feat(prompt-agent): 新增 PromptAgent Eino Chain 调用 LLM 优化提示词

- Eino Chain: formatMetaPrompt → llmRefine → 三段式输出 - callLLMRefine 调用 OpenAI 兼容 Chat Completions API - 支持 SSE 流式与非流式响应解析 - 无 API key 或调用失败时自动回退模板生成 - 新增完整的单元测试与集成测试（含 mock HTTP server）
2026-05-24 20:45:09 +08:00
parent ef5ddcfbb5
commit 788cd021ef
2 changed files with 656 additions and 0 deletions
@@ -0,0 +1,313 @@
+package service
+
+import (
+	"bufio"
+	"bytes"
+	"context"
+	"encoding/json"
+	"fmt"
+	"io"
+	"log"
+	"net/http"
+	"strings"
+
+	"gen2d/internal/config"
+
+	"github.com/cloudwego/eino/compose"
+)
+
+// llmCfg 保存 LLM 配置，由 main 通过 InitLLMConfig 注入。
+var llmCfg config.LLMConfig
+
+// InitLLMConfig 注入 LLM 配置。
+func InitLLMConfig(cfg config.LLMConfig) {
+	llmCfg = cfg
+}
+
+// PromptAgentInput 提示词优化 Agent 输入。
+type PromptAgentInput struct {
+	Tags      []string `json:"tags"`              // 用户选择的标签
+	AssetType string   `json:"assetType"`          // 素材类型：sprite/background/ui/animation
+	Prompt    string   `json:"prompt,omitempty"`   // 用户原始提示词
+	UserNote  string   `json:"userNote,omitempty"` // 用户额外描述
+}
+
+// PromptAgentOutput 提示词优化 Agent 输出。
+type PromptAgentOutput struct {
+	Prompt  string `json:"prompt"`  // 优化后的标准提示词
+	RawText string `json:"rawText"` // LLM 原始返回文本
+}
+
+// NewPromptAgentGraph 创建提示词优化 Agent Chain。
+//
+//	START → formatMetaPrompt → llmRefine → END
+func NewPromptAgentGraph() (*compose.Chain[PromptAgentInput, PromptAgentOutput], error) {
+	chain := compose.NewChain[PromptAgentInput, PromptAgentOutput]()
+
+	chain.AppendLambda(
+		compose.InvokableLambda(func(ctx context.Context, in PromptAgentInput) (string, error) {
+			return buildMetaPrompt(in), nil
+		}),
+	)
+
+	chain.AppendLambda(
+		compose.InvokableLambda(func(ctx context.Context, metaPrompt string) (PromptAgentOutput, error) {
+			return callLLMRefine(ctx, metaPrompt)
+		}),
+	)
+
+	return chain, nil
+}
+
+// RunPromptAgent 编译并执行提示词优化 Agent。
+func RunPromptAgent(ctx context.Context, in PromptAgentInput) (*PromptAgentOutput, error) {
+	g, err := NewPromptAgentGraph()
+	if err != nil {
+		return nil, fmt.Errorf("create prompt agent: %w", err)
+	}
+
+	r, err := g.Compile(ctx)
+	if err != nil {
+		return nil, fmt.Errorf("compile prompt agent: %w", err)
+	}
+
+	output, err := r.Invoke(ctx, in)
+	if err != nil {
+		return nil, fmt.Errorf("invoke prompt agent: %w", err)
+	}
+
+	return &output, nil
+}
+
+// buildMetaPrompt 构建发送给 LLM 的元提示词。
+func buildMetaPrompt(in PromptAgentInput) string {
+	var sb strings.Builder
+
+	sb.WriteString("你是一个专业的 2D 游戏素材提示词工程师。\n")
+	sb.WriteString("根据用户提供的标签和素材类型，生成一个详细、规范的中文提示词。\n\n")
+
+	sb.WriteString("输出要求：\n")
+	sb.WriteString("1. 三段式结构：【主题】描述画面主体与场景,【风格】描述艺术风格与视觉特征,【技术】描述分辨率、方向数等技术参数\n")
+	sb.WriteString("2. 使用专业术语，描述具体、可执行\n")
+	sb.WriteString("3. 风格一致，适合游戏资产管线\n\n")
+
+	tagStr := strings.Join(in.Tags, "、")
+	sb.WriteString(fmt.Sprintf("用户选择标签: %s\n", tagStr))
+	sb.WriteString(fmt.Sprintf("素材类型: %s\n", in.AssetType))
+	if in.Prompt != "" {
+		sb.WriteString(fmt.Sprintf("用户原始描述: %s\n", in.Prompt))
+	}
+	if in.UserNote != "" {
+		sb.WriteString(fmt.Sprintf("补充说明: %s\n", in.UserNote))
+	}
+
+	return sb.String()
+}
+
