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docs: expand usage instructions with detailed steps
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119
README.md
119
README.md
@ -48,34 +48,131 @@ ts/
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## 在 WaterFlow 中使用
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### 方式一：作为独立模块引用
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本模块支持两种使用方式：**作为模块直接引用** 或 **作为 Skill 调用**。
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将 `ts/` 目录复制到你的 WaterFlow 项目中：
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### 方式一：作为模块直接引用（适合开发者集成）
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#### 步骤 1：复制源码
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将本项目的 `ts/` 目录复制到你的 WaterFlow 项目中，例如：
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```
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你的WaterFlow项目/
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├── src/
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│ └── runtime/
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│ └── core/
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│ └── graph_memory/ # 从 ts/src/runtime/core/ 复制
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└── ts/ # 或直接放在项目根目录
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└── bundled-skills/ # Skill 文件
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```
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#### 步骤 2：编译 TypeScript
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```bash
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cd ts/
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npm install
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npm run build
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```
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编译后的文件会输出到 `ts/dist/` 目录。
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#### 步骤 3：在代码中引用
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```typescript
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import { GraphMemoryTool, createGraphMemoryTool } from './runtime/core/tools/builtin/graph_memory_tool';
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import { createGraphMemoryTool } from './runtime/core/tools/builtin/graph_memory_tool';
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const tool = createGraphMemoryTool();
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const result = await tool.handler({
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// 创建工具实例，可以传入 sessionId 来区分不同会话
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const tool = createGraphMemoryTool('my-session-id');
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// 准备执行上下文
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const context = {
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toolCallId: 'call-123',
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workingDirectory: '/project',
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abortController: { signal: {} },
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config: { timeout: 30000 },
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logger: {
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info: console.log,
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warn: console.warn,
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error: console.error,
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debug: console.debug
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}
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};
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// 写入记忆示例
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const commitResult = await tool.handler({
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action: 'commit',
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params: {
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triplets: [
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{ subject: '用户', relation: '喜欢', object: '编程' }
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{ subject: '用户', relation: '喜欢', object: '编程' },
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{ subject: '用户', relation: '正在学习', object: 'TypeScript' }
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]
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}
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}, context);
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console.log(commitResult);
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// 输出: {"success":true,"data":{"createdEntities":4,"createdRelations":2}}
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// 检索记忆示例
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const recallResult = await tool.handler({
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action: 'recall',
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params: {
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queryIntent: '用户 编程'
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}
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}, context);
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console.log(recallResult);
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// 输出: {"success":true,"data":{"entities":[...],"relations":[...],"message":"找到 X 个实体, Y 条关系"}}
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```
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### 方式二：使用 Skill
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### 方式二：使用 Skill（推荐，适合 AI Agent 调用）
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GraphMemory 已配置为 bundled skill，可直接调用：
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#### 步骤 1：配置 Skill 来源
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在你的 WaterFlow 项目中，找到 Skill 配置文件，添加 bundled 来源指向本项目的 Skill 目录：
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```typescript
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// skill_interface.ts 或配置文件中
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import { DEFAULT_SKILL_LOADER_CONFIG } from './skill_interface';
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const config = {
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...DEFAULT_SKILL_LOADER_CONFIG,
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sources: {
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...DEFAULT_SKILL_LOADER_CONFIG.sources,
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bundled: './ts/bundled-skills' // 指向本项目的 Skill 目录
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},
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enabledSources: ['project', 'bundled']
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};
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```
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#### 步骤 2：通过 Agent 调用 Skill
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在你的 Agent 或 Workflow 中，通过 Tool 调用 Skill：
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```
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使用 skill:graph_memory 进行记忆操作
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使用 skill:graph_memory_persona 进行人设管理
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使用 skill:graph_memory_task 进行任务管理
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使用 skill:graph_memory 进行以下操作:
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1. 写入记忆: 我喜欢编程，正在学习 TypeScript
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2. 检索记忆: 找出我和编程相关的记忆
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```
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或者通过代码调用：
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```typescript
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// 通过 SkillTool 调用
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const skillResult = await skillTool.handler({
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skill: 'graph_memory',
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args: 'recall - queryIntent: "用户 学习"'
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}, context);
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```
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#### 可用 Skill 列表
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| Skill 名称 | 功能 | 使用场景 |
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|------------|------|----------|
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| `graph_memory` | 记忆 CRUD | 读取/写入/删除记忆 |
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| `graph_memory_persona` | 人设管理 | 设置 AI 角色性格 |
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| `graph_memory_task` | 任务管理 | 创建/更新长期任务 |
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---
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## API
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119
README_EN.md
119
README_EN.md
@ -49,34 +49,131 @@ ts/
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## Usage in WaterFlow
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### Method 1: Import as standalone module
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This module supports two usage methods: **import as module** or **use as Skill**.
