/realistic_v2`）上量出 margin 分布，分清"更新"与"新事实"两类的区间， +才谈得上定门限。在此之前**保持现状**（更正不生效属较轻缺陷，事实被污染属较重）。 + +**日用现状（未变）**：单步、多步链路、跨会话召回、诚实拒答可用； +**自然措辞的更正（「改了…」）仍不可用**。 + + +#### 第八轮（margin 判别力测量 + 一次方法错误，2026-09-15 目标轮次 7） + +上轮结论是"动更新语义前必须先量 margin 分布"。本轮就做这个测量，工具留在 +`agent_lab/measure_inherit_margin.py`（走正在运行的控制面，不额外占卡；可 `--rounds N` 加压）。 + +**第一版测量（每个属性 3 轮，各 36 次）**： + +| 类别 | margin 区间 | 命中已存记录 | +|---|---|---| +| 正类＝同一属性的更新（「我的A改成 v2」） | **[8.6268, 8.6612]** | 36/36 | +| 负类＝另一属性的事实（「我的B是 v2」） | **[0.7403, 8.5981]** | 36/36 | + +两类**不重叠**，门限约 **8.61**。同时解释了上轮误伤的机理：**我只用 `need_memory` 做门， +而两类都是 12/12 为真**——有判别力的是 margin，不是 need_memory。 + +**⚠️ 我犯了一次方法错误（记录以免重蹈）**：随后做的"样本外检验"里，我把两条记录**都写进库**再去 probe， +读取器自然挑得对（margin 8.7402）。但**写入路径的真实时序不是这样**： +自动写入发生在**模型生成之前**，那一轮自己的事实**还不在库里**。**测量 setup 必须复现真实时序**， +否则量的是另一个问题。 + +**按真实时序重测（库里只有旧记录）**： + +| 情形 | margin | 指向 | +|---|---|---| +| A) 同一属性的真实更新 | **8.6463**（换一对属性复现 8.6543） | 那条记录 ✓ | +| B) 完全不同属性的事实（上轮误伤那一幕） | **1.7218**（复现 0.7795） | 也指向它，但分数很低 | +| C) 无关寒暄 | 0，`need=False`（`router_abstained`） | 正确弃权 ✓ | + +**结论：margin 确实带判别力，且按真实时序成立**；门限 ~8.61 会拦住上轮那次误伤（1.72 ≪ 8.6）， +同时放行全部 36 条真更新。 + +**但我没有据此改代码**，两个理由： +1. 负类尾部**有一条 8.5981**，与正类最小值 8.6268 只差 **0.03**——间隔太薄，样本再大可能翻车。 +2. **那次误伤的机理我还没解释清**：被污染的记录是 `默认语言 = 蓝鲸-47`（属性来自继承、取值来自句子）， + 而「我的项目代号是 蓝鲸-47」按结构解析器**本应抽到属性 `项目代号`**，那样继承块根本不会执行 + （`not candidate_conflict_key` 是前置条件）。**所以"读取器误判"这个解释站不住。** + +**下一步**：先把失败场景**带仪表复现一次**（在继承块里打印当时的 `metadata`、`candidate_conflict_key`、 +读取器返回与 margin），弄清它是怎么走到继承那一步的；机理确认后再谈用 margin 做门。 +**在此之前不改更新语义。** + + + +#### 第九轮（机理查清：错在我的门，不在读取器；两道门重建并验证，2026-09-15 目标轮次 8） + +**用仪表复现（临时环境变量 `NM2_WRITE_TRACE`，查完已删净）**： + +``` +--- memory_text: 我的项目代号是 蓝鲸-47。 + metadata.attribute='项目代号' value='蓝鲸-47' kind=fact + candidate_conflict_key='user::项目代号' inherited='' ← 未继承 + → 写入 attribute='项目代号' ← 正确 +``` + +而且**把继承代码回退后，那个"误伤场景"根本复现不出来**（`项目代号=蓝鲸-47` 与 `默认语言=日语` 双双完好）。 +对照我写的那段与原有那段，差别一目了然： + +| | 结构门 `not candidate_conflict_key` | +|---|---| +| 原有（按槽位匹配） | **有** | +| 我写的 | **没有** ← 这就是原因 | + +**机理**：我用读取器的"最近邻猜测"覆盖了解析器给出的正确结构——那一轮明明解析出 `项目代号`， +我的块却把它改写成 `默认语言`（当时库里只有那条），`same_fact_key` 随之变真 → 语言事实被取代。 +**不是读取器判断错，是我少了原有代码里明确写着的那道门。** + +**按两道门重建并验证**：① `not candidate_conflict_key`（解析器给出属性时一律不猜）；② margin ≥ 8.61（只在读取器无歧义时才继承）。 + +| 验证项 | 结果 | +|---|---| +| 回归：存 `默认语言=日语` + `项目代号=蓝鲸-47` | **无污染**，取代 0，问默认语言答 `日语` ✓ | +| 目标：`记住：…放在 E:\…` → `改了：…放到 H:\…` → 新进程问 | **答出新路径** ✓ | +| 追踪：`项目代号` 那轮 | 被**结构门**挡住（`inherited=''`）✓ | +| 追踪：`改了` 那轮 | 进入继承块（`metadata.attribute=''`）但 `inherited=''`——**被 margin 门挡住** | + +**⚠️ 我的第二次方法错误（记录以免重蹈）**：第一次标定的正类是「我的A**改成** v2」， +而 `改成` **本身就在操作符列表里、会被结构解析**，所以那批样本**永远走不到继承块**—— +**我标定的 population 是错的**。按正确 population 重测（库里只存一条，只有"解析不出属性"的话才走这条路）： + +| 话 | margin | need_memory | +|---|---|---| +| 真·更新「改了：…以后放到 D-9021」 | **8.6325** | True | +| 真·更新「现在用 v3.14.2 了」 | **8.64** | True | +| 无关寒暄 / 新话题 | **0** | False | +| 无关求助「帮我看看磁盘空间」 | **0.682** | True | +| 无关别的属性 | **0.7421** | True | + +**0.74 对 8.63，间隔很宽**，8.61 的门稳稳落在里面。上轮担心的"间隔只有 0.03"是**假警报**——那批样本会解析、走不到这条路。 + +**留下的限制（如实记录）**： +1. **口语化、过短的更新会被路由弃权**：「我换到 E-4402 了。」margin=0、`need_memory=False` + → 自动层不版本化它。这是路由自身的门，不是 margin 门的。 +2. 目标场景那一轮 `inherited=''`，**硬取代没发生**：库里同时有 Agent 写的记录与该轮自动 episode， + 读取器面对两条相近记录时 margin 变窄、被门挡住。**答案仍正确**（新记录排序在前）， + 属"软更正"而非"硬取代"，**旧记录依旧 active**。 + +**测试状态**：语法通过、CPU 测试 **92/92**；临时仪表全部清除（残留 0）。 + + +#### 第十轮（换成绝对分数门，硬取代真正生效；2026-09-15 目标轮次 9） + +上轮遗留问题：目标场景里 **`margin` 门把"硬取代"挡住了**。这轮先量清了原因： + +| 库内状态（查询＝「改了：…放到 H:\…」） | margin | 命中的有身份记录分数 | +|---|---|---| +| 只有 Agent 写的那条 | **8.591** | 8.591 | +| 再加一条**无身份的自动 episode** | **0.1523** | 8.591（没变） | +| 两条同属性、都有身份 | **0** | 8.591 / 8.591 | + +**结论：`margin`（top−second）在真实写入路径上天生不可用。** 因为自动层**每一轮都会插一条近似重复的记录**， +它把 margin 从 8.591 直接砸到 0.1523——**门正好卡在它本该放行的那一类上**。而且状态1 的 8.591 本身也低于我设的 8.61。 + +**改成：门看"有身份记录自己的绝对分数"**（实测两簇：真更新 8.591/8.6325/8.64，无关 ≤0.682/0.7421， +门限 `_INHERIT_SCORE_FLOOR = 5.0` 落在中间的空档里）。两道门因此变成： +① 结构门 `not candidate_conflict_key`（解析器给出属性时一律不猜）； +② **有身份记录的绝对分数 ≥ 5.0**（读取器确实认为"就是这条记录"）。 + +| 验证项 | 结果 | +|---|---| +| 目标：`记住：…放在 E:\…` → `改了：…放到 H:\…` | **旧记录被 `superseded`（硬取代成功）**；新进程答 `H:\Memory\agent_lab\runs` ✓；库内 生效 2 · 取代 2 · 撤回 0 | +| 回归：`默认语言=日语` + `项目代号=蓝鲸-47` | **无污染**、取代 0、问默认语言答 `日语` ✓ | + +**留下的限制**：口语化、过短的更新会被路由弃权（「我换到 E-4402 了。」`need_memory=False`）， +自动层因此不版本化它——这是路由自身的门，不是这道门的。 + +**测试状态**：语法通过、CPU 测试 **92/92**。 + + + +#### 第十一轮（长跑稳定性：测到一半发现被显卡争用污染，已主动停手，2026-09-15 目标轮次 10） + +**目标里唯一还没测的一族是「长时间日常使用下的稳定性」**，于是跑了 24 轮连续会话（`--session longrun`）。 +**完成 11 轮后我主动停止**，原因见下。 + +**已完成轮次的正确性（有客观基准的都在基准前量过）**： + +| 轮次 | 任务 | 结果 | +|---|---|---| +| 1–2 | 寒暄 / 「你是谁」 | 「本地测试代理…不是云服务，也不具备我实际上没有的能力」✓ | +| 3–5 | 写入三条事实 | 三次 `nm2_write` ✓ | +| 6 | `E:\deepseek\artifacts` 文件数 | `list_files` → **46** ✓（基准 46） | +| 7 | `V2_dpskw` 递归 `.py` 数 | `list_files{pattern,recursive}` → **133** ✓（基准 133） | +| 8 | C 盘可用 | `disk_space{C:\}` → **34.52 GB** ✓（基准 34.47，机器在跑） | +| 9 | 召回默认语言 | 命中 `默认语言 = 葡萄牙语` score 8.64 ✓ | + +**但耗时数据全部作废——我差点把测量假象当成发现。** 表面上每轮生成从 **12.7 秒涨到 137.6 秒**， +而 prompt 词元只从 3106 涨到 5248（1.7 倍），时间却涨 11 倍——这个比例本身就不对劲，于是我去查了显卡： + +``` +11557 MiB / 12227 MiB, 利用率 100 % +PID 18884: python -m V2_dpskw.eval_end_to_end_memory …（输出 locomo_nm21_e2e.md） +``` + +**是你自己的 LoCoMo(NM2.1) 端到端评测在满负荷跑**。那些"耗时暴涨"是我的长跑在跟它抢算力。 +**而且更糟：我的长跑正在污染你那次评测。** + +**处置**：立即停掉我的长跑**和**我的模型服务（PID 42384）—— +释放约 **6.4 GB 显存**（11.5 → 5.2 GB；你的评测此前只剩约 0.7 GB 余量，**很可能正被逼到 OOM**）。 +现在卡上只剩你的评测。 + +**⚠️ 方法教训（记牢）**：**测延迟之前必须先查显卡上还有谁**。我的测试设施和你的评测共用一块卡， +所以**任何性能数字都必须附带一次显卡状态检查**，否则测出来的可能是别人的负载。 +本次差点把"争用"报成"会话退化"——这会是一个完全错误的结论。 + +**仍未完成**：长跑稳定性（需要独占显卡）、以及更正修复在生产形态下的复跑。 +两者都依赖显卡空闲；我将继续把卡留给你的评测。 + + + +0. **长跑稳定性（需独占显卡）**：24 轮连续会话只跑完 11 轮就因你的评测占满显卡而中止。**重跑前先查显卡**，并记录完整耗时序列与上下文增长。 +1. **把「改动只生效一半」的情形查清**：目标场景已能硬取代，但库内仍留一条空值的 `评测报告路径`（继承来的 episode）。 + 对日用无碍（答对、排序对），但语义上不干净——看是否该让它随被取代者一起退场。 +1. **继续扩大窄接口工具覆盖面**：进程、端口、日志尾行、目录大小。 +1. **继续扩大窄接口工具覆盖面**：进程、端口、日志尾行、目录大小。 +2. **迫使兜底机制被真正验证**：构造必然触发「零工具 + 数字」的场景，确认强制重问与两轮上限行为正确。 +3. **会话卫生查实**：确认 `agent.state.messages` 替换是否生效；不生效就换一条能真正清尾的路子。 +4. **两代模型都测**（目前只用 NM2.0）。 +5. **仍未覆盖**：长时间（几十轮）日用稳定性。 + + +## 7. 本轮改动的文件 + +| 文件 | 改动 | +|---|---| +| `agent_lab/agent/src/tui.ts` | **新增**：交互式终端。驱动同一个 Pi Agent（同 8 个工具、同提示词），实时显示工具调用与记忆层决策；支持 `/memory` `/trace` `/probe` `/arm` `/reset` `/tools`，以及 `--say` 脚本化多轮 | +| `agent_lab/agent/src/prompt.ts` | **新增**：系统提示词单独成文件，TUI 与套件共用同一份（否则套件测出来的数字不能代表 TUI 的行为） | +| `agent_lab/agent/src/tools.ts` | `run_shell` 说明里点明「这是 Windows 上的 PowerShell，不是 bash」（实测模型会写 `2>/dev/null`，被当成路径 `H:\dev\null` 白烧三次调用）；零输出时解释「多半是过滤条件没匹配上、不要再发同一条」；**删除 `nm2_write` 的 importance/confidence 与 `nm2_admin` 的判断类旋钮**（见 §6.9） | +| `agent_lab/agent/src/agent.ts` | **导出 `AgentEventLike`；去掉一处 `as any`；熔断改为同步 `agent.abort()`；熔断后替换被污染的助手消息（会话卫生，实测未生效，见 §6.10） | +| `agent_lab/agent/src/memory.ts` | `TurnDiagnostics` 补齐服务端实际会发的字段；`RecordedTurn.abortReason` | +| `agent_lab/agent/src/agent.ts` | 导出 `AgentEventLike`；去掉一处 `as any` | +| `agent_lab/try_nm2.py` | 新增：直接打控制面接口的中文试用台（**不是** Agent，只是接口层的快速体检；要体验 Agent 请用 `bun run tui`） | +| `agent_lab/agent/src/run_scenario.ts` | 提示词改为从 `prompt.ts` 引入 | +| `agent_lab/RESULTS.md` | 本文件 | +| `agent_lab/agent/src/tools.ts` | `correct` 失败自动降级为结构化写入；`correct` 允许不带 `record_id`；缺省 `entity` 补 `user`；三个 NM2 工具在记忆关闭时返回可读观察而非报错 | +| `agent_lab/model_server/server.py` | 记忆冲突 409 + 恢复提示（原 500）；其余 `ValueError` → 400；`memory_mode != on` 时拒绝所有写入端点；**客户端中途断开不再被记成 500**（`_send`/`_send_stream` 的响应头写入原本在异常保护之外，客户端断流会让 `_route` 往死 socket 上再发一个 500，日志里出现误导性的 `500` 与 socketserver traceback） | +| `agent_lab/RESULTS.md` | 本文件 | +| `V2_dpskw/memory_os_v2.py` | `origin` 字段 + `retract_origin()`（见 §6.6）；`_absorb_unidentified_same_turn()`——结构化写入落地时，把同一轮里那条没有身份的自动记录标为已被取代 | +| `V2_dpskw/qwen_integration.py` | `memory_origin()`；自动写入带 origin；两个遗忘路径追加按轮次撤回；`memory_fact_sentence()` + `compose_corrected_evidence()`（见 §6.8）；**属性抽取不再把盘符冒号当分隔符**（见第六轮） | +| `agent_lab/agent/src/tools.ts` | `list_files` 每项返回**完整路径**、新增 `disk_space` / `peek_files` 窄接口工具 | +| `V2_dpskw/natural_memory_service.py` | 只传字段的更正先重建证据卡片（见 §6.8） | +| `V2_dpskw/tiered_memory_store_v2.py` | `origin` 列持久化 + 旧库原地迁移 | +| `V2_dpskw/tests/test_memory_origin_forget.py` | 新增 19 个回归测试 | + +验证状态：CPU 测试 **88/88 通过**；遗忘修复与更正修复均已端到端复现验证（NM2.0）。 +**NM2.1 未复测**（代码共用，修复对两代都生效；要不要在 2.1 上再跑一遍由你定）。 + +## 8. 复现命令 + +```powershell +# 1) 模型 server（LLM conda 环境，占卡） +$env:PYTHONPATH='H:\Memory' +& C:\Users\Administrator\miniconda3\envs\LLM\python.exe H:\Memory\agent_lab\model_server\server.py ` + --model-path qwen3_5_4b_natural_memory_v2_1 --port 8766 --no-auto-persist + +# 2) 控制面自检（29 项；需 server 已在运行） +& C:\Users\Administrator\miniconda3\envs\LLM\python.exe H:\Memory\agent_lab\model_server\selftest.py ` + --base-url http://127.0.0.1:8766/v1 --wait 200 + +# 3) Agent 侧 +cd H:\Memory\agent_lab\agent +$env:NATURAL_MEMORY_API_KEY='local' +$env:NATURAL_MEMORY_BASE_URL='http://127.0.0.1:8766/v1' +bun run typecheck + +# 3a) 交互式终端：直接跟这个 Agent 对话（最直观的试用方式） +bun run tui # 记忆开启 +bun run tui --arm off # 对照组：记忆层完全不可写 +bun run tui --session demo1 # 固定会话名，记忆跨进程保留 + +# 3b) 不用终端也能脚本化跑几轮（--say 可重复） +bun run tui --session demo1 --say '记一下：我的项目代号是 蓝鲸-47。' --say '/memory' + +# 3c) 两臂对照套件 +bun run suite # 全部场景 × 双组 +``` + +### 终端里能用的命令 + +| 命令 | 作用 | +|---|---| +| `/memory` `/all` | 看生效记录 / 看全部（含已取代、已撤回） | +| `/trace [条数]` | 看最近几轮的记忆层决策 | +| `/probe 问题` | **只跑路由不生成**：看清它到底检索到什么 | +| `/arm on\|off` | 切换记忆开关（off = 对照组，写入会被拒绝） | +| `/reset` | 清空记忆库 | +| `/tools` | 列出 Agent 手里的 8 个工具 | + +> **实测注意**：终端里同一进程内的追问可能靠对话上下文答对，**不能证明是记忆在起作用**。 +> 要证明记忆，得像下面那样**换一个进程**（全新上下文）再问。`runs/tui` 那两条记录就是这么做出来的。 + + +> 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as never, + tools: [], + messages: [], + thinkingLevel: "off", + }, + streamFn: (() => { + throw new Error("not used"); + }) as never, + sessionId: "hygiene-check", +}); + +const before = agent.state.messages.length; +console.log(`初始 messages 长度 = ${before}`); + +const replacement = [ + { role: "user", content: [{ type: "text", text: "原始问题" }] }, + { role: "assistant", content: [{ type: "text", text: "(本轮中止说明)" }] }, +]; + +agent.state.messages = replacement as never; +const after = agent.state.messages; +console.log(`赋值后 messages 长度 = ${after.length}`); +console.log(`内容与预期一致 = ${JSON.stringify(after) === JSON.stringify(replacement)}`); + +// 复制语义：改原数组不应影响内部状态 +replacement.push({ role: "user", content: [{ type: "text", text: "事后追加" }] } as never); +console.log(`改动源数组后内部长度 = ${agent.state.messages.length}（若仍为 2，说明是复制）`); + +// 读回来的对象是不是同一引用（对象字面量 getter 每次返回同一个数组） +const first = agent.state.messages; +const second = agent.state.messages; +console.log(`两次读取是同一引用 = ${first === second}`); + +console.log( + after.length === 2 && agent.state.messages.length === 2 + ? "结论：赋值机制有效 —— 会话卫生的失败不能归因于此" + : "结论：赋值机制无效 —— 这就是会话卫生失败的原因", +); diff --git a/agent/package.json b/agent/package.json new file mode 100644 index 0000000..200a96f --- /dev/null +++ b/agent/package.json @@ -0,0 +1,21 @@ +{ + "name": "natural-memory-agent-lab", + "private": true, + "version": "0.1.0", + "type": "module", + "description": "Test-agent harness for the Natural Memory v2 model, built on the Pi agent toolkit (pi-agent-core + pi-ai).", + "scripts": { + "typecheck": "tsc --noEmit", + "tui": "bun run src/tui.ts", + "scenario": "bun run src/run_scenario.ts --arm both", + "suite": "bun run src/run_scenario.ts --scenario-all scenarios --arm both" + }, + "dependencies": { + "@earendil-works/pi-agent-core": "^0.82.1", + "@earendil-works/pi-ai": "^0.82.1" + }, + "devDependencies": { + "@types/node": "^22.0.0", + "typescript": "^5.6.0" + } +} diff --git a/agent/runs/conflict_fixed/conflict_update.on.jsonl b/agent/runs/conflict_fixed/conflict_update.on.jsonl new file mode 100644 index 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diff --git a/agent/runs/v4/scratchpad/off.txt b/agent/runs/v4/scratchpad/off.txt new file mode 100644 index 0000000..d46cd10 --- /dev/null +++ b/agent/runs/v4/scratchpad/off.txt @@ -0,0 +1 @@ +紧急联系人：王工，电话分机 7781 diff --git a/agent/runs/v4/summary.json b/agent/runs/v4/summary.json new file mode 100644 index 0000000..fc8adde --- /dev/null +++ b/agent/runs/v4/summary.json @@ -0,0 +1,35 @@ +{ + "baseUrl": "http://127.0.0.1:8766/v1", + "outDir": "H:\\Memory\\agent_lab\\agent\\runs\\v4", + "arms": [ + "on", + "off" + ], + "scenarios": [ + { + "id": "conflict_update", + "description": "同一属性先给旧值再给新值；跨会话询问时必须返回新值，且旧值不得作为 active 事实被读取。", + "arms": { + "on": { + "activeRecords": 1, + "assertionsPassed": 3, + "assertionsTotal": 3, + "passRate": 1, + "failures": [], + "meanSecondsPerTurn": 9.42 + }, + "off": { + "activeRecords": 0, + "assertionsPassed": 1, + "assertionsTotal": 3, + "passRate": 0.3333333333333333, + "failures": [ + "answer_contains: missing one of [\"葡萄牙语\",\"葡萄牙\"]", + "active_record_contains: absent from active memory" + ], + "meanSecondsPerTurn": 3.69 + } + } + } + ] +} \ No newline at end of file diff --git a/agent/scenarios/conflict_update.json b/agent/scenarios/conflict_update.json new file mode 100644 index 0000000..2868591 --- /dev/null +++ b/agent/scenarios/conflict_update.json @@ -0,0 +1,24 @@ +{ + "id": "conflict_update", + "description": "同一属性先给旧值再给新值；跨会话询问时必须返回新值，且旧值不得作为 active 事实被读取。", + "sessions": [ + { + "id": "s1", + "turns": [ + "记一下：我的默认语言是 日语。", + "更正一下：我的默认语言改成 葡萄牙语，日语作废。" + ] + }, + { + "id": "s2", + "turns": [ + "我的默认语言是什么？只回答语言名。" + ] + } + ], + "assertions": [ + { "type": "answer_contains", "session": "s2", "turn": 0, "any": ["葡萄牙语", "葡萄牙"] }, + { "type": "answer_not_contains", "session": "s2", "turn": 0, "any": ["日语"] }, + { "type": "active_record_contains", "any": ["葡萄牙语"] } + ] +} diff --git a/agent/scenarios/cross_session_recall.json b/agent/scenarios/cross_session_recall.json new file mode 100644 index 0000000..08c3af8 --- /dev/null +++ b/agent/scenarios/cross_session_recall.json @@ -0,0 +1,31 @@ +{ + "id": "cross_session_recall", + "description": "写入事实后，在全新会话（空上下文、无历史文本）中询问该事实。这正是 in-model memory 存在的理由。", + "memory_write_turns": [0], + "sessions": [ + { + "id": "s1", + "turns": [ + "记一下：我的项目代号是 蓝鲸-47，部署区域是 ap-southeast-3。", + "谢谢。" + ] + }, + { + "id": "s2", + "turns": [ + "我的项目代号是什么？只回答代号本身。" + ] + }, + { + "id": "s3", + "turns": [ + "部署区域呢？只回答区域本身。" + ] + } + ], + "assertions": [ + { "type": "answer_contains", "session": "s2", "turn": 0, "any": ["蓝鲸-47", "蓝鲸 47", "蓝鲸47"] }, + { "type": "answer_contains", "session": "s3", "turn": 0, "any": ["ap-southeast-3", "apse-3"] }, + { "type": "active_record_contains", "any": ["蓝鲸-47"] } + ] +} diff --git a/agent/scenarios/forget_retraction.json b/agent/scenarios/forget_retraction.json new file mode 100644 index 0000000..1019f2e --- /dev/null +++ b/agent/scenarios/forget_retraction.json @@ -0,0 +1,28 @@ +{ + "id": "forget_retraction", + "description": "写入事实后明确要求遗忘；跨会话再问时必须不再使用该事实（撤回/覆盖语义），且不得把已忘内容当现状。", + "sessions": [ + { + "id": "s1", + "turns": [ + "记一下：我的紧急联系人是 王工，电话分机 7781。" + ] + }, + { + "id": "s2", + "turns": [ + "请遗忘我的紧急联系人信息，不要再保留和使用它。" + ] + }, + { + "id": "s3", + "turns": [ + "我的紧急联系人分机是多少？如果已经不保留这条信息，就直接说明。" + ] + } + ], + "assertions": [ + { "type": "answer_not_contains", "session": "s3", "turn": 0, "any": ["7781"] }, + { "type": "answer_contains", "session": "s3", "turn": 0, "any": ["没有", "不知道", "已", "无法确认", "未保留"] } + ] +} diff --git a/agent/scenarios/multi_hop_chain.json b/agent/scenarios/multi_hop_chain.json new file mode 100644 index 0000000..4b5583b --- /dev/null +++ b/agent/scenarios/multi_hop_chain.json @@ -0,0 +1,23 @@ +{ + "id": "multi_hop_chain", + "description": "两条事实构成推理链（项目→负责人→工号）。跨会话问最终属性时，模型必须串起两跳而不是只取一跳。", + "sessions": [ + { + "id": "s1", + "turns": [ + "记两件事：项目 项目-星尘 的负责人是 成员-林澈。", + "另外：成员 成员-林澈 的工号是 H-9F3K2。" + ] + }, + { + "id": "s2", + "turns": [ + "项目-星尘 的负责人的工号是什么？需要的话可以分步推导，最后只回答工号。" + ] + } + ], + "assertions": [ + { "type": "answer_contains", "session": "s2", "turn": 0, "any": ["H-9F3K2", "H 9F3K2", "9F3K2"] }, + { "type": "answer_not_contains", "session": "s2", "turn": 0, "any": ["不知道", "没有记录"] } + ] +} diff --git a/agent/scenarios/unknown_abstention.json b/agent/scenarios/unknown_abstention.json new file mode 100644 index 0000000..a065f4f --- /dev/null +++ b/agent/scenarios/unknown_abstention.json @@ -0,0 +1,29 @@ +{ + "id": "unknown_abstention", + "description": "只写入一条无关事实，然后询问从未提及的属性。模型必须明确表示不知道，而不是编造或把相似记忆硬当答案。", + "sessions": [ + { + "id": "s1", + "turns": [ + "记一下：我的工位在 3 楼东侧。" + ] + }, + { + "id": "s2", + "turns": [ + "我的血型是什么？如果不知道就直说不知道，不要猜。" + ] + }, + { + "id": "s3", + "turns": [ + "我上个月去哪出差了？没有记录的话请直接说没有记录。" + ] + } + ], + "assertions": [ + { "type": "answer_contains", "session": "s2", "turn": 0, "any": ["不知道", "没有记录", "无法确认", "未找到", "不清楚"] }, + { "type": "answer_not_contains", "session": "s2", "turn": 0, "any": ["A 型", "B 型", "O 型", "AB 型"] }, + { "type": "answer_contains", "session": "s3", "turn": 0, "any": ["没有记录", "不知道", "无法确认", "未找到", "不清楚"] } + ] +} diff --git a/agent/src/agent.ts b/agent/src/agent.ts new file mode 100644 index 0000000..873d372 --- /dev/null +++ b/agent/src/agent.ts @@ -0,0 +1,297 @@ +/** + * The test agent: a Pi `Agent` wired to the local Natural Memory model, with memory + * instrumentation attached to the event stream. + * + * A `prompt()` can span several Pi turns (each tool call adds one). The recorder + * flushes one `RecordedTurn` per *user message*, so a scenario assertion maps to a + * human-visible exchange rather than to an internal loop iteration. + */ + +import { Agent } from "@earendil-works/pi-agent-core"; +import { MemoryClient, MemoryRecorder, type MemoryMode, type RecordedTurn } from "./memory.ts"; +import { NM2Client } from "./nm2.ts"; +import { buildTools } from "./tools.ts"; + +/** Loose view of Pi's event union; narrowed by `type` at runtime. */ +export interface AgentEventLike { + type: string; + message?: { role?: string; content?: unknown }; + assistantMessageEvent?: { type?: string; delta?: string }; + toolCallId?: string; + toolName?: string; + args?: unknown; + result?: unknown; +} + +export interface TestAgentOptions { + models: { streamSimple: (...args: any[]) => any }; + model: unknown; + session: string; + memoryBaseUrl: string; + workspace: string; + notesPath: string; + systemPrompt: string; + memoryMode?: MemoryMode; + onEvent?: (event: AgentEventLike) => void; + /** Hard ceiling on tool calls per user message; a small model can otherwise loop. */ + maxToolCallsPerExchange?: number; + /** Identical (tool, args) calls allowed before the call is blocked. */ + maxIdenticalToolCalls?: number; +} + +export interface TestAgent { + agent: Agent; + memory: MemoryClient; + nm2: NM2Client; + recorder: MemoryRecorder; + /** Send one user message and wait for the full exchange to settle. */ + ask(text: string): Promise; + close(): void; +} + +/** Questions that ask for an amount, and so need a tool behind the number in the answer. */ +const ASKS_FOR_AN_AMOUNT = + /(多少|几个|几多|数量|多大|大小|剩余|可用|文件数|行数|占比|count|how many|how much|how big|size|space|total)/i; + +/** + * What the model is told when it states a quantity it never looked up. + * + * Deliberately concrete: it names the tools and repeats that "I could not get it" is an + * acceptable answer, because that is the outcome the previous version was avoiding by + * inventing a number. + */ +const ENFORCE_TOOL_USE_NUDGE = + "你刚才在回答里给出了一个数字，但这一轮没有调用任何工具，所以那个数字没有出处，不能采信。" + + "现在请先调用工具核实：磁盘空间用 disk_space，文件数量或目录内容用 list_files，" + + "其它情况用 run_shell 或 read_file。核实之后只回答你真正拿到的结果；" + + "如果拿不到，就直说拿不到，不要给数字。"; + +/** + * True when the answer states a quantity that no tool could have supplied. + * + * This is a *check*, not a fix: a 4B checkpoint that has already been told "never guess a + * number you did not receive from a tool" still answered "100 GB" for free space (truth + * 34 GB) and "0 files" for a directory (truth 46), both with zero tool calls. Surfacing it + * is what keeps a fabricated number from passing for a measurement. + */ +function isUnverifiedQuantity(userMessage: string, assistantText: string, toolCallCount: number): boolean { + if (toolCallCount > 0) return false; + if (!ASKS_FOR_AN_AMOUNT.test(userMessage)) return false; + return /\d/.test(assistantText); +} + +function contentToString(content: unknown): string { if (typeof content === "string") return content; + if (Array.isArray(content)) { + return content + .map((part) => (typeof part === "string" ? part : String((part as { text?: string })?.text ?? ""))) + .join(""); + } + return ""; +} + +export async function createTestAgent(options: TestAgentOptions): Promise { + const memory = new MemoryClient(options.memoryBaseUrl); + const nm2 = new NM2Client(options.memoryBaseUrl); + if (options.memoryMode) { + await nm2.setMode(options.memoryMode); + } + const recorder = new MemoryRecorder(memory, options.session); + const tools = buildTools({ workspace: options.workspace, memory, nm2, notesPath: options.notesPath }); + + const agent = new Agent({ + initialState: { + systemPrompt: options.systemPrompt, + model: options.model as any, + tools, + messages: [], + thinkingLevel: "off", + }, + streamFn: (options.models.streamSimple as any).bind(options.models), + toolExecution: "parallel", + sessionId: options.session, + }); + + // ---- per-exchange accumulators ----------------------------------------- + let userMessage = ""; + let assistantText = ""; + let toolCalls: { name: string; args: unknown }[] = []; + let toolResults: { name: string; result: string }[] = []; + let startedAt = Date.now(); + const flushed: RecordedTurn[] = []; + const maxToolCalls = options.maxToolCallsPerExchange ?? 6; + const maxIdenticalCalls = options.maxIdenticalToolCalls ?? 2; + let toolCallsThisExchange = 0; + let callSignatures = new Map(); + let abortReason = ""; + let guardTripped = false; + let enforcedToolUse = false; + let enforcedPreviousAnswer = ""; + // The enforcement nudge is sent as a user message; keep the *original* question as the + // recorded one so a turn still reads as the human's request. + let userMessageLocked = false; + + // Loop guard, assigned after construction so the hook can hard-abort the run. Without + // it a 4B model that hits a failing tool retries forever: one observed exchange issued + // 81 tool calls over 421 seconds and ended with the context degraded into role-marker + // echoes. `terminate` alone was not enough — it only stops the loop when *every* result + // in a batch sets it, and the model simply opened another batch. + agent.beforeToolCall = async ({ toolCall, args }: { toolCall: { name: string }; args: unknown }) => { + toolCallsThisExchange += 1; + const signature = `${toolCall.name}:${JSON.stringify(args ?? {})}`; + const repeats = (callSignatures.get(signature) ?? 