chore: dead code cleanup (pyflakes-clean)
- Remove unused imports: time/asyncio/Path/Optional/re/Counter/jieba across backfill_embeddings, bucket_manager, embedding_engine, import_memory - Drop unused local var `old_primary` in reclassify_domains - Fix 3 placeholder-less f-strings in backfill_embeddings, migrate_to_domains, reclassify_domains - Delete legacy quick-check scripts test_smoke.py / test_tools.py (replaced by tests/ pytest suite, never collected by pytest) - Delete backup_20260405_2124/ (stale local snapshot, no code references) Verified: pyflakes *.py → 0 warnings; pytest tests/ → 237 passed, 7 skipped.
This commit is contained in:
P0luz
@@ -13,7 +13,6 @@ Free tier: 1500 requests/day, so ~75 batches of 20.
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import asyncio
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import asyncio
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import argparse
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import argparse
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import sys
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import sys
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import time
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sys.path.insert(0, ".")
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sys.path.insert(0, ".")
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from utils import load_config
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from utils import load_config
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@@ -79,7 +78,7 @@ async def backfill(batch_size: int = 20, dry_run: bool = False):
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print(f" ERROR: {b['id'][:12]} ({name[:30]}): {e}")
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print(f" ERROR: {b['id'][:12]} ({name[:30]}): {e}")
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if i + batch_size < total:
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if i + batch_size < total:
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print(f" Waiting 2s before next batch...")
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print(" Waiting 2s before next batch...")
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await asyncio.sleep(2)
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await asyncio.sleep(2)
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print(f"\n=== Done: {success} success, {failed} failed, {total - success - failed} skipped ===")
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print(f"\n=== Done: {success} success, {failed} failed, {total - success - failed} skipped ===")
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@@ -1,205 +0,0 @@
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# Ombre Brain
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一个给 Claude 用的长期情绪记忆系统。基于 Russell 效价/唤醒度坐标打标，Obsidian 做存储层，MCP 接入，带遗忘曲线。
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A long-term emotional memory system for Claude. Tags memories using Russell's valence/arousal coordinates, stores them as Obsidian-compatible Markdown, connects via MCP, and has a forgetting curve.
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---
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## 它是什么 / What is this
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Claude 没有跨对话记忆。每次对话结束，之前聊过的所有东西都会消失。
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Ombre Brain 给了它一套持久记忆——不是那种冷冰冰的键值存储，而是带情感坐标的、会自然衰减的、像人类记忆一样会遗忘和浮现的系统。
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Claude has no cross-conversation memory. Everything from a previous chat vanishes once it ends.
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Ombre Brain gives it persistent memory — not cold key-value storage, but a system with emotional coordinates, natural decay, and forgetting/surfacing mechanics that loosely mimic how human memory works.
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核心特点 / Key features:
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- **情感坐标打标 / Emotional tagging**: 每条记忆用 Russell 环形情感模型的 valence（效价）和 arousal（唤醒度）两个连续维度标记。不是"开心/难过"这种离散标签。
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Each memory is tagged with two continuous dimensions from Russell's circumplex model: valence and arousal. Not discrete labels like "happy/sad".
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- **自然遗忘 / Natural forgetting**: 改进版艾宾浩斯遗忘曲线。不活跃的记忆自动衰减归档，高情绪强度的记忆衰减更慢。
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Modified Ebbinghaus forgetting curve. Inactive memories naturally decay and archive. High-arousal memories decay slower.
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- **权重池浮现 / Weight pool surfacing**: 记忆不是被动检索的，它们会主动浮现——未解决的、情绪强烈的记忆权重更高，会在对话开头自动推送。
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Memories aren't just passively retrieved — they actively surface. Unresolved, emotionally intense memories carry higher weight and get pushed at conversation start.
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- **Obsidian 原生 / Obsidian-native**: 每个记忆桶就是一个 Markdown 文件，YAML frontmatter 存元数据。可以直接在 Obsidian 里浏览、编辑、搜索。自动注入 `[[双链]]`。
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Each memory bucket is a Markdown file with YAML frontmatter. Browse, edit, and search directly in Obsidian. Wikilinks are auto-injected.
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- **API 降级 / API degradation**: 脱水压缩和自动打标优先用廉价 LLM API（DeepSeek 等），API 不可用时自动降级到本地关键词分析——始终可用。
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Dehydration and auto-tagging prefer a cheap LLM API (DeepSeek etc.). When the API is unavailable, it degrades to local keyword analysis — always functional.
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## 边界说明 / Design boundaries
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官方记忆功能已经在做身份层的事了——你是谁，你有什么偏好，你们的关系是什么。那一层交给它，Ombre Brain不打算造重复的轮子。
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Ombre Brain 的边界是时间里发生的事，不是你是谁。它记住的是：你们聊过什么，经历了什么，哪些事情还悬在那里没有解决。两层配合用，才是完整的。
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每次新对话，Claude 从零开始——但它能从 Ombre Brain 里找回跟你有关的一切。不是重建，是接续。
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---
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Official memory already handles the identity layer — who you are, what you prefer, what your relationship is. That layer belongs there. Ombre Brain isn't trying to duplicate it.
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Ombre Brain's boundary is *what happened in time*, not *who you are*. It holds conversations, experiences, unresolved things. The two layers together are what make it feel complete.
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Each new conversation starts fresh — but Claude can reach back through Ombre Brain and find everything that happened between you. Not a rebuild. A continuation.
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## 架构 / Architecture
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```
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Claude ←→ MCP Protocol ←→ server.py
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│
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┌───────────────┼───────────────┐
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│ │ │
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bucket_manager dehydrator decay_engine
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(CRUD + 搜索) (压缩 + 打标) (遗忘曲线)
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│
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Obsidian Vault (Markdown files)
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```
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5 个 MCP 工具 / 5 MCP tools:
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| 工具 Tool | 作用 Purpose |
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|-----------|-------------|
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| `breath` | 浮现或检索记忆。无参数=推送未解决记忆；有参数=关键词+情感检索 / Surface or search memories |
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| `hold` | 存储单条记忆，自动打标+合并相似桶 / Store a single memory with auto-tagging |
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| `grow` | 日记归档，自动拆分长内容为多个记忆桶 / Diary digest, auto-split into multiple buckets |
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| `trace` | 修改元数据、标记已解决、删除 / Modify metadata, mark resolved, delete |
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| `pulse` | 系统状态 + 所有记忆桶列表 / System status + bucket listing |
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## 安装 / Setup
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### 环境要求 / Requirements
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- Python 3.11+
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- 一个 Obsidian Vault（可选，不用也行，会在项目目录下自建 `buckets/`）
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An Obsidian vault (optional — without one, it uses a local `buckets/` directory)
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### 步骤 / Steps
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```bash
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git clone https://github.com/P0lar1zzZ/Ombre-Brain.git
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cd Ombre-Brain
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python -m venv .venv
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source .venv/bin/activate # Windows: .venv\Scripts\activate
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pip install -r requirements.txt
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```
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复制配置文件并按需修改 / Copy config and edit as needed:
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```bash
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cp config.example.yaml config.yaml
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```
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如果你要用 API 做脱水压缩和自动打标（推荐，效果好很多），设置环境变量：
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If you want API-powered dehydration and tagging (recommended, much better quality):
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```bash
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export OMBRE_API_KEY="your-api-key"
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```
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支持任何 OpenAI 兼容 API。在 `config.yaml` 里改 `base_url` 和 `model` 就行。
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Supports any OpenAI-compatible API. Just change `base_url` and `model` in `config.yaml`.
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### 接入 Claude Desktop / Connect to Claude Desktop
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在 Claude Desktop 配置文件中添加（macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`）：
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Add to your Claude Desktop config:
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```json
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{
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"mcpServers": {
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"ombre-brain": {
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"command": "python",
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"args": ["/path/to/Ombre-Brain/server.py"],
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"env": {
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"OMBRE_API_KEY": "your-api-key"
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}
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}
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}
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}
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```
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### 接入 Claude.ai (远程) / Connect to Claude.ai (remote)
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需要 HTTP 传输 + 隧道。可以用 Docker：
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Requires HTTP transport + tunnel. Docker setup:
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```bash
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echo "OMBRE_API_KEY=your-api-key" > .env
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docker-compose up -d
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```
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`docker-compose.yml` 里配好了 Cloudflare Tunnel。你需要自己在 `~/.cloudflared/` 下放凭证和路由配置。
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The `docker-compose.yml` includes Cloudflare Tunnel. You'll need your own credentials under `~/.cloudflared/`.
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### 指向 Obsidian / Point to Obsidian
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在 `config.yaml` 里设置 `buckets_dir`：
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Set `buckets_dir` in `config.yaml`:
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```yaml
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buckets_dir: "/path/to/your/Obsidian Vault/Ombre Brain"
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```
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不设的话，默认用项目目录下的 `buckets/`。
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If not set, defaults to `buckets/` in the project directory.
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## 配置 / Configuration
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所有参数在 `config.yaml`（从 `config.example.yaml` 复制）。关键的几个：
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All parameters in `config.yaml` (copy from `config.example.yaml`). Key ones:
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| 参数 Parameter | 说明 Description | 默认 Default |
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|---|---|---|
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| `transport` | `stdio`（本地）/ `streamable-http`（远程）| `stdio` |
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| `buckets_dir` | 记忆桶存储路径 / Bucket storage path | `./buckets/` |
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| `dehydration.model` | 脱水用的 LLM 模型 / LLM model for dehydration | `deepseek-chat` |
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| `dehydration.base_url` | API 地址 / API endpoint | `https://api.deepseek.com/v1` |
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| `decay.lambda` | 衰减速率，越大越快忘 / Decay rate | `0.05` |
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| `decay.threshold` | 归档阈值 / Archive threshold | `0.3` |
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| `merge_threshold` | 合并相似度阈值 (0-100) / Merge similarity | `75` |
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敏感配置用环境变量：
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Sensitive config via env vars:
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- `OMBRE_API_KEY` — LLM API 密钥
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- `OMBRE_TRANSPORT` — 覆盖传输方式
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- `OMBRE_BUCKETS_DIR` — 覆盖存储路径
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## 衰减公式 / Decay Formula
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$$Score = Importance \times activation\_count^{0.3} \times e^{-\lambda \times days} \times (base + arousal \times boost)$$
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- `importance`: 1-10，记忆重要性 / memory importance
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- `activation_count`: 被检索的次数，越常被想起衰减越慢 / retrieval count; more recalls = slower decay
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- `days`: 距上次激活的天数 / days since last activation
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- `arousal`: 唤醒度，越强烈的记忆越难忘 / arousal; intense memories are harder to forget
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- 已解决的记忆权重降到 5%，沉底等被关键词唤醒 / resolved memories drop to 5%, sink until keyword-triggered
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## 给 Claude 的使用指南 / Usage Guide for Claude
|
|
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|
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`CLAUDE_PROMPT.md` 是写给 Claude 看的使用说明。放到你的 system prompt 或 custom instructions 里就行。
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|
||||||
|
|
||||||
`CLAUDE_PROMPT.md` is the usage guide written for Claude. Put it in your system prompt or custom instructions.