+// ======================== ChatModel 调用层 ========================
+
+// chatMessage OpenAI 兼容的消息结构。
+type chatMessage struct {
+	Role    string `json:"role"`
+	Content string `json:"content"`
+}
+
+// chatRequest OpenAI 兼容的请求体。
+type chatRequest struct {
+	Model       string        `json:"model"`
+	Messages    []chatMessage `json:"messages"`
+	Temperature float64       `json:"temperature"`
+	MaxTokens   int           `json:"max_tokens,omitempty"`
+	Stream      bool          `json:"stream"`
+}
+
+// chatResponse OpenAI 兼容的非流式响应。
+type chatResponse struct {
+	Choices []struct {
+		Message chatMessage `json:"message"`
+	} `json:"choices"`
+}
+
+// callLLMRefine 调用 LLM 生成规范化提示词。未配置 API key 时回退到模板生成。
+func callLLMRefine(ctx context.Context, metaPrompt string) (PromptAgentOutput, error) {
+	if llmCfg.APIKey == "" {
+		log.Println("[prompt_agent] LLM API key not configured, using template fallback")
+		return fallbackRefine(metaPrompt), nil
+	}
+
+	messages := []chatMessage{
+		{Role: "system", Content: "你是一个专业的2D游戏素材提示词工程师，只输出优化后的中文提示词，不要任何解释。"},
+		{Role: "user", Content: metaPrompt},
+	}
+
+	result, err := chatCompletion(ctx, messages)
+	if err != nil {
+		log.Printf("[prompt_agent] LLM API call failed: %v, using template fallback", err)
+		return fallbackRefine(metaPrompt), nil
+	}
+
+	return PromptAgentOutput{
+		Prompt:  result,
+		RawText: result,
+	}, nil
+}
+
+// chatCompletion 调用 OpenAI 兼容的 Chat Completions API。
+func chatCompletion(ctx context.Context, messages []chatMessage) (string, error) {
+	reqBody := chatRequest{
+		Model:       llmCfg.Model,
+		Messages:    messages,
+		Temperature: llmCfg.Temperature,
+		MaxTokens:   llmCfg.MaxTokens,
+		Stream:      false,
+	}
+
+	body, err := json.Marshal(reqBody)
+	if err != nil {
+		return "", fmt.Errorf("marshal request: %w", err)
+	}
+
+	url := strings.TrimRight(llmCfg.BaseURL, "/") + "/chat/completions"
+	req, err := http.NewRequestWithContext(ctx, http.MethodPost, url, bytes.NewReader(body))
+	if err != nil {
+		return "", fmt.Errorf("create request: %w", err)
+	}
+	req.Header.Set("Content-Type", "application/json")
+	req.Header.Set("Authorization", "Bearer "+llmCfg.APIKey)
+
+	resp, err := http.DefaultClient.Do(req)
+	if err != nil {
+		return "", fmt.Errorf("send request: %w", err)
+	}
+	defer resp.Body.Close()
+
+	if resp.StatusCode != http.StatusOK {
+		b, _ := io.ReadAll(io.LimitReader(resp.Body, 4096))
+		return "", fmt.Errorf("llm api error %d: %s", resp.StatusCode, string(b))
+	}
+
+	// 处理流式 (SSE) 与非流式响应
+	if resp.Header.Get("Content-Type") == "text/event-stream" {
+		return parseStreamResponse(resp.Body)
+	}
+
+	var chatResp chatResponse
+	if err := json.NewDecoder(resp.Body).Decode(&chatResp); err != nil {
+		return "", fmt.Errorf("decode response: %w", err)
+	}
+	if len(chatResp.Choices) == 0 {
+		return "", fmt.Errorf("llm returned empty choices")
+	}
+
+	return chatResp.Choices[0].Message.Content, nil
+}
+
+// parseStreamResponse 解析 SSE 流式响应。
+func parseStreamResponse(r io.Reader) (string, error) {
+	var sb strings.Builder
+	scanner := bufio.NewScanner(r)
+	for scanner.Scan() {
+		line := scanner.Text()
+		if !strings.HasPrefix(line, "data: ") {