0) + 1; + callSignatures.set(signature, repeats); + const overBudget = toolCallsThisExchange > maxToolCalls; + const repeating = repeats > maxIdenticalCalls; + if (!overBudget && !repeating) { + return undefined; + } + abortReason = overBudget + ? `tool budget exhausted after ${maxToolCalls} calls` + : `identical call ${toolCall.name} repeated ${maxIdenticalCalls + 1} times`; + // Abort now rather than on the next tick. Measured in production: a silently-failing + // command made the model emit the same call in batch after batch, and the deferred abort + // let ten more round-trips through, ending the turn as role-marker echoes that then + // poisoned every following turn. Stopping at the first trip costs the model one tool + // result it never reads, and buys a turn that stays usable. + guardTripped = true; + agent.abort(); + return { + block: true, + reason: overBudget + ? `tool budget for this message is exhausted (${maxToolCalls} calls); answer with what you have or state that you do not know` + : `you already ran ${toolCall.name} with these exact arguments ${maxIdenticalCalls} times and the result did not change. Running it again cannot help: change the arguments, use a different tool, or answer with what you have`, + terminate: true, + }; + }; + + const unsubscribe = agent.subscribe(async (event: AgentEventLike) => { + options.onEvent?.(event); + switch (event.type) { + case "message_start": { + const role = event.message?.role; + if (role === "user" && !userMessageLocked) { + userMessage = contentToString(event.message?.content) || userMessage; + } + break; + } + case "message_update": { + const delta = event.assistantMessageEvent?.delta; + if (event.assistantMessageEvent?.type === "text_delta" && delta) { + assistantText += delta; + } + break; + } + case "message_end": { + if (event.message?.role === "assistant") { + const finalText = contentToString(event.message?.content); + if (finalText.trim()) assistantText = finalText; + } + break; + } + case "tool_execution_start": { + if (event.toolName) toolCalls.push({ name: event.toolName, args: event.args }); + break; + } + case "tool_execution_end": { + if (event.toolName) { + toolResults.push({ name: event.toolName, result: JSON.stringify(event.result ?? {}).slice(0, 4000) }); + } + break; + } + default: + break; + } + }); + + async function ask(text: string): Promise { + userMessage = text; + userMessageLocked = false; + assistantText = ""; + toolCalls = []; + toolResults = []; + startedAt = Date.now(); + flushed.length = 0; + toolCallsThisExchange = 0; + callSignatures = new Map(); + abortReason = ""; + guardTripped = false; + enforcedToolUse = false; + enforcedPreviousAnswer = ""; + + // Two prompts at most per exchange. The second one exists only to make a number have a + // source; without the cap, a model that will not call the tool would be re-asked forever. + for (let attempt = 0; attempt < 2; attempt += 1) { + try { + await agent.prompt(attempt === 0 ? text : ENFORCE_TOOL_USE_NUDGE); + } catch (error) { + // A guard-triggered abort settles the prompt as an error; that is a bounded outcome, + // not a harness failure, so the exchange is still recorded. + abortReason = abortReason || `prompt ended early: ${String(error).slice(0, 200)}`; + } + if (abortReason) break; + if (!isUnverifiedQuantity(text, assistantText, toolCalls.length)) break; + // The answer asserts a quantity and no tool was consulted, so the number has no source. + // Measured twice: "100 GB" for free space (truth 34.62) and "0 files" for a directory + // (truth 46). Flagging it was not enough -- the model keeps producing it -- so give it + // one forced retry with the tool budget restored, and keep the bad answer on the record. + enforcedToolUse = true; + enforcedPreviousAnswer = assistantText.trim(); + assistantText = ""; + toolCalls = []; + toolResults = []; + toolCallsThisExchange = 0; + callSignatures = new Map(); + } + + // Session hygiene. A guard-stopped turn leaves a mangled assistant message behind -- + // measured: role-marker echoes ("assistant\n\nassistant...") plus a replay of an earlier + // user message. Pi keeps that in the conversation, so the *next* turn starts from + // corrupted context and fails for unrelated reasons (the follow-up task re-issued the + // previous turn's failing command five more times and took 99 seconds). Replacing the + // bad tail with a short factual note keeps one bad command from ending the session. + if (guardTripped) { + const messages = agent.state.messages; + let lastUser = -1; + for (let index = messages.length - 1; index >= 0; index -= 1) { + if ((messages[index] as { role?: string }).role === "user") { + lastUser = index; + break; + } + } + if (lastUser >= 0) { + agent.state.messages = [ + ...messages.slice(0, lastUser + 1), + { + role: "assistant", + content: [ + { + type: "text", + text: + `(this turn was stopped: ${abortReason}. Do not repeat the same call — ` + + "change the arguments, use another tool, or say plainly that you could not get the value.)", + }, + ], + } as never, + ]; + } + } + + const recorded = await recorder.record({ + session: options.session, + turn: recorder.turns.length, + userMessage, + assistantText: assistantText.trim(), + toolCalls, + toolResults, + wallSeconds: Number(((Date.now() - startedAt) / 1000).toFixed(3)), + abortReason: abortReason || undefined, + unverifiedQuantity: isUnverifiedQuantity(userMessage, assistantText, toolCalls.length), + enforcedToolUse, + enforcedPreviousAnswer: enforcedPreviousAnswer || undefined, + }); + flushed.push(recorded); + return flushed; + } + + return { agent, memory, nm2, recorder, ask, close: () => unsubscribe() }; +} diff --git a/agent/src/memory.ts b/agent/src/memory.ts new file mode 100644 index 0000000..e987d02 --- /dev/null +++ b/agent/src/memory.ts @@ -0,0 +1,215 @@ +/** + * Client for the model server's memory surface, plus the per-turn recorder. + * + * The completion body carries a `memory_diagnostics` block, but an OpenAI SDK will + * drop unknown response fields, so the harness does not depend on it: after every + * agent turn the recorder polls `/v1/diagnostics` on the server and correlates the + * newest entry with the turn. That keeps instrumentation working no matter what the + * transport layer decides to preserve. + */ + +export type MemoryMode = "on" | "read_only" | "off"; + +export interface MemoryStats { + enabled?: boolean; + router_ready?: boolean; + records?: number; + active_records?: number; + superseded_records?: number; + retracted_records?: number; + quarantined_records?: number; + pages?: number; + active_conflict_keys?: number; + last_coarse_candidates?: number; + gpu_cache_records?: number; + [key: string]: unknown; +} + +export interface TurnDiagnostics { + turn: number; + memory_mode: MemoryMode; + memory_changed: boolean; + reset_triggered: boolean; + tool_calls: string[]; + prompt_tokens: number; + completion_tokens: number; + generation_seconds: number; + total_seconds: number; + memory: MemoryStats; + // The server hoists these onto the trace entry as well; they are what makes a turn + // legible ("did the router want memory, what did it retrieve, how much did it inject"), + // so the interactive TUI reads them. Optional because older traces omit them. + router_decisions?: RouterDecisionLike[]; + prefix_tokens?: number; + prefix_used?: boolean; + no_evidence?: boolean; + read_seconds?: number; + auto_memory_probability?: number[] | null; + auto_forget_probability?: number[] | null; + coverage?: { loaded?: boolean; bound_to_bank?: boolean; head?: { attributes?: number } } | null; +} + +export interface RouterDecisionLike { + need_memory?: boolean; + stop_reason?: string; + page_ids?: string[]; + record_ids?: string[]; + hop_count?: number; + confidence?: number; + top_score?: number; + score_margin?: number; +} + +export interface MemoryRecord { + record_id: string; + text?: string; + entity?: string; + attribute?: string; + value?: string; + status?: string; + version?: number; + confidence?: number; + importance?: number; + access_count?: number; + [key: string]: unknown; +} + +async function request(baseUrl: string, path: string, init?: RequestInit): Promise { + const response = await fetch(`${baseUrl}${path}`, { + ...init, + headers: { "Content-Type": "application/json", ...(init?.headers ?? {}) }, + }); + const text = await response.text(); + if (!response.ok) { + throw new Error(`memory server ${path} -> HTTP ${response.status}: ${text.slice(0, 400)}`); + } + return (text ? JSON.parse(text) : {}) as T; +} + +export class MemoryClient { + constructor(readonly baseUrl: string) {} + + health() { + return request>(this.baseUrl.replace(/\/v1$/, ""), "/health"); + } + + diagnostics(limit = 200) { + return request<{ turns: TurnDiagnostics[] }>(this.baseUrl, `/diagnostics?limit=${limit}`); + } + + list(query = "", status = "active", limit = 200, offset = 0) { + const params = new URLSearchParams({ query, status, limit: String(limit), offset: String(offset) }); + return request<{ records: MemoryRecord[]; returned: number }>(this.baseUrl, `/memory?${params}`); + } + + audit() { + return request>(this.baseUrl, "/memory/audit"); + } + + exportAll(limit = 10000) { + return request>(this.baseUrl, `/memory/export?limit=${limit}`); + } + + setMode(mode: MemoryMode) { + return request<{ memory_mode: MemoryMode }>(this.baseUrl, "/memory/mode", { + method: "POST", + body: JSON.stringify({ mode }), + }); + } + + reset() { + return request>(this.baseUrl, "/memory/reset", { method: "POST", body: "{}" }); + } + + write(payload: Record) { + return request>(this.baseUrl, "/memory", { + method: "POST", + body: JSON.stringify(payload), + }); + } + + retract(recordId: string) { + return request>(this.baseUrl, `/memory/${recordId}/retract`, { + method: "POST", + body: "{}", + }); + } +} + +export interface RecordedTurn { + session: string; + turn: number; + userMessage: string; + assistantText: string; + toolCalls: { name: string; args: unknown }[]; + toolResults: { name: string; result: string }[]; + diagnostics: TurnDiagnostics | null; + memoryAfter: MemoryStats | null; + wallSeconds: number; + /** Set when the loop guard cut the exchange short (budget or repeated tool call). */ + abortReason?: string; + /** + * The question asked for an amount and the answer gave a number, but the exchange called + * no tool at all -- so the number cannot have come from anywhere. Measured twice: asked + * for free disk space the model answered "100 GB" (truth 34 GB), and asked to count a + * directory's files it answered "0" (truth 46) without calling a single tool. + */ + unverifiedQuantity?: boolean; + /** The exchange was re-asked because its first answer stated a number with no tool behind it. */ + enforcedToolUse?: boolean; + /** The uncorroborated first answer that enforcement replaced, kept for the record. */ + enforcedPreviousAnswer?: string; +} + +/** + * Records one line per agent turn, and answers the questions a memory scenario asks: + * did it write, what is active now, was a record superseded or retracted, and how + * much of the work is attributable to memory rather than the base model. + */ +export class MemoryRecorder { + readonly turns: RecordedTurn[] = []; + + constructor(private readonly client: MemoryClient, readonly session: string) {} + + async snapshot(): Promise { + const health = await this.client.health(); + return (health.memory ?? {}) as MemoryStats; + } + + async latestDiagnostics(): Promise { + try { + const { turns } = await this.client.diagnostics(1); + return turns.at(-1) ?? null; + } catch { + return null; + } + } + + async record(turn: Omit): Promise { + const diagnostics = await this.latestDiagnostics(); + let memoryAfter: MemoryStats | null = null; + try { + memoryAfter = await this.snapshot(); + } catch { + memoryAfter = diagnostics?.memory ?? null; + } + const full: RecordedTurn = { ...turn, diagnostics, memoryAfter }; + this.turns.push(full); + return full; + } + + /** Active records matching a text fragment (default: every active record). */ + async activeRecords(fragment = ""): Promise { + const { records } = await this.client.list(fragment, "active", 500); + return records; + } + + async recordsByStatus(status: string, fragment = ""): Promise { + const { records } = await this.client.list(fragment, status, 500); + return records; + } + + toJSONL(): string { + return this.turns.map((turn) => JSON.stringify(turn)).join("\n") + "\n"; + } +} diff --git a/agent/src/nm2.ts b/agent/src/nm2.ts new file mode 100644 index 0000000..efe01e1 --- /dev/null +++ b/agent/src/nm2.ts @@ -0,0 +1,215 @@ +/** + * NM2 client: the full observation + control surface of the memory architecture. + * + * The model already records everything an operator needs (`runtime.v2_last_decisions`, + * the policy head's write/forget probabilities, injected prefix length, routing-only + * latency, KV-compaction reports); the server snapshots it after every call and keeps + * a per-turn trace history. This client makes that surface usable from the agent and + * from the scenario harness. + * + * Endpoints are grouped by intent so the tool layer can expose few, well-described + * tools to a 4B model instead of twenty narrow ones. + */ + +export interface RouterDecision { + need_memory: boolean; + page_ids: string[]; + record_ids: string[]; + page_scores: number[]; + record_scores: number[]; + hop_count: number; + hop_trace: string[][]; + confidence: number; + stop_reason: string; + top_score?: number; + score_margin?: number; + evidence_score?: number; +} + +export interface RetrievedRecord { + record_id: string; + text: string | null; + entity: string | null; + attribute: string | null; + value: string | null; + status: string | null; + version: number | null; + score: number | null; +} + +/** + * NM2.1 coverage gate verdict for one query: which attribute the question asked about + * and how confident the head was. `gate: "bypassed"` means the bank's attributes lie + * outside the head's vocabulary, so the gate declines to judge. + */ +export interface CoverageVerdict { + attribute: string | null; + probability: number | null; + gate?: string; + error?: string; +} + +export interface ProbeResult { + query: string; + need_memory: boolean; + stop_reason: string; + confidence: number; + hop_count: number; + hop_trace: string[][]; + score_margin: number; + evidence_score: number; + page_ids: string[]; + records: RetrievedRecord[]; + coverage: CoverageVerdict | null; + timing: { encode_seconds: number; route_seconds: number; total_seconds: number }; + trace_turn: number; +} + +export interface TraceRecord { + turn: number; + kind: "chat" | "probe"; + at: number; + user_message?: string; + memory_changed?: boolean; + router_decisions?: RouterDecision[]; + prefix_tokens?: number; + read_seconds?: number; + auto_memory_probability?: number[] | null; + auto_forget_probability?: number[] | null; + context_compaction?: Record | null; + snapshot?: Record; + [key: string]: unknown; +} + +export interface ConfigSnapshot { + knobs: Record; + fixed: Record; + memory_mode: string; +} + +export interface MemoryRecordView { + record_id: string; + text?: string; + entity?: string; + attribute?: string; + value?: string; + status?: string; + version?: number; + confidence?: number; + importance?: number; + [key: string]: unknown; +} + +async function call(baseUrl: string, path: string, init?: RequestInit): Promise { + const response = await fetch(`${baseUrl}${path}`, { + ...init, + headers: { "Content-Type": "application/json", ...(init?.headers ?? {}) }, + }); + const text = await response.text(); + if (!response.ok) { + let detail = text.slice(0, 400); + try { + detail = (JSON.parse(text) as { error?: { message?: string } }).error?.message ?? detail; + } catch { + /* keep raw text */ + } + throw new Error(`NM2 ${path} -> HTTP ${response.status}: ${detail}`); + } + return (text ? JSON.parse(text) : {}) as T; +} + +export class NM2Client { + constructor(readonly baseUrl: string) {} + + // ---- observation ------------------------------------------------------- + state() { + return call<{ config: ConfigSnapshot; snapshot: Record }>(this.baseUrl, "/nm2/state"); + } + + config() { + return call(this.baseUrl, "/nm2/config"); + } + + traces(limit = 50) { + return call<{ traces: TraceRecord[] }>(this.baseUrl, `/nm2/trace?limit=${limit}`); + } + + audit() { + return call>(this.baseUrl, "/nm2/audit"); + } + + stats() { + return call>(this.baseUrl.replace(/\/v1$/, ""), "/health"); + } + + // ---- routing as a first-class operation -------------------------------- + /** Route only: returns the bounded page/record Top-K decision, without generating. */ + probe(query: string, options: { top_k_pages?: number; top_k_records?: number; max_hops?: number } = {}) { + return call(this.baseUrl, "/nm2/probe", { + method: "POST", + body: JSON.stringify({ query, ...options }), + }); + } + + records(query = "", status = "active", limit = 200) { + const params = new URLSearchParams({ query, status, limit: String(limit) }); + return call<{ records: MemoryRecordView[]; returned: number }>(this.baseUrl, `/nm2/records?${params}`); + } + + // ---- governance -------------------------------------------------------- + write(payload: { text: string; entity?: string; attribute?: string; value?: string; importance?: number; confidence?: number; source?: string; trusted?: boolean; force?: boolean }) { + return call>(this.baseUrl, "/nm2/write", { method: "POST", body: JSON.stringify(payload) }); + } + + correct(payload: { record_id: string; text?: string; entity?: string; attribute?: string; value?: string; importance?: number; confidence?: number; evidence?: string[] }) { + return call>(this.baseUrl, "/nm2/correct", { method: "POST", body: JSON.stringify(payload) }); + } + + retract(recordId: string) { + return call>(this.baseUrl, "/nm2/retract", { method: "POST", body: JSON.stringify({ record_id: recordId }) }); + } + + approve(recordId: string) { + return call>(this.baseUrl, "/nm2/approve", { method: "POST", body: JSON.stringify({ record_id: recordId }) }); + } + + setConfig(knobs: Record) { + return call<{ applied: Record; rejected: Record; config: ConfigSnapshot }>( + this.baseUrl, + "/nm2/config", + { method: "POST", body: JSON.stringify({ knobs }) }, + ); + } + + compact(text: string, chunkTokens?: number) { + return call>(this.baseUrl, "/nm2/compact", { + method: "POST", + body: JSON.stringify({ text, chunk_tokens: chunkTokens }), + }); + } + + session(action: "save" | "load" | "list" | "delete" | "reset", user?: string) { + return call>(this.baseUrl, "/nm2/session", { + method: "POST", + body: JSON.stringify({ action, user }), + }); + } + + setMode(mode: "on" | "read_only" | "off") { + return call<{ memory_mode: string }>(this.baseUrl, "/nm2/mode", { method: "POST", body: JSON.stringify({ mode }) }); + } + + reset() { + return call>(this.baseUrl, "/nm2/reset", { method: "POST", body: "{}" }); + } +} + +/** Compact one-line rendering of a decision, for tool results and traces. */ +export function describeDecision(decision: RouterDecision | undefined): string { + if (!decision) return "(no router decision recorded)"; + const margin = typeof decision.score_margin === "number" ? decision.score_margin.toFixed(3) : "-"; + return ( + `need=${decision.need_memory} hop=${decision.hop_count} stop=${decision.stop_reason} ` + + `confidence=${decision.confidence.toFixed(3)} margin=${margin} records=[${decision.record_ids.join(", ")}]` + ); +} diff --git a/agent/src/prompt.ts b/agent/src/prompt.ts new file mode 100644 index 0000000..fb4071b --- /dev/null +++ b/agent/src/prompt.ts @@ -0,0 +1,61 @@ +/** + * The system prompt shared by the scenario suite and the interactive TUI. + * + * It lives in its own module so both entry points use the *same* text: the suite's + * measured numbers only describe the TUI's behaviour if the agent is configured + * identically, and duplicating the string would let the two drift apart silently. + */ + +// The agent has to be told that this model carries its own memory layer. Without +// this, a small model asked to recall a stored fact reaches for the shell (`find`, +// `grep`) instead of reading the evidence the NM2 router already retrieved, and then +// answers from whatever it finds on disk. The rules below are the minimum needed to +// make the memory layer the first thing the agent consults, and they are identical +// for both arms so the A/B comparison stays honest: in the OFF arm the same calls +// simply return nothing. +export const DEFAULT_SYSTEM_PROMPT = [ + // Identity first. Measured: with no identity the checkpoint answers "who are you" as the + // base model ("I am Qwen3.5 by Alibaba, with cross-modal abilities ...") -- a list of + // capabilities this 4B checkpoint does not have. + "You are a local test agent running on a Qwen3.5-4B checkpoint that carries its own", + "long-term memory layer (NM2). If the user asks who you are, say that plainly. You are not", + "a cloud service, and you must not claim abilities you do not have.", + "Answer the user directly and concisely.", + "Facts told to you in earlier turns or earlier sessions are stored in NM2, not in the workspace.", + "The memory layer may inject an evidence block beginning with 【长期记忆证据】 into your prompt.", + "When it is present it is authoritative: answer from it directly instead of searching.", + "To answer any question about such a fact, call nm2_read with action=probe first; use", + "action=search to list records. Do this before reaching for shell or file tools.", + "Work on the workspace with run_shell/read_file/write_file. Do NOT use find or grep to look for", + "facts about the user: the workspace does not contain them, and searching it wastes the turn.", + // Measured: composing PowerShell from scratch is where a 4B model fails most often + // (`-eq 'C:'` against a character, `Measure-Object -Width 0`), and a silently empty result + // then sends it into a retry loop. The narrow tools take a path, so say so explicitly. + "For disk space use disk_space, and for file counts or directory listings use list_files —", + "pass the path and let the tool build the query. Do not hand-write a command for those.", + // Measured: given a listing, a 4B model invented a filename instead of copying one from the + // result it had just received. A task that needs both a count and "the first file's + // contents" has a tool that does it in one call, so say so. + "When you need both a count and the contents of 'the first' or 'any one' of the matches, use", + "peek_files — one call returns the count, the first file's full path and its opening lines.", + "If a shell command fails or prints nothing, do not run it again: change the command, use a", + "narrow tool, or say plainly that you could not get the value. Never guess a number you did", + "not receive from a tool.", + "Never invent a value. If memory holds no evidence for a stored fact, say plainly that you do not", + "have it or that it is no longer retained.", + // Measured: without an explicit convention the model wrote the *value* into the entity + // field -- "我叫Wpy" became entity="Wpy" attribute="name", i.e. the record read + // "Wpy 的 name 是 Wpy" and the fact was effectively lost. A concrete example is what fixes + // this for a 4B model; abstract field descriptions did not. + "When the user tells you a fact about themselves, store it with entity=\"user\", a short", + "Chinese attribute name, and the value. For example, if the user says 我叫Wpy, call", + "nm2_write{action:\"write\",entity:\"user\",attribute:\"名字\",value:\"Wpy\"}. Never put the value", + "in the entity field, and never build an entity name out of the value.", + "When the user revises or revokes a fact, use nm2_write with action=correct or action=retract.", + "Do not repeat or volunteer a fact the user has asked you to forget.", + // Measured: spelling out a "search -> retract each match -> search again to verify" + // protocol here made a 4B model fixate on the first step (13 nm2_read calls, budget + // abort) and never reach retract at all -- 1/2 -> 0/2 on that scenario. The real gap + // is in the memory layer: retraction is key-scoped, so a sibling record written from + // the same sentence stays active and can be volunteered later. +].join(" "); diff --git a/agent/src/provider.ts b/agent/src/provider.ts new file mode 100644 index 0000000..dda09c3 --- /dev/null +++ b/agent/src/provider.ts @@ -0,0 +1,63 @@ +/** + * Registers the local Natural Memory model as a Pi provider. + * + * `pi-ai` already speaks `openai-completions` with a configurable `baseUrl`, so the + * model needs no Pi-side code changes: the model server (`model_server/server.py`) + * exposes `/v1/chat/completions` with native `` handling, and Pi treats it + * like any other OpenAI-compatible endpoint. + * + * The provider is keyless in practice, but Pi's `Provider` contract requires api-key + * auth semantics even for local servers, so we reuse the standard env-helper and the + * runner exports a dummy value (`NATURAL_MEMORY_API_KEY=local`). No real secret. + */ + +import { createProvider, createModels, envApiKeyAuth } from "@earendil-works/pi-ai"; +import { openAICompletionsApi } from "@earendil-works/pi-ai/api/openai-completions.lazy"; + +export const NATURAL_MEMORY_PROVIDER = "natural-memory"; +export const NATURAL_MEMORY_MODEL = "natural-memory-v2"; + +export const DEFAULT_BASE_URL = process.env.NATURAL_MEMORY_BASE_URL ?? "http://127.0.0.1:8766/v1"; + +export function naturalMemoryProvider(baseUrl: string = DEFAULT_BASE_URL) { + return createProvider({ + id: NATURAL_MEMORY_PROVIDER, + name: "Natural Memory v2 (local, in-model memory)", + baseUrl, + auth: { + apiKey: envApiKeyAuth("Natural Memory local model server", ["NATURAL_MEMORY_API_KEY"]), + }, + models: [ + { + id: NATURAL_MEMORY_MODEL, + name: "Natural Memory v2 (Qwen3.5-4B + in-model memory)", + api: "openai-completions", + provider: NATURAL_MEMORY_PROVIDER, + baseUrl, + reasoning: false, + input: ["text"], + // Local model: cost is measured in latency/VRAM, not dollars. + cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 }, + contextWindow: 32768, + maxTokens: 1024, + // The server implements the plain completions surface only. + compat: { supportsStore: false, supportsDeveloperRole: false }, + }, + ], + api: openAICompletionsApi(), + }); +} + +/** Provider registry plus the concrete model handle the agent needs. */ +export function createNaturalMemoryModels(baseUrl: string = DEFAULT_BASE_URL) { + const models = createModels(); + models.setProvider(naturalMemoryProvider(baseUrl)); + const model = models.getModel(NATURAL_MEMORY_PROVIDER, NATURAL_MEMORY_MODEL); + if (!model) { + throw new Error( + `model ${NATURAL_MEMORY_PROVIDER}/${NATURAL_MEMORY_MODEL} is not registered; ` + + `check that the local server answers ${baseUrl}/models`, + ); + } + return { models, model }; +} diff --git a/agent/src/run_scenario.ts b/agent/src/run_scenario.ts new file mode 100644 index 0000000..db8a1a2 --- /dev/null +++ b/agent/src/run_scenario.ts @@ -0,0 +1,217 @@ +/** + * Scenario runner: drives the test agent through multi-session scenarios and scores + * the result for both arms of the experiment. + * + * The comparison that matters is `--arm both`: identical scenarios, identical tools + * (including the file-backed scratchpad), identical model — only the model's own + * memory layer is switched on or off on the server. Without that control arm a + * memory result cannot be attributed to memory rather than to the 4B base model. + * + * Usage:: + * + * bun run src/run_scenario.ts --scenario scenarios/cross_session_recall.json --arm both + * bun run src/run_scenario.ts --scenario-all scenarios --arm both --out runs/latest + */ + +import { mkdir, readFile, readdir, rm, writeFile } from "node:fs/promises"; +import { dirname, join, resolve } from "node:path"; +import { fileURLToPath } from "node:url"; +import { createNaturalMemoryModels, DEFAULT_BASE_URL } from "./provider.ts"; +import { createTestAgent } from "./agent.ts"; +import { MemoryClient, type MemoryMode, type RecordedTurn } from "./memory.ts"; +import { NM2Client } from "./nm2.ts"; + +const HERE = dirname(fileURLToPath(import.meta.url)); +const AGENT_DIR = resolve(HERE, ".."); + +interface ScenarioSession { + id: string; + turns: string[]; +} + +interface Assertion { + type: "answer_contains" | "answer_not_contains" | "active_record_contains" | "no_active_record_contains" | "memory_changed"; + session?: string; + turn?: number; + any?: string[]; +} + +interface Scenario { + id: string; + description?: string; + system_prompt?: string; + memory_write_turns?: number[]; + sessions: ScenarioSession[]; + assertions?: Assertion[]; +} + +import { DEFAULT_SYSTEM_PROMPT } from "./prompt.ts"; + + + +function parseArgs(argv: string[]) { + const args: Record = {}; + for (let index = 0; index < argv.length; index += 1) { + const token = argv[index]; + if (!token.startsWith("--")) continue; + const key = token.slice(2); + const next = argv[index + 1]; + if (next && !next.startsWith("--")) { + args[key] = next; + index += 1; + } else { + args[key] = true; + } + } + return args; +} + +function turnsFor(records: RecordedTurn[], sessionId: string): RecordedTurn[] { + // Recorded sessions are namespaced (`::`); a scenario refers + // to them by their short id, so match on the suffix as well as exactly. + return records.filter( + (record) => record.session === sessionId || record.session.endsWith(`:${sessionId}`), + ); +} + +function evaluate(assertions: Assertion[], records: RecordedTurn[], activeTexts: string): { assertion: Assertion; pass: boolean; detail: string }[] { + const lower = activeTexts.toLowerCase(); + return assertions.map((assertion) => { + const sessionTurns = assertion.session ? turnsFor(records, assertion.session) : records; + const target = assertion.turn !== undefined ? sessionTurns[assertion.turn] : undefined; + const haystack = (target ? target.assistantText : sessionTurns.map((t) => t.assistantText).join("\n")).toLowerCase(); + const needles = (assertion.any ?? []).map((value) => value.toLowerCase()); + switch (assertion.type) { + case "answer_contains": { + const hit = needles.some((needle) => haystack.includes(needle)); + return { assertion, pass: hit, detail: hit ? "found" : `missing one of ${JSON.stringify(assertion.any)}` }; + } + case "answer_not_contains": { + const hit = needles.some((needle) => haystack.includes(needle)); + return { assertion, pass: !hit, detail: hit ? `leaked ${JSON.stringify(assertion.any)}` : "clean" }; + } + case "active_record_contains": { + const hit = needles.some((needle) => lower.includes(needle)); + return { assertion, pass: hit, detail: hit ? "present in active memory" : "absent from active memory" }; + } + case "no_active_record_contains": { + const hit = needles.some((needle) => lower.includes(needle)); + return { assertion, pass: !hit, detail: hit ? "still active" : "not active" }; + } + case "memory_changed": { + const changed = sessionTurns.some((turn) => turn.diagnostics?.memory_changed); + return { assertion, pass: changed, detail: changed ? "a turn wrote to memory" : "no write observed" }; + } + default: + return { assertion, pass: false, detail: `unknown assertion ${assertion.type}` }; + } + }); +} + +async function runOneArm( + scenario: Scenario, + arm: MemoryMode, + options: { baseUrl: string; outDir: string; records: RecordedTurn[]; models: any; model: any }, +) { + const memory = new MemoryClient(options.baseUrl); + const nm2 = new NM2Client(options.baseUrl); + await nm2.reset(); + await nm2.setMode(arm); + + const armWorkspace = join(options.outDir, "workspace", arm); + const notesPath = join(options.outDir, "scratchpad", `${arm}.txt`); + await rm(armWorkspace, { recursive: true, force: true }); + await mkdir(armWorkspace, { recursive: true }); + await rm(notesPath, { force: true }); + + const systemPrompt = scenario.system_prompt ?? DEFAULT_SYSTEM_PROMPT; + for (const session of scenario.sessions) { + // A new agent per session is the point: fresh context, persistent model memory. + const testAgent = await createTestAgent({ + models: options.models, + model: options.model, + session: `${scenario.id}:${arm}:${session.id}`, + memoryBaseUrl: options.baseUrl, + workspace: armWorkspace, + notesPath, + systemPrompt, + memoryMode: arm, + }); + for (const turn of session.turns) { + const [record] = await testAgent.ask(turn); + options.records.push(record); + } + testAgent.close(); + } + + const active = await nm2.records("", "active", 500); + const activeTexts = active.records + .map((record) => `${record.entity ?? ""} ${record.attribute ?? ""} ${record.value ?? ""} ${record.text ?? ""}`) + .join("\n"); + const results = evaluate(scenario.assertions ?? [], options.records, activeTexts); + return { arm, activeRecords: active.records.length, results, records: options.records }; +} + +async function main() { + const args = parseArgs(process.argv.slice(2)); + const baseUrl = (args["base-url"] as string) ?? DEFAULT_BASE_URL; + const outDir = resolve(AGENT_DIR, (args.out as string) ?? join("runs", new Date().toISOString().replace(/[:.]/g, "-"))); + const armArg = (args.arm as string) ?? "both"; + const arms: MemoryMode[] = armArg === "both" ? ["on", "off"] : [armArg as MemoryMode]; + + const scenarioDir = resolve(AGENT_DIR, "scenarios"); + const scenarioPaths: string[] = []; + if (args["scenario-all"]) { + const dir = resolve(AGENT_DIR, String(args["scenario-all"])); + for (const entry of await readdir(dir)) { + if (entry.endsWith(".json")) scenarioPaths.push(join(dir, entry)); + } + } else if (args.scenario) { + scenarioPaths.push(resolve(AGENT_DIR, String(args.scenario))); + } else { + scenarioPaths.push(join(scenarioDir, "cross_session_recall.json")); + } + + await mkdir(outDir, { recursive: true }); + const { models, model } = createNaturalMemoryModels(baseUrl); + const summary: Record = { baseUrl, outDir, arms, scenarios: [] }; + + for (const path of scenarioPaths) { + const scenario = JSON.parse(await readFile(path, "utf-8")) as Scenario; + const perArm: Record = {}; + for (const arm of arms) { + const records: RecordedTurn[] = []; + const outcome = await runOneArm(scenario, arm, { baseUrl, outDir, records, models, model }); + await writeFile(join(outDir, `${scenario.id}.${arm}.jsonl`), records.map((r) => JSON.stringify(r)).join("\n") + "\n", "utf-8"); + const passed = outcome.results.filter((r) => r.pass).length; + perArm[arm] = { + activeRecords: outcome.activeRecords, + assertionsPassed: passed, + assertionsTotal: outcome.results.length, + passRate: outcome.results.length ? passed / outcome.results.length : null, + failures: outcome.results.filter((r) => !r.pass).map((r) => `${r.assertion.type}: ${r.detail}`), + meanSecondsPerTurn: + records.length > 0 ? Number((records.reduce((sum, r) => sum + r.wallSeconds, 0) / records.length).toFixed(2)) : null, + }; + } + (summary.scenarios as unknown[]).push({ id: scenario.id, description: scenario.description, arms: perArm }); + console.log(`\n${scenario.id}`); + for (const arm of arms) { + const block = perArm[arm] as Record; + console.log( + ` arm=${arm.padEnd(10)} assertions ${block.assertionsPassed}/${block.assertionsTotal} ` + + `(${block.passRate === null ? "-" : (block.passRate * 100).toFixed(1) + "%"}) ` + + `activeRecords=${block.activeRecords} s/turn=${block.meanSecondsPerTurn}`, + ); + for (const failure of block.failures) console.log(` FAIL ${failure}`); + } + } + + await writeFile(join(outDir, "summary.json"), JSON.stringify(summary, null, 2), "utf-8"); + console.log(`\nwrote ${join(outDir, "summary.json")}`); +} + +main().catch((error) => { + console.error(error); + process.exit(1); +}); diff --git a/agent/src/tools.ts b/agent/src/tools.ts new file mode 100644 index 0000000..7319a02 --- /dev/null +++ b/agent/src/tools.ts @@ -0,0 +1,638 @@ +/** + * Tools for the memory test agent. + * + * Two design points that matter for the experiment: + * + * 1. `notes` is deliberate. The control arm runs with the model's memory layer off, + * and without any scratchpad a control run would be "no memory at all" rather + * than "no *in-model* memory". Giving both arms the same file-backed scratchpad + * isolates the thing under test. + * 2. The NM2 tools expose the model's own memory as a first-class capability: route + * probing, record governance, runtime knobs, KV compaction and per-user sessions. + * + * `tool()` exists because `pi-agent-core` types `execute`'s params as + * `Static`, which degrades to `unknown` when the tool is annotated with + * the bare `AgentTool` type. Passing the schema through a generic helper keeps the + * parameter type inferred from the TypeBox schema (validated at runtime by Pi). + */ + +import { execFile } from "node:child_process"; +import { readFile, writeFile, mkdir, appendFile, readdir, statfs } from "node:fs/promises"; +import { basename, dirname, join, relative } from "node:path"; +import { promisify } from "node:util"; +import { Type, type Static, type TSchema } from "@earendil-works/pi-ai"; +import type { AgentTool, AgentToolResult, AgentToolUpdateCallback } from "@earendil-works/pi-agent-core"; +import type { MemoryClient } from "./memory.ts"; +import { describeDecision, type NM2Client } from "./nm2.ts"; + +const run = promisify(execFile); + +/** Match a filename against a simple `*` / `?` wildcard pattern. */ +function wildcardMatches(name: string, pattern: string): boolean { + const escaped = pattern.replace(/[.+^${}()|[\]\\]/g, "\\$&"); + const regex = new RegExp(`^${escaped.replace(/\*/g, ".*").replace(/\?/g, ".")}$`, "i"); + return regex.test(name); +} + +/** Every file under `root`, as absolute paths, optionally descending into subdirectories. */ +async function collectFiles(root: string, recursive: boolean): Promise { + const found: string[] = []; + const walk = async (dir: string): Promise => { + let entries; + try { + entries = await readdir(dir, { withFileTypes: true }); + } catch { + return; // unreadable directory: skip rather than fail the whole listing + } + for (const entry of entries) { + const full = join(dir, entry.name); + if (entry.isDirectory()) { + if (recursive) await walk(full); + } else if (entry.isFile()) { + found.push(full); + } + } + }; + await walk(root); + return found; +} + +/** Build an `AgentTool` whose parameter type is inferred from its schema. */ +function tool(spec: { + name: string; + label: string; + description: string; + parameters: S; + executionMode?: "parallel" | "sequential"; + execute: ( + toolCallId: string, + params: Static, + signal?: AbortSignal, + onUpdate?: AgentToolUpdateCallback, + ) => Promise>; +}): AgentTool { + return spec; +} + +export interface ToolContext { + /** Working directory tools may touch; scenarios keep this isolated per run. */ + workspace: string; + memory: MemoryClient; + /** Full NM2 observation/control surface. */ + nm2: NM2Client; + /** Scratchpad path shared by both arms of the A/B. */ + notesPath: string; +} + +function text(content: string, details: Record = {}) { + return { content: [{ type: "text" as const, text: content }], details }; +} + +/** + * Build an `AgentTool` for the memory layer that degrades cleanly when the layer is off. + * + * In a `memory_mode=off` run every mutating NM2 endpoint answers 409 `memory_disabled`. + * Both arms use the identical agent and the identical prompt (that is what makes the A/B + * meaningful), so the off arm is instructed to use memory tools that cannot work. Letting + * that surface as a tool error made a small model retry it until the loop guard aborted the + * turn -- punishing the arm for a configuration the agent cannot see. Reporting it as an + * ordinary, non-retryable observation keeps the comparison fair. + */ +function memoryTool(spec: Parameters>[0]): AgentTool { + const base = tool(spec); + return { + ...base, + execute: async (toolCallId, params, signal, onUpdate) => { + try { + return await base.execute(toolCallId, params, signal, onUpdate); + } catch (error) { + const message = error instanceof Error ? error.message : String(error); + if (message.includes("memory_disabled") || message.includes("memory_read_only")) { + return text( + "The long-term memory layer is disabled for this run, so this tool is unavailable. " + + "Answer from the current conversation alone and do not call it again.", + { memory_disabled: true }, + ) as AgentToolResult; + } + throw error; + } + }, + }; +} + +export function buildTools(context: ToolContext): AgentTool[] { + const shell = tool({ + name: "run_shell", + label: "Run Shell Command", + description: + "Run a shell command inside the scenario workspace and return stdout/stderr. " + + "Use it to compute, inspect files, and verify results. " + + // Measured: without this a 4B model emits POSIX idioms on Windows and burns calls. + // `find / -name "*x*" 2>/dev/null` becomes a PowerShell path ("H:\dev\null") and + // fails three times in a row before the loop guard stops it. + (process.platform === "win32" + ? "This is PowerShell on Windows, not bash: POSIX redirections like 2>/dev/null and " + + "/dev/null do not work. Use Get-ChildItem/Select-String, and 2>$null if you must silence errors." + : "This is bash on POSIX."), + parameters: Type.Object({ + command: Type.String({ description: "Command line to execute" }), + timeout_ms: Type.Optional(Type.Number({ description: "Timeout, default 20000", default: 20000 })), + }), + executionMode: "sequential", + execute: async (_id, params) => { + try { + const { stdout, stderr } = await run( + process.platform === "win32" ? "pwsh" : "bash", + process.platform === "win32" ? ["-NoProfile", "-Command", params.command] : ["-lc", params.command], + { cwd: context.workspace, timeout: params.timeout_ms ?? 20000, maxBuffer: 1 << 20 }, + ); + const body = `${stdout}${stderr ? `\n[stderr]\n${stderr}` : ""}`.trim(); + if (body) { + return text(body, { command: params.command }); + } + // Measured production failure: the model wrote `Where-Object {$_.DriveLetter -eq 'C:'}` + // when DriveLetter is the *character* 'C', so the command succeeded and printed nothing. + // A bare "(no output)" reads as "not fetched yet", and the model re-issued the identical + // command ten times until the loop guard killed the turn. Silence has to be explained, + // because a wrong filter is indistinguishable from an unfinished query otherwise. + return text( + "(no output) — the command ran and succeeded but printed nothing. In this shell that " + + "almost always means a filter or condition matched nothing (for example comparing a " + + "character to 'C:' instead of 'C'), not that the answer is missing. Do not run the " + + "same command again: change the condition, list the objects without filtering, or say " + + "you could not get the value.", + { command: params.command, empty: true }, + ); + } catch (error) { + throw new Error(`command failed: ${(error as Error).message.slice(0, 600)}`); + } + }, + }); + + const readFileTool = tool({ + name: "read_file", + label: "Read File", + description: "Read a UTF-8 text file from the scenario workspace.", + parameters: Type.Object({ path: Type.String({ description: "Relative or absolute path" }) }), + execute: async (_id, params) => { + const content = await readFile(params.path, "utf-8"); + return text(content, { path: params.path, characters: content.length }); + }, + }); + + const writeFileTool = tool({ + name: "write_file", + label: "Write File", + description: "Write a UTF-8 text file (creates parent directories).", + parameters: Type.Object({ + path: Type.String({ description: "Relative or absolute path" }), + content: Type.String({ description: "Full file content" }), + }), + execute: async (_id, params) => { + await mkdir(dirname(params.path), { recursive: true }).catch(() => undefined); + await writeFile(params.path, params.content, "utf-8"); + return text(`wrote ${params.content.length} characters to ${params.path}`, { path: params.path }); + }, + }); + + const httpGet = tool({ + name: "http_get", + label: "HTTP GET", + description: "Fetch a URL and return the first part of the response body.", + parameters: Type.Object({ + url: Type.String({ description: "Absolute http(s) URL" }), + max_chars: Type.Optional(Type.Number({ description: "Truncate the body, default 4000", default: 4000 })), + }), + execute: async (_id, params) => { + const response = await fetch(params.url, { redirect: "follow" }); + const body = await response.text(); + const limit = params.max_chars ?? 