wunianxiao
b9de3665d9
Phase 1 ✅ CLI 框架 + 配置系统 - clap 子命令: login / listen / send / whoami / usage - config.json + env var 替换 - tracing 日志系统 - state 持久化（auth/runtime 文件存 + PostgreSQL） Phase 2 ✅ 微信通道 - wechat::client — 完整 iLink Bot HTTP API 实现 - 扫码登录（终端二维码 + 轮询状态） - 长轮询 getupdates / 消息收发 / 监听注册 Phase 3 ✅ AI 对话（纯文本 + function calling） - LlmProvider trait: DeepSeek + LM Studio 实现 - SSE 流式解析（text / reasoning / tool_calls delta / usage） - Conversation: 消息历史 + chat / chat_with_tools 工具循环 Phase 4 ✅ PostgreSQL 集成 - app_state（认证 KV 存储） - chat_records（消息收发记录） - llm_usage（Token 用量统计缓存命中率） - user_memories（长期记忆持久化） - pending_approvals（审批确认码） - scheduled_tasks（定时任务表） Phase 5 ✅ 一切皆 Skill（工具系统） - SkillRegistry: 系统 + 用户 skills 双目录合并 - SKILL.md 解析器 + 子进程执行器（stdin JSON → stdout） - 9 个系统 Skills: datetime / weather / search / email / shell / schedule / memos / read_memories / read_summaries - ApprovalManager: High 风险技能 → 确认码审批（透明模式） - High 风险技能：确认码审批（透明模式） Phase 6 ✅ 定时任务调度器 上下文管理 - ChatSession: checkpoint + token budget (28K) + summaries - Token 估算器（中英文自适应） - 12h 空闲 → trigger_idle_summary（不入会话） - Budget 溢出 → trigger_overflow_summary（入会话 + drain 旧消息） - Summarizer: LLM 生成自然语言摘要（fallback 简单截断） - 长期记忆 / 摘要 通过 read_memories / read_summaries 工具按需读取 工具调用日志 + Token 统计 - INFO: 工具名 + 参数 + 结果摘要 - DEBUG: 子进程 exit/stdout/stderr - ias usage --since --until --model 查看用量和缓存命中率
126 lines
3.5 KiB
Rust
126 lines
3.5 KiB
Rust
use serde::{Deserialize, Serialize};
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// ─── 角色 ───
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#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
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#[serde(rename_all = "lowercase")]
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pub enum Role {
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System,
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User,
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Assistant,
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Tool,
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}
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// ─── 消息 ───
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct Message {
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pub role: Role,
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#[serde(default)]
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pub content: String,
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#[serde(skip_serializing_if = "Option::is_none")]
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pub tool_call_id: Option<String>,
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#[serde(skip_serializing_if = "Option::is_none")]
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pub name: Option<String>,
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#[serde(skip_serializing_if = "Option::is_none")]
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pub tool_calls: Option<Vec<ToolCall>>,
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}
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impl Message {
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pub fn system(content: impl Into<String>) -> Self {
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Self { role: Role::System, content: content.into(), tool_call_id: None, name: None, tool_calls: None }
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}
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pub fn user(content: impl Into<String>) -> Self {
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Self { role: Role::User, content: content.into(), tool_call_id: None, name: None, tool_calls: None }
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}
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pub fn assistant(content: impl Into<String>) -> Self {
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Self { role: Role::Assistant, content: content.into(), tool_call_id: None, name: None, tool_calls: None }
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}
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pub fn assistant_with_tool_calls(tool_calls: Vec<ToolCall>) -> Self {
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Self { role: Role::Assistant, content: String::new(), tool_call_id: None, name: None, tool_calls: Some(tool_calls) }
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}
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pub fn tool_result(tool_call_id: &str, name: &str, result: &str) -> Self {
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Self { role: Role::Tool, content: result.to_string(), tool_call_id: Some(tool_call_id.to_string()), name: Some(name.to_string()), tool_calls: None }
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}
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}
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// ─── 工具调用 ───
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ToolCall {
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pub id: String,
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#[serde(rename = "type")]
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pub call_type: String, // "function"
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pub function: ToolFunctionCall,
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}
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ToolFunctionCall {
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pub name: String,
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pub arguments: String, // JSON string
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}
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// ─── 流式响应块 ───
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#[derive(Debug, Clone)]
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pub enum StreamChunk {
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/// 文本片段 delta
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Text(String),
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/// 思考/推理内容（DeepSeek thinking）
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Reasoning(String),
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/// 完成（携带完整文本，以及可选的工具调用）
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Done {
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text: String,
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reasoning: Option<String>,
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tool_calls: Option<Vec<ToolCall>>,
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usage: Option<Usage>,
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},
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/// 错误
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Error(String),
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}
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// ─── Token 用量 ───
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#[derive(Debug, Clone, Serialize, Deserialize, Default)]
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pub struct Usage {
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pub prompt_tokens: u32,
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pub completion_tokens: u32,
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pub total_tokens: u32,
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#[serde(default)]
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pub prompt_cache_hit_tokens: u32,
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#[serde(default)]
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pub prompt_cache_miss_tokens: u32,
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}
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// ─── 对话配置 ───
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#[derive(Debug, Clone)]
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pub struct ConversationConfig {
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pub system_prompt: String,
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pub model: String,
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pub temperature: f32,
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pub max_tokens: u32,
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pub thinking: bool,
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/// LLM function calling 的工具定义（JSON 数组）
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pub tools: Option<Vec<serde_json::Value>>,
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}
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impl Default for ConversationConfig {
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fn default() -> Self {
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Self {
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system_prompt: "You are a concise and helpful assistant replying in Chinese. \
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Keep replies practical and natural."
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.to_string(),
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model: "deepseek-v4-flash".to_string(),
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temperature: 0.7,
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max_tokens: 4096,
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thinking: true,
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tools: None,
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}
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}
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}
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