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## 工具脚本 / Utility Scripts
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|
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| 脚本 Script | 用途 Purpose |
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||||||
|---|---|
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| `write_memory.py` | 手动写入记忆，绕过 MCP / Manually write memories, bypass MCP |
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| `migrate_to_domains.py` | 迁移平铺文件到域子目录 / Migrate flat files to domain subdirs |
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| `reclassify_domains.py` | 基于关键词重分类 / Reclassify by keywords |
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| `reclassify_api.py` | 用 API 重打标未分类桶 / Re-tag uncategorized buckets via API |
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|
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| `test_smoke.py` | 冒烟测试 / Smoke test |
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|
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||||||
## License
|
|
||||||
|
|
||||||
MIT
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|
||||||
@@ -1,755 +0,0 @@
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|||||||
# ============================================================
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|
||||||
# Module: Memory Bucket Manager (bucket_manager.py)
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|
||||||
# 模块：记忆桶管理器
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|
||||||
#
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|
||||||
# CRUD operations, multi-dimensional index search, activation updates
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|
||||||
# for memory buckets.
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|
||||||
# 记忆桶的增删改查、多维索引搜索、激活更新。
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|
||||||
#
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|
||||||
# Core design:
|
|
||||||
# 核心逻辑：
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|
||||||
# - Each bucket = one Markdown file (YAML frontmatter + body)
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|
||||||
# 每个记忆桶 = 一个 Markdown 文件
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|
||||||
# - Storage by type: permanent / dynamic / archive
|
|
||||||
# 存储按类型分目录
|
|
||||||
# - Multi-dimensional soft index: domain + valence/arousal + fuzzy text
|
|
||||||
# 多维软索引：主题域 + 情感坐标 + 文本模糊匹配
|
|
||||||
# - Search strategy: domain pre-filter → weighted multi-dim ranking
|
|
||||||
# 搜索策略：主题域预筛 → 多维加权精排
|
|
||||||
# - Emotion coordinates based on Russell circumplex model:
|
|
||||||
# 情感坐标基于环形情感模型（Russell circumplex）：
|
|
||||||
# valence (0~1): 0=negative → 1=positive
|
|
||||||
# arousal (0~1): 0=calm → 1=excited
|
|
||||||
#
|
|
||||||
# Depended on by: server.py, decay_engine.py
|
|
||||||
# 被谁依赖：server.py, decay_engine.py
|
|
||||||
# ============================================================
|
|
||||||
|
|
||||||
import os
|
|
||||||
import math
|
|
||||||
import logging
|
|
||||||
import re
|
|
||||||
import shutil
|
|
||||||
from collections import Counter
|
|
||||||
from datetime import datetime
|
|
||||||
from pathlib import Path
|
|
||||||
from typing import Optional
|
|
||||||
|
|
||||||
import frontmatter
|
|
||||||
import jieba
|
|
||||||
from rapidfuzz import fuzz
|
|
||||||
|
|
||||||
from utils import generate_bucket_id, sanitize_name, safe_path, now_iso
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|
||||||
|
|
||||||
logger = logging.getLogger("ombre_brain.bucket")
|
|
||||||
|
|
||||||
|
|
||||||
class BucketManager:
|
|
||||||
"""
|
|
||||||
Memory bucket manager — entry point for all bucket CRUD operations.
|
|
||||||
Buckets are stored as Markdown files with YAML frontmatter for metadata
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|
||||||
and body for content. Natively compatible with Obsidian browsing/editing.
|
|
||||||
记忆桶管理器 —— 所有桶的 CRUD 操作入口。
|
|
||||||
桶以 Markdown 文件存储，YAML frontmatter 存元数据，正文存内容。
|
|
||||||
天然兼容 Obsidian 直接浏览和编辑。
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, config: dict):
|
|
||||||
# --- Read storage paths from config / 从配置中读取存储路径 ---
|
|
||||||
self.base_dir = config["buckets_dir"]
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|
||||||
self.permanent_dir = os.path.join(self.base_dir, "permanent")
|
|
||||||
self.dynamic_dir = os.path.join(self.base_dir, "dynamic")
|
|
||||||
self.archive_dir = os.path.join(self.base_dir, "archive")
|
|
||||||
self.fuzzy_threshold = config.get("matching", {}).get("fuzzy_threshold", 50)
|
|
||||||
self.max_results = config.get("matching", {}).get("max_results", 5)
|
|
||||||
|
|
||||||
# --- Wikilink config / 双链配置 ---
|
|
||||||
wikilink_cfg = config.get("wikilink", {})
|
|
||||||
self.wikilink_enabled = wikilink_cfg.get("enabled", True)
|
|
||||||
self.wikilink_use_tags = wikilink_cfg.get("use_tags", False)
|
|
||||||
self.wikilink_use_domain = wikilink_cfg.get("use_domain", True)
|
|
||||||
self.wikilink_use_auto_keywords = wikilink_cfg.get("use_auto_keywords", True)
|
|
||||||
self.wikilink_auto_top_k = wikilink_cfg.get("auto_top_k", 8)
|
|
||||||
self.wikilink_min_len = wikilink_cfg.get("min_keyword_len", 2)
|
|
||||||
self.wikilink_exclude_keywords = set(wikilink_cfg.get("exclude_keywords", []))
|
|
||||||
self.wikilink_stopwords = {
|
|
||||||
"的", "了", "在", "是", "我", "有", "和", "就", "不", "人",
|
|
||||||
"都", "一个", "上", "也", "很", "到", "说", "要", "去",
|
|
||||||
"你", "会", "着", "没有", "看", "好", "自己", "这", "他", "她",
|
|
||||||
"我们", "你们", "他们", "然后", "今天", "昨天", "明天", "一下",
|
|
||||||
"the", "and", "for", "are", "but", "not", "you", "all", "can",
|
|
||||||
"had", "her", "was", "one", "our", "out", "has", "have", "with",
|
|
||||||
"this", "that", "from", "they", "been", "said", "will", "each",
|
|
||||||
}
|
|
||||||
self.wikilink_stopwords |= {w.lower() for w in self.wikilink_exclude_keywords}
|
|
||||||
|
|
||||||
# --- Search scoring weights / 检索权重配置 ---
|
|
||||||
scoring = config.get("scoring_weights", {})
|
|
||||||
self.w_topic = scoring.get("topic_relevance", 4.0)
|
|
||||||
self.w_emotion = scoring.get("emotion_resonance", 2.0)
|
|
||||||
self.w_time = scoring.get("time_proximity", 1.5)
|
|
||||||
self.w_importance = scoring.get("importance", 1.0)
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Create a new bucket
|
|
||||||
# 创建新桶
|
|
||||||
# Write content and metadata into a .md file
|
|
||||||
# 将内容和元数据写入一个 .md 文件
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
async def create(
|
|
||||||
self,
|
|
||||||
content: str,
|
|
||||||
tags: list[str] = None,
|
|
||||||
importance: int = 5,
|
|
||||||
domain: list[str] = None,
|
|
||||||
valence: float = 0.5,
|
|
||||||
arousal: float = 0.3,
|
|
||||||
bucket_type: str = "dynamic",
|
|
||||||
name: str = None,
|
|
||||||
) -> str:
|
|
||||||
"""
|
|
||||||
Create a new memory bucket, return bucket ID.
|
|
||||||
创建一个新的记忆桶，返回桶 ID。
|
|
||||||
"""
|
|
||||||
bucket_id = generate_bucket_id()
|
|
||||||
bucket_name = sanitize_name(name) if name else bucket_id
|
|
||||||
domain = domain or ["未分类"]
|
|
||||||
tags = tags or []
|
|
||||||
linked_content = self._apply_wikilinks(content, tags, domain, bucket_name)
|
|
||||||
|
|
||||||
# --- Build YAML frontmatter metadata / 构建元数据 ---
|
|
||||||
metadata = {
|
|
||||||
"id": bucket_id,
|
|
||||||
"name": bucket_name,
|
|
||||||
"tags": tags,
|
|
||||||
"domain": domain,
|
|
||||||
"valence": max(0.0, min(1.0, valence)),
|
|
||||||
"arousal": max(0.0, min(1.0, arousal)),
|
|
||||||
"importance": max(1, min(10, importance)),
|
|
||||||
"type": bucket_type,
|
|
||||||
"created": now_iso(),
|
|
||||||
"last_active": now_iso(),
|
|
||||||
"activation_count": 1,
|
|
||||||
}
|
|
||||||
|
|
||||||
# --- Assemble Markdown file (frontmatter + body) ---
|
|
||||||
# --- 组装 Markdown 文件 ---
|
|
||||||
post = frontmatter.Post(linked_content, **metadata)
|
|
||||||
|
|
||||||
# --- Choose directory by type + primary domain ---
|
|
||||||
# --- 按类型 + 主题域选择存储目录 ---
|
|
||||||
type_dir = self.permanent_dir if bucket_type == "permanent" else self.dynamic_dir
|
|
||||||
primary_domain = sanitize_name(domain[0]) if domain else "未分类"
|
|
||||||
target_dir = os.path.join(type_dir, primary_domain)
|
|
||||||
os.makedirs(target_dir, exist_ok=True)
|
|
||||||
|
|
||||||
# --- Filename: readable_name_bucketID.md (Obsidian friendly) ---
|
|
||||||
# --- 文件名：可读名称_桶ID.md ---
|
|
||||||
if bucket_name and bucket_name != bucket_id:
|
|
||||||
filename = f"{bucket_name}_{bucket_id}.md"
|
|
||||||
else:
|
|
||||||
filename = f"{bucket_id}.md"
|
|
||||||
file_path = safe_path(target_dir, filename)
|
|
||||||
|
|
||||||
try:
|
|
||||||
with open(file_path, "w", encoding="utf-8") as f:
|
|
||||||
f.write(frontmatter.dumps(post))
|
|
||||||
except OSError as e:
|
|
||||||
logger.error(f"Failed to write bucket file / 写入桶文件失败: {file_path}: {e}")
|
|
||||||
raise
|
|
||||||
|
|
||||||
logger.info(
|
|
||||||
f"Created bucket / 创建记忆桶: {bucket_id} ({bucket_name}) → {primary_domain}/"
|
|
||||||
)
|
|
||||||
return bucket_id
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Read bucket content
|
|
||||||
# 读取桶内容
|
|
||||||
# Returns {"id", "metadata", "content", "path"} or None
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
async def get(self, bucket_id: str) -> Optional[dict]:
|
|
||||||
"""
|
|
||||||
Read a single bucket by ID.
|
|
||||||
根据 ID 读取单个桶。
|
|
||||||
"""
|
|
||||||
if not bucket_id or not isinstance(bucket_id, str):
|
|
||||||
return None
|
|
||||||
file_path = self._find_bucket_file(bucket_id)
|
|
||||||
if not file_path:
|
|
||||||
return None
|
|
||||||
return self._load_bucket(file_path)
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Update bucket
|
|
||||||
# 更新桶
|
|
||||||
# Supports: content, tags, importance, valence, arousal, name, resolved
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
async def update(self, bucket_id: str, **kwargs) -> bool:
|
|
||||||
"""
|
|
||||||
Update bucket content or metadata fields.
|
|
||||||
更新桶的内容或元数据字段。
|
|
||||||
"""
|
|
||||||
file_path = self._find_bucket_file(bucket_id)
|
|
||||||
if not file_path:
|
|
||||||
return False
|
|
||||||
|
|
||||||
try:
|
|
||||||
post = frontmatter.load(file_path)
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"Failed to load bucket for update / 加载桶失败: {file_path}: {e}")
|
|
||||||
return False
|
|
||||||
|
|
||||||
# --- Update only fields that were passed in / 只改传入的字段 ---
|
|
||||||
if "content" in kwargs:
|
|
||||||
next_tags = kwargs.get("tags", post.get("tags", []))
|
|
||||||
next_domain = kwargs.get("domain", post.get("domain", []))
|
|
||||||
next_name = kwargs.get("name", post.get("name", ""))
|
|
||||||
post.content = self._apply_wikilinks(
|
|
||||||
kwargs["content"],
|
|
||||||
next_tags,
|
|
||||||
next_domain,
|
|
||||||
next_name,
|
|
||||||
)
|
|
||||||
if "tags" in kwargs:
|
|
||||||
post["tags"] = kwargs["tags"]
|
|
||||||
if "importance" in kwargs:
|
|
||||||
post["importance"] = max(1, min(10, int(kwargs["importance"])))
|
|
||||||
if "domain" in kwargs:
|
|
||||||
post["domain"] = kwargs["domain"]
|
|
||||||
if "valence" in kwargs:
|
|
||||||
post["valence"] = max(0.0, min(1.0, float(kwargs["valence"])))
|
|
||||||
if "arousal" in kwargs:
|
|
||||||
post["arousal"] = max(0.0, min(1.0, float(kwargs["arousal"])))
|
|
||||||
if "name" in kwargs:
|
|
||||||
post["name"] = sanitize_name(kwargs["name"])
|
|
||||||
if "resolved" in kwargs:
|
|
||||||
post["resolved"] = bool(kwargs["resolved"])
|
|
||||||
|
|
||||||
# --- Auto-refresh activation time / 自动刷新激活时间 ---
|
|
||||||
post["last_active"] = now_iso()
|
|
||||||
|
|
||||||
try:
|
|
||||||
with open(file_path, "w", encoding="utf-8") as f:
|
|
||||||
f.write(frontmatter.dumps(post))
|
|
||||||
except OSError as e:
|
|
||||||
logger.error(f"Failed to write bucket update / 写入桶更新失败: {file_path}: {e}")
|
|
||||||
return False
|
|
||||||
|
|
||||||
logger.info(f"Updated bucket / 更新记忆桶: {bucket_id}")
|
|
||||||
return True
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Wikilink injection
|
|
||||||
# 自动添加 Obsidian 双链
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
def _apply_wikilinks(
|
|
||||||
self,
|
|
||||||
content: str,
|
|
||||||
tags: list[str],
|
|
||||||
domain: list[str],
|
|
||||||
name: str,
|
|
||||||
) -> str:
|
|
||||||
"""
|
|
||||||
Auto-inject Obsidian wikilinks, avoiding double-wrapping existing [[...]].