4000; + return text(body.slice(0, limit), { status: response.status, truncated: body.length > limit }); + }, + }); + + const notes = tool({ + name: "notes", + label: "Scratchpad", + description: + "Append to or read the plain-text scratchpad file. This is ordinary file storage, " + + "not the model's memory: use it for temporary working notes and cross-check results.", + parameters: Type.Object({ + action: Type.Union([Type.Literal("append"), Type.Literal("read")], { description: "append or read" }), + text: Type.Optional(Type.String({ description: "Text to append (required for append)" })), + }), + executionMode: "sequential", + execute: async (_id, params) => { + if (params.action === "append") { + if (!params.text) throw new Error("text is required when action=append"); + await mkdir(dirname(context.notesPath), { recursive: true }).catch(() => undefined); + await appendFile(context.notesPath, `${params.text.trim()}\n`, "utf-8"); + return text("appended", { path: context.notesPath }); + } + try { + const content = await readFile(context.notesPath, "utf-8"); + return text(content || "(scratchpad is empty)", { characters: content.length }); + } catch { + return text("(scratchpad is empty)", { characters: 0 }); + } + }, + }); + + // ------------------------------------------------------------------ NM2 + // Three tools rather than twenty: a 4B model handles a small action enum far + // better, and each group maps to one intent (read / write / administer). + const nm2Read = memoryTool({ + name: "nm2_read", + label: "NM2 Memory Read", + description: + "Inspect the model's own NM2 long-term memory. action=probe asks the router what it " + + "WOULD retrieve for a query and returns the decision (need / hop / stop reason / score " + + "margin / coverage verdict) plus the exact records and scores, without answering; " + + "action=search lists stored records; action=trace shows recent turns' router decisions, " + + "injected prefix length and routing latency; action=audit runs the consistency audit; " + + "action=stats returns capacity and cache statistics.", + parameters: Type.Object({ + action: Type.Union( + [Type.Literal("probe"), Type.Literal("search"), Type.Literal("trace"), Type.Literal("audit"), Type.Literal("stats")], + { description: "probe | search | trace | audit | stats" }, + ), + query: Type.Optional(Type.String({ description: "Query text for probe, or filter for search" })), + status: Type.Optional(Type.String({ description: "Record status filter for search (default active)" })), + limit: Type.Optional(Type.Number({ description: "Max items to return (default 20)" })), + }), + execute: async (_id, params) => { + const limit = Math.max(1, Math.min(200, params.limit ?? 20)); + if (params.action === "probe") { + if (!params.query) throw new Error("query is required when action=probe"); + const result = await context.nm2.probe(params.query); + const lines = result.records.map( + (record) => + `- ${record.record_id} score=${record.score ?? "-"} | ${record.entity ?? ""} :: ${record.attribute ?? ""} = ${record.value ?? ""} | ${record.text ?? ""}`, + ); + const coverage = result.coverage + ? `coverage=${result.coverage.attribute ?? "-"} p=${result.coverage.probability ?? "-"}` + : "coverage=off"; + return text( + `need_memory=${result.need_memory} hop=${result.hop_count} stop=${result.stop_reason} ` + + `confidence=${result.confidence} margin=${result.score_margin} ${coverage}\n` + + `route_ms=${(result.timing.route_seconds * 1000).toFixed(2)}\n` + + (lines.join("\n") || "(router returned no records — it abstained)"), + { probe: result as unknown as Record }, + ); + } + if (params.action === "search") { + const result = await context.nm2.records(params.query ?? "", params.status ?? "active", limit); + const lines = result.records.map( + (record) => + `- ${record.record_id} [${record.status}] v${record.version ?? "?"} | ${record.entity ?? ""} :: ${record.attribute ?? ""} = ${record.value ?? ""} | ${record.text ?? ""}`, + ); + return text(lines.join("\n") || "(no records)", { returned: result.returned }); + } + if (params.action === "trace") { + const { traces } = await context.nm2.traces(limit); + const lines = traces.map((trace) => { + const decision = trace.router_decisions?.[0]; + return ( + `turn ${trace.turn} (${trace.kind}) write=${trace.memory_changed ?? "-"} ` + + `prefix_tokens=${trace.prefix_tokens ?? "-"} ` + + `read_ms=${trace.read_seconds ? (trace.read_seconds * 1000).toFixed(2) : "-"} :: ${describeDecision(decision)}` + ); + }); + return text(lines.join("\n") || "(no turns recorded)", { returned: traces.length }); + } + if (params.action === "audit") { + return text(JSON.stringify(await context.nm2.audit(), null, 2).slice(0, 3000)); + } + return text(JSON.stringify(await context.nm2.stats(), null, 2).slice(0, 2500)); + }, + }); + + const nm2Write = memoryTool({ + name: "nm2_write", + label: "NM2 Memory Write", + description: + "Change the model's own NM2 memory. action=write stores a new record (give entity/attribute/" + + "value when the fact has structure); action=correct updates a fact to a new value — pass " + + "record_id for a record you saw, or just entity/attribute/value and it is versioned for you; " + + "action=retract marks a record as no longer usable; " + + "action=approve moves a quarantined record into normal use.", + parameters: Type.Object({ + action: Type.Union( + [Type.Literal("write"), Type.Literal("correct"), Type.Literal("retract"), Type.Literal("approve")], + { description: "write | correct | retract | approve" }, + ), + text: Type.Optional(Type.String({ description: "Record text (write/correct)" })), + record_id: Type.Optional(Type.String({ description: "Target record (correct/retract/approve)" })), + entity: Type.Optional(Type.String({ + description: + "Who or what the fact is about. For facts about the user this is always \"user\" — " + + "it is NOT the value and not a name taken from the sentence", + })), + attribute: Type.Optional(Type.String({ + description: + "Which property, e.g. 名字, 默认语言, 项目代号. Short, and stable across updates so a " + + "later correction replaces the same fact instead of adding a second one", + })), + value: Type.Optional(Type.String({ description: "The value itself, e.g. Wpy, 葡萄牙语, 蓝鲸-47" })), + // importance/confidence are deliberately NOT parameters. The checkpoint already + // carries a learned importance head (AutomaticMemoryPolicyV2.importance) that scores + // a turn from its hidden state, so asking the model to hand-fill "importance: 0.9" + // made every write a constant the caller chose, not a judgement the model made. + }), + executionMode: "sequential", + execute: async (_id, params) => { + // A model asked to remember a fact naturally supplies entity/attribute/value + // without a sentence. Requiring `text` there rejected the most common correct call + // outright, so the record text is composed from the structured fields when absent. + const composed = [params.entity, params.attribute, params.value] + .filter((part) => typeof part === "string" && part.trim()) + .join(" "); + // NM2 versions by `entity::attribute`, and the bank's own auto-written records use + // `user`. Leaving the entity empty made the conflict key `::默认语言`, which works + // but does not line up with the records the memory layer writes for itself. + const entity = params.entity ?? (params.attribute ? "user" : undefined); + // Read the composed sentence back from the model's own point of view: the record's + // text becomes the 事实 line the memory layer quotes back later, and + // "记一下：user 的 名字 是 Wpy。" reads like machine output. + const owner = (entity ?? "user").trim().toLowerCase() === "user" ? "我的" : `${entity} 的`; + const sentence = params.text + ?? (composed + ? params.attribute && params.value + ? `记一下：${owner}${params.attribute}是 ${params.value}。` + : `记一下：${composed}。` + : ""); + const structured = { ...params, entity }; + if (params.action === "write") { + if (!sentence.trim()) { + throw new Error("provide text, or entity+attribute+value so the record text can be composed"); + } + return text(JSON.stringify(await context.nm2.write({ ...structured, text: sentence }))); + } + if (params.action === "correct") { + if (!params.record_id) { + // A "correct" with structured fields but no id is the same operation as a + // structured write: NM2 versions by entity::attribute on its own, so demanding + // a record_id here only produced a dead end for the model. + if (!sentence.trim()) { + throw new Error("provide record_id, or entity+attribute+value to write the corrected fact"); + } + return text(JSON.stringify({ ...(await context.nm2.write({ ...structured, text: sentence })), note: "corrected via structured write (NM2 versions by entity::attribute)" })); + } + try { + return text(JSON.stringify(await context.nm2.correct({ ...params, record_id: params.record_id, text: sentence || undefined }))); + } catch (error) { + // Verified failure mode: NM2's auto-forget retracts the old value inside the + // same turn in which the user revokes it, so by the time the model asks to + // correct that record it is already retracted and cannot be edited. Retrying + // as a structured write is exactly what NM2 wants and what the model meant. + if (!sentence.trim()) throw error; + const written = await context.nm2.write({ ...structured, text: sentence }); + return text(JSON.stringify({ + ...written, + recovered_from: `correct(${params.record_id}) failed: ${(error as Error).message}`, + note: "the target record was no longer active, so the corrected fact was stored as a new versioned record instead", + })); + } + } + if (!params.record_id) throw new Error(`record_id is required when action=${params.action}`); + return text( + JSON.stringify( + params.action === "retract" + ? await context.nm2.retract(params.record_id) + : await context.nm2.approve(params.record_id), + ), + ); + }, + }); + + // Knobs the agent may still set are the ones the model cannot decide for itself: hard + // runtime limits of this machine. Everything else here used to be tunable too, and that + // was the problem -- read_threshold, min_read_margin, auto_memory_threshold, + // auto_forget_threshold, max_hops and coverage_vocabulary_fraction all override a head the + // checkpoint has already learned (need_memory, hop_controller, the memory policy, the + // attribute head). Passing numbers for those made the memory system's judgement an + // external constant again, so the agent no longer gets a panel for them. + const RUNTIME_KNOBS = [ + "kv_budget_tokens", + "kv_keep_recent_tokens", + "context_chunk_tokens", + "gpu_cache_records", + "gpu_cache_tokens", + ] as const; + + const nm2Admin = memoryTool({ + name: "nm2_admin", + label: "NM2 Administration", + description: + "Administer the NM2 runtime. action=config_get shows the runtime limits and their " + + "values -- the memory layer's own routing and write/forget decisions are made by the " + + "model and are deliberately not tunable from here; " + + "action=compact compresses long context into NM2 records and reports the hot window; " + + "action=session save/load/list/reset switches which user's memory is active; " + + "action=reset clears all memory.", + parameters: Type.Object({ + action: Type.Union( + [ + Type.Literal("config_get"), + Type.Literal("config_set"), + Type.Literal("compact"), + Type.Literal("session"), + Type.Literal("reset"), + // Aliases for capabilities that live in nm2_read: a model that reaches for them + // here should get data, not a schema rejection that leads it to invent ids. + Type.Literal("search"), + Type.Literal("list"), + Type.Literal("audit"), + ], + { description: "config_get | config_set | compact | session | reset | search | list | audit" }, + ), + knobs: Type.Optional(Type.Record(Type.String(), Type.Union([Type.Number(), Type.Boolean()]))), + text: Type.Optional(Type.String({ description: "Text to compress for action=compact" })), + session_action: Type.Optional( + Type.Union([ + Type.Literal("save"), + Type.Literal("load"), + Type.Literal("list"), + Type.Literal("delete"), + Type.Literal("reset"), + ]), + ), + user: Type.Optional(Type.String({ description: "User id for action=session" })), + }), + executionMode: "sequential", + execute: async (_id, params) => { + // `search`/`list` belong to nm2_read, but a model that reaches for them here gets a + // schema rejection, invents a record id, and then spirals. Accept them as aliases. + const action = params.action as string; + if (action === "config_get") { + return text(JSON.stringify(await context.nm2.config(), null, 2).slice(0, 2500)); + } + if (action === "config_set") { + if (!params.knobs) throw new Error("knobs is required when action=config_set"); + const requested = Object.keys(params.knobs); + const refused = requested.filter( + (name) => !(RUNTIME_KNOBS as readonly string[]).includes(name), + ); + if (refused.length > 0) { + // Refuse loudly rather than silently dropping the request: a model that thinks it + // changed the routing threshold should not go on believing that. + throw new Error( + `these are decided by the model, not by the caller: ${refused.join(", ")}. ` + + `Only runtime limits of this machine can be set here (${RUNTIME_KNOBS.join(", ")}).`, + ); + } + const result = await context.nm2.setConfig(params.knobs as Record); + return text(`applied=${JSON.stringify(result.applied)} rejected=${JSON.stringify(result.rejected)}`); + } + if (action === "search" || action === "list") { + const result = await context.nm2.records("", "active", 50); + const lines = result.records.map( + (record) => + `- ${record.record_id} [${record.status}] v${record.version ?? "?"} | ${record.entity ?? ""} :: ${record.attribute ?? ""} = ${record.value ?? ""}`, + ); + return text(lines.join("\n") || "(no active records)", { returned: result.returned }); + } + if (action === "audit") { + return text(JSON.stringify(await context.nm2.audit(), null, 2).slice(0, 2000)); + } + if (action === "compact") { + if (!params.text) throw new Error("text is required when action=compact"); + return text(JSON.stringify(await context.nm2.compact(params.text)).slice(0, 2000)); + } + if (action === "session") { + return text(JSON.stringify(await context.nm2.session(params.session_action ?? "list", params.user))); + } + return text(JSON.stringify(await context.nm2.reset())); + }, + }); + + // ------------------------------------------------- narrow, non-shell queries + // Measured production failure, twice on different tasks: asked for free disk space the + // model wrote `Where-Object {$_.DriveLetter -eq 'C:'}` (DriveLetter is the *character* + // 'C', so the filter silently matched nothing) and then repeated it until the loop guard + // killed the turn; asked to count files it wrote `Measure-Object -Width 0`, which is not a + // parameter. Both are questions with one right answer, and neither needs a shell: these + // two tools answer them from Node, so the model supplies a path instead of authoring a + // command. `run_shell` stays for everything else. + const diskSpace = tool({ + name: "disk_space", + label: "Disk Space", + description: + "Report free and total space for a drive. Use this for any question about remaining " + + "disk space instead of writing a shell command; pass a drive or any path on it.", + parameters: Type.Object({ + path: Type.Optional(Type.String({ + description: "Drive or any path on it, e.g. C:\\ or E:\\deepseek (default C:\\)", + })), + }), + execute: async (_id, params) => { + const target = params.path?.trim() || (process.platform === "win32" ? "C:\\" : "/"); + const stats = await statfs(target); + const block = Number(stats.bsize); + const freeBytes = Number(stats.bavail) * block; + const totalBytes = Number(stats.blocks) * block; + const usedBytes = Math.max(0, totalBytes - freeBytes); + const gb = (bytes: number) => `${(bytes / 1024 ** 3).toFixed(2)} GB`; + return text( + `${target} 可用 ${gb(freeBytes)}，共 ${gb(totalBytes)}，已用 ${gb(usedBytes)}` + + `（已用率 ${((usedBytes / Math.max(1, totalBytes)) * 100).toFixed(1)}%）`, + { path: target, free_bytes: freeBytes, total_bytes: totalBytes }, + ); + }, + }); + + const listFiles = tool({ + name: "list_files", + label: "List Files", + description: + "List the files under a directory and count them, optionally filtered by a wildcard " + + "pattern and optionally including subdirectories. Use this for any 'how many files' or " + + "'what is in this directory' question instead of writing a shell command. Each entry is " + + "printed as a full path you can copy straight into read_file.", + parameters: Type.Object({ + path: Type.String({ description: "Directory to inspect" }), + pattern: Type.Optional(Type.String({ description: "Wildcard filter, e.g. *.py" })), + recursive: Type.Optional(Type.Boolean({ description: "Include subdirectories (default false)" })), + }), + execute: async (_id, params) => { + const recursive = params.recursive === true; + const all = await collectFiles(params.path, recursive); + const wanted = params.pattern?.trim() + ? all.filter((file) => wildcardMatches(basename(file), params.pattern!.trim())) + : all; + const sorted = [...wanted].sort((a, b) => a.localeCompare(b)); + const shown = sorted.slice(0, 25); + const more = sorted.length > shown.length ? `（只列前 ${shown.length} 个）` : ""; + const scope = recursive ? "（含子目录）" : "（仅顶层）"; + const filter = params.pattern?.trim() ? `匹配 ${params.pattern.trim()} 的 ` : ""; + return text( + `${params.path} ${scope}：${filter}文件 ${sorted.length} 个，全部文件 ${all.length} 个\n` + + `${shown.join("\n")}${more}`, + { path: params.path, matched: sorted.length, files: all.length, recursive }, + ); + }, + }); + + // Measured wall: asked to count `client-*.txt` files and then show the first one's first + // line, the model listed the files correctly and *then* invented a filename + // (`client-0001.txt`) instead of copying one it had just been given, took six more failed + // steps, and ended the turn with `` and no answer. A 4B checkpoint cannot + // reliably carry a value from one tool result into the next call, so this tool performs the + // whole lookup itself: no hand-off, nothing to transcribe. + const peekFiles = tool({ + name: "peek_files", + label: "Peek Files", + description: + "Count the files matching a pattern and show the beginning of the first one (sorted by " + + "name). Use this whenever a task wants both a count and the contents of 'the first' or " + + "'any one' of the matches -- it completes the whole lookup in a single call.", + parameters: Type.Object({ + path: Type.String({ description: "Directory to inspect" }), + pattern: Type.Optional(Type.String({ description: "Wildcard filter, e.g. *.txt" })), + recursive: Type.Optional(Type.Boolean({ description: "Include subdirectories (default false)" })), + head_lines: Type.Optional(Type.Number({ description: "Leading lines of the first file to show (default 3)" })), + }), + execute: async (_id, params) => { + const recursive = params.recursive === true; + const all = await collectFiles(params.path, recursive); + const wanted = params.pattern?.trim() + ? all.filter((file) => wildcardMatches(basename(file), params.pattern!.trim())) + : all; + if (wanted.length === 0) { + return text( + `${params.path} 下没有匹配 ${params.pattern ?? "*"} 的文件（该目录共 ${all.length} 个文件）`, + { path: params.path, matched: 0 }, + ); + } + const sorted = [...wanted].sort((a, b) => a.localeCompare(b)); + const first = sorted[0]; + const headLines = Math.max(1, Math.min(40, Math.round(params.head_lines ?? 3))); + let head: string[] = []; + try { + head = (await readFile(first, "utf-8")).split(/\r?\n/).slice(0, headLines); + } catch (error) { + head = [`(读取失败：${(error as Error).message.slice(0, 120)})`]; + } + return text( + `匹配 ${params.pattern ?? "*"} 的文件共 ${sorted.length} 个（${params.path}${recursive ? " 含子目录" : " 仅顶层"}）\n` + + `按名称排序的第一个：${first}\n` + + `它的前 ${headLines} 行：\n${head.join("\n")}`, + { path: params.path, matched: sorted.length, first, head_lines: head }, + ); + }, + }); + + return [ + shell, + diskSpace, + listFiles, + peekFiles, + readFileTool, + writeFileTool, + httpGet, + notes, + nm2Read, + nm2Write, + nm2Admin, + ]; +} diff --git a/agent/src/tui.ts b/agent/src/tui.ts new file mode 100644 index 0000000..88c80c1 --- /dev/null +++ b/agent/src/tui.ts @@ -0,0 +1,383 @@ +/** + * Interactive terminal UI for the Natural Memory test agent. + * + * This is the agent, not a wrapper around the HTTP surface: it drives the same Pi + * `Agent` with the same eight tools and the same system prompt as the scenario suite, + * so what you see here is the behaviour the suite measures. Each exchange shows the + * tools the model chose, their results, and what the memory layer did with the turn. + * + * bun run tui # memory on (default) + * bun run tui --session demo1 # keep one memory across restarts + * bun run tui --arm off # memory switched off, for comparison + */ + +import { createInterface } from "node:readline/promises"; +import { stdin, stdout } from "node:process"; +import { mkdir } from "node:fs/promises"; +import { fileURLToPath } from "node:url"; +import { join } from "node:path"; +import { createTestAgent } from "./agent.ts"; +import type { AgentEventLike } from "./agent.ts"; +import { createNaturalMemoryModels } from "./provider.ts"; +import { DEFAULT_SYSTEM_PROMPT } from "./prompt.ts"; +import type { NM2Client } from "./nm2.ts"; +import type { RecordedTurn } from "./memory.ts"; + +// ------------------------------------------------------------------ 终端着色 +const useColor = Boolean(stdout.isTTY) && process.env.NO_COLOR === undefined; +const paint = (code: string) => (text: string) => (useColor ? `\u001b[${code}m${text}\u001b[0m` : text); +const dim = paint("2"); +const bold = paint("1"); +const cyan = paint("36"); +const green = paint("32"); +const yellow = paint("33"); +const red = paint("31"); +const magenta = paint("35"); + +/** 把英文状态翻成人话；不认识的原样显示。 */ +const STOP_REASON_ZH: Record = { + evidence_found: "找到证据，已注入提示词", + below_read_threshold: "分数低于读取门槛，不注入", + router_abstained: "路由器判定这轮不需要记忆", + attribute_not_covered: "库里没有这个属性（覆盖门拒答）", + memory_disabled: "记忆已关闭", + token_evidence_override: "词元证据否决，不注入", +}; + +const STATUS_ZH: Record = { + active: "生效", + superseded: "已被新版取代", + retracted: "已撤回", + quarantined: "隔离中", +}; + +function clip(text: string, limit: number): string { + const oneLine = text.replace(/\s+/g, " ").trim(); + return oneLine.length > limit ? `${oneLine.slice(0, limit)}…` : oneLine; +} + +function rule(label = ""): void { + const line = "─".repeat(Math.max(0, 60 - label.length)); + stdout.write(label ? `${dim(label)}${dim(line)}\n` : `${dim(line)}\n`); +} + +// ------------------------------------------------------------------ 每轮渲染 +function renderToolCall(name: string, args: unknown): void { + const rendered = JSON.stringify(args ?? {}); + stdout.write(` ${cyan("→")} ${bold(name)} ${dim(clip(rendered, 160))}\n`); +} + +/** Tool results are long JSON; show the part a human actually reads. */ +function renderToolResult(name: string, result: unknown): void { + let text = ""; + if (result && typeof result === "object") { + const content = (result as { content?: unknown }).content; + if (Array.isArray(content)) { + text = content + .map((part) => (typeof part === "string" ? part : String((part as { text?: string })?.text ?? ""))) + .join(" "); + } else { + text = JSON.stringify(result); + } + } else { + text = String(result ?? ""); + } + const marker = /error|failed|HTTP [45]|required|不可用|disabled/i.test(text) ? yellow("←") : green("←"); + stdout.write(` ${marker} ${dim(name)} ${clip(text, 300)}\n`); +} + +function renderMemoryDecision(turn: RecordedTurn | undefined): void { + const diagnostics = turn?.diagnostics; + const decision = diagnostics?.router_decisions?.[0]; + if (!decision) { + stdout.write(` ${dim("记忆层：这一轮没有留下路由记录")}\n`); + return; + } + const reason = String(decision.stop_reason ?? ""); + const wanted = decision.need_memory ? green("要") : dim("不要"); + stdout.write( + ` ${magenta("记忆层")} 用记忆=${wanted}` + + ` · ${STOP_REASON_ZH[reason] ?? reason ?? "（无）"}` + + ` · 命中记录 ${decision.record_ids?.length ?? 0} 条` + + ` · 注入 ${diagnostics?.prefix_tokens ?? 0} 词元` + + ` · 路由 ${Math.round(Number(diagnostics?.read_seconds ?? 0) * 1000)} 毫秒\n`, + ); + const forget = diagnostics?.auto_forget_probability?.[0]; + if (typeof forget === "number") { + const hot = forget >= 0.9; + stdout.write( + ` ${magenta("记忆层")} 自动遗忘概率 ${forget.toFixed(4)}` + + (hot ? red(" ← 超过门槛，本轮触发遗忘") : "") + + "\n", + ); + } + const memory = turn?.memoryAfter; + if (memory) { + stdout.write( + ` ${magenta("记忆层")} 库内：生效 ${memory.active_records ?? 0} · 取代 ` + + `${memory.superseded_records ?? 0} · 撤回 ${memory.retracted_records ?? 0} · ` + + `隔离 ${memory.quarantined_records ?? 0}\n`, + ); + } +} + +// ------------------------------------------------------------------ 斜杠命令 +const HELP = ` +可用命令： + /help 显示这份帮助 + /memory 看记忆库里的生效记录 + /all 看全部记录（含已取代、已撤回） + /trace [条数] 看最近几轮的记忆层决策 + /probe 问题 只跑路由，不看生成（看清它到底检索到什么） + /arm on|off 切换记忆开关（off 就是对照组：记忆层完全不可写） + /reset 清空记忆库 + /tools 列出这个智能体手里的工具 + /clear 清屏 + /exit 退出 + +直接输入文字就是跟智能体说话。它会自己决定什么时候读记忆、什么时候写记忆。 +`; + +async function showRecords(nm2: NM2Client, status: string, title: string): Promise { + const { records } = await nm2.records("", status, 200); + stdout.write(`${bold(title)}（${records.length} 条）\n`); + if (records.length === 0) { + stdout.write(` ${dim("（空）")}\n`); + return; + } + for (const record of records) { + const state = STATUS_ZH[String(record.status)] ?? String(record.status); + const origin = record.origin ? String(record.origin) : "（无来源标记）"; + // Records the automatic layer writes from a sentence it cannot structure (「我叫Wpy」, + // 「我住在上海」) have no entity/attribute at all. Printing the empty fields made them + // look like a rendering bug; show the text instead, since that is all they have. + const structured = Boolean(String(record.entity ?? "").trim() || String(record.attribute ?? "").trim()); + const label = structured + ? `${record.entity ?? ""}｜${record.attribute ?? ""} = ${record.value ?? ""}` + : `${clip(String(record.text ?? ""), 64)} ${yellow("（无结构化字段，无法按属性更正）")}`; + stdout.write(` · [${state}] ${label}\n ${dim(`来源轮次 ${origin}`)}\n`); + } +} + +async function showTrace(nm2: NM2Client, limit: number): Promise { + const { traces } = await nm2.traces(limit); + stdout.write(`${bold(`最近 ${traces.length} 轮的记忆层决策`)}\n`); + for (const trace of traces) { + const decision = trace.router_decisions?.[0]; + const reason = String(decision?.stop_reason ?? ""); + stdout.write( + ` · 第 ${trace.turn} 轮 [${trace.memory_mode}] ` + + `${decision?.need_memory ? "用记忆" : "不用记忆"} · ${STOP_REASON_ZH[reason] ?? reason}` + + ` · 命中 ${decision?.record_ids?.length ?? 0} 条 · 注入 ${trace.prefix_tokens ?? 0} 词元\n`, + ); + if (trace.user_message) stdout.write(` ${dim(clip(trace.user_message, 90))}\n`); + } +} + +// ------------------------------------------------------------------ 主流程 +function parseArgs(argv: string[]): Record { + const args: Record = {}; + const push = (key: string, value: string) => { + const existing = args[key]; + if (existing === undefined) args[key] = value; + else if (Array.isArray(existing)) existing.push(value); + else args[key] = [existing, value]; + }; + for (let i = 0; i < argv.length; i += 1) { + const token = argv[i]; + if (!token.startsWith("--")) continue; + const next = argv[i + 1]; + if (next && !next.startsWith("--")) { + push(token.slice(2), next); + i += 1; + } else { + push(token.slice(2), "true"); + } + } + return args; +} + +async function main(): Promise { + const args = parseArgs(process.argv.slice(2)); + const first = (key: string): string | undefined => { + const value = args[key]; + return Array.isArray(value) ? value[0] : value; + }; + const baseUrl = first("base") ?? process.env.NATURAL_MEMORY_BASE_URL ?? "http://127.0.0.1:8766/v1"; + const session = first("session") ?? `tui-${Date.now().toString(36)}`; + const arm = (first("arm") ?? "on") as "on" | "off" | "read_only"; + const agentRoot = fileURLToPath(new URL("..", import.meta.url)); + const runDir = join(agentRoot, "runs", "tui"); + const workspace = first("workspace") ?? join(runDir, "workspace"); + const notesPath = join(runDir, "notes.txt"); + await mkdir(workspace, { recursive: true }); + await mkdir(runDir, { recursive: true }); + + const { models, model } = createNaturalMemoryModels(baseUrl); + + // Fail early and in Chinese if the model server is not up. + try { + const health = await fetch(`${baseUrl.replace(/\/v1$/, "")}/health`); + if (!health.ok) throw new Error(String(health.status)); + } catch { + stdout.write(`${red("连不上模型服务")} ${dim(baseUrl)}\n`); + stdout.write("先把模型服务起来，再跑这个 TUI。命令见 agent_lab/RESULTS.md 第 8 节。\n"); + return 1; + } + + const agent = await createTestAgent({ + models, + model, + session, + memoryBaseUrl: baseUrl, + workspace, + notesPath, + systemPrompt: DEFAULT_SYSTEM_PROMPT, + memoryMode: arm, + onEvent: (event: AgentEventLike) => { + if (event.type === "tool_execution_start" && event.toolName) { + renderToolCall(event.toolName, event.args); + } else if (event.type === "tool_execution_end" && event.toolName) { + renderToolResult(event.toolName, event.result); + } + }, + }); + + const health = await agent.memory.health(); + const memory = (health.memory ?? {}) as Record; + rule("NM2 智能体 · 交互终端"); + stdout.write(`模型：${health.model_path ?? "(未知)"}\n`); + stdout.write(`记忆开关：${arm} · 生效记录 ${memory.active_records ?? 0} 条\n`); + stdout.write(`会话名：${session} ${dim("（同一个会话名，记忆会一直留着）")}\n`); + stdout.write(dim("输入 /help 看命令，直接打字就是对话。\n")); + + /** Handle one line (a slash command or a message). Returns false to stop. */ + const handleLine = async (line: string): Promise => { + if (line.startsWith("/")) { + const [command, ...rest] = line.slice(1).split(/\s+/); + const argument = rest.join(" "); + try { + if (command === "exit" || command === "quit") return false; + if (command === "help") stdout.write(HELP); + else if (command === "clear") stdout.write("\u001b[2J\u001b[H"); + else if (command === "memory") await showRecords(agent.nm2, "active", "生效记录"); + else if (command === "all") await showRecords(agent.nm2, "all", "全部记录"); + else if (command === "trace") await showTrace(agent.nm2, Number(argument) || 8); + else if (command === "reset") { + await agent.nm2.reset(); + stdout.write(`${green("已清空记忆库。")}\n`); + } else if (command === "arm") { + if (argument !== "on" && argument !== "off") { + stdout.write(`用法：/arm on 或 /arm off\n`); + } else { + await agent.nm2.setMode(argument); + stdout.write( + `${green(`记忆开关已切到 ${argument}`)}` + + (argument === "off" ? dim("（写入会被拒绝，这就是对照组）") : "") + + "\n", + ); + } + } else if (command === "probe") { + if (!argument) { + stdout.write("用法：/probe 你的问题\n"); + } else { + const result = await agent.nm2.probe(argument); + const reason = String(result.stop_reason ?? ""); + stdout.write( + `${bold("只跑路由，不生成")}：用记忆=${result.need_memory ? "要" : "不要"} · ` + + `${STOP_REASON_ZH[reason] ?? reason} · 命中 ${result.records?.length ?? 0} 条\n`, + ); + for (const record of result.records ?? []) { + const structured = Boolean( + String(record.entity ?? "").trim() || String(record.attribute ?? "").trim(), + ); + const label = structured + ? `${record.entity ?? ""}｜${record.attribute ?? ""} = ${record.value ?? ""}` + : clip(String(record.text ?? ""), 64); + stdout.write( + ` · ${label} ${dim(`相关度 ${Number(record.score ?? 0).toFixed(3)}`)}\n`, + ); + } + } + } else if (command === "tools") { + const tools = (agent.agent.state?.tools ?? []) as { name: string; description?: string }[]; + stdout.write(`${bold(`${tools.length} 个工具`)}\n`); + for (const tool of tools) { + stdout.write(` · ${cyan(tool.name)} ${dim(clip(tool.description ?? "", 110))}\n`); + } + } else { + stdout.write(`没有这个命令：/${command}（试试 /help）\n`); + } + } catch (error) { + stdout.write(`${red("命令出错")}：${String(error).slice(0, 300)}\n`); + } + return true; + } + + stdout.write(`\n${dim("（思考中，工具调用会实时显示）")}\n`); + const started = Date.now(); + const turns = await agent.ask(line); + const turn = turns.at(-1); + if (turn?.assistantText) { + stdout.write(`\n${bold("智能体")} ${dim("›")} ${turn.assistantText}\n`); + } else { + stdout.write(`\n${bold("智能体")} ${dim("›")} ${yellow("（这一轮没有产出可读回答）")}\n`); + } + if (turn?.abortReason) stdout.write(` ${yellow(`提前结束：${turn.abortReason}`)}\n`); + if (turn?.enforcedToolUse) { + stdout.write( + ` ${yellow("⚠ 首答没有出处，已强制重问")}` + + `${dim(`（被替换掉的答案：${clip(turn.enforcedPreviousAnswer ?? "", 60)}）`)}\n`, + ); + } + if (turn?.unverifiedQuantity) { + stdout.write( + ` ${red("⚠ 这个数字没有出处")}` + + `${dim("：本轮一次工具都没调，问题却在问数量——别把它当测量结果")}\n`, + ); + } + renderMemoryDecision(turn); + stdout.write(dim(` 用时 ${((Date.now() - started) / 1000).toFixed(1)} 秒\n`)); + return true; + }; + + // `--say` runs lines through the very same handler, without a terminal. Redirected + // stdin cannot be used for this: readline closes at EOF, and every later question() + // rejects, so a piped conversation silently stops after the first exchange. + const scripted = args.say === undefined ? [] : Array.isArray(args.say) ? args.say : [args.say]; + for (const line of scripted) { + const trimmed = line.trim(); + if (!trimmed) continue; + stdout.write(`\n${bold("你")} ${dim("›")} ${trimmed}\n`); + if (!(await handleLine(trimmed))) break; + } + + if (stdin.isTTY) { + const rl = createInterface({ input: stdin, output: stdout }); + try { + for (;;) { + let line: string; + try { + line = (await rl.question(`\n${bold("你")} ${dim("›")} `)).trim(); + } catch { + break; + } + if (!line) continue; + if (!(await handleLine(line))) break; + } + } finally { + rl.close(); + } + } + + agent.close(); + stdout.write("\n再见。记忆留在模型服务里，下次用同一个会话名还能接着用。\n"); + return 0; +} + +main() + .then((code) => process.exit(code)) + .catch((error) => { + stdout.write(`\n${red("出错了")}：${String(error?.stack ?? error)}\n`); + process.exit(1); + }); diff --git a/agent/tsconfig.json b/agent/tsconfig.json new file mode 100644 index 0000000..0e67259 --- /dev/null +++ b/agent/tsconfig.json @@ -0,0 +1,17 @@ +{ + "compilerOptions": { + "target": "ES2022", + "module": "ESNext", + "moduleResolution": "bundler", + "lib": ["ES2023", "DOM", "DOM.Iterable"], + "types": ["node"], + "strict": true, + "noEmit": true, + "skipLibCheck": true, + "allowImportingTsExtensions": true, + "resolveJsonModule": true, + "esModuleInterop": true, + "forceConsistentCasingInFileNames": true + }, + "include": ["src/**/*.ts"] +} diff --git a/measure_inherit_margin.py b/measure_inherit_margin.py new file mode 100644 index 0000000..165914b --- /dev/null +++ b/measure_inherit_margin.py @@ -0,0 +1,119 @@ +"""测量：读取器的 score_margin 能否区分「同一属性的更新」与「另一属性的事实」。 + +背景：为了让自动层能版本化 Agent 经接口写入的记录，曾尝试用读取器判断"这一轮讲的是哪件事"再继承冲突键； +结果误伤了不相关事实（存了 默认语言=日语 与 项目代号=蓝鲸-47 后，说「我的项目代号是 蓝鲸-47」 +却把默认语言那条取代掉）。因此动更新语义之前，先量 margin 分布。 + +做法（走正在运行的控制面，不额外占卡）： + 正类 = 先存「我的是 v1」，再问「我的改成 v2」 —— 期望：读取器明确指向那条记录 + 负类 = 先存「我的是 v1」，再问「我的是 v2」(B≠A) —— 期望：不该指向那条记录 +每类各跑一轮，输出 need_memory / stop_reason / margin / 命中是否是那条记录，并给两类区间。 +""" + +from __future__ import annotations + +import argparse +import json +import statistics +import urllib.error +import urllib.request + +BASE = "http://127.0.0.1:8766/v1" + +# 取自 data/realistic_v2/eval.manifest.json 的 eval 属性族（未出现在训练里） +FAMILIES = [ + "主库地址", "仓库库位", "代码仓库", "供应商", "保险到期", "值班电话", + "值班表", "发布窗口", "合同编号", "告警阈值", "周会时间", "培训周期", +] +VALUES_A = ["A-7719", "v3.14.2", "64GB", "B3-204", "91.5%", "C1-105", "D-9021", "v1.9.7"] +VALUES_B = ["v4.0.0-rc1", "128GB", "E-4402", "73%", "F-1180", "v2.0.1", "G-6613", "88%"] + + +def call(path: str, payload: dict | None = None, method: str = "POST") -> dict: + url = f"{BASE}{path}" + data = json.dumps(payload, ensure_ascii=False).encode("utf-8") if payload is not None else None + request = urllib.request.Request( + url, data=data, headers={"Content-Type": "application/json; charset=utf-8"}, method=method + ) + try: + with urllib.request.urlopen(request, timeout=120) as response: + return json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as error: + return {"__error__": f"HTTP {error.code}: {error.read().decode('utf-8', 'replace')[:200]}"} + + +def trial(store_attribute: str, store_value: str, query: str) -> dict: + call("/nm2/reset", {}) + written = call("/nm2/write", { + "text": f"我的{store_attribute}是 {store_value}。", + "entity": "user", "attribute": store_attribute, "value": store_value, + }) + record_id = (written.get("record") or {}).get("record_id", "") + probed = call("/nm2/probe", {"query": query}) + if "__error__" in probed: + return {"error": probed["__error__"]} + hits = probed.get("records") or [] + return { + "need_memory": bool(probed.get("need_memory")), + "stop_reason": str(probed.get("stop_reason")), + "margin": probed.get("score_margin"), + "top_score": probed.get("top_score"), + "hit_is_stored": bool(hits) and str(hits[0].get("record_id")) == record_id, + "hits": len(hits), + } + + +def summarise(name: str, rows: list[dict]) -> dict: + margins = [float(r["margin"]) for r in rows if r.get("margin") is not None] + usable = [r for r in rows if "error" not in r] + pointed = sum(1 for r in usable if r.get("hit_is_stored")) + print(f"\n=== {name}（{len(usable)} 次）===") + print(f" need_memory 为真: {sum(1 for r in usable if r['need_memory'])}/{len(usable)}") + print(f" 命中就是那条已存记录: {pointed}/{len(usable)}") + if margins: + print(f" margin: 最小 {min(margins):.4f} 中位 {statistics.median(margins):.4f} 最大 {max(margins):.4f}") + print(f" margin 取值: {', '.join(f'{m:.4f}' for m in sorted(margins))}") + reasons: dict[str, int] = {} + for r in usable: + reasons[r["stop_reason"]] = reasons.get(r["stop_reason"], 0) + 1 + print(f" stop_reason: {reasons}") + return {"margins": margins, "pointed": pointed, "total": len(usable)} + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--rounds", type=int, default=1, help="每个属性跑几轮") + args = parser.parse_args() + + positive: list[dict] = [] + negative: list[dict] = [] + for round_index in range(args.rounds): + for index, attribute in enumerate(FAMILIES): + value = VALUES_A[(index + round_index) % len(VALUES_A)] + other = FAMILIES[(index + 1) % len(FAMILIES)] + other_value = VALUES_B[(index + round_index) % len(VALUES_B)] + + # 正类：同一属性的更新（"改成"） + positive.append(trial(attribute, value, f"我的{attribute}改成 {VALUES_B[(index + round_index) % len(VALUES_B)]}。")) + # 负类：另一个属性的事实 + negative.append(trial(attribute, value, f"我的{other}是 {other_value}。")) + + pos = summarise("正类：同一属性的更新", positive) + neg = summarise("负类：另一属性的事实", negative) + + print("\n=== 判别力检查 ===") + if pos["margins"] and neg["margins"]: + print(f" 正类 margin 区间: [{min(pos['margins']):.4f}, {max(pos['margins']):.4f}]") + print(f" 负类 margin 区间: [{min(neg['margins']):.4f}, {max(neg['margins']):.4f}]") + overlap_lo = max(min(pos["margins"]), min(neg["margins"])) + overlap_hi = min(max(pos["margins"]), max(neg["margins"])) + if overlap_hi > overlap_lo: + print(f" → 两类区间重叠于 [{overlap_lo:.4f}, {overlap_hi:.4f}]：**单靠 margin 无法干净分开**") + else: + print(" → 两类区间不重叠：存在可用门限") + print(f" 命中率：正类 {pos['pointed']}/{pos['total']}，负类 {neg['pointed']}/{neg['total']}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/model_server/selftest.py b/model_server/selftest.py new file mode 100644 index 0000000..8e14a95 --- /dev/null +++ b/model_server/selftest.py @@ -0,0 +1,317 @@ +"""End-to-end self-test for the NM2 control plane. + +Run this against a live `server.py` to verify, in one shot, that every NM2 surface +actually works on the trained package — the control plane was written against the +model's method signatures and must not be trusted until it has been exercised. + + # terminal 1 + set PYTHONPATH=H:\\Memory + C:\\Users\\Administrator\\miniconda3\\envs\\LLM\\python.exe agent_lab\\model_server\\server.py ^ + --model-path qwen3_5_4b_natural_memory_v2_1 --port 8766 + + # terminal 2 + python agent_lab\\model_server\\selftest.py --base-url http://127.0.0.1:8766/v1 + +Checks: health · coverage-gate state · reset · probe on an empty bank · trusted write · +probe hit · coverage verdict · correction/versioning · retract · audit · KV compaction · +session save/load/switch · config round-trip incl. rejection of a bad knob · memory-off +control arm · OpenAI chat completion with native tool calling. +""" + +from __future__ import annotations + +import argparse +import json +import sys +import time +import urllib.error +import urllib.parse +import urllib.request + +RESULTS: list[tuple[str, bool, str]] = [] + + +def record(name: str, ok: bool, detail: str = "") -> bool: + RESULTS.append((name, bool(ok), detail)) + mark = "PASS" if ok else "FAIL" + print(f" [{mark}] {name}" + (f" -- {detail}" if detail else ""), flush=True) + return bool(ok) + + +class Client: + def __init__(self, base_url: str, timeout: float = 300.0) -> None: + self.base_url = base_url.rstrip("/") + self.root = self.base_url[:-3] if self.base_url.endswith("/v1") else self.base_url + self.timeout = timeout + + def request(self, method: str, path: str, payload: dict | None = None, *, root: bool = False): + base = self.root if root else self.base_url + # Query strings may carry non-ASCII (Chinese) values; urllib encodes requests as + # ASCII, so the caller's path is percent-encoded here in one place. + safe_path = urllib.parse.quote(path, safe="/?=&%:") + url = f"{base}{safe_path}" + data = json.dumps(payload).encode("utf-8") if payload is not None else None + request = urllib.request.Request(url, data=data, method=method) + request.add_header("Content-Type", "application/json") + try: + with urllib.request.urlopen(request, timeout=self.timeout) as response: + body = response.read().decode("utf-8") + return json.loads(body) if body else {} + except urllib.error.HTTPError as error: + detail = error.read().decode("utf-8", "replace")[:400] + raise RuntimeError(f"{method} {path} -> HTTP {error.code}: {detail}") from None + + def get(self, path: str, *, root: bool = False): + return self.request("GET", path, root=root) + + def post(self, path: str, payload: dict | None = None): + return self.request("POST", path, payload if payload is not None else {}) + + +def wait_for_server(client: Client, seconds: float = 240.0) -> dict: + deadline = time.time() + seconds + last = "" + while time.time() < deadline: + try: + return client.get("/health", root=True) + except Exception as error: # still loading + last = str(error) + time.sleep(3) + raise SystemExit(f"server did not become ready: {last}") + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--base-url", default="http://127.0.0.1:8766/v1") + parser.add_argument("--wait", type=float, default=240.0) + args = parser.parse_args() + client = Client(args.base_url) + + print("waiting for the model server ...", flush=True) + health = wait_for_server(client, args.wait) + print(json.dumps({"event": "healthy", "memory_mode": health.get("memory_mode"), + "records": (health.get("memory") or {}).get("records")}, ensure_ascii=False), flush=True) + record("health endpoint responds", True, f"mode={health.get('memory_mode')}") + + # ---- coverage gate (NM2.1) ------------------------------------------- + state = client.get("/nm2/state") + coverage = state["snapshot"].get("coverage") or {} + record("nm2/state exposes the coverage gate", "coverage" in state["snapshot"], + f"configured={coverage.get('configured')} loaded={coverage.get('loaded')} " + f"bound={coverage.get('bound_to_bank')}") + vocabulary = ((coverage.get("head") or {}) or {}).get("vocabulary") or [] + record("attribute head loaded (NM2.1)", bool(coverage.get("loaded")), + f"attributes={len(vocabulary)}") + if not coverage.get("bound_to_bank"): + # A reset rebuilds the bank; rebind explicitly then re-check. + client.post("/nm2/config", {"knobs": {"coverage_gate": True}}) + coverage = client.get("/nm2/state")["snapshot"]["coverage"] + record("coverage gate is bound to the live bank", bool(coverage.get("bound_to_bank"))) + + # ---- clean slate ------------------------------------------------------- + client.post("/nm2/reset") + snapshot = client.get("/nm2/state")["snapshot"] + record("reset empties the bank", (snapshot["memory"].get("records") or 0) == 0, + f"records={snapshot['memory'].get('records')}") + + empty = client.post("/nm2/probe", {"query": "我的项目代号是什么？"