+			continue
+		}
+		data := strings.TrimPrefix(line, "data: ")
+		if data == "[DONE]" {
+			break
+		}
+		var chunk struct {
+			Choices []struct {
+				Delta struct {
+					Content string `json:"content"`
+				} `json:"delta"`
+			} `json:"choices"`
+		}
+		if err := json.Unmarshal([]byte(data), &chunk); err != nil {
+			continue
+		}
+		for _, c := range chunk.Choices {
+			sb.WriteString(c.Delta.Content)
+		}
+	}
+	return sb.String(), scanner.Err()
+}
+
+// ======================== 模板回退 ========================
+
+func fallbackRefine(metaPrompt string) PromptAgentOutput {
+	tags, assetType := parseTagsFromMeta(metaPrompt)
+	prompt := generateStructuredPrompt(tags, assetType)
+	return PromptAgentOutput{Prompt: prompt, RawText: prompt}
+}
+
+func parseTagsFromMeta(meta string) (tags []string, assetType string) {
+	lines := strings.Split(meta, "\n")
+	for _, line := range lines {
+		if strings.HasPrefix(line, "用户选择标签:") {
+			tagStr := strings.TrimPrefix(line, "用户选择标签: ")
+			for _, t := range strings.Split(tagStr, "、") {
+				t = strings.TrimSpace(t)
+				if t != "" {
+					tags = append(tags, t)
+				}
+			}
+		}
+		if strings.HasPrefix(line, "素材类型:") {
+			assetType = strings.TrimSpace(strings.TrimPrefix(line, "素材类型: "))
+		}
+	}
+	return
+}
+
+func generateStructuredPrompt(tags []string, assetType string) string {
+	var sb strings.Builder
+	sb.WriteString("【主题】")
+	sb.WriteString(buildSubject(tags, assetType))
+	sb.WriteString("\n")
+	sb.WriteString("【风格】")
+	sb.WriteString(buildStyle(tags))
+	sb.WriteString("\n")
+	sb.WriteString("【技术】")
+	sb.WriteString(buildTechNotes(assetType))
+	sb.WriteString("\n")
+	return sb.String()
+}
+
+func buildSubject(tags []string, assetType string) string {
+	tagStr := strings.Join(tags, "、")
+	switch assetType {
+	case "sprite":
+		return fmt.Sprintf("一个融合%s元素的游戏角色精灵图，正面站立姿势，轮廓清晰，适合作为2D游戏角色", tagStr)
+	case "background":
+		return fmt.Sprintf("一个%s风格的游戏场景背景，层次分明，包含前景、中景和远景", tagStr)
+	case "ui":
+		return fmt.Sprintf("一套%s风格的游戏UI元素，包括按钮、面板和图标", tagStr)
+	case "animation":
+		return fmt.Sprintf("一个%s风格的角色动画帧序列，动作流畅连贯", tagStr)
+	default:
+		return fmt.Sprintf("一个%s风格的游戏素材，高质量，适合2D游戏使用", tagStr)
+	}
+}
+
+func buildStyle(tags []string) string {
+	parts := []string{"色彩鲜明", "细节丰富", "统一的光源方向"}
+	for _, t := range tags {
+		parts = append(parts, t+"风格")
+	}
+	return strings.Join(parts, "；")
+}
+
+func buildTechNotes(assetType string) string {
+	switch assetType {
+	case "sprite":
+		return "输出格式: spritesheet；分辨率: 64x64 或 128x128；透明背景"
+	case "background":
+		return "输出格式: 独立PNG；分辨率: 1920x1080；层次分明的前中后景"
+	case "ui":
+		return "输出格式: 独立PNG素材；分辨率: 按元素适配；支持九宫格缩放"
+	case "animation":
+		return "输出格式: spritesheet或帧序列；建议4方向x4帧；透明背景"
+	default:
+		return "输出格式: PNG；分辨率: 标准2D游戏分辨率"
+	}
+}
@@ -0,0 +1,343 @@
+package service
+
+import (
+	"context"
+	"net/http"
+	"net/http/httptest"
+	"strings"
+	"testing"
+)
+
+func TestRunPromptAgent_Fallback(t *testing.T) {
+	// 无 API key 时走模板回退路径
+	output, err := RunPromptAgent(context.Background(), PromptAgentInput{
+		Tags:      []string{"像素", "中世纪", "战士"},
+		AssetType: "sprite",
+		UserNote:  "持盾",
+	})