|
|
||||||
自动添加 Obsidian 双链，避免重复包裹已有 [[...]]。
|
|
||||||
"""
|
|
||||||
if not self.wikilink_enabled or not content:
|
|
||||||
return content
|
|
||||||
|
|
||||||
keywords = self._collect_wikilink_keywords(content, tags, domain, name)
|
|
||||||
if not keywords:
|
|
||||||
return content
|
|
||||||
|
|
||||||
# Split on existing wikilinks to avoid wrapping them again
|
|
||||||
# 按已有双链切分，避免重复包裹
|
|
||||||
segments = re.split(r"(\[\[[^\]]+\]\])", content)
|
|
||||||
pattern = re.compile("|".join(re.escape(kw) for kw in keywords))
|
|
||||||
for i, segment in enumerate(segments):
|
|
||||||
if segment.startswith("[[") and segment.endswith("]]"):
|
|
||||||
continue
|
|
||||||
updated = pattern.sub(lambda m: f"[[{m.group(0)}]]", segment)
|
|
||||||
segments[i] = updated
|
|
||||||
return "".join(segments)
|
|
||||||
|
|
||||||
def _collect_wikilink_keywords(
|
|
||||||
self,
|
|
||||||
content: str,
|
|
||||||
tags: list[str],
|
|
||||||
domain: list[str],
|
|
||||||
name: str,
|
|
||||||
) -> list[str]:
|
|
||||||
"""
|
|
||||||
Collect candidate keywords from tags/domain/auto-extraction.
|
|
||||||
汇总候选关键词：可选 tags/domain + 自动提词。
|
|
||||||
"""
|
|
||||||
candidates = []
|
|
||||||
|
|
||||||
if self.wikilink_use_tags:
|
|
||||||
candidates.extend(tags or [])
|
|
||||||
if self.wikilink_use_domain:
|
|
||||||
candidates.extend(domain or [])
|
|
||||||
if name:
|
|
||||||
candidates.append(name)
|
|
||||||
if self.wikilink_use_auto_keywords:
|
|
||||||
candidates.extend(self._extract_auto_keywords(content))
|
|
||||||
|
|
||||||
return self._normalize_keywords(candidates)
|
|
||||||
|
|
||||||
def _normalize_keywords(self, keywords: list[str]) -> list[str]:
|
|
||||||
"""
|
|
||||||
Deduplicate and sort by length (longer first to avoid short words
|
|
||||||
breaking long ones during replacement).
|
|
||||||
去重并按长度排序，优先替换长词。
|
|
||||||
"""
|
|
||||||
if not keywords:
|
|
||||||
return []
|
|
||||||
|
|
||||||
seen = set()
|
|
||||||
cleaned = []
|
|
||||||
for keyword in keywords:
|
|
||||||
if not isinstance(keyword, str):
|
|
||||||
continue
|
|
||||||
kw = keyword.strip()
|
|
||||||
if len(kw) < self.wikilink_min_len:
|
|
||||||
continue
|
|
||||||
if kw in self.wikilink_exclude_keywords:
|
|
||||||
continue
|
|
||||||
if kw.lower() in self.wikilink_stopwords:
|
|
||||||
continue
|
|
||||||
if kw in seen:
|
|
||||||
continue
|
|
||||||
seen.add(kw)
|
|
||||||
cleaned.append(kw)
|
|
||||||
|
|
||||||
return sorted(cleaned, key=len, reverse=True)
|
|
||||||
|
|
||||||
def _extract_auto_keywords(self, content: str) -> list[str]:
|
|
||||||
"""
|
|
||||||
Auto-extract keywords from body text, prioritizing high-frequency words.
|
|
||||||
从正文自动提词，优先高频词。
|
|
||||||
"""
|
|
||||||
if not content:
|
|
||||||
return []
|
|
||||||
|
|
||||||
try:
|
|
||||||
zh_words = [w.strip() for w in jieba.lcut(content) if w.strip()]
|
|
||||||
except Exception:
|
|
||||||
zh_words = []
|
|
||||||
en_words = re.findall(r"[A-Za-z][A-Za-z0-9_-]{2,20}", content)
|
|
||||||
|
|
||||||
# Chinese bigrams / 中文双词组合
|
|
||||||
zh_bigrams = []
|
|
||||||
for i in range(len(zh_words) - 1):
|
|
||||||
left = zh_words[i]
|
|
||||||
right = zh_words[i + 1]
|
|
||||||
if len(left) < self.wikilink_min_len or len(right) < self.wikilink_min_len:
|
|
||||||
continue
|
|
||||||
if not re.fullmatch(r"[\u4e00-\u9fff]+", left + right):
|
|
||||||
continue
|
|
||||||
if len(left + right) > 8:
|
|
||||||
continue
|
|
||||||
zh_bigrams.append(left + right)
|
|
||||||
|
|
||||||
merged = []
|
|
||||||
for word in zh_words + zh_bigrams + en_words:
|
|
||||||
if len(word) < self.wikilink_min_len:
|
|
||||||
continue
|
|
||||||
if re.fullmatch(r"\d+", word):
|
|
||||||
continue
|
|
||||||
if word.lower() in self.wikilink_stopwords:
|
|
||||||
continue
|
|
||||||
merged.append(word)
|
|
||||||
|
|
||||||
if not merged:
|
|
||||||
return []
|
|
||||||
|
|
||||||
counter = Counter(merged)
|
|
||||||
return [w for w, _ in counter.most_common(self.wikilink_auto_top_k)]
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Delete bucket
|
|
||||||
# 删除桶
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
async def delete(self, bucket_id: str) -> bool:
|
|
||||||
"""
|
|
||||||
Delete a memory bucket file.
|
|
||||||
删除指定的记忆桶文件。
|
|
||||||
"""
|
|
||||||
file_path = self._find_bucket_file(bucket_id)
|
|
||||||
if not file_path:
|
|
||||||
return False
|
|
||||||
|
|
||||||
try:
|
|
||||||
os.remove(file_path)
|
|
||||||
except OSError as e:
|
|
||||||
logger.error(f"Failed to delete bucket file / 删除桶文件失败: {file_path}: {e}")
|
|
||||||
return False
|
|
||||||
|
|
||||||
logger.info(f"Deleted bucket / 删除记忆桶: {bucket_id}")
|
|
||||||
return True
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Touch bucket (refresh activation time + increment count)
|
|
||||||
# 触碰桶（刷新激活时间 + 累加激活次数）
|
|
||||||
# Called on every recall hit; affects decay score.
|
|
||||||
# 每次检索命中时调用，影响衰减得分。
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
async def touch(self, bucket_id: str) -> None:
|
|
||||||
"""
|
|
||||||
Update a bucket's last activation time and count.
|
|
||||||
更新桶的最后激活时间和激活次数。
|
|
||||||
"""
|
|
||||||
file_path = self._find_bucket_file(bucket_id)
|
|
||||||
if not file_path:
|
|
||||||
return
|
|
||||||
|
|
||||||
try:
|
|
||||||
post = frontmatter.load(file_path)
|
|
||||||
post["last_active"] = now_iso()
|
|
||||||
post["activation_count"] = post.get("activation_count", 0) + 1
|
|
||||||
|
|
||||||
with open(file_path, "w", encoding="utf-8") as f:
|
|
||||||
f.write(frontmatter.dumps(post))
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"Failed to touch bucket / 触碰桶失败: {bucket_id}: {e}")
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Multi-dimensional search (core feature)
|
|
||||||
# 多维搜索（核心功能）
|
|
||||||
#
|
|
||||||
# Strategy: domain pre-filter → weighted multi-dim ranking
|
|
||||||
# 策略：主题域预筛 → 多维加权精排
|
|
||||||
#
|
|
||||||
# Ranking formula:
|
|
||||||
# total = topic(×w_topic) + emotion(×w_emotion)
|
|
||||||
# + time(×w_time) + importance(×w_importance)
|
|
||||||
#
|
|
||||||
# Per-dimension scores (normalized to 0~1):
|
|
||||||
# topic = rapidfuzz weighted match (name/tags/domain/body)
|
|
||||||
# emotion = 1 - Euclidean distance (query v/a vs bucket v/a)
|
|
||||||
# time = e^(-0.02 × days) (recent memories first)
|
|
||||||
# importance = importance / 10
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
async def search(
|
|
||||||
self,
|
|
||||||
query: str,
|
|
||||||
limit: int = None,
|
|
||||||
domain_filter: list[str] = None,
|
|
||||||
query_valence: float = None,
|
|
||||||
query_arousal: float = None,
|
|
||||||
) -> list[dict]:
|
|
||||||
"""
|
|
||||||
Multi-dimensional indexed search for memory buckets.
|
|
||||||
多维索引搜索记忆桶。
|
|
||||||
|
|
||||||
domain_filter: pre-filter by domain (None = search all)
|
|
||||||
query_valence/arousal: emotion coordinates for resonance scoring
|
|
||||||
"""
|
|
||||||
if not query or not query.strip():
|
|
||||||
return []
|
|
||||||
|
|
||||||
limit = limit or self.max_results
|
|
||||||
all_buckets = await self.list_all(include_archive=False)
|
|
||||||
|
|
||||||
if not all_buckets:
|
|
||||||
return []
|
|
||||||
|
|
||||||
# --- Layer 1: domain pre-filter (fast scope reduction) ---
|
|
||||||
# --- 第一层：主题域预筛（快速缩小范围）---
|
|
||||||
if domain_filter:
|
|
||||||
filter_set = {d.lower() for d in domain_filter}
|
|
||||||
candidates = [
|
|
||||||
b for b in all_buckets
|
|
||||||
if {d.lower() for d in b["metadata"].get("domain", [])} & filter_set
|
|
||||||
]
|
|
||||||
# Fall back to full search if pre-filter yields nothing
|
|
||||||
# 预筛为空则回退全量搜索
|
|
||||||
if not candidates:
|
|
||||||
candidates = all_buckets
|
|
||||||
else:
|
|
||||||
candidates = all_buckets
|
|
||||||
|
|
||||||
# --- Layer 2: weighted multi-dim ranking ---
|
|
||||||
# --- 第二层：多维加权精排 ---
|
|
||||||
scored = []
|
|
||||||
for bucket in candidates:
|
|
||||||
meta = bucket.get("metadata", {})
|
|
||||||
|
|
||||||
try:
|
|
||||||
# Dim 1: topic relevance (fuzzy text, 0~1)
|
|
||||||
topic_score = self._calc_topic_score(query, bucket)
|
|
||||||
|
|
||||||
# Dim 2: emotion resonance (coordinate distance, 0~1)
|
|
||||||
emotion_score = self._calc_emotion_score(
|
|
||||||
query_valence, query_arousal, meta
|
|
||||||
)
|
|
||||||
|
|
||||||
# Dim 3: time proximity (exponential decay, 0~1)
|
|
||||||
time_score = self._calc_time_score(meta)
|
|
||||||
|
|
||||||
# Dim 4: importance (direct normalization)
|
|
||||||
importance_score = max(1, min(10, int(meta.get("importance", 5)))) / 10.0
|
|
||||||
|
|
||||||
# --- Weighted sum / 加权求和 ---
|
|
||||||
total = (
|
|
||||||
topic_score * self.w_topic
|
|
||||||
+ emotion_score * self.w_emotion
|
|
||||||
+ time_score * self.w_time
|
|
||||||
+ importance_score * self.w_importance
|
|
||||||
)
|
|
||||||
# Normalize to 0~100 for readability
|
|
||||||
weight_sum = self.w_topic + self.w_emotion + self.w_time + self.w_importance
|
|
||||||
normalized = (total / weight_sum) * 100 if weight_sum > 0 else 0
|
|
||||||
|
|
||||||
# Resolved buckets get ranking penalty (but still reachable by keyword)
|
|
||||||
# 已解决的桶降权排序（但仍可被关键词激活）
|
|
||||||
if meta.get("resolved", False):
|
|
||||||
normalized *= 0.3
|
|
||||||
|
|
||||||
if normalized >= self.fuzzy_threshold:
|
|
||||||
bucket["score"] = round(normalized, 2)
|
|
||||||
scored.append(bucket)
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(
|
|
||||||
f"Scoring failed for bucket {bucket.get('id', '?')} / "
|
|
||||||
f"桶评分失败: {e}"
|
|
||||||
)
|
|
||||||
continue
|
|
||||||
|
|
||||||
scored.sort(key=lambda x: x["score"], reverse=True)
|
|
||||||
return scored[:limit]
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Topic relevance sub-score:
|
|
||||||
# name(×3) + domain(×2.5) + tags(×2) + body(×1)
|
|
||||||
# 文本相关性子分：桶名(×3) + 主题域(×2.5) + 标签(×2) + 正文(×1)
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
def _calc_topic_score(self, query: str, bucket: dict) -> float:
|
|
||||||
"""
|
|
||||||
Calculate text dimension relevance score (0~1).
