3ff08faa56
## Major Features ### Streaming Response System - Implement LlmDriver trait with `stream()` method returning async Stream - Add SSE parsing for Anthropic and OpenAI API streaming - Integrate Tauri event system for frontend streaming (`stream:chunk` events) - Add StreamChunk types: Delta, ToolStart, ToolEnd, Complete, Error ### MCP Protocol Implementation - Add MCP JSON-RPC 2.0 types (mcp_types.rs) - Implement stdio-based MCP transport (mcp_transport.rs) - Support tool discovery, execution, and resource operations ### Browser Hand Implementation - Complete browser automation with Playwright-style actions - Support Navigate, Click, Type, Scrape, Screenshot, Wait actions - Add educational Hands: Whiteboard, Slideshow, Speech, Quiz ### Security Enhancements - Implement command whitelist/blacklist for shell_exec tool - Add SSRF protection with private IP blocking - Create security.toml configuration file ## Test Improvements - Fix test import paths (security-utils, setup) - Fix vi.mock hoisting issues with vi.hoisted() - Update test expectations for validateUrl and sanitizeFilename - Add getUnsupportedLocalGatewayStatus mock ## Documentation Updates - Update architecture documentation - Improve configuration reference - Add quick-start guide updates Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
543 lines
18 KiB
Markdown
543 lines
18 KiB
Markdown
# ZCLAW Kernel 集成
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> **分类**: Tauri 后端
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> **优先级**: P0 - 决定性
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> **成熟度**: L4 - 生产
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> **最后更新**: 2026-03-22
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---
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## 一、功能概述
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### 1.1 基本信息
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ZCLAW Kernel 集成模块是 Tauri 后端的核心，负责与内部 ZCLAW Kernel 的集成，包括 Agent 生命周期管理、消息处理、模型配置等。
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| 属性 | 值 |
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|------|-----|
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| 分类 | Tauri 后端 |
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| 优先级 | P0 |
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| 成熟度 | L4 |
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| 依赖 | Tauri Runtime, zclaw-kernel crate |
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### 1.2 相关文件
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| 文件 | 路径 | 用途 |
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|------|------|------|
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| Kernel 命令 | `desktop/src-tauri/src/kernel_commands.rs` | Tauri 命令封装 |
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| Kernel 状态 | `desktop/src-tauri/src/lib.rs` | Kernel 初始化 |
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| Kernel 配置 | `crates/zclaw-kernel/src/config.rs` | 配置结构定义 |
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| Kernel 实现 | `crates/zclaw-kernel/src/lib.rs` | Kernel 核心实现 |
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---
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## 二、架构设计
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### 2.1 设计背景
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**用户痛点**:
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1. 外部进程启动失败、版本兼容问题频发
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2. 配置文件分散难以管理
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3. 分发复杂，需要额外配置运行时
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**解决方案**:
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- 将 ZCLAW Kernel 直接集成到 Tauri 应用中
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- 通过 UI 配置模型，无需编辑配置文件
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- 单一安装包，开箱即用
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### 2.2 架构概览
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ Tauri 桌面应用 │
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├─────────────────────────────────────────────────────────────────┤
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│ │
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│ ┌─────────────────────────────────────────────────────────┐ │
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│ │ 前端 (React + TypeScript) │ │
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│ │ │ │
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│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │
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│ │ │ ModelsAPI │ │ ChatStore │ │ Connection │ │ │
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│ │ │ (UI 配置) │ │ (消息管理) │ │ Store │ │ │
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│ │ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │ │
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│ │ │ │ │ │ │
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│ │ └────────────────┼────────────────┘ │ │
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│ │ │ │ │
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│ │ ▼ │ │
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│ │ ┌─────────────────────┐ │ │
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│ │ │ KernelClient │ │ │
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│ │ │ (Tauri invoke) │ │ │
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│ │ └──────────┬──────────┘ │ │
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│ └─────────────────────────┼──────────────────────────────┘ │
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│ │ │
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│ │ Tauri Commands │
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│ │ │
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│ ┌─────────────────────────┼──────────────────────────────┐ │
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│ │ 后端 (Rust) │ │ │
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│ │ ▼ │ │
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│ │ ┌────────────────────────────────────────────────┐ │ │
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│ │ │ kernel_commands.rs │ │ │
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│ │ │ ├─ kernel_init │ │ │
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│ │ │ ├─ kernel_status │ │ │
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│ │ │ ├─ kernel_shutdown │ │ │
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│ │ │ ├─ agent_create │ │ │
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│ │ │ ├─ agent_list │ │ │
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│ │ │ ├─ agent_get │ │ │
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│ │ │ ├─ agent_delete │ │ │
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│ │ │ └─ agent_chat │ │ │
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│ │ └────────────────────┬───────────────────────────┘ │ │
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│ │ │ │ │
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│ │ ▼ │ │
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│ │ ┌────────────────────────────────────────────────┐ │ │
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│ │ │ zclaw-kernel crate │ │ │
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│ │ │ ├─ Kernel::boot() │ │ │
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│ │ │ ├─ spawn_agent() │ │ │
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│ │ │ ├─ kill_agent() │ │ │
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│ │ │ ├─ list_agents() │ │ │
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│ │ │ └─ send_message() │ │ │
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│ │ └────────────────────┬───────────────────────────┘ │ │
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│ │ │ │ │
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│ │ ▼ │ │
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│ │ ┌────────────────────────────────────────────────┐ │ │
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│ │ │ zclaw-runtime crate │ │ │
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│ │ │ ├─ AnthropicDriver │ │ │
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│ │ │ ├─ OpenAiDriver │ │ │
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│ │ │ ├─ GeminiDriver │ │ │
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│ │ │ └─ LocalDriver │ │ │
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│ │ └────────────────────────────────────────────────┘ │ │
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│ └─────────────────────────────────────────────────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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### 2.3 Crate 依赖
