97 lines
3.3 KiB
Markdown
97 lines
3.3 KiB
Markdown
# 实现细节
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> 2024-06-18 当前状态
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---
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## 关键文件清单
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```
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src/
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├── daemon.rs → 主控 (LLM/Tool/Send Consumer, Registry, execute_builtin)
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├── main.rs → CLI (cmd_tool_dynamic, cmd_tools)
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├── cli.rs → clap 命令定义
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├── logger.rs → 三路日志初始化
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├── tools/
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│ ├── mod.rs → 模块声明 (6 个)
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│ ├── spec.rs → ToolSpec + ParamSpec
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│ ├── parser.rs → parse_dir() YAML 解析 (4 tests)
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│ ├── registry.rs → Registry (load/list/dispatch)
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│ ├── executor.rs → execute_loop (spawn→stdout→http/db/result)
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│ └── approval.rs → ApprovalManager (6位确认码)
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├── llm/
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│ ├── types.rs → Message, ToolCall, StreamChunk, ConversationConfig
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│ ├── conversation.rs → Conversation (run_tool_loop, summarize)
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│ ├── deepseek.rs → DeepSeek provider (tool_call 流式解析)
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│ └── provider.rs → LlmProvider trait
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└── context/
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├── types.rs → ChatSession, add_tool_result
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└── builder.rs → build_context (system prompt + 摘要 + 消息)
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```
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---
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## 工具调用全链路
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```
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用户消息
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→ LLM Consumer: 创建 Conversation, 注册 2 元工具
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→ LLM: 调 query_capabilities (tool_call)
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→ ToolExecutor → PipelineMessage::ToolCall → 消息队列
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→ ToolConsumer → execute_builtin → query_capabilities → 返回工具列表
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→ ToolResult → LLM Consumer → resume
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→ LLM: 调 call_capability(name="weather_now", prompt='{"location":"北京"}')
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→ ToolConsumer → execute_builtin → call_capability 解包
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→ registry.dispatch("weather_now", {"location":"北京"})
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→ executor::execute_loop
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→ spawn weather --command now
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→ stdout: {"type":"http","url":"...","desc":"..."}
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→ 审批(如需要) → reqwest::get → response
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→ spawn weather --command now (带 response)
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→ stdout: {"type":"result","content":"北京晴，25°C"}
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→ ToolResult
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→ LLM Consumer → resume → 生成回复
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→ Send Consumer → 微信
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```
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---
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## executor::execute_loop 状态机
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```
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spawn tool
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→ stdout JSON parse
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├─ {"type":"result","content":"..."} → 返回 content
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├─ {"type":"http","method":"GET","url":"..."}
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│ → 审批(high risk) → HTTP → 存入 response
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│ → re-spawn (带 response 字段)
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└─ {"type":"db","operation":"read_memories",...}
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→ 审批 → MemoryStore 操作 → 存入 db_response
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→ re-spawn (带 db_response 字段)
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```
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---
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## 消息队列路由
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```rust
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MessageKind::UserMessage | ToolResult | ScheduledTask → LLM Consumer
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MessageKind::ToolCall | ApprovalRequest → Tool Consumer
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MessageKind::LLMReply → Send Consumer
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```
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---
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## System Prompt
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```
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你是 iAs 智能助手，通过微信与用户交流。回复简洁自然。
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你可以通过 function calling 调用两个元工具：
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1. query_capabilities — 查询所有可用能力工具
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2. call_capability — 调用能力工具，参数 {"name":"工具名","prompt":"{\"参数\":\"值\"}"}
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流程：收到消息 → query_capabilities → call_capability → 回复
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```
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