|
|
||||||
计算文本维度的相关性得分。
|
|
||||||
"""
|
|
||||||
meta = bucket.get("metadata", {})
|
|
||||||
|
|
||||||
name_score = fuzz.partial_ratio(query, meta.get("name", "")) * 3
|
|
||||||
domain_score = (
|
|
||||||
max(
|
|
||||||
(fuzz.partial_ratio(query, d) for d in meta.get("domain", [])),
|
|
||||||
default=0,
|
|
||||||
)
|
|
||||||
* 2.5
|
|
||||||
)
|
|
||||||
tag_score = (
|
|
||||||
max(
|
|
||||||
(fuzz.partial_ratio(query, tag) for tag in meta.get("tags", [])),
|
|
||||||
default=0,
|
|
||||||
)
|
|
||||||
* 2
|
|
||||||
)
|
|
||||||
content_score = fuzz.partial_ratio(query, bucket.get("content", "")[:500]) * 1
|
|
||||||
|
|
||||||
return (name_score + domain_score + tag_score + content_score) / (100 * 8.5)
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Emotion resonance sub-score:
|
|
||||||
# Based on Russell circumplex Euclidean distance
|
|
||||||
# 情感共鸣子分：基于环形情感模型的欧氏距离
|
|
||||||
# No emotion in query → neutral 0.5 (doesn't affect ranking)
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
def _calc_emotion_score(
|
|
||||||
self, q_valence: float, q_arousal: float, meta: dict
|
|
||||||
) -> float:
|
|
||||||
"""
|
|
||||||
Calculate emotion resonance score (0~1, closer = higher).
|
|
||||||
计算情感共鸣度（0~1，越近越高）。
|
|
||||||
"""
|
|
||||||
if q_valence is None or q_arousal is None:
|
|
||||||
return 0.5 # No emotion coordinates → neutral / 无情感坐标时给中性分
|
|
||||||
|
|
||||||
try:
|
|
||||||
b_valence = float(meta.get("valence", 0.5))
|
|
||||||
b_arousal = float(meta.get("arousal", 0.3))
|
|
||||||
except (ValueError, TypeError):
|
|
||||||
return 0.5
|
|
||||||
|
|
||||||
# Euclidean distance, max sqrt(2) ≈ 1.414
|
|
||||||
dist = math.sqrt((q_valence - b_valence) ** 2 + (q_arousal - b_arousal) ** 2)
|
|
||||||
return max(0.0, 1.0 - dist / 1.414)
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Time proximity sub-score:
|
|
||||||
# More recent activation → higher score
|
|
||||||
# 时间亲近子分：距上次激活越近分越高
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
def _calc_time_score(self, meta: dict) -> float:
|
|
||||||
"""
|
|
||||||
Calculate time proximity score (0~1, more recent = higher).
|
|
||||||
计算时间亲近度。
|
|
||||||
"""
|
|
||||||
last_active_str = meta.get("last_active", meta.get("created", ""))
|
|
||||||
try:
|
|
||||||
last_active = datetime.fromisoformat(str(last_active_str))
|
|
||||||
days = max(0.0, (datetime.now() - last_active).total_seconds() / 86400)
|
|
||||||
except (ValueError, TypeError):
|
|
||||||
days = 30
|
|
||||||
return math.exp(-0.02 * days)
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# List all buckets
|
|
||||||
# 列出所有桶
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
async def list_all(self, include_archive: bool = False) -> list[dict]:
|
|
||||||
"""
|
|
||||||
Recursively walk directories (including domain subdirs), list all buckets.
|
|
||||||
递归遍历目录（含域子目录），列出所有记忆桶。
|
|
||||||
"""
|
|
||||||
buckets = []
|
|
||||||
|
|
||||||
dirs = [self.permanent_dir, self.dynamic_dir]
|
|
||||||
if include_archive:
|
|
||||||
dirs.append(self.archive_dir)
|
|
||||||
|
|
||||||
for dir_path in dirs:
|
|
||||||
if not os.path.exists(dir_path):
|
|
||||||
continue
|
|
||||||
for root, _, files in os.walk(dir_path):
|
|
||||||
for filename in files:
|
|
||||||
if not filename.endswith(".md"):
|
|
||||||
continue
|
|
||||||
file_path = os.path.join(root, filename)
|
|
||||||
bucket = self._load_bucket(file_path)
|
|
||||||
if bucket:
|
|
||||||
buckets.append(bucket)
|
|
||||||
|
|
||||||
return buckets
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Statistics (counts per category + total size)
|
|
||||||
# 统计信息（各分类桶数量 + 总体积）
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
async def get_stats(self) -> dict:
|
|
||||||
"""
|
|
||||||
Return memory bucket statistics (including domain subdirs).
|
|
||||||
返回记忆桶的统计数据。
|
|
||||||
"""
|
|
||||||
stats = {
|
|
||||||
"permanent_count": 0,
|
|
||||||
"dynamic_count": 0,
|
|
||||||
"archive_count": 0,
|
|
||||||
"total_size_kb": 0.0,
|
|
||||||
"domains": {},
|
|
||||||
}
|
|
||||||
|
|
||||||
for subdir, key in [
|
|
||||||
(self.permanent_dir, "permanent_count"),
|
|
||||||
(self.dynamic_dir, "dynamic_count"),
|
|
||||||
(self.archive_dir, "archive_count"),
|
|
||||||
]:
|
|
||||||
if not os.path.exists(subdir):
|
|
||||||
continue
|
|
||||||
for root, _, files in os.walk(subdir):
|
|
||||||
for f in files:
|
|
||||||
if f.endswith(".md"):
|
|
||||||
stats[key] += 1
|
|
||||||
fpath = os.path.join(root, f)
|
|
||||||
try:
|
|
||||||
stats["total_size_kb"] += os.path.getsize(fpath) / 1024
|
|
||||||
except OSError:
|
|
||||||
pass
|
|
||||||
# Per-domain counts / 每个域的桶数量
|
|
||||||
domain_name = os.path.basename(root)
|
|
||||||
if domain_name != os.path.basename(subdir):
|
|
||||||
stats["domains"][domain_name] = stats["domains"].get(domain_name, 0) + 1
|
|
||||||
|
|
||||||
return stats
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Archive bucket (move from permanent/dynamic into archive)
|
|
||||||
# 归档桶（从 permanent/dynamic 移入 archive）
|
|
||||||
# Called by decay engine to simulate "forgetting"
|
|
||||||
# 由衰减引擎调用，模拟"遗忘"
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
async def archive(self, bucket_id: str) -> bool:
|
|
||||||
"""
|
|
||||||
Move a bucket into the archive directory (preserving domain subdirs).
|
|
||||||
将指定桶移入归档目录（保留域子目录结构）。
|
|
||||||
"""
|
|
||||||
file_path = self._find_bucket_file(bucket_id)
|
|
||||||
if not file_path:
|
|
||||||
return False
|
|
||||||
|
|
||||||
try:
|
|
||||||
# Read once, get domain info and update type / 一次性读取
|
|
||||||
post = frontmatter.load(file_path)
|
|
||||||
domain = post.get("domain", ["未分类"])
|
|
||||||
primary_domain = sanitize_name(domain[0]) if domain else "未分类"
|
|
||||||
archive_subdir = os.path.join(self.archive_dir, primary_domain)
|
|
||||||
os.makedirs(archive_subdir, exist_ok=True)
|
|
||||||
|
|
||||||
dest = safe_path(archive_subdir, os.path.basename(file_path))
|
|
||||||
|
|
||||||
# Update type marker then move file / 更新类型标记后移动文件
|
|
||||||
post["type"] = "archived"
|
|
||||||
with open(file_path, "w", encoding="utf-8") as f:
|
|
||||||
f.write(frontmatter.dumps(post))
|
|
||||||
|
|
||||||
# Use shutil.move for cross-filesystem safety
|
|
||||||
# 使用 shutil.move 保证跨文件系统安全
|
|
||||||
shutil.move(file_path, str(dest))
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(
|
|
||||||
f"Failed to archive bucket / 归档桶失败: {bucket_id}: {e}"
|
|
||||||
)
|
|
||||||
return False
|
|
||||||
|
|
||||||
logger.info(f"Archived bucket / 归档记忆桶: {bucket_id} → archive/{primary_domain}/")
|
|
||||||
return True
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Internal: find bucket file across all three directories
|
|
||||||
# 内部：在三个目录中查找桶文件
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
def _find_bucket_file(self, bucket_id: str) -> Optional[str]:
|
|
||||||
"""
|
|
||||||
Recursively search permanent/dynamic/archive for a bucket file
|
|
||||||
matching the given ID.
|
|
||||||
在 permanent/dynamic/archive 中递归查找指定 ID 的桶文件。
|
|
||||||
"""
|
|
||||||
if not bucket_id:
|
|
||||||
return None
|
|
||||||
for dir_path in [self.permanent_dir, self.dynamic_dir, self.archive_dir]:
|
|
||||||
if not os.path.exists(dir_path):
|
|
||||||
continue
|
|
||||||
for root, _, files in os.walk(dir_path):
|
|
||||||
for fname in files:
|
|
||||||
if not fname.endswith(".md"):
|
|
||||||
continue
|
|
||||||
# Match by exact ID segment in filename
|
|
||||||
# 通过文件名中的 ID 片段精确匹配
|
|
||||||
if bucket_id in fname:
|
|
||||||
return os.path.join(root, fname)
|
|
||||||
return None
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Internal: load bucket data from .md file
|
|
||||||
# 内部：从 .md 文件加载桶数据
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
def _load_bucket(self, file_path: str) -> Optional[dict]:
|
|
||||||
"""
|
|
||||||
Parse a Markdown file and return structured bucket data.
|
|
||||||
解析 Markdown 文件，返回桶的结构化数据。
|
|
||||||
"""
|
|
||||||
try:
|
|
||||||
post = frontmatter.load(file_path)
|
|
||||||
return {
|
|
||||||
"id": post.get("id", Path(file_path).stem),
|
|
||||||
"metadata": dict(post.metadata),
|
|
||||||
"content": post.content,
|
|
||||||
"path": file_path,
|
|
||||||
}
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(
|
|
||||||
f"Failed to load bucket file / 加载桶文件失败: {file_path}: {e}"
|
|
||||||
)
|
|
||||||
return None
|
|
||||||
@@ -1,242 +0,0 @@
|
|||||||
# ============================================================
|
|
||||||
# Module: Memory Decay Engine (decay_engine.py)
|
|
||||||
# 模块：记忆衰减引擎
|
|
||||||
#
|
|
||||||
# Simulates human forgetting curve; auto-decays inactive memories and archives them.
|
|
||||||
# 模拟人类遗忘曲线，自动衰减不活跃记忆并归档。
|
|
||||||
#
|
|
||||||
# Core formula (improved Ebbinghaus + emotion coordinates):
|
|
||||||
# 核心公式（改进版艾宾浩斯遗忘曲线 + 情感坐标）：
|
|
||||||
# Score = Importance × (activation_count^0.3) × e^(-λ×days) × emotion_weight
|
|
||||||
#
|
|
||||||
# Emotion weight (continuous coordinate, not discrete labels):
|
|
||||||
# 情感权重（基于连续坐标而非离散列举）：
|
|
||||||
# emotion_weight = base + (arousal × arousal_boost)
|
|
||||||
# Higher arousal → higher emotion weight → slower decay
|
|
||||||
# 唤醒度越高 → 情感权重越大 → 记忆衰减越慢
|
|
||||||
#
|
|
||||||
# Depended on by: server.py
|
|
||||||
# 被谁依赖：server.py
|
|
||||||
# ============================================================
|
|
||||||
|
|
||||||
import math
|
|
||||||
import asyncio
|
|
||||||
import logging
|
|
||||||
from datetime import datetime
|
|
||||||
|
|
||||||
logger = logging.getLogger("ombre_brain.decay")
|
|
||||||
|
|
||||||
|
|
||||||
class DecayEngine:
|
|
||||||
"""
|
|
||||||
Memory decay engine — periodically scans all dynamic buckets,
|
|
||||||
calculates decay scores, auto-archives low-activity buckets
|
|
||||||
to simulate natural forgetting.
|
|
||||||
记忆衰减引擎 —— 定期扫描所有动态桶，
|
|
||||||
计算衰减得分，将低活跃桶自动归档，模拟自然遗忘。
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, config: dict, bucket_mgr):
|
|
||||||
# --- Load decay parameters / 加载衰减参数 ---
|
|
||||||
decay_cfg = config.get("decay", {})
|
|
||||||
self.decay_lambda = decay_cfg.get("lambda", 0.05)
|
|
||||||
self.threshold = decay_cfg.get("threshold", 0.3)
|
|
||||||
self.check_interval = decay_cfg.get("check_interval_hours", 24)
|
|
||||||
|
|
||||||
# --- Emotion weight params (continuous arousal coordinate) ---
|
|
||||||
# --- 情感权重参数（基于连续 arousal 坐标）---
|
|
||||||
emotion_cfg = decay_cfg.get("emotion_weights", {})
|
|
||||||
self.emotion_base = emotion_cfg.get("base", 1.0)
|
|
||||||
self.arousal_boost = emotion_cfg.get("arousal_boost", 0.8)
|
|
||||||
|
|
||||||
self.bucket_mgr = bucket_mgr
|
|
||||||
|
|
||||||
# --- Background task control / 后台任务控制 ---
|
|
||||||
self._task: asyncio.Task | None = None
|
|
||||||
self._running = False
|
|
||||||
|
|
||||||
@property
|
|
||||||
def is_running(self) -> bool:
|
|
||||||
"""Whether the decay engine is running in the background.