+	if err != nil {
+		t.Fatalf("RunPromptAgent failed: %v", err)
+	}
+
+	if output.Prompt == "" {
+		t.Fatal("expected non-empty prompt")
+	}
+	for _, section := range []string{"【主题】", "【风格】", "【技术】"} {
+		if !strings.Contains(output.Prompt, section) {
+			t.Errorf("output missing section %s: %s", section, output.Prompt)
+		}
+	}
+}
+
+func TestRunPromptAgent_Sprite(t *testing.T) {
+	output, err := RunPromptAgent(context.Background(), PromptAgentInput{
+		Tags:      []string{"像素", "中世纪", "战士"},
+		AssetType: "sprite",
+	})
+	if err != nil {
+		t.Fatalf("RunPromptAgent failed: %v", err)
+	}
+	if !strings.Contains(output.Prompt, "spritesheet") {
+		t.Errorf("sprite output should mention spritesheet: %s", output.Prompt)
+	}
+}
+
+func TestRunPromptAgent_Background(t *testing.T) {
+	output, err := RunPromptAgent(context.Background(), PromptAgentInput{
+		Tags:      []string{"森林", "暗黑"},
+		AssetType: "background",
+	})
+	if err != nil {
+		t.Fatalf("RunPromptAgent failed: %v", err)
+	}
+	if !strings.Contains(output.Prompt, "1920x1080") {
+		t.Errorf("background output should mention 1920x1080: %s", output.Prompt)
+	}
+}
+
+func TestRunPromptAgent_UI(t *testing.T) {
+	output, err := RunPromptAgent(context.Background(), PromptAgentInput{
+		Tags:      []string{"简约", "科幻"},
+		AssetType: "ui",
+	})
+	if err != nil {
+		t.Fatalf("RunPromptAgent failed: %v", err)
+	}
+	if !strings.Contains(output.Prompt, "九宫格") {
+		t.Errorf("ui output should mention 九宫格: %s", output.Prompt)
+	}
+}
+
+func TestRunPromptAgent_Animation(t *testing.T) {
+	output, err := RunPromptAgent(context.Background(), PromptAgentInput{
+		Tags:      []string{"火焰", "魔法"},
+		AssetType: "animation",
+	})
+	if err != nil {
+		t.Fatalf("RunPromptAgent failed: %v", err)
+	}
+	if !strings.Contains(output.Prompt, "帧") {
+		t.Errorf("animation output should mention 帧: %s", output.Prompt)
+	}
+}
+
+func TestRunPromptAgent_EmptyUserNote(t *testing.T) {
+	output, err := RunPromptAgent(context.Background(), PromptAgentInput{
+		Tags:      []string{"水彩"},
+		AssetType: "sprite",
+	})
+	if err != nil {
+		t.Fatalf("RunPromptAgent failed: %v", err)
+	}
+	if output.Prompt == "" {
+		t.Fatal("expected non-empty prompt")
+	}
+}
+
+func TestRunPromptAgent_SingleTag(t *testing.T) {
+	output, err := RunPromptAgent(context.Background(), PromptAgentInput{
+		Tags:      []string{"赛博朋克"},
+		AssetType: "background",
+	})
+	if err != nil {
+		t.Fatalf("RunPromptAgent with single tag failed: %v", err)
+	}
+	if output.Prompt == "" {
+		t.Fatal("expected non-empty prompt")
+	}
+}
+
+func TestRunPromptAgent_WithChatModel(t *testing.T) {
+	// 启动一个 mock OpenAI 兼容 API
+	server := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+		if r.URL.Path != "/chat/completions" {
+			http.NotFound(w, r)
+			return
+		}
+		w.Header().Set("Content-Type", "application/json")
+		w.Write([]byte(`{"choices":[{"message":{"role":"assistant","content":"【主题】一个像素战士\n【风格】像素风格\n【技术】spritesheet"}}]}`))
+	}))
+	defer server.Close()
+
+	// 保存原始配置，测试后恢复
+	orig := llmCfg
+	defer func() { llmCfg = orig }()
+	llmCfg.BaseURL = server.URL
+	llmCfg.APIKey = "test-key"
+	llmCfg.Model = "test-model"