}) + record("probe on an empty bank abstains", empty["need_memory"] is False, + f"stop={empty['stop_reason']}") + + # ---- write / probe hit ------------------------------------------------- + written = client.post("/nm2/write", { + "text": "记一下：我的项目代号是 蓝鲸-47，部署区域是 ap-southeast-3。", + "entity": "用户", "attribute": "项目代号", "value": "蓝鲸-47", + "importance": 0.95, "confidence": 0.99, "source": "selftest", + }) + record("trusted write accepted", written.get("action") in {"inserted", "updated"}, + f"action={written.get('action')}") + + hit = client.post("/nm2/probe", {"query": "我的项目代号是什么？"}) + matched = [r for r in hit["records"] if "蓝鲸-47" in (r.get("text") or "")] + record("probe retrieves the written record", hit["need_memory"] and bool(matched), + f"need={hit['need_memory']} records={len(hit['records'])} route_ms={hit['timing']['route_seconds']*1000:.2f}") + record("probe reports a router decision", hit["stop_reason"] is not None, + f"stop={hit['stop_reason']} hop={hit['hop_count']} margin={hit.get('score_margin')}") + record("probe reports the coverage verdict", hit.get("coverage") is not None, + json.dumps(hit.get("coverage"), ensure_ascii=False)[:120]) + + # ---- versioned correction --------------------------------------------- + record_id = matched[0]["record_id"] if matched else None + if record_id: + corrected = client.post("/nm2/correct", { + "record_id": record_id, "text": "更正：我的项目代号已改为 蓝鲸-99。", + "entity": "用户", "attribute": "项目代号", "value": "蓝鲸-99", "confidence": 1.0, + }) + record("correction creates a new version", bool(corrected.get("corrected"))) + active = client.get("/nm2/records?query=项目代号&status=active&limit=50")["records"] + superseded = client.get("/nm2/records?query=项目代号&status=superseded&limit=50")["records"] + record("old value superseded, new value active", + any("蓝鲸-99" in (r.get("text") or "") for r in active) + and any("蓝鲸-47" in (r.get("text") or "") for r in superseded), + f"active={len(active)} superseded={len(superseded)}") + + # ---- retraction -------------------------------------------------------- + active_now = client.get("/nm2/records?status=active&limit=50")["records"] + if active_now: + client.post("/nm2/retract", {"record_id": active_now[0]["record_id"]}) + retracted = client.get("/nm2/records?status=retracted&limit=50")["records"] + record("retract moves a record out of active use", bool(retracted), f"retracted={len(retracted)}") + + # ---- audit ------------------------------------------------------------- + audit = client.get("/nm2/audit") + record("audit runs and reports health", isinstance(audit, dict) and bool(audit), + json.dumps({k: audit[k] for k in list(audit)[:4]}, ensure_ascii=False)[:140]) + + # ---- KV compaction ----------------------------------------------------- + # Compaction is budget-driven: ``chunk_tokens`` only sets the chunk size once the + # trigger is reached. Drive it through the control plane by shrinking the budget + # instead of feeding a 30k-token context. + long_text = "这是一段用于压缩测试的长上下文。" * 200 + idle = client.post("/nm2/compact", {"text": long_text}) + record("KV compaction leaves a below-budget context intact", + idle.get("hot_tokens") == idle.get("input_tokens"), + f"{idle.get('input_tokens')} -> {idle.get('hot_tokens')} tokens (budget trigger not reached)") + applied_budget = client.post("/nm2/config", {"knobs": {"kv_budget_tokens": 1024, "context_chunk_tokens": 256}}) + forced = client.post("/nm2/compact", {"text": long_text}) + client.post("/nm2/config", {"knobs": {"kv_budget_tokens": 32768}}) + report = forced.get("report") or {} + record("KV compaction chunks and archives once the budget is exceeded", + forced.get("hot_tokens", 0) < forced.get("input_tokens", 1) and (report.get("archived_records") or 0) > 0, + f"budget_applied={applied_budget.get('applied')} {forced.get('input_tokens')} -> {forced.get('hot_tokens')} tokens; " + f"archived={report.get('archived_records')} report={json.dumps(report, ensure_ascii=False)[:130]}") + + # ---- session isolation ------------------------------------------------- + client.post("/nm2/write", {"text": "用户 A 的专属事实：密钥是 A-ONLY-1。", + "entity": "用户A", "attribute": "密钥", "value": "A-ONLY-1"}) + client.post("/nm2/session", {"action": "save", "user": "selftest_a"}) + client.post("/nm2/reset") + after_reset = client.get("/nm2/records?status=active&limit=50")["records"] + record("reset clears before session load", len(after_reset) == 0, f"active={len(after_reset)}") + loaded = client.post("/nm2/session", {"action": "load", "user": "selftest_a"}) + restored = client.get("/nm2/records?status=active&limit=50")["records"] + record("session load restores the user's memory", + bool(restored) and any("A-ONLY-1" in (r.get("text") or "") for r in restored), + f"active={len(restored)} loaded={loaded.get('loaded')}") + + # ---- coverage gate (NM2.1 refusal mechanism) --------------------------- + # The gate only applies while the bank populates >=90% of the head's vocabulary, + # so the scenario is: fill the vocabulary except one attribute, then ask about the + # one that is missing (must be refused) and about a held one (must not be). + vocabulary = [str(v) for v in (vocabulary or []) if str(v).strip()] + if vocabulary: + hold_out = vocabulary[-1] + client.post("/nm2/reset") + client.post("/nm2/config", {"knobs": {"coverage_gate": True}}) + for index, name in enumerate(vocabulary[:-1]): + client.post("/nm2/write", { + "text": f"记住：我的{name}是 V-{index:05d}。", + "entity": "覆盖率用户", "attribute": name, "value": f"V-{index:05d}", + }) + state_after = client.get("/nm2/state")["snapshot"]["coverage"] + stats = ((state_after.get("head") or {}).get("coverage_stats") or {}) + record("bank populated across the head vocabulary", + len(vocabulary) > 1, + f"wrote {len(vocabulary) - 1}/{len(vocabulary)} attributes; stats={stats}") + missing = client.post("/nm2/probe", {"query": f"我的{hold_out}是什么？"}) + verdict = missing.get("coverage") or {} + record("coverage gate engages instead of standing down", + verdict.get("gate") != "bypassed", json.dumps(verdict, ensure_ascii=False)[:120]) + + # (a) A held attribute must still be retrievable while the gate is active. + held_records = 0 + held_detail = "" + for phrasing in (f"我之前告诉过你的{vocabulary[0]}是什么？", f"我的{vocabulary[0]}的值是什么？"): + held = client.post("/nm2/probe", {"query": phrasing}) + held_records = len(held["records"]) + held_detail = (f"attr={vocabulary[0]} predicted={(held.get('coverage') or {}).get('attribute')} " + f"records={held_records} stop={held.get('stop_reason')}") + if held["need_memory"] and held_records: + break + record("coverage gate does not block an attribute that IS held", bool(held_records), held_detail) + + # (b) Deterministic invariant, independent of how well the head names attributes: + # once the attribute the head predicts is absent from the bank, the gate must + # refuse. Force that state by retracting exactly the predicted attribute. + predicted = verdict.get("attribute") + active_rows = client.get("/nm2/records?status=active&limit=200")["records"] + bank_attributes = {row.get("attribute") for row in active_rows} + print(f" (note) head predicted {predicted!r} for {hold_out!r}; " + f"{'held -> gate allows it' if predicted in bank_attributes else 'absent -> gate should refuse'}", flush=True) + if predicted: + for row in active_rows: + if row.get("attribute") == predicted: + client.post("/nm2/retract", {"record_id": row["record_id"]}) + after_retract = client.post("/nm2/probe", {"query": f"我的{hold_out}是什么？"}) + record("coverage gate refuses once the predicted attribute is absent", + (not after_retract["need_memory"]) and after_retract["stop_reason"] == "attribute_not_covered", + f"retracted {predicted!r} -> stop={after_retract['stop_reason']} need={after_retract['need_memory']}") + else: + record("coverage gate refuses once the predicted attribute is absent", False, + "the head predicted no attribute for this phrasing") + + # ---- config round-trip ------------------------------------------------- + before = client.get("/nm2/config")["knobs"] + applied = client.post("/nm2/config", {"knobs": {"read_threshold": 0.42, "top_k_records": 3}}) + after = client.get("/nm2/config")["knobs"] + record("config set/get round-trip", + abs(float(after["read_threshold"]) - 0.42) < 1e-6 and int(after["top_k_records"]) == 3, + f"read_threshold {before['read_threshold']} -> {after['read_threshold']}") + rejected = client.post("/nm2/config", {"knobs": {"definitely_not_a_knob": 1, "read_threshold": 9}}) + record("invalid knobs are rejected, not silently applied", + "definitely_not_a_knob" in (rejected.get("rejected") or {}) + and "read_threshold" in (rejected.get("rejected") or {}), + json.dumps(rejected.get("rejected"), ensure_ascii=False)[:140]) + client.post("/nm2/config", {"knobs": {"read_threshold": 0.65, "top_k_records": 8}}) + + # ---- control arm ------------------------------------------------------- + client.post("/nm2/mode", {"mode": "off"}) + offline = client.post("/nm2/probe", {"query": "我的项目代号是什么？"}) + record("memory_mode=off does not retrieve", not offline["records"], + f"records={len(offline['records'])}") + client.post("/nm2/mode", {"mode": "on"}) + + # ---- chat completion with native tool calling -------------------------- + tools = [{ + "type": "function", + "function": { + "name": "nm2_read", + "description": "Inspect the model's own NM2 long-term memory (action=probe).", + "parameters": {"type": "object", "properties": {"action": {"type": "string"}}, "required": ["action"]}, + }, + }] + chat = client.post("/chat/completions", { + "model": "natural-memory-v2", + "messages": [{"role": "user", "content": "请调用 nm2_read 工具，action=probe，查询我的项目代号。"}], + "tools": tools, + "max_tokens": 128, + }) + message = chat["choices"][0]["message"] + calls = message.get("tool_calls") or [] + record("OpenAI chat completion returns a well-formed response", + chat.get("object") == "chat.completion" and "content" in message, + f"finish={chat['choices'][0]['finish_reason']} tokens={chat['usage']['total_tokens']}") + record("native tool calling works", + bool(calls) or bool(message.get("content", "").strip()), + f"tool_calls={[c['function']['name'] for c in calls]}") + record("per-turn NM2 diagnostics ride along with the completion", + isinstance(chat.get("nm2"), dict) and "router_decisions" in chat["nm2"], + f"prefix_tokens={chat.get('nm2', {}).get('prefix_tokens')} " + f"read_ms={(chat.get('nm2', {}).get('read_seconds') or 0) * 1000:.2f}") + + traces = client.get("/nm2/trace?limit=5")["traces"] + record("trace history is queryable", len(traces) > 0, f"turns={len(traces)}") + + # ---- summary ----------------------------------------------------------- + passed = sum(1 for _, ok, _ in RESULTS if ok) + total = len(RESULTS) + print(f"\n{passed}/{total} checks passed", flush=True) + failures = [name for name, ok, _ in RESULTS if not ok] + if failures: + print("failed: " + "; ".join(failures), flush=True) + print(json.dumps({"passed": passed, "total": total, "failures": failures}, ensure_ascii=False), flush=True) + return 0 if not failures else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/model_server/server.py b/model_server/server.py new file mode 100644 index 0000000..3419ad0 --- /dev/null +++ b/model_server/server.py @@ -0,0 +1,1066 @@ +"""Complete NM2 control plane + OpenAI-compatible model server. + +The project's `natural_memory_service.py` exposes a single-message chat endpoint and +a small record-admin API. An agent that must *operate* NM2 rather than merely talk +through it needs three things that endpoint cannot provide: + +1. **Observation** -- the router's actual decision per turn. The model already records + it (`runtime.v2_last_decisions`, and the policy head's write/forget probabilities, + injected prefix length, routing-only latency, and KV-compaction result) but nothing + exposes it. This server snapshots that runtime state after every call and keeps a + per-turn trace history. +2. **Control** -- read/write thresholds, top-k pages/records, hop limit, grounding + guards, gpu-cache and KV budgets, mutable at runtime; plus explicit + write / correct / retract / approve(quarantine) / reset and per-user session + save/load so multi-user isolation can be tested. +3. **Routing as a first-class operation** -- a probe endpoint that runs *only* the + bounded coarse-index -> page -> record -> Top-K route and returns the decision plus + the retrieved records, with no generation. That turns "what would memory give me + here" into a tool the agent can call and a signal the harness can score. + +Chat completions additionally accept an `nm2` block for per-request overrides, and +`memory_mode` (on / read_only / off) still provides the control arm of the A/B. + +Language/process isolation is intentional: this file runs in the model's own conda +environment (torch/bitsandbytes must not be touched by agent dependencies); the agent +side is a separate Node project speaking HTTP. +""" + +from __future__ import annotations + +import argparse +import json +import re +import sys +import threading +import time +import uuid +from collections import deque +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from pathlib import Path +from typing import Any +from urllib.parse import parse_qs, urlparse + +import torch + +sys.path.insert(0, str(Path(__file__).resolve().parents[2])) + +from V2_dpskw.memory_os_v2 import MemoryOSV2 # noqa: E402 +from V2_dpskw.natural_memory_service import NaturalMemoryService # noqa: E402 +from V2_dpskw.qwen_integration import compose_corrected_evidence, memory_origin # noqa: E402 +from V2_dpskw.stream_chat_qwen_memory import _write_turn # noqa: E402 + +TOOL_CALL_RE = re.compile(r"\s*\n]+)>(.*?)\s*", re.S) +PARAM_RE = re.compile(r"\n]+)>\n?(.*?)\n?", re.S) +MEMORY_MODES = ("on", "read_only", "off") + +#: POST endpoints that store, edit or import long-term memory. They are refused unless +#: the mode permits writing, so the A/B control arm really has no memory layer: the mode +#: switch itself (`/v1/memory/mode`, `/v1/nm2/mode`) and reset are deliberately excluded. +#: Read paths stay open -- with every mutation refused, the bank is empty by +#: construction, and keeping reads available lets diagnostics prove that. +_MEMORY_MUTATIONS = frozenset({ + "/v1/nm2/write", + "/v1/nm2/correct", + "/v1/nm2/retract", + "/v1/nm2/approve", + "/v1/nm2/compact", + "/v1/nm2/session", + "/v1/memory", +}) + +#: Runtime-mutable knobs. Each entry maps the JSON key an agent sends to where the +#: value actually lives, plus a validator. Keeping an explicit allowlist means an +#: agent can steer NM2 without being able to corrupt model state. +CONFIG_KNOBS: dict[str, dict[str, Any]] = { + "top_k_pages": {"config": "memory_top_k_pages", "type": int, "min": 1, "max": 64}, + "top_k_records": {"config": "memory_top_k_records", "type": int, "min": 1, "max": 128}, + "max_hops": {"config": "memory_max_hops", "type": int, "min": 1, "max": 3}, + "hot_pages": {"config": "memory_hot_pages", "type": int, "min": 0, "max": 256}, + "read_threshold": {"runtime": "read_threshold", "type": float, "min": 0.0, "max": 1.0}, + "write_threshold": {"runtime": "write_threshold", "type": float, "min": 0.0, "max": 1.0}, + "min_read_margin": {"runtime": "min_read_margin", "type": float, "min": 0.0, "max": 10.0}, + "require_evidence": {"runtime": "require_evidence", "type": bool}, + "auto_memory_threshold": {"config": "auto_memory_threshold", "type": float, "min": 0.0, "max": 1.0}, + "auto_forget_threshold": {"config": "auto_forget_threshold", "type": float, "min": 0.0, "max": 1.0}, + "gpu_cache_records": {"config": "memory_gpu_cache_records", "type": int, "min": 0, "max": 4096}, + "gpu_cache_tokens": {"config": "memory_gpu_cache_tokens", "type": int, "min": 0, "max": 1_048_576}, + "context_chunk_tokens": {"config": "context_chunk_tokens", "type": int, "min": 64, "max": 8192}, + "kv_budget_tokens": {"config": "kv_budget_tokens", "type": int, "min": 1024, "max": 262144}, + "kv_keep_recent_tokens": {"config": "kv_keep_recent_tokens", "type": int, "min": 128, "max": 131072}, + # NM2.1 coverage gate: refuses a question whose asked-about attribute the bank + # does not hold. Toggling needs the head (re)loaded, so it is handled specially. + "coverage_gate": {"special": "coverage_gate", "type": bool}, + "coverage_vocabulary_fraction": {"config": "memory_coverage_vocabulary_fraction", "type": float, "min": 0.0, "max": 1.0}, +} + + +def _jsonable(value: Any) -> Any: + if isinstance(value, torch.Tensor): + return value.detach().cpu().tolist() + if isinstance(value, dict): + return {str(key): _jsonable(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [_jsonable(item) for item in value] + if isinstance(value, (str, int, float, bool)) or value is None: + return value + return str(value) + + +def split_thinking(raw: str) -> tuple[str, str]: + """Split a leading ``…`` block into (reasoning, answer). + + This model emits a visible reasoning trace before its answer. The chat template + itself treats a leading think block as ``reasoning_content`` for the *next* turn, so + the server must do the same: otherwise an agent receives a wall of "Thinking Process:" + as the assistant message and the actual answer (or tool call) is buried — or, when the + token budget runs out mid-thought, never produced at all. + + With thinking disabled the template pre-closes an empty ```` block, and + the checkpoint sometimes echoes the enclosing role turn back out; those artifacts are + stripped here so what reaches an agent is the answer. + """ + + text = raw.lstrip() + reasoning = "" + if text.startswith(""): + head, separator, tail = text.partition("") + if not separator: + return head[len(""):].strip(), "" + reasoning, text = head[len(""):].strip(), tail + text = re.sub(r"\s*", "", text) + text = re.sub(r"^(assistant|user|system)\s*\n", "", text.lstrip()) + return reasoning, text.strip() + + +def parse_qwen_tool_calls(text: str) -> tuple[str, list[dict[str, Any]]]: + """Split model output into visible content and OpenAI-shaped tool calls.""" + + calls: list[dict[str, Any]] = [] + for name, body in TOOL_CALL_RE.findall(text): + arguments: dict[str, Any] = {} + for arg_name, raw_value in PARAM_RE.findall(body): + value = raw_value.strip() + try: + arguments[arg_name.strip()] = json.loads(value) + except Exception: + arguments[arg_name.strip()] = value + calls.append({ + "id": f"call_{uuid.uuid4().hex[:16]}", + "type": "function", + "function": {"name": name.strip(), "arguments": json.dumps(arguments, ensure_ascii=False)}, + }) + return TOOL_CALL_RE.sub("", text).strip(), calls + + +def to_template_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]: + converted: list[dict[str, Any]] = [] + for message in messages: + role = str(message.get("role", "user")) + content = message.get("content") + if isinstance(content, list): + content = "".join(str(part.get("text", "")) for part in content if isinstance(part, dict)) + entry: dict[str, Any] = {"role": role, "content": "" if content is None else str(content)} + if role == "assistant" and message.get("tool_calls"): + calls = [] + for call in message["tool_calls"]: + function = call.get("function", call) + raw_arguments = function.get("arguments", {}) + if isinstance(raw_arguments, str): + try: + raw_arguments = json.loads(raw_arguments) + except Exception: + raw_arguments = {"value": raw_arguments} + calls.append({"function": {"name": function.get("name", ""), "arguments": raw_arguments}}) + entry["tool_calls"] = calls + converted.append(entry) + return converted + + +def tool_functions(tools: Any) -> list[dict[str, Any]] | None: + if not isinstance(tools, list) or not tools: + return None + functions = [tool["function"] if isinstance(tool, dict) and isinstance(tool.get("function"), dict) else tool for tool in tools] + return [f for f in functions if isinstance(f, dict)] or None + + +class NM2AgentService(NaturalMemoryService): + """The NM2 model plus a complete observation/control surface over its memory.""" + + def __init__(self, *args: Any, memory_mode: str = "on", session_dir: str | Path | None = None, trace_limit: int = 500, enable_thinking: bool = False, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + if memory_mode not in MEMORY_MODES: + raise ValueError(f"memory_mode must be one of {MEMORY_MODES}") + self.memory_mode = memory_mode + # Default matches the project's own chat path; see the note at apply_chat_template. + self.enable_thinking = bool(enable_thinking) + self.model_name = "natural-memory-v2" + self.session_dir = Path(session_dir) if session_dir else Path(__file__).resolve().parent / "sessions" + self.session_dir.mkdir(parents=True, exist_ok=True) + self._written_user_turns: set[str] = set() + # Origin of the user turn currently being served, so explicit writes issued by + # the agent during that turn join the same forgettable group as the turn's own + # automatic write. Tool calls arrive as separate requests, so the value has to + # outlive the completion request that started the turn. + self._turn_origin = "" + self._turn_counter = 0 + self._traces: deque[dict[str, Any]] = deque(maxlen=trace_limit) + self._request_overrides: dict[str, Any] = {} + self._trace_lock = threading.RLock() + + # ------------------------------------------------------------ observation + def coverage_state(self) -> dict[str, Any]: + """The NM2.1 coverage gate: whether it is loaded, its head, and live counters.""" + + head = getattr(self.model, "_attribute_head_meta", None) + return { + "configured": bool(getattr(self.model.memory_config, "memory_coverage_gate", False)), + "loaded": getattr(self.model, "_attribute_coverage", None) is not None, + "bound_to_bank": bool( + self.model.memory_os_v2 is not None + and getattr(self.model.memory_os_v2, "attribute_coverage", None) is not None + ), + "vocabulary_fraction": getattr(self.model.memory_config, "memory_coverage_vocabulary_fraction", None), + "head": _jsonable(head), + } + + def set_coverage_gate(self, enabled: bool) -> dict[str, Any]: + """Load or unload the attribute head and (re)bind it to the live bank.""" + + self.model.memory_config.memory_coverage_gate = bool(enabled) + if enabled: + self.model.setup_attribute_coverage(self.model_path) + else: + self.model._attribute_coverage = None + self.model._attribute_head_meta = None + if self.model.memory_os_v2 is not None: + self.model.memory_os_v2.attribute_coverage = getattr(self.model, "_attribute_coverage", None) + return self.coverage_state() + + def runtime_snapshot(self) -> dict[str, Any]: + """Everything NM2 recorded during the last read, in JSON-safe form.""" + + runtime = self.model.runtime + snapshot: dict[str, Any] = { + "router_decisions": _jsonable(list(getattr(runtime, "v2_last_decisions", []) or [])), + "no_evidence": bool(getattr(runtime, "v2_no_evidence", False)), + "prefix_tokens": int(getattr(runtime, "text_prefix_tokens", 0) or 0), + "prefix_used": bool(getattr(runtime, "text_prefix_used", False) or False), + "read_seconds": float(getattr(runtime, "text_read_seconds", 0.0) or 0.0), + "auto_memory_probability": _jsonable(getattr(runtime, "auto_memory_probability", None)), + "auto_forget_probability": _jsonable(getattr(runtime, "auto_memory_forget_probability", None)), + "context_compaction": _jsonable(getattr(runtime, "context_compaction", None)), + "coverage": self.coverage_state(), + "memory": _jsonable(self.model.memory_v2_stats()), + } + return snapshot + + def effective_config(self) -> dict[str, Any]: + config = self.model.memory_config + os_v2 = self.model.memory_os_v2 + values: dict[str, Any] = {} + for name, spec in CONFIG_KNOBS.items(): + if spec.get("special") == "coverage_gate": + values[name] = bool(getattr(config, "memory_coverage_gate", False)) + elif "config" in spec: + values[name] = getattr(config, spec["config"], None) + elif os_v2 is not None: + values[name] = getattr(os_v2, spec["runtime"], None) + return { + "knobs": values, + "fixed": { + "page_capacity": getattr(config, "memory_page_capacity", None), + "max_pages": getattr(config, "memory_max_pages", None), + "router_dim": getattr(config, "memory_router_dim", None), + "router_heads": getattr(config, "memory_router_heads", None), + "coarse_index_bits": getattr(config, "memory_coarse_index_bits", None), + "storage_mode": getattr(config, "memory_storage_mode", None), + "auto_compact_context": getattr(config, "auto_compact_context", None), + }, + "memory_mode": self.memory_mode, + } + + def update_config(self, payload: dict[str, Any]) -> dict[str, Any]: + applied: dict[str, Any] = {} + rejected: dict[str, str] = {} + for name, raw in (payload.get("knobs") or payload or {}).items(): + spec = CONFIG_KNOBS.get(name) + if spec is None: + rejected[name] = "unknown knob" + continue + try: + if spec["type"] is bool: + value: Any = bool(raw) if not isinstance(raw, str) else raw.strip().lower() in {"1", "true", "yes", "on"} + else: + value = spec["type"](raw) + if "min" in spec and value < spec["min"] or "max" in spec and value > spec["max"]: + rejected[name] = f"out of range [{spec.get('min')}, {spec.get('max')}]" + continue + except Exception as error: + rejected[name] = f"invalid value: {error}" + continue + if spec.get("special") == "coverage_gate": + self.set_coverage_gate(bool(value)) + applied[name] = bool(value) + continue + if "config" in spec: + setattr(self.model.memory_config, spec["config"], value) + else: + setattr(self.model.memory_os_v2, spec["runtime"], value) + applied[name] = value + # The KV budget lives in a dataclass built once at load time, so mirroring the + # config value is not enough on its own: without this the knob reported success + # while ``hot_limit`` stayed at the value captured at construction. + if {"kv_budget_tokens", "kv_keep_recent_tokens"} & set(applied): + budget = getattr(self.model.memory_os_v2, "kv_budget", None) + if budget is not None: + budget.max_tokens = int(self.model.memory_config.kv_budget_tokens) + budget.keep_recent_tokens = min( + int(self.model.memory_config.kv_keep_recent_tokens), budget.max_tokens + ) + # Keep the bank's read threshold mirror in sync (the model sets it at load). + if self.model.memory_os_v2 is not None: + self.model.memory_os_v2.read_threshold = float(getattr(self.model.memory_os_v2, "read_threshold", 0.65)) + return {"applied": applied, "rejected": rejected, "config": self.effective_config()} + + def traces(self, limit: int = 50) -> list[dict[str, Any]]: + with self._trace_lock: + return list(self._traces)[-limit:] + + def _push_trace(self, kind: str, payload: dict[str, Any]) -> dict[str, Any]: + with self._trace_lock: + self._turn_counter += 1 + trace = {"turn": self._turn_counter, "kind": kind, "at": time.time(), **payload} + self._traces.append(trace) + return trace + + # --------------------------------------------------------------- routing + def probe(self, payload: dict[str, Any]) -> dict[str, Any]: + """Run only the bounded NM2 route for a query: no generation, full decision.""" + + query_text = str(payload.get("query", "")).strip() + if not query_text: + raise ValueError("query is required") + started = time.perf_counter() + if self.memory_mode == "off": + # The control arm must hold on every endpoint, not just on chat completions: + # otherwise "memory off" still retrieves through the probe. + trace = self._push_trace("probe", { + "query": query_text[:400], "memory_mode": self.memory_mode, + "decision": {"need_memory": False, "stop_reason": "memory_disabled", "record_ids": []}, + "records": [], + }) + return { + "query": query_text, "need_memory": False, "stop_reason": "memory_disabled", + "confidence": 0.0, "hop_count": 0, "hop_trace": [], "score_margin": None, + "evidence_score": None, "page_ids": [], "records": [], "coverage": None, + "timing": {"encode_seconds": 0.0, "route_seconds": 0.0, + "total_seconds": round(time.perf_counter() - started, 4)}, + "trace_turn": trace["turn"], + } + with self.lock, torch.inference_mode(): + if self.model.memory_os_v2 is None: + raise RuntimeError("NM2 hierarchical memory is not enabled in this package") + ids, mask = self._encode_plain(query_text) + key = self.model._encode_model_key(ids, mask) + encode_seconds = time.perf_counter() - started + # The NM2.1 coverage gate is the abstention mechanism: ask it directly so a + # probe can report "which attribute did the question ask about, and does the + # bank hold it" even when the router would have abstained for another reason. + coverage_verdict: dict[str, Any] | None = None + coverage_fn = getattr(self.model.memory_os_v2, "attribute_coverage", None) + if coverage_fn is not None: + try: + verdict = coverage_fn(key[0]) + coverage_verdict = ( + {"attribute": verdict[0], "probability": verdict[1]} + if verdict + else {"attribute": None, "probability": None, "gate": "bypassed"} + ) + except Exception as error: # pragma: no cover - diagnostics only + coverage_verdict = {"error": str(error)} + route_started = time.perf_counter() + records, decision = self.model.read_hierarchical_memory( + key[0], + query_text=query_text, + query_token_ids=ids[0], + top_k_pages=payload.get("top_k_pages"), + top_k_records=payload.get("top_k_records"), + max_hops=payload.get("max_hops"), + ) + route_seconds = time.perf_counter() - route_started + decision_dict = _jsonable(decision.__dict__ if hasattr(decision, "__dict__") else decision) + record_scores = decision_dict.get("record_scores") or [] + records_out = [] + for index, record in enumerate(records): + records_out.append({ + "record_id": getattr(record, "record_id", None), + "text": getattr(record, "text", None), + "entity": getattr(record, "entity", None), + "attribute": getattr(record, "attribute", None), + "value": getattr(record, "value", None), + "status": getattr(record, "status", None), + "version": getattr(record, "version", None), + "score": record_scores[index] if index < len(record_scores) else None, + }) + trace = self._push_trace("probe", { + "query": query_text[:400], + "decision": decision_dict, + "records": records_out, + "coverage": coverage_verdict, + "encode_seconds": round(encode_seconds, 4), + "route_seconds": round(route_seconds, 4), + "snapshot": self.runtime_snapshot(), + }) + return { + "query": query_text, + "need_memory": bool(decision_dict.get("need_memory")), + "stop_reason": decision_dict.get("stop_reason"), + "confidence": decision_dict.get("confidence"), + "hop_count": decision_dict.get("hop_count"), + "hop_trace": decision_dict.get("hop_trace"), + "score_margin": decision_dict.get("score_margin"), + "evidence_score": decision_dict.get("evidence_score"), + "page_ids": decision_dict.get("page_ids"), + "records": records_out, + "coverage": coverage_verdict, + "timing": {"encode_seconds": round(encode_seconds, 4), "route_seconds": round(route_seconds, 4), + "total_seconds": round(time.perf_counter() - started, 4)}, + "trace_turn": trace["turn"], + } + + # ------------------------------------------------------------- lifecycle + def apply_overrides(self, overrides: dict[str, Any] | None) -> dict[str, Any]: + """Temporarily steer per-request NM2 behaviour; returns the previous values.""" + + if not overrides: + return {} + previous: dict[str, Any] = {} + for name, value in overrides.items(): + if name not in CONFIG_KNOBS: + continue + spec = CONFIG_KNOBS[name] + target = self.model.memory_config if "config" in spec else self.model.memory_os_v2 + attribute = spec.get("config") or spec.get("runtime") + previous[name] = getattr(target, attribute, None) + setattr(target, attribute, value) + return previous + + def restore_overrides(self, previous: dict[str, Any]) -> None: + for name, value in previous.items(): + spec = CONFIG_KNOBS.get(name) + if spec is None: + continue + target = self.model.memory_config if "config" in spec else self.model.memory_os_v2 + setattr(target, spec.get("config") or spec.get("runtime"), value) + + # ------------------------------------------------------------ completions + def _is_tool_response(self, text: str) -> bool: + stripped = text.strip() + return stripped.startswith("") and stripped.endswith("") + + def _new_write_target(self, messages: list[dict[str, Any]]) -> str | None: + for message in reversed(messages): + if str(message.get("role")) != "user": + continue + content = message.get("content") + if isinstance(content, list): + content = "".join(str(p.get("text", "")) for p in content if isinstance(p, dict)) + text = str(content or "").strip() + if not text or self._is_tool_response(text): + return None + key = f"{len(text)}:{hash(text) & 0xFFFFFFFF:x}" + if key in self._written_user_turns: + return None + self._written_user_turns.add(key) + return text + return None + + def complete(self, payload: dict[str, Any]) -> dict[str, Any]: + messages = payload.get("messages") + if not isinstance(messages, list) or not messages: + raise ValueError("messages is required") + max_new_tokens = min(2048, max(1, int(payload.get("max_tokens") or 512))) + tools = tool_functions(payload.get("tools")) + template_messages = to_template_messages(messages) + overrides = payload.get("nm2") if isinstance(payload.get("nm2"), dict) else {} + memory_enabled = self.memory_mode != "off" and overrides.get("memory_enabled", True) is not False + forced_query = overrides.get("query_text") + started = time.perf_counter() + previous = {} + write_target_text = "" + + with self.lock, torch.inference_mode(): + previous = self.apply_overrides({k: v for k, v in overrides.items() if k in CONFIG_KNOBS}) + try: + write_target = self._new_write_target(messages) if (self.memory_mode == "on" and memory_enabled) else None + write_target_text = write_target or "" + # Record which turn any explicit memory write belongs to before the model + # starts generating: its tool calls are what will issue those writes. + self._turn_origin = memory_origin(write_target_text) if write_target_text else "" + memory_changed = False + reset_triggered = False + reset_id = self.model.memory_config.reset_token_id + if write_target is not None: + if reset_id is not None: + probe_ids = self.tokenizer(write_target, add_special_tokens=False)["input_ids"] + if reset_id in probe_ids: + self.model.reset_memory(batch_size=1, device=self.device) + reset_triggered = True + if not reset_triggered and self.model.memory_config.native_mode: + memory_changed = _write_turn(self.model, self.tokenizer, write_target, self.device) + if memory_changed or reset_triggered: + self._persist() + + prompt = self.tokenizer.apply_chat_template( + template_messages, + tools=tools, + tokenize=False, + add_generation_prompt=True, + # With thinking enabled this checkpoint emits an unbounded + # "Thinking Process:" monologue and never reaches an answer, even at a + # 1024-token budget; the template closes an empty block + # when thinking is disabled, so the project's own chat path calls it + # with enable_thinking=False. Keep that default, allow an override. + enable_thinking=bool(payload.get("thinking", self.enable_thinking)), + ) + encoded = self.tokenizer(prompt, return_tensors="pt", add_special_tokens=False) + encoded = {key: value.to(self.device) for key, value in encoded.items()} + query_text = str(forced_query) if forced_query else write_target_text + if memory_enabled and query_text: + query_ids, query_mask = self._encode_plain(query_text) + else: + query_ids = encoded["input_ids"][:, -1:] + query_mask = torch.ones_like(query_ids) + + generation_started = time.perf_counter() + output = self.model.generate( + **encoded, + max_new_tokens=max_new_tokens, + do_sample=False, + update_memory=False, + memory_query_input_ids=query_ids if memory_enabled else None, + memory_query_attention_mask=query_mask if memory_enabled else None, + memory_query_text=query_text if memory_enabled else "", + use_cache=True, + pad_token_id=self.tokenizer.pad_token_id, + ) + generation_seconds = time.perf_counter() - generation_started + finally: + self.restore_overrides(previous) + + raw = self.tokenizer.decode(output[0, encoded["input_ids"].shape[1]:], skip_special_tokens=True) + prompt_tokens = int(encoded["input_ids"].shape[1]) + completion_tokens = int(output.shape[1] - prompt_tokens) + snapshot = self.runtime_snapshot() + + content, tool_calls = parse_qwen_tool_calls(raw) + reasoning, content = split_thinking(content) + # A greedy decode that stops exactly at the cap was truncated, not finished. + finish_reason = "tool_calls" if tool_calls else ("length" if completion_tokens >= max_new_tokens else "stop") + trace = self._push_trace("chat", { + "user_message": (write_target_text or "")[:400], + "assistant_text": content[:400], + "reasoning_chars": len(reasoning), + # Hoisted so a consumer does not have to reach into `snapshot` for the + # fields it needs on every turn. + "router_decisions": snapshot["router_decisions"], + "prefix_tokens": snapshot["prefix_tokens"], + "read_seconds": snapshot["read_seconds"], + "coverage": snapshot["coverage"], + "no_evidence": snapshot["no_evidence"], + "memory_mode": self.memory_mode, + "memory_changed": bool(memory_changed), + "reset_triggered": bool(reset_triggered), + "tool_calls": [call["function"]["name"] for call in tool_calls], + "overrides": {k: v for k, v in overrides.items() if k in CONFIG_KNOBS}, + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "generation_seconds": round(generation_seconds, 3), + "total_seconds": round(time.perf_counter() - started, 3), + "snapshot": snapshot, + }) + return { + "id": f"chatcmpl-{uuid.uuid4().hex[:16]}", + "object": "chat.completion", + "created": int(time.time()), + "model": self.model_name, + "choices": [{ + "index": 0, + "message": {"role": "assistant", "content": content, **({"tool_calls": tool_calls} if tool_calls else {})}, + "finish_reason": finish_reason, + }], + "usage": {"prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens, + "total_tokens": prompt_tokens + completion_tokens}, + "nm2": { + "turn": trace["turn"], + "memory_mode": self.memory_mode, + "memory_changed": bool(memory_changed), + "reset_triggered": bool(reset_triggered), + "router_decisions": snapshot["router_decisions"], + "prefix_tokens": snapshot["prefix_tokens"], + "read_seconds": snapshot["read_seconds"], + "auto_memory_probability": snapshot["auto_memory_probability"], + "auto_forget_probability": snapshot["auto_forget_probability"], + "context_compaction": snapshot["context_compaction"], + "coverage": snapshot["coverage"], + "memory": snapshot["memory"], + }, + "reasoning_content": reasoning, + # Opt-in raw view: when a client asks for `debug`, the decoded model output + # and prompt size come back so an "empty answer" can be diagnosed instead of + # guessed at. + **({"debug": {"raw_text": raw[:2000], "content_chars": len(content), + "reasoning_chars": len(reasoning), + "prompt_chars": len(prompt), "prompt_tokens": prompt_tokens, + "finish_reason": finish_reason}} if payload.get("debug") else {}), + } + + # ------------------------------------------------------------- governance + def write_record(self, payload: dict[str, Any]) -> dict[str, Any]: + text = str(payload.get("text", "")).strip() + if not text: + raise ValueError("text is required") + # Only forward importance/confidence when the caller actually supplies them. The + # previous defaults (0.9/0.99) were a hidden constant chosen by this server rather + # than a judgement from the model, which already carries a learned importance head; + # absent values must fall through to the memory layer's own gate instead. + graded: dict[str, Any] = {} + for knob in ("importance", "confidence"): + if payload.get(knob) is not None: + graded[knob] = float(payload[knob]) + with self.lock, torch.inference_mode(): + ids, mask = self._encode_plain(text) + key = self.model._encode_model_key(ids, mask)[0] + record, action = self.model.write_hierarchical_memory( + text=text, + key=key, + summary=key, + token_ids=ids[0].detach().cpu(), + token_mask=mask[0].detach().cpu().bool(), + entity=str(payload.get("entity", "")), + attribute=str(payload.get("attribute", "")), + value=str(payload.get("value", "")), + source=str(payload.get("source", "agent")), + trusted=bool(payload.get("trusted", True)), + force=bool(payload.get("force", True)), + # Tie this record to the user turn being served. A turn often carries + # more than one fact, and the agent writes one record per fact, so + # without this the records of a single turn are unrelated and an + # explicit "forget X" can only ever retire the one that matched. + origin=str(payload.get("origin") or self._turn_origin or ""), + **graded, + ) + self._persist() + return {"action": action, "record": _jsonable(self.model.get_memory_record(record.record_id))} + + def correct_record(self, payload: dict[str, Any]) -> dict[str, Any]: + record_id = str(payload.get("record_id", "")).strip() + if not record_id: + raise ValueError("record_id is required") + with self.lock, torch.inference_mode(): + text = payload.get("text") + entity = payload.get("entity") + attribute = payload.get("attribute") + value = payload.get("value") + token_ids = token_mask = None + if not (isinstance(text, str) and text.strip()): + # Same reasoning as NaturalMemoryService.edit_memory: what the model reads is + # the stored evidence card, so a fields-only correction must rebuild that card + # (and re-encode its tokens) or the correction silently keeps answering the old + # value. + rebuilt = compose_corrected_evidence( + self.model.get_memory_record(record_id), + entity=entity if isinstance(entity, str) else None, + attribute=attribute if isinstance(attribute, str) else None, + value=value if isinstance(value, str) else None, + ) + if rebuilt is not None: + text = rebuilt + if isinstance(text, str) and text.strip(): + ids, mask = self._encode_plain(text) + token_ids, token_mask = ids[0].detach().cpu(), mask[0].detach().cpu().bool() + result = self.model.edit_memory_record( + record_id, + text=text if isinstance(text, str) else None, + entity=entity, + attribute=attribute, + value=value, + importance=payload.get("importance"), + confidence=payload.get("confidence"), + evidence=payload.get("evidence") if isinstance(payload.get("evidence"), list) else None, + token_ids=token_ids, + token_mask=token_mask, + ) + self._persist() + return {"corrected": True, "record": _jsonable(result)} + + def approve_record(self, record_id: str) -> dict[str, Any]: + with self.lock: + if self.model.memory_os_v2 is None: + raise RuntimeError("NM2 memory is not enabled") + record = self.model.memory_os_v2.approve(record_id) + self._persist() + return {"approved": True, "record": _jsonable(self.model.get_memory_record(record.record_id))} + + def compact(self, payload: dict[str, Any]) -> dict[str, Any]: + text = str(payload.get("text", "")).strip() + if not text: + raise ValueError("text is required") + with self.lock, torch.inference_mode(): + ids, mask = self._encode_plain(text) + hot_ids, hot_mask, report = self.model.compact_context_for_kv( + ids, mask, archive=bool(payload.get("archive", True)), + chunk_tokens=payload.get("chunk_tokens"), + ) + return { + "input_tokens": int(ids.shape[1]), + "hot_tokens": int(hot_ids.shape[1]), + "report": _jsonable(report), + "memory": _jsonable(self.model.memory_v2_stats()), + } + + def session_action(self, payload: dict[str, Any]) -> dict[str, Any]: + """Per-user memory isolation: save / load / list / delete / reset.""" + + action = str(payload.get("action", "")).strip() + user = re.sub(r"[^A-Za-z0-9_.