|
|
||||||
衰减引擎是否正在后台运行。"""
|
|
||||||
return self._running
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Core: calculate decay score for a single bucket
|
|
||||||
# 核心：计算单个桶的衰减得分
|
|
||||||
#
|
|
||||||
# Higher score = more vivid memory; below threshold → archive
|
|
||||||
# 得分越高 = 记忆越鲜活，低于阈值则归档
|
|
||||||
# Permanent buckets never decay / 固化桶永远不衰减
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
def calculate_score(self, metadata: dict) -> float:
|
|
||||||
"""
|
|
||||||
Calculate current activity score for a memory bucket.
|
|
||||||
计算一个记忆桶的当前活跃度得分。
|
|
||||||
|
|
||||||
Formula: Score = Importance × (act_count^0.3) × e^(-λ×days) × (base + arousal×boost)
|
|
||||||
"""
|
|
||||||
if not isinstance(metadata, dict):
|
|
||||||
return 0.0
|
|
||||||
|
|
||||||
# --- Permanent buckets never decay / 固化桶永不衰减 ---
|
|
||||||
if metadata.get("type") == "permanent":
|
|
||||||
return 999.0
|
|
||||||
|
|
||||||
importance = max(1, min(10, int(metadata.get("importance", 5))))
|
|
||||||
activation_count = max(1, int(metadata.get("activation_count", 1)))
|
|
||||||
|
|
||||||
# --- Days since last activation / 距离上次激活过了多少天 ---
|
|
||||||
last_active_str = metadata.get("last_active", metadata.get("created", ""))
|
|
||||||
try:
|
|
||||||
last_active = datetime.fromisoformat(str(last_active_str))
|
|
||||||
days_since = max(0.0, (datetime.now() - last_active).total_seconds() / 86400)
|
|
||||||
except (ValueError, TypeError):
|
|
||||||
days_since = 30 # Parse failure → assume 30 days / 解析失败假设已过 30 天
|
|
||||||
|
|
||||||
# --- Emotion weight: continuous arousal coordinate ---
|
|
||||||
# --- 情感权重：基于连续 arousal 坐标计算 ---
|
|
||||||
# Higher arousal → stronger emotion → higher weight → slower decay
|
|
||||||
# arousal 越高 → 情感越强烈 → 权重越大 → 衰减越慢
|
|
||||||
try:
|
|
||||||
arousal = max(0.0, min(1.0, float(metadata.get("arousal", 0.3))))
|
|
||||||
except (ValueError, TypeError):
|
|
||||||
arousal = 0.3
|
|
||||||
emotion_weight = self.emotion_base + arousal * self.arousal_boost
|
|
||||||
|
|
||||||
# --- Apply decay formula / 套入衰减公式 ---
|
|
||||||
score = (
|
|
||||||
importance
|
|
||||||
* (activation_count ** 0.3)
|
|
||||||
* math.exp(-self.decay_lambda * days_since)
|
|
||||||
* emotion_weight
|
|
||||||
)
|
|
||||||
|
|
||||||
# --- Weight pool modifiers / 权重池修正因子 ---
|
|
||||||
# Resolved events drop to 5%, sink to bottom awaiting keyword reactivation
|
|
||||||
# 已解决的事件权重骤降到 5%，沉底等待关键词激活
|
|
||||||
resolved_factor = 0.05 if metadata.get("resolved", False) else 1.0
|
|
||||||
# High-arousal unresolved buckets get urgency boost for priority surfacing
|
|
||||||
# 高唤醒未解决桶额外加成，优先浮现
|
|
||||||
urgency_boost = 1.5 if (arousal > 0.7 and not metadata.get("resolved", False)) else 1.0
|
|
||||||
|
|
||||||
return round(score * resolved_factor * urgency_boost, 4)
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Execute one decay cycle
|
|
||||||
# 执行一轮衰减周期
|
|
||||||
# Scan all dynamic buckets → score → archive those below threshold
|
|
||||||
# 扫描所有动态桶 → 算分 → 低于阈值的归档
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
async def run_decay_cycle(self) -> dict:
|
|
||||||
"""
|
|
||||||
Execute one decay cycle: iterate dynamic buckets, archive those
|
|
||||||
scoring below threshold.
|
|
||||||
执行一轮衰减：遍历动态桶，归档得分低于阈值的桶。
|
|
||||||
|
|
||||||
Returns stats: {"checked": N, "archived": N, "lowest_score": X}
|
|
||||||
"""
|
|
||||||
try:
|
|
||||||
buckets = await self.bucket_mgr.list_all(include_archive=False)
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"Failed to list buckets for decay / 衰减周期列桶失败: {e}")
|
|
||||||
return {"checked": 0, "archived": 0, "lowest_score": 0, "error": str(e)}
|
|
||||||
|
|
||||||
checked = 0
|
|
||||||
archived = 0
|
|
||||||
lowest_score = float("inf")
|
|
||||||
|
|
||||||
for bucket in buckets:
|
|
||||||
meta = bucket.get("metadata", {})
|
|
||||||
|
|
||||||
# Skip permanent buckets / 跳过固化桶
|
|
||||||
if meta.get("type") == "permanent":
|
|
||||||
continue
|
|
||||||
|
|
||||||
checked += 1
|
|
||||||
try:
|
|
||||||
score = self.calculate_score(meta)
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(
|
|
||||||
f"Score calculation failed for {bucket.get('id', '?')} / "
|
|
||||||
f"计算得分失败: {e}"
|
|
||||||
)
|
|
||||||
continue
|
|
||||||
|
|
||||||
lowest_score = min(lowest_score, score)
|
|
||||||
|
|
||||||
# --- Below threshold → archive (simulate forgetting) ---
|
|
||||||
# --- 低于阈值 → 归档（模拟遗忘）---
|
|
||||||
if score < self.threshold:
|
|
||||||
try:
|
|
||||||
success = await self.bucket_mgr.archive(bucket["id"])
|
|
||||||
if success:
|
|
||||||
archived += 1
|
|
||||||
logger.info(
|
|
||||||
f"Decay archived / 衰减归档: "
|
|
||||||
f"{meta.get('name', bucket['id'])} "
|
|
||||||
f"(score={score:.4f}, threshold={self.threshold})"
|
|
||||||
)
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(
|
|
||||||
f"Archive failed for {bucket.get('id', '?')} / "
|
|
||||||
f"归档失败: {e}"
|
|
||||||
)
|
|
||||||
|
|
||||||
result = {
|
|
||||||
"checked": checked,
|
|
||||||
"archived": archived,
|
|
||||||
"lowest_score": lowest_score if checked > 0 else 0,
|
|
||||||
}
|
|
||||||
logger.info(f"Decay cycle complete / 衰减周期完成: {result}")
|
|
||||||
return result
|
|
||||||
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
# Background decay task management
|
|
||||||
# 后台衰减任务管理
|
|
||||||
# ---------------------------------------------------------
|
|
||||||
async def ensure_started(self) -> None:
|
|
||||||
"""
|
|
||||||
Ensure the decay engine is started (lazy init on first call).
|
|
||||||
确保衰减引擎已启动（懒加载，首次调用时启动）。
|
|
||||||
"""
|
|
||||||
if not self._running:
|
|
||||||
await self.start()
|
|
||||||
|
|
||||||
async def start(self) -> None:
|
|
||||||
"""Start the background decay loop.
|
|
||||||
启动后台衰减循环。"""
|
|
||||||
if self._running:
|
|
||||||
return
|
|
||||||
self._running = True
|
|
||||||
self._task = asyncio.create_task(self._background_loop())
|
|
||||||
logger.info(
|
|
||||||
f"Decay engine started, interval: {self.check_interval}h / "
|
|
||||||
f"衰减引擎已启动，检查间隔: {self.check_interval} 小时"
|
|
||||||
)
|
|
||||||
|
|
||||||
async def stop(self) -> None:
|
|
||||||
"""Stop the background decay loop.
|
|
||||||
停止后台衰减循环。"""
|
|
||||||
self._running = False
|
|
||||||
if self._task:
|
|
||||||
self._task.cancel()
|
|
||||||
try:
|
|
||||||
await self._task
|
|
||||||
except asyncio.CancelledError:
|
|
||||||
pass
|
|
||||||
logger.info("Decay engine stopped / 衰减引擎已停止")
|
|
||||||
|
|
||||||
async def _background_loop(self) -> None:
|
|
||||||
"""Background loop: run decay → sleep → repeat.
|
|
||||||
后台循环体：执行衰减 → 睡眠 → 重复。"""
|
|
||||||
while self._running:
|
|
||||||
try:
|
|
||||||
await self.run_decay_cycle()
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"Decay cycle error / 衰减周期出错: {e}")
|
|
||||||
# --- Wait for next cycle / 等待下一个周期 ---
|
|
||||||
try:
|
|
||||||
await asyncio.sleep(self.check_interval * 3600)
|
|
||||||
except asyncio.CancelledError:
|
|
||||||
break
|
|
||||||
@@ -1,536 +0,0 @@
|
|||||||
# ============================================================
|
|
||||||
# Module: MCP Server Entry Point (server.py)
|
|
||||||
# 模块：MCP 服务器主入口
|
|
||||||
#
|
|
||||||
# Starts the Ombre Brain MCP service and registers memory
|
|
||||||
# operation tools for Claude to call.
|
|
||||||
# 启动 Ombre Brain MCP 服务，注册记忆操作工具供 Claude 调用。
|
|
||||||
#
|
|
||||||
# Core responsibilities:
|
|
||||||
# 核心职责：
|
|
||||||
# - Initialize config, bucket manager, dehydrator, decay engine
|
|
||||||
# 初始化配置、记忆桶管理器、脱水器、衰减引擎
|
|
||||||
# - Expose 5 MCP tools:
|
|
||||||
# 暴露 5 个 MCP 工具：
|
|
||||||
# breath — Surface unresolved memories or search by keyword
|
|
||||||
# 浮现未解决记忆 或 按关键词检索
|
|
||||||
# hold — Store a single memory
|
|
||||||
# 存储单条记忆
|
|
||||||
# grow — Diary digest, auto-split into multiple buckets
|
|
||||||
# 日记归档，自动拆分多桶
|
|
||||||
# trace — Modify metadata / resolved / delete
|
|
||||||
# 修改元数据 / resolved 标记 / 删除
|
|
||||||
# pulse — System status + bucket listing
|
|
||||||
# 系统状态 + 所有桶列表
|
|
||||||
#
|
|
||||||
# Startup:
|
|
||||||
# 启动方式：
|
|
||||||
# Local: python server.py
|
|
||||||
# Remote: OMBRE_TRANSPORT=streamable-http python server.py
|
|
||||||
# Docker: docker-compose up
|
|
||||||
# ============================================================
|
|
||||||
|
|
||||||
import os
|
|
||||||
import sys
|
|
||||||
import random
|
|
||||||
import logging
|
|
||||||
import asyncio
|
|
||||||
import httpx
|
|
||||||
|
|
||||||
# --- Ensure same-directory modules can be imported ---
|
|
||||||
# --- 确保同目录下的模块能被正确导入 ---
|
|
||||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
|
||||||
|
|
||||||
from mcp.server.fastmcp import FastMCP
|
|
||||||
|
|
||||||
from bucket_manager import BucketManager
|
|
||||||
from dehydrator import Dehydrator
|
|
||||||
from decay_engine import DecayEngine
|
|
||||||
from utils import load_config, setup_logging
|
|
||||||
|
|
||||||
# --- Load config & init logging / 加载配置 & 初始化日志 ---
|
|
||||||
config = load_config()
|
|
||||||
setup_logging(config.get("log_level", "INFO"))
|
|
||||||
logger = logging.getLogger("ombre_brain")
|
|
||||||
|
|
||||||
# --- Initialize three core components / 初始化三大核心组件 ---
|
|
||||||
bucket_mgr = BucketManager(config) # Bucket manager / 记忆桶管理器
|
|
||||||
dehydrator = Dehydrator(config) # Dehydrator / 脱水器
|
|
||||||
decay_engine = DecayEngine(config, bucket_mgr) # Decay engine / 衰减引擎
|
|
||||||
|
|
||||||
# --- Create MCP server instance / 创建 MCP 服务器实例 ---
|
|
||||||
# host="0.0.0.0" so Docker container's SSE is externally reachable
|
|
||||||
# stdio mode ignores host (no network)
|
|
||||||
mcp = FastMCP(
|
|
||||||
"Ombre Brain",
|
|
||||||
host="0.0.0.0",
|
|
||||||
port=8000,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
# =============================================================
|
|
||||||
# /health endpoint: lightweight keepalive
|
|
||||||
# 轻量保活接口
|
|
||||||
# For Cloudflare Tunnel or reverse proxy to ping, preventing idle timeout
|
|
||||||
# 供 Cloudflare Tunnel 或反代定期 ping，防止空闲超时断连
|
|
||||||
# =============================================================
|
|
||||||
@mcp.custom_route("/health", methods=["GET"])
|
|
||||||
async def health_check(request):
|
|
||||||
from starlette.responses import JSONResponse
|
|
||||||
try:
|
|
||||||
stats = await bucket_mgr.get_stats()
|
|
||||||
return JSONResponse({
|
|
||||||
"status": "ok",
|
|
||||||
"buckets": stats["permanent_count"] + stats["dynamic_count"],
|
|
||||||
"decay_engine": "running" if decay_engine.is_running else "stopped",
|
|
||||||
})
|
|
||||||
except Exception as e:
|
|
||||||
return JSONResponse({"status": "error", "detail": str(e)}, status_code=500)
|
|
||||||
|
|
||||||
|
|
||||||
# =============================================================
|
|
||||||
# Internal helper: merge-or-create
|
|
||||||
# 内部辅助：检查是否可合并，可以则合并，否则新建
|
|
||||||
# Shared by hold and grow to avoid duplicate logic
|
|
||||||
# hold 和 grow 共用，避免重复逻辑
|
|
||||||
# =============================================================
|
|
||||||
async def _merge_or_create(
|
|
||||||
content: str,
|
|
||||||
tags: list,
|
|
||||||
importance: int,
|
|
||||||
domain: list,
|
|
||||||
valence: float,
|
|
||||||
arousal: float,
|
|
||||||
name: str = "",
|
|
||||||
) -> tuple[str, bool]:
|
|
||||||
"""
|
|
||||||
Check if a similar bucket exists for merging; merge if so, create if not.