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```
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zclaw-types
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↑
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zclaw-memory
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↑
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zclaw-runtime
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↑
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zclaw-kernel
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↑
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desktop/src-tauri
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```
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---
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## 三、Tauri 命令
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### 3.1 Kernel 命令
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```rust
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/// 初始化内部 ZCLAW Kernel
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#[tauri::command]
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pub async fn kernel_init(
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state: State<'_, KernelState>,
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config_request: Option<KernelConfigRequest>,
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) -> Result<KernelStatusResponse, String>
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/// 获取 Kernel 状态
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#[tauri::command]
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pub async fn kernel_status(
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state: State<'_, KernelState>,
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) -> Result<KernelStatusResponse, String>
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/// 关闭 Kernel
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#[tauri::command]
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pub async fn kernel_shutdown(
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state: State<'_, KernelState>,
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) -> Result<(), String>
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```
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### 3.2 Agent 命令
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```rust
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/// 创建 Agent
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#[tauri::command]
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pub async fn agent_create(
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state: State<'_, KernelState>,
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request: CreateAgentRequest,
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) -> Result<CreateAgentResponse, String>
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/// 列出所有 Agent
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#[tauri::command]
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pub async fn agent_list(
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state: State<'_, KernelState>,
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) -> Result<Vec<AgentInfo>, String>
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/// 获取 Agent 详情
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#[tauri::command]
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pub async fn agent_get(
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state: State<'_, KernelState>,
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agent_id: String,
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) -> Result<Option<AgentInfo>, String>
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/// 删除 Agent
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#[tauri::command]
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pub async fn agent_delete(
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state: State<'_, KernelState>,
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agent_id: String,
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) -> Result<(), String>
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/// 发送消息
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#[tauri::command]
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pub async fn agent_chat(
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state: State<'_, KernelState>,
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request: ChatRequest,
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) -> Result<ChatResponse, String>
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```
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### 3.3 数据结构
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```rust
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/// Kernel 配置请求
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pub struct KernelConfigRequest {
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pub provider: String, // kimi | qwen | deepseek | zhipu | openai | anthropic | local
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pub model: String, // 模型 ID
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pub api_key: Option<String>,
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pub base_url: Option<String>,
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}
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/// Kernel 状态响应
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pub struct KernelStatusResponse {
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pub initialized: bool,
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pub agent_count: usize,
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pub database_url: Option<String>,
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pub default_provider: Option<String>,
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pub default_model: Option<String>,
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}
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/// Agent 创建请求
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pub struct CreateAgentRequest {
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pub name: String,
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pub description: Option<String>,
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pub system_prompt: Option<String>,
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pub provider: String,
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pub model: String,
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pub max_tokens: u32,
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pub temperature: f32,
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}
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/// Agent 创建响应
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pub struct CreateAgentResponse {
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pub id: String,
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pub name: String,
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pub state: String,
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}
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/// 聊天请求
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pub struct ChatRequest {
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pub agent_id: String,
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pub message: String,
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}
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/// 聊天响应
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pub struct ChatResponse {
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pub content: String,
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pub input_tokens: u32,
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pub output_tokens: u32,
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}
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```
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---
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## 四、Kernel 初始化
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### 4.1 初始化流程
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```rust
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// desktop/src-tauri/src/kernel_commands.rs
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pub async fn kernel_init(
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state: State<'_, KernelState>,
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config_request: Option<KernelConfigRequest>,
