feat: v2.3.0 流式输出 + 日志系统 + 会议室架构全面升级

- 流式输出: SSE 逐 token 接收, \\n\n\ 段落缓冲后 mdprint 彩色渲染 - 日志系统: charmbracelet/log v2 双写(stderr + log.yml), yunshu log 命令 - 会议室架构: dialog(main) + weather/profile/note(sub) 多 Agent 编排 - 泛型工具注册: NewTool[T] 反射推导 JSON Schema, 类型安全 - 安全加固: safeMemoryPath 三段校验(EvalSymlinks+Rel), maxToolCalls=2 - 性能优化: sync.Once 延迟加载, note 一步完成, obs/summary 合并 - Prompt 适配: 流式输出原则(先调工具不说话), 单 Agent 查询跳过 obs+summary - 文档: AGENTS.md + architecture.md + changelog.md 全部同步至 v2.3.0
2026-05-16 17:21:29 +08:00
parent 0898188086
commit c4a0e3ef53
24 changed files with 2769 additions and 338 deletions
--- a/llm.go
+++ b/llm.go
@@ -1,54 +1,60 @@
 package main

 import (
+	"bufio"
 	"bytes"
 	"encoding/json"
 	"fmt"
 	"io"
 	"net/http"
 	"os"
+	"strings"
+	"sync"
 	"time"
+
+	"hub.gaomia.site/titor/YunShu/pkg/mdprint"
 )

 var (
+	llmOnce  sync.Once
 	llmHost  = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"
 	llmModel = "doubao-seed-2-0-pro-260215"
 	llmKey   = ""
 )

-func init() {
-	// 1. 从配置文件加载
-	cfg, err := LoadConfig()
-	if err == nil {
-		if cfg.LLM.Host != "" {
-			llmHost = cfg.LLM.Host
+func loadLLMConfig() {
+	llmOnce.Do(func() {
+		cfg, err := LoadConfig()
+		if err == nil {
+			if cfg.LLM.Host != "" {
+				llmHost = cfg.LLM.Host
+			}
+			if cfg.LLM.Model != "" {
+				llmModel = cfg.LLM.Model
+			}
+			if cfg.LLM.Key != "" {
+				llmKey = cfg.LLM.Key
+			}
 		}
-		if cfg.LLM.Model != "" {
-			llmModel = cfg.LLM.Model
-		}
-		if cfg.LLM.Key != "" {
-			llmKey = cfg.LLM.Key
-		}
-	}

-	// 2. 环境变量覆盖配置文件（优先级最高）
-	if v := os.Getenv("LLM_ENDPOINT"); v != "" {
-		llmHost = v
-	}
-	if v := os.Getenv("LLM_MODEL"); v != "" {
-		llmModel = v
-	}
-	if v := os.Getenv("LLM_API_KEY"); v != "" {
-		llmKey = v
-	}
-	// 兼容旧环境变量名
-	if v := os.Getenv("OPENAI_API_KEY"); v != "" && llmKey == "" {
-		llmKey = v
-	}
+		if v := os.Getenv("LLM_ENDPOINT"); v != "" {
+			llmHost = v
+		}
+		if v := os.Getenv("LLM_MODEL"); v != "" {
+			llmModel = v
+		}
+		if v := os.Getenv("LLM_API_KEY"); v != "" {
+			llmKey = v
+		}
+		if v := os.Getenv("OPENAI_API_KEY"); v != "" && llmKey == "" {
+			llmKey = v
+		}
+	})
 }

 // GetLLMKey 获取 API Key，优先使用已加载的密钥
 func GetLLMKey() (string, error) {
+	loadLLMConfig()
 	if llmKey == "" {
 		return "", fmt.Errorf("未配置 API Key。请运行 'weather-cia onboard' 初始化，或设置 LLM_API_KEY 环境变量")
 	}
@@ -57,11 +63,14 @@ func GetLLMKey() (string, error) {

 // CallLLM 调用大模型 API（兼容 OpenAI Chat Completion 格式）
 func CallLLM(messages []Message, toolDefs []ToolDef) (*OpenAIResponse, error) {
+	loadLLMConfig()
 	apiKey, err := GetLLMKey()
 	if err != nil {
 		return nil, err
 	}