|
|
||||||
Returns (bucket_id_or_name, is_merged).
|
|
||||||
检查是否有相似桶可合并，有则合并，无则新建。
|
|
||||||
返回 (桶ID或名称, 是否合并)。
|
|
||||||
"""
|
|
||||||
try:
|
|
||||||
existing = await bucket_mgr.search(content, limit=1)
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"Search for merge failed, creating new / 合并搜索失败，新建: {e}")
|
|
||||||
existing = []
|
|
||||||
|
|
||||||
if existing and existing[0].get("score", 0) > config.get("merge_threshold", 75):
|
|
||||||
bucket = existing[0]
|
|
||||||
try:
|
|
||||||
merged = await dehydrator.merge(bucket["content"], content)
|
|
||||||
await bucket_mgr.update(
|
|
||||||
bucket["id"],
|
|
||||||
content=merged,
|
|
||||||
tags=list(set(bucket["metadata"].get("tags", []) + tags)),
|
|
||||||
importance=max(bucket["metadata"].get("importance", 5), importance),
|
|
||||||
domain=list(set(bucket["metadata"].get("domain", []) + domain)),
|
|
||||||
valence=valence,
|
|
||||||
arousal=arousal,
|
|
||||||
)
|
|
||||||
return bucket["metadata"].get("name", bucket["id"]), True
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"Merge failed, creating new / 合并失败，新建: {e}")
|
|
||||||
|
|
||||||
bucket_id = await bucket_mgr.create(
|
|
||||||
content=content,
|
|
||||||
tags=tags,
|
|
||||||
importance=importance,
|
|
||||||
domain=domain,
|
|
||||||
valence=valence,
|
|
||||||
arousal=arousal,
|
|
||||||
name=name or None,
|
|
||||||
)
|
|
||||||
return bucket_id, False
|
|
||||||
|
|
||||||
|
|
||||||
# =============================================================
|
|
||||||
# Tool 1: breath — Breathe
|
|
||||||
# 工具 1：breath — 呼吸
|
|
||||||
#
|
|
||||||
# No args: surface highest-weight unresolved memories (active push)
|
|
||||||
# 无参数：浮现权重最高的未解决记忆
|
|
||||||
# With args: search by keyword + emotion coordinates
|
|
||||||
# 有参数：按关键词+情感坐标检索记忆
|
|
||||||
# =============================================================
|
|
||||||
@mcp.tool()
|
|
||||||
async def breath(
|
|
||||||
query: str = "",
|
|
||||||
max_results: int = 3,
|
|
||||||
domain: str = "",
|
|
||||||
valence: float = -1,
|
|
||||||
arousal: float = -1,
|
|
||||||
) -> str:
|
|
||||||
"""检索记忆或浮现未解决记忆。query 为空时自动推送权重最高的未解决桶；有 query 时按关键词+情感检索。domain 逗号分隔，valence/arousal 传 0~1 启用情感共鸣，-1 忽略。"""
|
|
||||||
await decay_engine.ensure_started()
|
|
||||||
|
|
||||||
# --- No args: surfacing mode (weight pool active push) ---
|
|
||||||
# --- 无参数：浮现模式（权重池主动推送）---
|
|
||||||
if not query.strip():
|
|
||||||
try:
|
|
||||||
all_buckets = await bucket_mgr.list_all(include_archive=False)
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"Failed to list buckets for surfacing / 浮现列桶失败: {e}")
|
|
||||||
return "记忆系统暂时无法访问。"
|
|
||||||
|
|
||||||
unresolved = [
|
|
||||||
b for b in all_buckets
|
|
||||||
if not b["metadata"].get("resolved", False)
|
|
||||||
and b["metadata"].get("type") != "permanent"
|
|
||||||
]
|
|
||||||
if not unresolved:
|
|
||||||
return "权重池平静，没有需要处理的记忆。"
|
|
||||||
|
|
||||||
scored = sorted(
|
|
||||||
unresolved,
|
|
||||||
key=lambda b: decay_engine.calculate_score(b["metadata"]),
|
|
||||||
reverse=True,
|
|
||||||
)
|
|
||||||
top = scored[:2]
|
|
||||||
results = []
|
|
||||||
for b in top:
|
|
||||||
try:
|
|
||||||
summary = await dehydrator.dehydrate(b["content"], b["metadata"])
|
|
||||||
await bucket_mgr.touch(b["id"])
|
|
||||||
score = decay_engine.calculate_score(b["metadata"])
|
|
||||||
results.append(f"[权重:{score:.2f}] {summary}")
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"Failed to dehydrate surfaced bucket / 浮现脱水失败: {e}")
|
|
||||||
continue
|
|
||||||
if not results:
|
|
||||||
return "权重池平静，没有需要处理的记忆。"
|
|
||||||
return "=== 浮现记忆 ===\n" + "\n---\n".join(results)
|
|
||||||
|
|
||||||
# --- With args: search mode / 有参数：检索模式 ---
|
|
||||||
domain_filter = [d.strip() for d in domain.split(",") if d.strip()] or None
|
|
||||||
q_valence = valence if 0 <= valence <= 1 else None
|
|
||||||
q_arousal = arousal if 0 <= arousal <= 1 else None
|
|
||||||
|
|
||||||
try:
|
|
||||||
matches = await bucket_mgr.search(
|
|
||||||
query,
|
|
||||||
limit=max_results,
|
|
||||||
domain_filter=domain_filter,
|
|
||||||
query_valence=q_valence,
|
|
||||||
query_arousal=q_arousal,
|
|
||||||
)
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"Search failed / 检索失败: {e}")
|
|
||||||
return "检索过程出错，请稍后重试。"
|
|
||||||
|
|
||||||
results = []
|
|
||||||
for bucket in matches:
|
|
||||||
try:
|
|
||||||
summary = await dehydrator.dehydrate(bucket["content"], bucket["metadata"])
|
|
||||||
await bucket_mgr.touch(bucket["id"])
|
|
||||||
results.append(summary)
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"Failed to dehydrate search result / 检索结果脱水失败: {e}")
|
|
||||||
continue
|
|
||||||
|
|
||||||
# --- Random surfacing: when search returns < 3, 40% chance to float old memories ---
|
|
||||||
# --- 随机浮现：检索结果不足 3 条时，40% 概率从低权重旧桶里漂上来 ---
|
|
||||||
if len(matches) < 3 and random.random() < 0.4:
|
|
||||||
try:
|
|
||||||
all_buckets = await bucket_mgr.list_all(include_archive=False)
|
|
||||||
matched_ids = {b["id"] for b in matches}
|
|
||||||
low_weight = [
|
|
||||||
b for b in all_buckets
|
|
||||||
if b["id"] not in matched_ids
|
|
||||||
and decay_engine.calculate_score(b["metadata"]) < 2.0
|
|
||||||
]
|
|
||||||
if low_weight:
|
|
||||||
drifted = random.sample(low_weight, min(random.randint(1, 3), len(low_weight)))
|
|
||||||
drift_results = []
|
|
||||||
for b in drifted:
|
|
||||||
summary = await dehydrator.dehydrate(b["content"], b["metadata"])
|
|
||||||
drift_results.append(f"[surface_type: random]\n{summary}")
|
|
||||||
results.append("--- 忽然想起来 ---\n" + "\n---\n".join(drift_results))
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"Random surfacing failed / 随机浮现失败: {e}")
|
|
||||||
|
|
||||||
if not results:
|
|
||||||
return "未找到相关记忆。"
|
|
||||||
|
|
||||||
return "\n---\n".join(results)
|
|
||||||
|
|
||||||
|
|
||||||
# =============================================================
|
|
||||||
# Tool 2: hold — Hold on to this
|
|
||||||
# 工具 2：hold — 握住，留下来
|
|
||||||
# =============================================================
|
|
||||||
@mcp.tool()
|
|
||||||
async def hold(
|
|
||||||
content: str,
|
|
||||||
tags: str = "",
|
|
||||||
importance: int = 5,
|
|
||||||
) -> str:
|
|
||||||
"""存储单条记忆。自动打标+合并相似桶。tags 逗号分隔，importance 1-10。"""
|
|
||||||
await decay_engine.ensure_started()
|
|
||||||
|
|
||||||
# --- Input validation / 输入校验 ---
|
|
||||||
if not content or not content.strip():
|
|
||||||
return "内容为空，无法存储。"
|
|
||||||
|
|
||||||
importance = max(1, min(10, importance))
|
|
||||||
extra_tags = [t.strip() for t in tags.split(",") if t.strip()]
|
|
||||||
|
|
||||||
# --- Step 1: auto-tagging / 自动打标 ---
|
|
||||||
try:
|
|
||||||
analysis = await dehydrator.analyze(content)
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"Auto-tagging failed, using defaults / 自动打标失败: {e}")
|
|
||||||
analysis = {
|
|
||||||
"domain": ["未分类"], "valence": 0.5, "arousal": 0.3,
|
|
||||||
"tags": [], "suggested_name": "",
|
|
||||||
}
|
|
||||||
|
|
||||||
domain = analysis["domain"]
|
|
||||||
valence = analysis["valence"]
|
|
||||||
arousal = analysis["arousal"]
|
|
||||||
auto_tags = analysis["tags"]
|
|
||||||
suggested_name = analysis.get("suggested_name", "")
|
|
||||||
|
|
||||||
all_tags = list(dict.fromkeys(auto_tags + extra_tags))
|
|
||||||
|
|
||||||
# --- Step 2: merge or create / 合并或新建 ---
|
|
||||||
result_name, is_merged = await _merge_or_create(
|
|
||||||
content=content,
|
|
||||||
tags=all_tags,
|
|
||||||
importance=importance,
|
|
||||||
domain=domain,
|
|
||||||
valence=valence,
|
|
||||||
arousal=arousal,
|
|
||||||
name=suggested_name,
|
|
||||||
)
|
|
||||||
|
|
||||||
if is_merged:
|
|
||||||
return (
|
|
||||||
f"已合并到现有记忆桶: {result_name}\n"
|
|
||||||
f"主题域: {', '.join(domain)} | 情感: V{valence:.1f}/A{arousal:.1f}"
|
|
||||||
)
|
|
||||||
return (
|
|
||||||
f"已创建新记忆桶: {result_name}\n"
|
|
||||||
f"主题域: {', '.join(domain)} | 情感: V{valence:.1f}/A{arousal:.1f} | 标签: {', '.join(all_tags)}"
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
# =============================================================
|
|
||||||
# Tool 3: grow — Grow, fragments become memories
|
|
||||||
# 工具 3：grow — 生长，一天的碎片长成记忆
|
|
||||||
# =============================================================
|
|
||||||
@mcp.tool()
|
|
||||||
async def grow(content: str) -> str:
|
|
||||||
"""日记归档。自动拆分长内容为多个记忆桶。"""
|
|
||||||
await decay_engine.ensure_started()
|
|
||||||
|
|
||||||
if not content or not content.strip():
|
|
||||||
return "内容为空，无法整理。"
|
|
||||||
|
|
||||||
# --- Step 1: let API split and organize / 让 API 拆分整理 ---
|
|
||||||
try:
|
|
||||||
items = await dehydrator.digest(content)
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"Diary digest failed / 日记整理失败: {e}")
|
|
||||||
return f"日记整理失败: {e}"
|
|
||||||
|
|
||||||
if not items:
|
|
||||||
return "内容为空或整理失败。"
|
|
||||||
|
|
||||||
results = []
|
|
||||||
created = 0
|
|
||||||
merged = 0
|
|
||||||
|
|
||||||
# --- Step 2: merge or create each item (with per-item error handling) ---
|
|
||||||
# --- 逐条合并或新建（单条失败不影响其他）---
|
|
||||||
for item in items:
|
|
||||||
try:
|
|
||||||
result_name, is_merged = await _merge_or_create(
|
|
||||||
content=item["content"],
|
|
||||||
tags=item.get("tags", []),
|
|
||||||
importance=item.get("importance", 5),
|
|
||||||
domain=item.get("domain", ["未分类"]),