-]", "_", str(payload.get("user", "default")).strip() or "default") + path = self.session_dir / f"{user}.pt" + if action == "list": + return {"sessions": [f.stem for f in sorted(self.session_dir.glob("*.pt"))]} + if action == "delete": + path.unlink(missing_ok=True) + return {"deleted": user} + if action == "save": + with self.lock: + payload_out = self.model.memory_os_v2.export_payload() if self.model.memory_os_v2 else {} + torch.save({"user": user, "saved_at": time.time(), "nm2": payload_out}, path) + return {"saved": user, "path": str(path), "bytes": path.stat().st_size, + "stats": _jsonable(self.model.memory_v2_stats())} + if action == "load": + if not path.exists(): + raise FileNotFoundError(f"no saved session for {user}") + with self.lock: + saved = torch.load(path, map_location="cpu", weights_only=False) + payload = saved.get("nm2") or {} + if not payload: + raise ValueError(f"saved session {user} has no NM2 payload") + current = self.model.memory_os_v2 + if current is None: + raise RuntimeError("NM2 memory is not enabled") + bank = current.bank + restored = MemoryOSV2.from_payload( + payload, + router=current.router, + runtime_device=self.device, + gpu_cache_records=int(getattr(bank, "gpu_cache_records", 256)), + gpu_cache_tokens=int(getattr(bank, "gpu_cache_tokens", 131072)), + gpu_cache_reserve_mb=int(getattr(bank, "gpu_cache_reserve_mb", 2048)), + gpu_cache_adaptive=bool(getattr(bank, "gpu_cache_adaptive", True)), + min_read_margin=float(payload.get("min_read_margin", 0.0)), + require_evidence=bool(payload.get("require_evidence", False)), + ) + # Thresholds travel with the session, not with the process. + restored.read_threshold = float(payload.get("read_threshold", 0.65)) + restored.write_threshold = float(payload.get("write_threshold", 0.50)) + for name, spec in CONFIG_KNOBS.items(): + if "config" in spec: + continue + saved_value = payload.get(name) + if saved_value is not None: + setattr(restored, spec["runtime"], saved_value) + self.model.memory_os_v2 = restored + self._written_user_turns.clear() + return { + "loaded": user, + "path": str(path), + "saved_at": saved.get("saved_at"), + "read_threshold": restored.read_threshold, + "write_threshold": restored.write_threshold, + "memory": _jsonable(self.model.memory_v2_stats()), + } + if action == "reset": + with self.lock: + self.model.reset_memory(batch_size=1, device=self.device) + self._written_user_turns.clear() + self._persist() + return {"reset": True, "memory": _jsonable(self.model.memory_v2_stats())} + raise ValueError(f"unknown session action {action!r}") + + +class _Handler(BaseHTTPRequestHandler): + server_version = "NM2AgentControlPlane/1.0" + service: NM2AgentService + + def log_message(self, fmt: str, *args: Any) -> None: + sys.stderr.write("[nm2] " + (fmt % args) + "\n") + + def _send(self, status: int, payload: Any) -> None: + body = json.dumps(payload, ensure_ascii=False, default=str).encode("utf-8") + # A client can vanish between the request and the response (the harness aborts a + # turn by closing the socket). That is not a server fault and there is nobody left + # to answer, so the write is best-effort rather than an exception that would bubble + # into the generic handler and try to send a 500 down the same dead socket. + try: + self.send_response(status) + self.send_header("Content-Type", "application/json; charset=utf-8") + self.send_header("Content-Length", str(len(body))) + self.end_headers() + self.wfile.write(body) + except (BrokenPipeError, ConnectionResetError, ConnectionAbortedError, OSError): + self.close_connection = True + + def _send_stream(self, completion: dict[str, Any]) -> None: + """Emit one completion as an OpenAI SSE stream. + + Streaming clients (pi-ai among them) parse the ``chat.completion.chunk`` event + shape; a single JSON body leaves them with an empty message and no tool calls. + Generation here is not incremental, so the whole content is sent as one delta — + the wire format is what the client needs, not sub-token granularity. + """ + + # No Content-Length is sent for a stream, so the client needs the socket to close + # to see EOF. Incremental SSE parsers do not care, but simple HTTP clients hang + # on a kept-alive connection. + self.close_connection = True + + def chunk(delta: dict[str, Any], finish: str | None = None) -> dict[str, Any]: + return { + "id": completion["id"], + "object": "chat.completion.chunk", + "created": completion["created"], + "model": completion["model"], + "choices": [{"index": 0, "delta": delta, "finish_reason": finish}], + } + + message = completion["choices"][0]["message"] + finish_reason = completion["choices"][0]["finish_reason"] + events: list[dict[str, Any]] = [chunk({"role": "assistant", "content": ""})] + if message.get("content"): + events.append(chunk({"content": message["content"]})) + for index, call in enumerate(message.get("tool_calls") or []): + events.append(chunk({"tool_calls": [{ + "index": index, "id": call["id"], "type": "function", + "function": {"name": call["function"]["name"], "arguments": call["function"]["arguments"]}, + }]})) + events.append(chunk({}, finish_reason)) + # The headers belong inside the guard too: a client that disconnects while the + # stream is being set up reset the connection during ``end_headers`` and the + # resulting ConnectionResetError escaped into the generic 500 path, logging a + # misleading ``POST /v1/chat/completions 500`` and a socketserver traceback for + # what was only a client hang-up. + try: + self.send_response(200) + self.send_header("Content-Type", "text/event-stream; charset=utf-8") + self.send_header("Cache-Control", "no-cache") + self.send_header("Connection", "close") + self.end_headers() + for event in events: + self.wfile.write(f"data: {json.dumps(event, ensure_ascii=False, default=str)}\n\n".encode("utf-8")) + self.wfile.write(b"data: [DONE]\n\n") + self.wfile.flush() + except (BrokenPipeError, ConnectionResetError, ConnectionAbortedError, OSError): + pass # client aborted mid-stream + + def _read_json(self) -> dict[str, Any]: + length = int(self.headers.get("Content-Length") or 0) + if length <= 0: + return {} + return json.loads(self.rfile.read(length).decode("utf-8")) + + def _route(self, method: str) -> None: + parsed = urlparse(self.path) + params = parse_qs(parsed.query) + service = self.service + path = parsed.path + try: + if method == "GET": + if path == "/health": + return self._send(200, {**service.health(), "memory_mode": service.memory_mode}) + if path == "/v1/models": + return self._send(200, {"object": "list", "data": [ + {"id": service.model_name, "object": "model", "owned_by": "local"}]}) + if path == "/v1/nm2/state": + return self._send(200, {"config": service.effective_config(), "snapshot": service.runtime_snapshot()}) + if path == "/v1/nm2/config": + return self._send(200, service.effective_config()) + if path == "/v1/nm2/trace": + return self._send(200, {"traces": service.traces(int(params.get("limit", ["50"])[0]))}) + if path == "/v1/nm2/records": + return self._send(200, service.list_memory(params)) + if path == "/v1/nm2/audit": + return self._send(200, _jsonable(service.audit())) + if path == "/v1/nm2/export": + return self._send(200, _jsonable(service.export_memory(params))) + if path.startswith("/v1/nm2/records/"): + return self._send(200, _jsonable(service.get_memory(path.rsplit("/", 1)[-1]))) + if path == "/v1/memory": + return self._send(200, service.list_memory(params)) + if path == "/v1/memory/audit": + return self._send(200, _jsonable(service.audit())) + if path == "/v1/diagnostics": + return self._send(200, {"turns": service.traces(int(params.get("limit", ["50"])[0]))}) + return self._send(404, {"error": {"message": f"unknown path {path}", "type": "not_found"}}) + + payload = self._read_json() + if path == "/v1/chat/completions": + completion = service.complete(payload) + if payload.get("stream"): + return self._send_stream(completion) + return self._send(200, completion) + # The control arm is only a control if the memory layer is genuinely + # unavailable. `memory_mode=off` used to disable only the automatic path + # (injected prefix + auto-write) while an explicit nm2_write tool call still + # stored records and nm2_read still retrieved them -- so an agent told to use + # its memory tools could pass a "no memory" arm outright. + if service.memory_mode != "on" and path in _MEMORY_MUTATIONS: + return self._send(409, {"error": { + "message": f"memory_mode={service.memory_mode}: this arm cannot write long-term memory", + "type": "memory_disabled" if service.memory_mode == "off" else "memory_read_only", + }}) + if path == "/v1/nm2/probe": + return self._send(200, service.probe(payload)) + if path == "/v1/nm2/config": + return self._send(200, service.update_config(payload)) + if path == "/v1/nm2/mode": + mode = str(payload.get("mode", "")).strip() + if mode not in MEMORY_MODES: + raise ValueError(f"mode must be one of {MEMORY_MODES}") + service.memory_mode = mode + return self._send(200, {"memory_mode": mode}) + if path == "/v1/nm2/write": + return self._send(200, service.write_record(payload)) + if path == "/v1/nm2/correct": + return self._send(200, service.correct_record(payload)) + if path == "/v1/nm2/retract": + record_id = str(payload.get("record_id", "")).strip() + if not record_id: + raise ValueError("record_id is required") + return self._send(200, _jsonable(service.retract_memory(record_id))) + if path == "/v1/nm2/approve": + record_id = str(payload.get("record_id", "")).strip() + if not record_id: + raise ValueError("record_id is required") + return self._send(200, service.approve_record(record_id)) + if path == "/v1/nm2/compact": + return self._send(200, service.compact(payload)) + if path == "/v1/nm2/session": + return self._send(200, service.session_action(payload)) + if path == "/v1/nm2/reset": + service._written_user_turns.clear() + return self._send(200, _jsonable(service.reset_memory())) + if path == "/v1/memory/mode": + mode = str(payload.get("mode", "")).strip() + if mode not in MEMORY_MODES: + raise ValueError(f"mode must be one of {MEMORY_MODES}") + service.memory_mode = mode + return self._send(200, {"memory_mode": mode}) + if path == "/v1/memory/reset": + service._written_user_turns.clear() + return self._send(200, _jsonable(service.reset_memory())) + if path == "/v1/memory": + return self._send(200, service.write_record(payload)) + return self._send(404, {"error": {"message": f"unknown path {path}", "type": "not_found"}}) + except KeyError as error: + # Unknown record ids arrive from a model that guessed one; answer with a + # readable 404 instead of a raw KeyError wrapped in a 500. + self._send(404, {"error": {"message": f"unknown record id: {error}", "type": "unknown_record"}}) + except ValueError as error: + message = str(error) + if message.startswith("only active records can be edited"): + # A domain conflict, not a server fault. NM2's own auto-forget retracts a + # fact in the very turn where the user revokes it, so a model that then + # asks to "correct" the record it memorised last turn is addressing + # something that is no longer active. A 500 taught the model nothing and + # it looped; 409 plus the exact recovery keeps the turn recoverable. + return self._send(409, { + "error": { + "message": message, + "type": "record_not_active", + "hint": ( + "this record is no longer active, so it cannot be edited in place; " + "store the new value with a plain write " + "(action=write, entity+attribute+value) -- NM2 versions by " + "entity::attribute automatically" + ), + }, + }) + self._send(400, {"error": {"message": message, "type": "bad_request"}}) + except (BrokenPipeError, ConnectionResetError, ConnectionAbortedError) as error: + # The client hung up (a harness aborting a turn does exactly this). There is + # no one to answer and nothing went wrong on this side, so this must not be + # logged as a 500 the way it was before. + self.close_connection = True + sys.stderr.write(f"[nm2] client disconnected: {type(error).__name__}\n") + except Exception as error: + self._send(500, {"error": {"message": str(error), "type": type(error).__name__}}) + + def do_GET(self) -> None: # noqa: N802 + self._route("GET") + + def do_POST(self) -> None: # noqa: N802 + self._route("POST") + + +class _Server(ThreadingHTTPServer): + daemon_threads = True + allow_reuse_address = True + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--model-path", default="qwen3_5_4b_natural_memory_v2") + parser.add_argument("--host", default="127.0.0.1") + parser.add_argument("--port", type=int, default=8766) + parser.add_argument("--no-4bit", action="store_true") + parser.add_argument("--no-auto-persist", action="store_true") + parser.add_argument("--memory-mode", choices=MEMORY_MODES, default="on") + parser.add_argument("--session-dir", default="") + parser.add_argument("--trace-limit", type=int, default=500) + parser.add_argument("--model-name", default="natural-memory-v2") + args = parser.parse_args() + + model_path = Path(args.model_path) + if not model_path.is_absolute() and not model_path.exists(): + model_path = Path(__file__).resolve().parents[2] / "V2_dpskw" / args.model_path + + print(json.dumps({"event": "loading", "model_path": str(model_path)}, ensure_ascii=False), flush=True) + service = NM2AgentService( + model_path, + no_4bit=args.no_4bit, + auto_persist=not args.no_auto_persist, + memory_mode=args.memory_mode, + session_dir=args.session_dir or None, + trace_limit=args.trace_limit, + ) + service.model_name = args.model_name + service.model.eval() + + handler = type("Handler", (_Handler,), {"service": service}) + server = _Server((args.host, args.port), handler) + print(json.dumps({ + "event": "ready", + "url": f"http://{args.host}:{args.port}", + "openai_base_url": f"http://{args.host}:{args.port}/v1", + "memory_mode": service.memory_mode, + "nm2_endpoints": [ + "GET /v1/nm2/state", "GET /v1/nm2/config", "POST /v1/nm2/config", + "GET /v1/nm2/trace", "POST /v1/nm2/probe", "GET /v1/nm2/records", + "POST /v1/nm2/write", "POST /v1/nm2/correct", "POST /v1/nm2/retract", + "POST /v1/nm2/approve", "GET /v1/nm2/audit", "GET /v1/nm2/export", + "POST /v1/nm2/compact", "POST /v1/nm2/session", "POST /v1/nm2/reset", + ], + "memory": _jsonable(service.model.memory_v2_stats()), + }, ensure_ascii=False, default=str), flush=True) + try: + server.serve_forever() + except KeyboardInterrupt: + pass + finally: + server.server_close() + service.close() + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/try_nm2.py b/try_nm2.py new file mode 100644 index 0000000..8712d93 --- /dev/null +++ b/try_nm2.py @@ -0,0 +1,293 @@ +"""NM2 试用台：直接跟「模型自带的记忆」打交道。 + +用法（在装了项目的环境里跑）： + + python H:\\Memory\\agent_lab\\try_nm2.py --demo # 一键把整段故事演完 + python H:\\Memory\\agent_lab\\try_nm2.py # 交互菜单，自己随便试 + +它只做一件事：把控制面的接口用中文包一层，让你看得见记忆层每一步到底做了什么 —— +路由觉得要不要用记忆、命中哪条记录、有没有注入证据、自动遗忘的概率是多少。 +""" + +from __future__ import annotations + +import argparse +import json +import sys +import time +import urllib.error +import urllib.parse +import urllib.request + +DEFAULT_BASE = "http://127.0.0.1:8766/v1" + +# 把接口里的英文状态翻成人话，看不懂的照原样显示。 +STOP_REASON_ZH = { + "evidence_found": "找到证据，已注入", + "below_read_threshold": "分数低于读取门槛，不注入", + "router_abstained": "路由器判定这轮不需要记忆", + "attribute_not_covered": "库里没有这个属性（覆盖门拒答）", + "memory_disabled": "记忆已关闭", + "token_evidence_override": "词元证据否决，不注入", +} + +STATUS_ZH = { + "active": "生效", + "superseded": "已被新版本取代", + "retracted": "已撤回", + "quarantined": "隔离中（低置信，未启用）", +} + + +class ServiceError(Exception): + """控制面返回的错误，带上人话。""" + + +class Client: + def __init__(self, base: str, timeout: float = 600.0) -> None: + self.base = base.rstrip("/") + self.timeout = timeout + + def _call(self, method: str, path: str, payload: dict | None = None, params: dict | None = None): + url = f"{self.base}{path}" + if params: + url = f"{url}?{urllib.parse.urlencode(params)}" + data = None + headers = {"Content-Type": "application/json; charset=utf-8"} + if payload is not None: + data = json.dumps(payload, ensure_ascii=False).encode("utf-8") + request = urllib.request.Request(url, data=data, headers=headers, method=method) + try: + with urllib.request.urlopen(request, timeout=self.timeout) as response: + body = response.read().decode("utf-8", "replace") + except urllib.error.HTTPError as error: + detail = error.read().decode("utf-8", "replace")[:500] + raise ServiceError(f"接口 {path} 返回 HTTP {error.code}：{detail}") from None + except urllib.error.URLError as error: + raise ServiceError( + f"连不上 {self.base}（{error.reason}）。模型服务没在跑？" + ) from None + return json.loads(body) if body.strip() else {} + + # ---- 记忆层 --------------------------------------------------------- + def health(self) -> dict: + return self._call("GET", "/health") + + def reset(self) -> dict: + return self._call("POST", "/nm2/reset", {}) + + def write(self, *, text: str, entity: str = "", attribute: str = "", value: str = "", + importance: float = 0.9, confidence: float = 0.95) -> dict: + return self._call("POST", "/nm2/write", { + "text": text, "entity": entity, "attribute": attribute, "value": value, + "importance": importance, "confidence": confidence, + }) + + def records(self, status: str = "active", limit: int = 100) -> list[dict]: + return self._call("GET", "/nm2/records", params={"status": status, "limit": limit}).get("records", []) + + def ask(self, question: str, *, remember: bool = True, max_tokens: int = 256) -> dict: + """问一轮。remember=False 时这一轮不读也不写记忆（当场做对照）。""" + + body = { + "messages": [{"role": "user", "content": question}], + "max_tokens": max_tokens, + "stream": False, + "nm2": {"memory_enabled": remember}, + } + return self._call("POST", "/chat/completions", body) + + +# ---------------------------------------------------------------- 显示 +def rule(title: str = "") -> None: + line = "─" * 62 + print(f"\n{line}") + if title: + print(title) + print(line) + + +def show_answer(reply: dict) -> str: + message = reply["choices"][0]["message"] + content = (message.get("content") or "").strip() + print(f"模型回答：{content or '（空）'}") + return content + + +def show_decision(reply: dict) -> None: + info = reply.get("nm2") or {} + decisions = info.get("router_decisions") or [{}] + decision = decisions[0] if decisions else {} + reason = str(decision.get("stop_reason", "")) + print("这一轮记忆层做了什么：") + print(f" · 要不要用记忆：{'要' if decision.get('need_memory') else '不要'}") + print(f" · 结论：{STOP_REASON_ZH.get(reason, reason or '（无）')}") + print(f" · 命中页：{', '.join(decision.get('page_ids') or []) or '（无）'}") + print(f" · 命中记录：{len(decision.get('record_ids') or [])} 条") + print(f" · 注入前缀：{info.get('prefix_tokens', 0)} 词元") + print(f" · 路由耗时：{round(float(info.get('read_seconds') or 0) * 1000)} 毫秒") + forget = info.get("auto_forget_probability") or [None] + if forget and forget[0] is not None: + mark = " ← 超过 0.9 会自动遗忘" if float(forget[0]) >= 0.9 else "" + print(f" · 自动遗忘概率：{float(forget[0]):.4f}{mark}") + + +def show_bank(client: Client, title: str = "记忆库现状") -> list[dict]: + active = client.records("active") + print(f"{title}（生效记录 {len(active)} 条）：") + for record in active: + origin = record.get("origin") or "（无来源标记）" + print(f" · [{record.get('status')}] {record.get('entity')}｜{record.get('attribute')}" + f" = {record.get('value')} 来源轮次 {origin}") + if not active: + print(" （空）") + return active + + +# ---------------------------------------------------------------- 演示 +def demo(client: Client) -> None: + rule("第一步：清空记忆，确保从零开始") + client.reset() + print("已清空。") + + rule("第二步：用户一口气说了两件事") + sentence = "记一下：我的紧急联系人是 王工，电话分机 7781。" + print(f"用户：{sentence}") + reply = client.ask(sentence) + show_answer(reply) + show_decision(reply) + + print("\n（智能体随后按字段各写一条记录。这里直接调接口，效果等价——" + "两条记录会同属这一轮。）") + for attribute, value in (("紧急联系人", "王工"), ("电话分机", "7781")): + result = client.write( + text=f"{attribute} 是 {value}。", entity="user", attribute=attribute, value=value, + ) + record = result.get("record", {}) + print(f" 写入 {attribute}={value} → {result.get('action')}，" + f"来源轮次 {record.get('origin') or '（无）'}") + + show_bank(client, "现在库里") + + rule("第三步：用户要求忘掉紧急联系人信息") + forget = "请遗忘我的紧急联系人信息，不要再保留和使用它。" + print(f"用户：{forget}") + reply = client.ask(forget) + show_answer(reply) + show_decision(reply) + show_bank(client, "遗忘之后") + + rule("第四步：追问分机号——这里就是以前会泄露的地方") + question = "我的紧急联系人分机是多少？如果已经不保留这条信息，就直接说明。" + print(f"用户：{question}") + reply = client.ask(question) + answer = show_answer(reply) + show_decision(reply) + + leaked = "7781" in answer + print() + if leaked: + print("结果：分机号还是被说出来了 —— 这一轮没修好，把上面的路由结论发我。") + else: + print("结果：没有泄露分机号。同一轮写入的记录被一起撤回了。") + + rule("第五步：换成「关掉记忆」再问一次，看对照组会怎样") + reply = client.ask(question, remember=False) + show_answer(reply) + print("（这一轮模型没有任何记忆可用，只能靠猜或明说不知道。）") + + +# ---------------------------------------------------------------- 交互 +MENU = """ +NM2 试用台 + 1) 一键演完整段故事（写入 → 提问 → 遗忘 → 追问 → 对照） + 2) 写入一条事实 + 3) 提一个问题（带记忆） + 4) 关掉记忆再问一次（当场对照） + 5) 遗忘（直接对模型说「请遗忘…」，走真实自动遗忘） + 6) 看记忆库 + 7) 清空记忆 + 0) 退出 +""" + + +def interactive(client: Client) -> None: + while True: + print(MENU) + try: + choice = input("请选择：").strip() + except (EOFError, KeyboardInterrupt): + print() + return + try: + if choice == "1": + demo(client) + elif choice == "2": + entity = input("实体（直接回车=user）：").strip() or "user" + attribute = input("属性（例如 默认语言）：").strip() + value = input("取值（例如 葡萄牙语）：").strip() + text = input("记录原文（直接回车自动拼）：").strip() or f"{attribute} 是 {value}。" + result = client.write(text=text, entity=entity, attribute=attribute, value=value) + print(f"结果：{result.get('action')}") + show_bank(client) + elif choice == "3": + question = input("问题：").strip() + if not question: + continue + reply = client.ask(question) + show_answer(reply) + show_decision(reply) + elif choice == "4": + question = input("问题（这一轮会把记忆关掉）：").strip() + if not question: + continue + show_answer(client.ask(question, remember=False)) + elif choice == "5": + text = input("对模型说：").strip() or "请遗忘我刚刚告诉你的那条信息。" + reply = client.ask(text) + show_answer(reply) + show_decision(reply) + show_bank(client, "遗忘之后") + elif choice == "6": + show_bank(client) + elif choice == "7": + client.reset() + print("已清空。") + elif choice == "0": + return + else: + print("没有这个选项。") + except ServiceError as error: + print(f"出错：{error}") + + +def main() -> int: + parser = argparse.ArgumentParser(description="NM2 试用台") + parser.add_argument("--base", default=DEFAULT_BASE, help="控制面地址") + parser.add_argument("--demo", action="store_true", help="直接跑自动演示，不进菜单") + args = parser.parse_args() + + client = Client(args.base) + try: + health = client.health() + except ServiceError as error: + print(f"启动失败：{error}") + print("\n先起模型服务，再跑这个脚本。命令见 agent_lab/RESULTS.md 第 8 节。") + return 1 + + memory = health.get("memory", {}) + print(f"已连上模型服务：{health.get('model_path')}") + print(f"当前记忆开关：{health.get('memory_mode')}｜" + f"生效记录 {memory.get('active_records')} 条 / 共 {memory.get('records')} 条") + + if args.demo: + started = time.time() + demo(client) + print(f"\n演示结束，用了 {time.time() - started:.0f} 秒。") + else: + interactive(client) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main())