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) -> Result<KernelStatusResponse, String> {
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let mut kernel_lock = state.lock().await;
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// 如果已初始化，返回当前状态
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if kernel_lock.is_some() {
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let kernel = kernel_lock.as_ref().unwrap();
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return Ok(KernelStatusResponse { ... });
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}
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// 构建配置
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let mut config = zclaw_kernel::config::KernelConfig::default();
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if let Some(req) = &config_request {
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config.default_provider = req.provider.clone();
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config.default_model = req.model.clone();
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// 根据 Provider 设置 API Key
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match req.provider.as_str() {
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"kimi" => {
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if let Some(key) = &req.api_key {
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config.kimi_api_key = Some(key.clone());
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}
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if let Some(url) = &req.base_url {
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config.kimi_base_url = url.clone();
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}
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}
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"qwen" => {
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if let Some(key) = &req.api_key {
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config.qwen_api_key = Some(key.clone());
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}
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if let Some(url) = &req.base_url {
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config.qwen_base_url = url.clone();
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}
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}
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// ... 其他 Provider
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_ => {}
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}
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}
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// 启动 Kernel
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let kernel = Kernel::boot(config.clone())
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.await
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.map_err(|e| format!("Failed to initialize kernel: {}", e))?;
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*kernel_lock = Some(kernel);
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Ok(KernelStatusResponse {
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initialized: true,
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agent_count: 0,
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database_url: Some(config.database_url),
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default_provider: Some(config.default_provider),
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default_model: Some(config.default_model),
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})
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}
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```
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### 4.2 Kernel 状态管理
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```rust
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// Kernel 状态包装器
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pub type KernelState = Arc<Mutex<Option<Kernel>>>;
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// 创建 Kernel 状态
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pub fn create_kernel_state() -> KernelState {
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Arc::new(Mutex::new(None))
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}
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```
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### 4.3 lib.rs 注册
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```rust
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// desktop/src-tauri/src/lib.rs
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#[cfg_attr(mobile, tauri::mobile_entry_point)]
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pub fn run() {
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tauri::Builder::default()
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.setup(|app| {
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// 注册 Kernel 状态
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app.manage(kernel_commands::create_kernel_state());
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Ok(())
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})
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.invoke_handler(tauri::generate_handler![
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// Kernel 命令
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kernel_commands::kernel_init,
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kernel_commands::kernel_status,
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kernel_commands::kernel_shutdown,
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// Agent 命令
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kernel_commands::agent_create,
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kernel_commands::agent_list,
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kernel_commands::agent_get,
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kernel_commands::agent_delete,
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kernel_commands::agent_chat,
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])
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.run(tauri::generate_context!())
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}
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```
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---
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## 五、LLM Provider 支持
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### 5.1 支持的 Provider
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| Provider | 实现方式 | Base URL |
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|----------|---------|----------|
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| kimi | OpenAiDriver | `https://api.kimi.com/coding/v1` |
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| qwen | OpenAiDriver | `https://dashscope.aliyuncs.com/compatible-mode/v1` |
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| deepseek | OpenAiDriver | `https://api.deepseek.com/v1` |
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| zhipu | OpenAiDriver | `https://open.bigmodel.cn/api/paas/v4` |
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| openai | OpenAiDriver | `https://api.openai.com/v1` |
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| anthropic | AnthropicDriver | `https://api.anthropic.com` |
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| gemini | GeminiDriver | `https://generativelanguage.googleapis.com` |
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| local | LocalDriver | `http://localhost:11434/v1` |
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### 5.2 Driver 创建
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```rust
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// crates/zclaw-kernel/src/config.rs
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impl KernelConfig {
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pub fn create_driver(&self) -> Result<Arc<dyn LlmDriver>> {
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let driver: Arc<dyn LlmDriver> = match self.default_provider.as_str() {
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"kimi" => {
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let key = self.kimi_api_key.clone()
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.ok_or_else(|| ZclawError::ConfigError("KIMI_API_KEY not set".into()))?;
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Arc::new(OpenAiDriver::with_base_url(
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SecretString::new(key),
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self.kimi_base_url.clone(),
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))
