init: first commit to Gitea mirror, update README with Docker quick start and new repo URL
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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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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| `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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`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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| 脚本 Script | 用途 Purpose |
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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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| `test_smoke.py` | 冒烟测试 / Smoke test |
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## License
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MIT
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