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Copy the `ts/` directory to your WaterFlow project:
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### Method 1: Import as Module (for developer integration)
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#### Step 1: Copy source files
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Copy the `ts/` directory to your WaterFlow project, for example:
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```
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your-waterflow-project/
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├── src/
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│ └── runtime/
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│ └── core/
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│ └── graph_memory/ # Copy from ts/src/runtime/core/
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└── ts/ # Or place in project root
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└── bundled-skills/ # Skill files
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```
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#### Step 2: Build TypeScript
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```bash
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cd ts/
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npm install
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npm run build
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```
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Compiled files will be output to `ts/dist/`.
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#### Step 3: Import in your code
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```typescript
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import { GraphMemoryTool, createGraphMemoryTool } from './runtime/core/tools/builtin/graph_memory_tool';
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import { createGraphMemoryTool } from './runtime/core/tools/builtin/graph_memory_tool';
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const tool = createGraphMemoryTool();
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const result = await tool.handler({
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// Create tool instance, can pass sessionId to distinguish different sessions
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const tool = createGraphMemoryTool('my-session-id');
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// Prepare execution context
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const context = {
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toolCallId: 'call-123',
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workingDirectory: '/project',
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abortController: { signal: {} },
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config: { timeout: 30000 },
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logger: {
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info: console.log,
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warn: console.warn,
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error: console.error,
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debug: console.debug
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}
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};
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// Commit memory example
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const commitResult = await tool.handler({
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action: 'commit',
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params: {
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triplets: [
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{ subject: 'User', relation: 'likes', object: 'Programming' }
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{ subject: 'User', relation: 'likes', object: 'Programming' },
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{ subject: 'User', relation: 'is learning', object: 'TypeScript' }
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]
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}
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}, context);
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console.log(commitResult);
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// Output: {"success":true,"data":{"createdEntities":4,"createdRelations":2}}
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// Recall memory example
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const recallResult = await tool.handler({
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action: 'recall',
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params: {
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queryIntent: 'User Programming'
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}
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}, context);
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console.log(recallResult);
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// Output: {"success":true,"data":{"entities":[...],"relations":[...],"message":"Found X entities, Y relations"}}
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```
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### Method 2: Use Skill
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### Method 2: Use Skill (recommended for AI Agent)
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GraphMemory is configured as a bundled skill and can be called directly:
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#### Step 1: Configure Skill source
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In your WaterFlow project, find the Skill configuration file and add bundled source pointing to this project's Skill directory:
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```typescript
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// skill_interface.ts or config file
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import { DEFAULT_SKILL_LOADER_CONFIG } from './skill_interface';
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const config = {
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...DEFAULT_SKILL_LOADER_CONFIG,
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sources: {
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...DEFAULT_SKILL_LOADER_CONFIG.sources,
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bundled: './ts/bundled-skills' // Point to this project's Skill directory
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},
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enabledSources: ['project', 'bundled']
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};
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```
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#### Step 2: Call Skill via Agent
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In your Agent or Workflow, call Skill via Tool:
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```
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Use skill:graph_memory for memory operations
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Use skill:graph_memory_persona for persona management
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Use skill:graph_memory_task for task management
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Use skill:graph_memory for:
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1. Commit memory: I like programming, learning TypeScript
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2. Recall memory: Find memories related to me and programming
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```
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Or call via code:
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```typescript
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// Call via SkillTool
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const skillResult = await skillTool.handler({
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skill: 'graph_memory',
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args: 'recall - queryIntent: "User learning"'
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}, context);
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```
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#### Available Skills
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| Skill Name | Function | Use Case |
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|------------|----------|----------|
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| `graph_memory` | Memory CRUD | Read/Write/Delete memories |
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| `graph_memory_persona` | Persona management | Set AI role/personality |
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| `graph_memory_task` | Task management | Create/update long-term tasks |
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---
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## API
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Reference in New Issue
Block a user