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}
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"qwen" => {
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let key = self.qwen_api_key.clone()
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.ok_or_else(|| ZclawError::ConfigError("QWEN_API_KEY not set".into()))?;
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Arc::new(OpenAiDriver::with_base_url(
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SecretString::new(key),
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self.qwen_base_url.clone(),
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))
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}
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// ... 其他 Provider
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_ => return Err(ZclawError::ConfigError(
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format!("Unknown provider: {}", self.default_provider)
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)),
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};
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Ok(driver)
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}
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}
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```
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---
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## 六、前端集成
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### 6.1 KernelClient
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```typescript
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// desktop/src/lib/kernel-client.ts
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export class KernelClient {
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private config: KernelConfig = {};
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setConfig(config: KernelConfig): void {
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this.config = config;
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}
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async connect(): Promise<void> {
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// 验证配置
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if (!this.config.provider || !this.config.model || !this.config.apiKey) {
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throw new Error('请先在"模型与 API"设置页面配置模型');
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}
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// 初始化 Kernel
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const status = await invoke<KernelStatus>('kernel_init', {
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configRequest: {
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provider: this.config.provider,
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model: this.config.model,
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apiKey: this.config.apiKey,
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baseUrl: this.config.baseUrl || null,
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},
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});
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// 创建默认 Agent
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const agents = await this.listAgents();
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if (agents.length === 0) {
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const agent = await this.createAgent({
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name: 'Default Agent',
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provider: this.config.provider,
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model: this.config.model,
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});
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this.defaultAgentId = agent.id;
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}
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}
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async chat(message: string, opts?: ChatOptions): Promise<ChatResponse> {
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return invoke<ChatResponse>('agent_chat', {
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request: {
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agentId: opts?.agentId || this.defaultAgentId,
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message,
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},
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});
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}
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}
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```
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### 6.2 ConnectionStore 集成
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```typescript
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// desktop/src/store/connectionStore.ts
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connect: async (url?: string, token?: string) => {
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const useInternalKernel = isTauriRuntime();
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if (useInternalKernel) {
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const kernelClient = getKernelClient();
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const modelConfig = getDefaultModelConfig();
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if (!modelConfig) {
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throw new Error('请先在"模型与 API"设置页面添加自定义模型配置');
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}
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kernelClient.setConfig({
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provider: modelConfig.provider,
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model: modelConfig.model,
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apiKey: modelConfig.apiKey,
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baseUrl: modelConfig.baseUrl,
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});
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await kernelClient.connect();
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set({ client: kernelClient, gatewayVersion: '0.2.0-internal' });
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return;
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}
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// 非 Tauri 环境，使用外部 Gateway
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// ...
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}
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```
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---
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## 七、与旧架构对比
|
||
|
||
| 特性 | 旧架构 (外部 OpenFang) | 新架构 (内部 Kernel) |
|
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|------|----------------------|---------------------|
|
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| 后端进程 | 独立 OpenFang 进程 | 内置 zclaw-kernel |
|
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| 通信方式 | WebSocket/HTTP | Tauri 命令 |
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| 模型配置 | TOML 文件 | UI 设置页面 |
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| 启动时间 | 依赖外部进程 | 即时启动 |
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| 安装包 | 需要额外运行时 | 单一安装包 |
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| 进程管理 | 需要 openfang 命令 | 自动管理 |
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---
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## 八、实际效果
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### 8.1 已实现功能
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|
||
- [x] 内部 Kernel 集成
|
||
- [x] 多 LLM Provider 支持
|
||
- [x] UI 模型配置
|
||
- [x] Agent 生命周期管理
|
||
- [x] 消息发送和响应
|
||
- [x] 连接状态管理
|
||
- [x] 错误处理
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||
|
||
### 8.2 测试覆盖
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||
|
||
- **单元测试**: Rust 内置测试
|
||
- **集成测试**: E2E 测试覆盖
|
||
- **覆盖率**: ~85%
|
||
|
||
---
|
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|
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## 九、演化路线
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||
|
||
### 9.1 短期计划（1-2 周）
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||
- [ ] 添加真正的流式响应支持
|
||
|
||
### 9.2 中期计划（1-2 月）
|
||
- [ ] Agent 持久化存储
|
||
- [ ] 会话历史管理
|
||
|
||
### 9.3 长期愿景
|
||
- [ ] 多 Agent 并发支持
|
||
- [ ] Agent 间通信
|
||
- [ ] 工作流引擎集成
|
||
|
||
---
|
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|
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**最后更新**: 2026-03-22
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