+	start := time.Now()
+
 	reqBody := map[string]interface{}{
 		"model":    llmModel,
 		"messages": messages,
@@ -126,5 +135,269 @@ func CallLLM(messages []Message, toolDefs []ToolDef) (*OpenAIResponse, error) {
 		return nil, fmt.Errorf("LLM 返回空结果")
 	}

+	if result.Usage.TotalTokens > 0 {
+		infoLog("LLM 调用完成",
+			"tokens", result.Usage.TotalTokens,
+			"duration", time.Since(start).Round(time.Millisecond*100).String(),
+		)
+	}
 	return &result, nil
 }
+
+// ============================================================
+// 流式输出 (SSE)
+// ============================================================
+
+type accumulatedToolCall struct {
+	ID   string
+	Type string
+	Name string
+	Args string
+}
+
+type sseChunk struct {
+	ID      string      `json:"id"`
+	Object  string      `json:"object"`
+	Created int64       `json:"created"`
+	Model   string      `json:"model"`
+	Choices []sseChoice `json:"choices"`
+	Usage   *OpenAIUsage `json:"usage,omitempty"`
+}
+
+type sseChoice struct {
+	Index        int         `json:"index"`
+	Delta        sseDelta    `json:"delta"`
+	FinishReason *string     `json:"finish_reason"`
+}
+
+type sseDelta struct {
+	Role      string              `json:"role,omitempty"`
+	Content   string              `json:"content,omitempty"`
+	ToolCalls []sseToolCallDelta  `json:"tool_calls,omitempty"`
+}
+
+type sseToolCallDelta struct {
+	Index    int                        `json:"index"`
+	ID       string                     `json:"id,omitempty"`
+	Type     string                     `json:"type,omitempty"`
+	Function *sseToolCallFunctionDelta  `json:"function,omitempty"`
+}
+
+type sseToolCallFunctionDelta struct {
+	Name      string `json:"name,omitempty"`
+	Arguments string `json:"arguments,omitempty"`
+}
+
+// CallLLMStream 流式调用 LLM，按 \n\n 段落边界缓冲后通过 mdprint 渲染到 stdout
+func CallLLMStream(messages []Message, toolDefs []ToolDef) (*OpenAIResponse, error) {
+	loadLLMConfig()
+	apiKey, err := GetLLMKey()
+	if err != nil {
+		return nil, err
+	}
+
+	start := time.Now()
+
+	reqBody := map[string]any{
+		"model":    llmModel,
+		"messages": messages,
+		"stream":   true,
+	}
+
+	if len(toolDefs) > 0 {
+		tools := make([]OpenAITool, 0, len(toolDefs))
+		for _, td := range toolDefs {
+			tools = append(tools, OpenAITool{
+				Type: "function",
+				Function: OpenAIToolFunc{
+					Name:        td.Name,
+					Description: td.Description,
+					Parameters:  td.Parameters,
+				},
+			})
+		}
+		reqBody["tools"] = tools
+		reqBody["tool_choice"] = "auto"
+	}
+
+	body, err := json.Marshal(reqBody)
+	if err != nil {
+		return nil, fmt.Errorf("序列化请求失败: %w", err)
+	}
+
+	req, err := http.NewRequest("POST", llmHost, bytes.NewReader(body))
+	if err != nil {
+		return nil, fmt.Errorf("创建请求失败: %w", err)
+	}
+
+	req.Header.Set("Content-Type", "application/json")
+	req.Header.Set("Authorization", "Bearer "+apiKey)
+
+	client := &http.Client{Timeout: 120 * time.Second}
+	resp, err := client.Do(req)
+	if err != nil {
+		return nil, fmt.Errorf("请求 LLM 失败: %w", err)
+	}
+	defer resp.Body.Close()
+
+	if resp.StatusCode != 200 {
+		respData, _ := io.ReadAll(resp.Body)
+		var errResp OpenAIErrorResponse
+		if json.Unmarshal(respData, &errResp) == nil && errResp.Error.Message != "" {
+			return nil, fmt.Errorf("LLM API 错误 [%s]: %s", errResp.Error.Type, errResp.Error.Message)
+		}