|
|
||||||
valence=item.get("valence", 0.5),
|
|
||||||
arousal=item.get("arousal", 0.3),
|
|
||||||
name=item.get("name", ""),
|
|
||||||
)
|
|
||||||
|
|
||||||
if is_merged:
|
|
||||||
results.append(f" 📎 合并 → {result_name}")
|
|
||||||
merged += 1
|
|
||||||
else:
|
|
||||||
domains_str = ",".join(item.get("domain", []))
|
|
||||||
results.append(
|
|
||||||
f" 📝 新建 [{item.get('name', result_name)}] "
|
|
||||||
f"主题:{domains_str} V{item.get('valence', 0.5):.1f}/A{item.get('arousal', 0.3):.1f}"
|
|
||||||
)
|
|
||||||
created += 1
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(
|
|
||||||
f"Failed to process diary item / 日记条目处理失败: "
|
|
||||||
f"{item.get('name', '?')}: {e}"
|
|
||||||
)
|
|
||||||
results.append(f" ⚠️ 失败: {item.get('name', '未知条目')}")
|
|
||||||
|
|
||||||
summary = f"=== 日记整理完成 ===\n拆分为 {len(items)} 条 | 新建 {created} 桶 | 合并 {merged} 桶\n"
|
|
||||||
return summary + "\n".join(results)
|
|
||||||
|
|
||||||
|
|
||||||
# =============================================================
|
|
||||||
# Tool 4: trace — Trace, redraw the outline of a memory
|
|
||||||
# 工具 4：trace — 描摹，重新勾勒记忆的轮廓
|
|
||||||
# Also handles deletion (delete=True)
|
|
||||||
# 同时承接删除功能
|
|
||||||
# =============================================================
|
|
||||||
@mcp.tool()
|
|
||||||
async def trace(
|
|
||||||
bucket_id: str,
|
|
||||||
name: str = "",
|
|
||||||
domain: str = "",
|
|
||||||
valence: float = -1,
|
|
||||||
arousal: float = -1,
|
|
||||||
importance: int = -1,
|
|
||||||
tags: str = "",
|
|
||||||
resolved: int = -1,
|
|
||||||
delete: bool = False,
|
|
||||||
) -> str:
|
|
||||||
"""修改记忆元数据。resolved=1 标记已解决（桶权重骤降沉底），resolved=0 重新激活，delete=True 删除桶。其余字段只传需改的，-1 或空串表示不改。"""
|
|
||||||
|
|
||||||
if not bucket_id or not bucket_id.strip():
|
|
||||||
return "请提供有效的 bucket_id。"
|
|
||||||
|
|
||||||
# --- Delete mode / 删除模式 ---
|
|
||||||
if delete:
|
|
||||||
success = await bucket_mgr.delete(bucket_id)
|
|
||||||
return f"已遗忘记忆桶: {bucket_id}" if success else f"未找到记忆桶: {bucket_id}"
|
|
||||||
|
|
||||||
bucket = await bucket_mgr.get(bucket_id)
|
|
||||||
if not bucket:
|
|
||||||
return f"未找到记忆桶: {bucket_id}"
|
|
||||||
|
|
||||||
# --- Collect only fields actually passed / 只收集用户实际传入的字段 ---
|
|
||||||
updates = {}
|
|
||||||
if name:
|
|
||||||
updates["name"] = name
|
|
||||||
if domain:
|
|
||||||
updates["domain"] = [d.strip() for d in domain.split(",") if d.strip()]
|
|
||||||
if 0 <= valence <= 1:
|
|
||||||
updates["valence"] = valence
|
|
||||||
if 0 <= arousal <= 1:
|
|
||||||
updates["arousal"] = arousal
|
|
||||||
if 1 <= importance <= 10:
|
|
||||||
updates["importance"] = importance
|
|
||||||
if tags:
|
|
||||||
updates["tags"] = [t.strip() for t in tags.split(",") if t.strip()]
|
|
||||||
if resolved in (0, 1):
|
|
||||||
updates["resolved"] = bool(resolved)
|
|
||||||
|
|
||||||
if not updates:
|
|
||||||
return "没有任何字段需要修改。"
|
|
||||||
|
|
||||||
success = await bucket_mgr.update(bucket_id, **updates)
|
|
||||||
if not success:
|
|
||||||
return f"修改失败: {bucket_id}"
|
|
||||||
|
|
||||||
changed = ", ".join(f"{k}={v}" for k, v in updates.items())
|
|
||||||
# Explicit hint about resolved state change semantics
|
|
||||||
# 特别提示 resolved 状态变化的语义
|
|
||||||
if "resolved" in updates:
|
|
||||||
if updates["resolved"]:
|
|
||||||
changed += " → 已沉底，只在关键词触发时重新浮现"
|
|
||||||
else:
|
|
||||||
changed += " → 已重新激活，将参与浮现排序"
|
|
||||||
return f"已修改记忆桶 {bucket_id}: {changed}"
|
|
||||||
|
|
||||||
|
|
||||||
# =============================================================
|
|
||||||
# Tool 5: pulse — Heartbeat, system status + memory listing
|
|
||||||
# 工具 5：pulse — 脉搏，系统状态 + 记忆列表
|
|
||||||
# =============================================================
|
|
||||||
@mcp.tool()
|
|
||||||
async def pulse(include_archive: bool = False) -> str:
|
|
||||||
"""系统状态和所有记忆桶摘要。include_archive=True 时包含归档桶。"""
|
|
||||||
try:
|
|
||||||
stats = await bucket_mgr.get_stats()
|
|
||||||
except Exception as e:
|
|
||||||
return f"获取系统状态失败: {e}"
|
|
||||||
|
|
||||||
status = (
|
|
||||||
f"=== Ombre Brain 记忆系统 ===\n"
|
|
||||||
f"固化记忆桶: {stats['permanent_count']} 个\n"
|
|
||||||
f"动态记忆桶: {stats['dynamic_count']} 个\n"
|
|
||||||
f"归档记忆桶: {stats['archive_count']} 个\n"
|
|
||||||
f"总存储大小: {stats['total_size_kb']:.1f} KB\n"
|
|
||||||
f"衰减引擎: {'运行中' if decay_engine.is_running else '已停止'}\n"
|
|
||||||
)
|
|
||||||
|
|
||||||
# --- List all bucket summaries / 列出所有桶摘要 ---
|
|
||||||
try:
|
|
||||||
buckets = await bucket_mgr.list_all(include_archive=include_archive)
|
|
||||||
except Exception as e:
|
|
||||||
return status + f"\n列出记忆桶失败: {e}"
|
|
||||||
|
|
||||||
if not buckets:
|
|
||||||
return status + "\n记忆库为空。"
|
|
||||||
|
|
||||||
lines = []
|
|
||||||
for b in buckets:
|
|
||||||
meta = b.get("metadata", {})
|
|
||||||
if meta.get("type") == "permanent":
|
|
||||||
icon = "📦"
|
|
||||||
elif meta.get("type") == "archived":
|
|
||||||
icon = "🗄️"
|
|
||||||
elif meta.get("resolved", False):
|
|
||||||
icon = "✅"
|
|
||||||
else:
|
|
||||||
icon = "💭"
|
|
||||||
try:
|
|
||||||
score = decay_engine.calculate_score(meta)
|
|
||||||
except Exception:
|
|
||||||
score = 0.0
|
|
||||||
domains = ",".join(meta.get("domain", []))
|
|
||||||
val = meta.get("valence", 0.5)
|
|
||||||
aro = meta.get("arousal", 0.3)
|
|
||||||
resolved_tag = " [已解决]" if meta.get("resolved", False) else ""
|
|
||||||
lines.append(
|
|
||||||
f"{icon} [{meta.get('name', b['id'])}]{resolved_tag} "
|
|
||||||
f"主题:{domains} "
|
|
||||||
f"情感:V{val:.1f}/A{aro:.1f} "
|
|
||||||
f"重要:{meta.get('importance', '?')} "
|
|
||||||
f"权重:{score:.2f} "
|
|
||||||
f"标签:{','.join(meta.get('tags', []))}"
|
|
||||||
)
|
|
||||||
|
|
||||||
return status + "\n=== 记忆列表 ===\n" + "\n".join(lines)
|
|
||||||
|
|
||||||
|
|
||||||
# --- Entry point / 启动入口 ---
|
|
||||||
if __name__ == "__main__":
|
|
||||||
transport = config.get("transport", "stdio")
|
|
||||||
logger.info(f"Ombre Brain starting | transport: {transport}")
|
|
||||||
|
|
||||||
# --- Application-level keepalive: remote mode only, ping /health every 60s ---
|
|
||||||
# --- 应用层保活：仅远程模式下启动，每 60 秒 ping 一次 /health ---
|
|
||||||
# Prevents Cloudflare Tunnel from dropping idle connections
|
|
||||||
if transport in ("sse", "streamable-http"):
|
|
||||||
async def _keepalive_loop():
|
|
||||||
await asyncio.sleep(10) # Wait for server to fully start
|
|
||||||
async with httpx.AsyncClient() as client:
|
|
||||||
while True:
|
|
||||||
try:
|
|
||||||
await client.get("http://localhost:8000/health", timeout=5)
|
|
||||||
logger.debug("Keepalive ping OK / 保活 ping 成功")
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"Keepalive ping failed / 保活 ping 失败: {e}")
|
|
||||||
await asyncio.sleep(60)
|
|
||||||
|
|
||||||
import threading
|
|
||||||
|
|
||||||
def _start_keepalive():
|
|
||||||
loop = asyncio.new_event_loop()
|
|
||||||
loop.run_until_complete(_keepalive_loop())
|
|
||||||
|
|
||||||
t = threading.Thread(target=_start_keepalive, daemon=True)
|
|
||||||
t.start()
|
|
||||||
|
|
||||||
mcp.run(transport=transport)
|
|
||||||
@@ -28,15 +28,12 @@
|
|||||||
import os
|
import os
|
||||||
import math
|
import math
|
||||||
import logging
|
import logging
|
||||||
import re
|
|
||||||
import shutil
|
import shutil
|
||||||
from collections import Counter
|
|
||||||
from datetime import datetime
|
from datetime import datetime
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
from typing import Optional
|
from typing import Optional
|
||||||
|
|
||||||
import frontmatter
|
import frontmatter
|
||||||
import jieba
|
|
||||||
from rapidfuzz import fuzz
|
from rapidfuzz import fuzz
|
||||||
|
|
||||||
from utils import generate_bucket_id, sanitize_name, safe_path, now_iso
|
from utils import generate_bucket_id, sanitize_name, safe_path, now_iso
|
||||||
|
|||||||
@@ -16,8 +16,6 @@ import json
|
|||||||
import math
|
import math
|
||||||
import sqlite3
|
import sqlite3
|
||||||
import logging
|
import logging
|
||||||
import asyncio
|
|
||||||
from pathlib import Path
|
|
||||||
|
|
||||||
from openai import AsyncOpenAI
|
from openai import AsyncOpenAI
|
||||||
|
|
||||||
|
|||||||
@@ -19,10 +19,8 @@ import os
|
|||||||
import json
|
import json
|
||||||
import hashlib
|
import hashlib
|
||||||
import logging
|
import logging
|
||||||
import asyncio
|
|
||||||
from datetime import datetime
|
from datetime import datetime
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
from typing import Optional
|
|
||||||
|
|
||||||
from utils import count_tokens_approx, now_iso
|
from utils import count_tokens_approx, now_iso
|
||||||
|
|
||||||
|
|||||||
@@ -117,7 +117,7 @@ def migrate():
|
|||||||
print(f" ✓ {filename}")
|
print(f" ✓ {filename}")
|
||||||
print(f" → {primary_domain}/{new_filename}")
|
print(f" → {primary_domain}/{new_filename}")
|
||||||
|
|
||||||
print(f"\n迁移完成。")
|
print("\n迁移完成。")
|
||||||
|
|
||||||
# 展示新结构
|
# 展示新结构
|
||||||
print("\n=== 新目录结构 ===")
|
print("\n=== 新目录结构 ===")
|
||||||
|
|||||||
@@ -165,7 +165,6 @@ def reclassify():
|
|||||||
new_domains = classify(body, old_domains)
|
new_domains = classify(body, old_domains)
|
||||||
|
|
||||||
primary = sanitize_name(new_domains[0])
|
primary = sanitize_name(new_domains[0])
|
||||||
old_primary = sanitize_name(old_domains[0]) if old_domains else "未分类"
|
|
||||||
|
|
||||||
if name and name != bucket_id:
|
if name and name != bucket_id:
|
||||||
new_filename = f"{sanitize_name(name)}_{bucket_id}.md"
|
new_filename = f"{sanitize_name(name)}_{bucket_id}.md"
|
||||||
@@ -197,7 +196,7 @@ def reclassify():
|
|||||||
os.rmdir(dp)
|
os.rmdir(dp)
|
||||||
print(f"\n 🗑 删除空目录: {d}/")
|
print(f"\n 🗑 删除空目录: {d}/")
|
||||||
|
|
||||||
print(f"\n重分类完成。\n")
|
print("\n重分类完成。\n")
|
||||||
|
|
||||||
# 展示新结构
|
# 展示新结构
|
||||||
print("=== 新目录结构 ===")
|
print("=== 新目录结构 ===")
|
||||||
|
|||||||
126
test_smoke.py
126
test_smoke.py
@@ -1,126 +0,0 @@
|
|||||||
"""Ombre Brain 冒烟测试：验证核心功能链路"""
|
|
||||||
import asyncio
|
|
||||||
import os
|
|
||||||
|
|
||||||
# 确保模块路径
|
|
||||||
import sys
|
|
||||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
|
||||||
|
|
||||||
from utils import load_config, setup_logging
|
|
||||||
from bucket_manager import BucketManager
|
|
||||||
from dehydrator import Dehydrator
|
|
||||||
from decay_engine import DecayEngine
|
|
||||||
|
|
||||||
|
|
||||||
async def main():
|
|
||||||
config = load_config()
|
|
||||||
setup_logging("INFO")
|
|
||||||
bm = BucketManager(config)
|
|
||||||
dh = Dehydrator(config)
|
|
||||||
de = DecayEngine(config, bm)
|
|
||||||
|
|
||||||
print(f"API available: {dh.api_available}")
|
|
||||||
print(f"base_url: {dh.base_url}")
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== 1. 自动打标 =====
|
|
||||||
print("=== 1. analyze (自动打标) ===")
|
|
||||||
try:
|
|
||||||
result = await dh.analyze("今天学了 Python 的 asyncio，感觉收获很大，心情不错")
|
|
||||||
print(f" domain: {result['domain']}")
|
|
||||||
print(f" valence: {result['valence']}, arousal: {result['arousal']}")
|
|
||||||
print(f" tags: {result['tags']}")
|
|
||||||
print(" [OK]")
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== 2. 建桶 =====
|
|
||||||
print("=== 2. create (建桶) ===")
|
|
||||||
try:
|
|
||||||
bid = await bm.create(
|
|
||||||
content="P酱喜欢猫，家里养了一只橘猫叫小橘",
|
|
||||||
tags=["猫", "宠物"],
|
|
||||||
importance=7,
|
|
||||||
domain=["生活"],
|
|
||||||
valence=0.8,
|
|
||||||
arousal=0.4,
|
|
||||||
)
|
|
||||||
print(f" bucket_id: {bid}")
|
|
||||||
print(" [OK]")
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
return
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== 3. 搜索 =====
|
|
||||||
print("=== 3. search (检索) ===")
|
|
||||||
try:
|
|
||||||
hits = await bm.search("猫", limit=3)
|
|
||||||
print(f" found {len(hits)} results")
|
|
||||||
for h in hits:
|
|
||||||
name = h["metadata"].get("name", h["id"])
|
|
||||||
print(f" - {name} (score={h['score']:.1f})")
|
|
||||||
print(" [OK]")
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== 4. 脱水压缩 =====
|
|
||||||
print("=== 4. dehydrate (脱水压缩) ===")
|
|
||||||
try:
|
|
||||||
text = (
|
|
||||||
"这是一段很长的内容用来测试脱水功能。"
|
|
||||||
"P酱今天去了咖啡厅，点了一杯拿铁，然后坐在窗边看书看了两个小时。"
|
|
||||||
"期间遇到了一个朋友，聊了聊最近的工作情况。回家之后写了会代码。"
|
|
||||||
)
|
|
||||||
summary = await dh.dehydrate(text, {})
|
|
||||||
print(f" summary: {summary[:120]}...")
|
|
||||||
print(" [OK]")
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== 5. 衰减评分 =====
|
|
||||||
print("=== 5. decay score (衰减评分) ===")
|
|
||||||
try:
|
|
||||||
bucket = await bm.get(bid)
|
|
||||||
score = de.calculate_score(bucket["metadata"])
|
|
||||||
print(f" score: {score:.3f}")
|
|
||||||
print(" [OK]")
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== 6. 日记整理 =====
|
|
||||||
print("=== 6. digest (日记整理) ===")
|
|
||||||
try:
|
|
||||||
diary = (
|
|
||||||
"今天上午写了个 Python 脚本处理数据，下午和朋友去吃了火锅很开心，"
|
|
||||||
"晚上失眠了有点焦虑，想了想明天的面试。"
|
|
||||||
)
|
|
||||||
items = await dh.digest(diary)
|
|
||||||
print(f" 拆分出 {len(items)} 条记忆:")
|
|
||||||
for it in items:
|
|
||||||
print(f" - [{it.get('name','')}] domain={it['domain']} V{it['valence']:.1f}/A{it['arousal']:.1f}")
|
|
||||||
print(" [OK]")
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== 7. 清理测试数据 =====
|
|
||||||
print("=== 7. cleanup (删除测试桶) ===")
|
|
||||||
try:
|
|
||||||
ok = await bm.delete(bid)
|
|
||||||
print(f" deleted: {ok}")
|
|
||||||
print(" [OK]")
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
print()
|
|
||||||
|
|
||||||
print("=" * 40)
|
|
||||||
print("冒烟测试完成!")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
asyncio.run(main())
|
|
||||||
159
test_tools.py
159
test_tools.py
@@ -1,159 +0,0 @@
|
|||||||
"""Ombre Brain MCP tool-level end-to-end test: direct calls to @mcp.tool() functions
|
|
||||||
Ombre Brain MCP 工具层端到端测试：直接调用 @mcp.tool() 函数"""
|
|
||||||
import asyncio
|
|
||||||
import os
|
|
||||||
import sys
|
|
||||||
|
|
||||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
|
||||||
|
|
||||||
from utils import load_config, setup_logging
|
|
||||||
|
|
||||||
config = load_config()
|
|
||||||
setup_logging("INFO")
|
|
||||||
|
|
||||||
# Must import after config is set, since server.py does module-level init
|
|
||||||
# 必须在配置好后导入，因为 server.py 有模块级初始化
|
|
||||||
from server import breath, hold, trace, pulse, grow
|
|
||||||
|
|
||||||
|
|
||||||
async def main():
|
|
||||||
passed = 0
|
|
||||||
failed = 0
|
|
||||||
|
|
||||||
# ===== pulse =====
|
|
||||||
print("=== [1/6] pulse ===")
|
|
||||||
try:
|
|
||||||
r = await pulse()
|
|
||||||
assert "Ombre Brain" in r
|
|
||||||
print(f" {r.splitlines()[0]}")
|
|
||||||
print(" [OK]")
|
|
||||||
passed += 1
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
failed += 1
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== hold =====
|
|
||||||
print("=== [2/6] hold ===")
|
|
||||||
try:
|
|
||||||
r = await hold(content="P酱最喜欢的编程语言是 Python，喜欢用 FastAPI 写后端", tags="编程,偏好", importance=8)
|
|
||||||
print(f" {r.splitlines()[0]}")
|
|
||||||
assert any(kw in r for kw in ["新建", "合并", "📌"])
|
|
||||||
print(" [OK]")
|
|
||||||
passed += 1
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
failed += 1
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== hold (merge test / 合并测试) =====
|
|
||||||
print("=== [2b/6] hold (合并测试) ===")
|
|
||||||
try:
|
|
||||||
r = await hold(content="P酱也喜欢用 Python 写爬虫和数据分析", tags="编程", importance=6)
|
|
||||||
print(f" {r.splitlines()[0]}")
|
|
||||||
print(" [OK]")
|
|
||||||
passed += 1
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
failed += 1
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== breath =====
|
|
||||||
print("=== [3/6] breath ===")
|
|
||||||
try:
|
|
||||||
r = await breath(query="Python 编程", max_results=3)
|
|
||||||
print(f" 结果前80字: {r[:80]}...")
|
|
||||||
assert "未找到" not in r
|
|
||||||
print(" [OK]")
|
|
||||||
passed += 1
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
failed += 1
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== breath (emotion resonance / 情感共鸣) =====
|
|
||||||
print("=== [3b/6] breath (情感共鸣检索) ===")
|
|
||||||
try:
|
|
||||||
r = await breath(query="编程", domain="编程", valence=0.8, arousal=0.5)
|
|
||||||
print(f" 结果前80字: {r[:80]}...")
|
|
||||||
print(" [OK]")
|
|
||||||
passed += 1
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
failed += 1
|
|
||||||
print()
|
|
||||||
|
|
||||||
# --- Get a bucket ID for subsequent tests / 取一个桶 ID 用于后续测试 ---
|
|
||||||
bucket_id = None
|
|
||||||
from bucket_manager import BucketManager
|
|
||||||
bm = BucketManager(config)
|
|
||||||
all_buckets = await bm.list_all()
|
|
||||||
if all_buckets:
|
|
||||||
bucket_id = all_buckets[0]["id"]
|
|
||||||
|
|
||||||
# ===== trace =====
|
|
||||||
print("=== [4/6] trace ===")
|
|
||||||
if bucket_id:
|
|
||||||
try:
|
|
||||||
r = await trace(bucket_id=bucket_id, domain="编程,创作", importance=9)
|
|
||||||
print(f" {r}")
|
|
||||||
assert "已修改" in r
|
|
||||||
print(" [OK]")
|
|
||||||
passed += 1
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
failed += 1
|
|
||||||
else:
|
|
||||||
print(" [SKIP] 没有可编辑的桶")
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== grow =====
|
|
||||||
print("=== [5/6] grow ===")
|
|
||||||
try:
|
|
||||||
diary = (
|
|
||||||
"今天早上复习了线性代数，搞懂了特征值分解。"
|
|
||||||
"中午和室友去吃了拉面，聊了聊暑假实习的事。"
|
|
||||||
"下午写了一个 Flask 项目的 API 接口。"
|
|
||||||
"晚上看了部电影叫《星际穿越》，被结尾感动哭了。"
|
|
||||||
)
|
|
||||||
r = await grow(content=diary)
|
|
||||||
print(f" {r.splitlines()[0]}")
|
|
||||||
for line in r.splitlines()[1:]:
|
|
||||||
if line.strip():
|
|
||||||
print(f" {line}")
|
|
||||||
assert "条|新" in r or "整理" in r
|
|
||||||
print(" [OK]")
|
|
||||||
passed += 1
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
failed += 1
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== cleanup via trace(delete=True) / 清理测试数据 =====
|
|
||||||
print("=== [6/6] cleanup (清理全部测试数据) ===")
|
|
||||||
try:
|
|
||||||
all_buckets = await bm.list_all()
|
|
||||||
for b in all_buckets:
|
|
||||||
r = await trace(bucket_id=b["id"], delete=True)
|
|
||||||
print(f" {r}")
|
|
||||||
print(" [OK]")
|
|
||||||
passed += 1
|
|
||||||
except Exception as e:
|
|
||||||
print(f" [FAIL] {e}")
|
|
||||||
failed += 1
|
|
||||||
print()
|
|
||||||
|
|
||||||
# ===== Confirm cleanup / 确认清理干净 =====
|
|
||||||
final = await pulse()
|
|
||||||
print(f"清理后: {final.splitlines()[0]}")
|
|
||||||
print()
|
|
||||||
print("=" * 50)
|
|
||||||
print(f"MCP tool test complete / 工具测试完成: {passed} passed / {failed} failed")
|
|
||||||
if failed == 0:
|
|
||||||
print("All passed ✓")
|
|
||||||
else:
|
|
||||||
print(f"{failed} failed ✗")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
asyncio.run(main())
|
|
||||||
Reference in New Issue
Block a user