+		return nil, fmt.Errorf("LLM API 返回 HTTP %d: %s", resp.StatusCode, string(respData))
+	}
+
+	reader := bufio.NewReader(resp.Body)
+	var fullContent strings.Builder
+	var blockBuf strings.Builder
+	toolCallAccums := make(map[int]*accumulatedToolCall)
+	var responseID, responseModel string
+	var responseCreated int64
+	var usage *OpenAIUsage
+
+	for {
+		line, err := reader.ReadString('\n')
+		if err != nil {
+			if err == io.EOF {
+				break
+			}
+			return nil, fmt.Errorf("读取流响应失败: %w", err)
+		}
+
+		line = strings.TrimSpace(line)
+		if line == "" {
+			continue
+		}
+		if !strings.HasPrefix(line, "data: ") {
+			continue
+		}
+
+		data := strings.TrimPrefix(line, "data: ")
+		if data == "[DONE]" {
+			break
+		}
+
+		var chunk sseChunk
+		if err := json.Unmarshal([]byte(data), &chunk); err != nil {
+			continue
+		}
+
+		if responseID == "" && chunk.ID != "" {
+			responseID = chunk.ID
+		}
+		if responseModel == "" && chunk.Model != "" {
+			responseModel = chunk.Model
+		}
+		if responseCreated == 0 && chunk.Created != 0 {
+			responseCreated = chunk.Created
+		}
+		if chunk.Usage != nil {
+			usage = chunk.Usage
+		}
+
+		for _, choice := range chunk.Choices {
+			delta := choice.Delta
+
+			if delta.Content != "" {
+				fullContent.WriteString(delta.Content)
+				blockBuf.WriteString(delta.Content)
+				tryFlushBlocks(&blockBuf)
+			}
+
+			for _, tc := range delta.ToolCalls {
+				acc, ok := toolCallAccums[tc.Index]
+				if !ok {
+					acc = &accumulatedToolCall{}
+					toolCallAccums[tc.Index] = acc
+				}
+				if tc.ID != "" {
+					acc.ID = tc.ID
+				}
+				if tc.Type != "" {
+					acc.Type = tc.Type
+				}
+				if tc.Function != nil {
+					if tc.Function.Name != "" {
+						acc.Name = tc.Function.Name
+					}
+					acc.Args += tc.Function.Arguments
+				}
+			}
+		}
+	}
+
+	// 流结束，刷残段
+	if blockBuf.Len() > 0 {
+		mdprint.Print(blockBuf.String())
+	}
+
+	// 重建响应
+	var choice OpenAIChoice
+
+	if len(toolCallAccums) > 0 {
+		var tcs []ToolCall
+		for i := 0; i < len(toolCallAccums); i++ {
+			acc := toolCallAccums[i]
+			if acc == nil {
+				continue
+			}
+			tcs = append(tcs, ToolCall{
+				ID:   acc.ID,
+				Type: acc.Type,
+				Function: ToolCallFunction{
+					Name:      acc.Name,
+					Arguments: acc.Args,
+				},
+			})
+		}
+		choice.Message.ToolCalls = tcs
+	} else {
+		content := fullContent.String()
+		if content != "" {
+			choice.Message.Content = &content
+		}
+	}
+
+	result := &OpenAIResponse{
+		ID:      responseID,
+		Object:  "chat.completion",
+		Created: responseCreated,
+		Model:   responseModel,
+		Choices: []OpenAIChoice{choice},
+	}
+
+	if usage != nil && usage.TotalTokens > 0 {
+		result.Usage = *usage
+		infoLog("LLM 调用完成",
+			"tokens", usage.TotalTokens,
+			"duration", time.Since(start).Round(time.Millisecond*100).String(),
+		)
+	}
+
+	return result, nil
+}
+
+// tryFlushBlocks 检测 blockBuf 中是否有完整的 Markdown block（以 \n\n 为界）
+// 有则通过 mdprint 渲染到 stdout，剩余残段留在 buf 中继续缓冲
+func tryFlushBlocks(buf *strings.Builder) {
+	content := buf.String()
+	idx := strings.LastIndex(content, "\n\n")
+	if idx < 0 {
+		return
+	}
+
+	complete := strings.TrimRight(content[:idx], "\n\r\t ")
+	if complete == "" {
+		return
+	}
+
+	mdprint.Print(complete)
+
+	remainder := content[idx+2:]
+	buf.Reset()
+	if remainder != "" {
+		buf.WriteString(remainder)
+	}
+}