+	llmCfg.Temperature = 0.7
+	llmCfg.MaxTokens = 512
+
+	output, err := RunPromptAgent(context.Background(), PromptAgentInput{
+		Tags:      []string{"像素", "战士"},
+		AssetType: "sprite",
+	})
+	if err != nil {
+		t.Fatalf("RunPromptAgent failed: %v", err)
+	}
+
+	if !strings.Contains(output.Prompt, "像素战士") {
+		t.Errorf("expected LLM response in output, got: %s", output.Prompt)
+	}
+	if output.RawText != output.Prompt {
+		t.Error("expected RawText == Prompt for non-streaming response")
+	}
+}
+
+func TestChatCompletion_NonStream(t *testing.T) {
+	server := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+		w.Header().Set("Content-Type", "application/json")
+		w.Write([]byte(`{"choices":[{"message":{"content":"test response"}}]}`))
+	}))
+	defer server.Close()
+
+	orig := llmCfg
+	defer func() { llmCfg = orig }()
+	llmCfg.BaseURL = server.URL
+	llmCfg.APIKey = "key"
+	llmCfg.Model = "m"
+	llmCfg.Temperature = 0.5
+
+	result, err := chatCompletion(context.Background(), []chatMessage{
+		{Role: "user", Content: "hello"},
+	})
+	if err != nil {
+		t.Fatalf("chatCompletion failed: %v", err)
+	}
+	if result != "test response" {
+		t.Errorf("expected 'test response', got %q", result)
+	}
+}
+
+func TestChatCompletion_Stream(t *testing.T) {
+	server := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+		w.Header().Set("Content-Type", "text/event-stream")
+		w.Write([]byte("data: {\"choices\":[{\"delta\":{\"content\":\"Hello\"}}]}\n\n"))
+		w.Write([]byte("data: {\"choices\":[{\"delta\":{\"content\":\" World\"}}]}\n\n"))
+		w.Write([]byte("data: [DONE]\n\n"))
+	}))
+	defer server.Close()
+
+	orig := llmCfg
+	defer func() { llmCfg = orig }()
+	llmCfg.BaseURL = server.URL
+	llmCfg.APIKey = "key"
+	llmCfg.Model = "m"
+	llmCfg.Temperature = 0.5
+
+	result, err := chatCompletion(context.Background(), []chatMessage{
+		{Role: "user", Content: "hi"},
+	})
+	if err != nil {
+		t.Fatalf("chatCompletion stream failed: %v", err)
+	}
+	if result != "Hello World" {
+		t.Errorf("expected 'Hello World', got %q", result)
+	}
+}
+
+func TestCallLLMRefine_FallbackOnError(t *testing.T) {
+	orig := llmCfg
+	defer func() { llmCfg = orig }()
+	llmCfg.BaseURL = "http://invalid-url.invalid"
+	llmCfg.APIKey = "key"
+	llmCfg.Model = "m"
+	llmCfg.Temperature = 0.5
+
+	// API 调用失败时应回退到模板生成
+	output, err := callLLMRefine(context.Background(), "用户选择标签: 测试\n素材类型: sprite")
+	if err != nil {
+		t.Fatalf("fallback should not error: %v", err)
+	}
+	if !strings.Contains(output.Prompt, "【主题】") {
+		t.Error("fallback output should contain 三段式 structure")
+	}
+}
+
+// 单元测试
+
+func TestBuildMetaPrompt(t *testing.T) {
+	in := PromptAgentInput{
+		Tags:      []string{"像素", "地牢"},
+		AssetType: "sprite",
+		UserNote:  "需要发光效果",
+	}
+	result := buildMetaPrompt(in)
+
+	checks := []string{
+		"2D 游戏素材提示词工程师",
+		"三段式结构",
+		"用户选择标签: 像素、地牢",
+		"素材类型: sprite",
+		"补充说明: 需要发光效果",
+	}
+	for _, c := range checks {
+		if !strings.Contains(result, c) {
+			t.Errorf("buildMetaPrompt missing %q", c)
+		}
+	}
+}
+
+func TestBuildMetaPrompt_NoUserNote(t *testing.T) {
+	in := PromptAgentInput{
+		Tags:      []string{"像素"},
+		AssetType: "sprite",
+	}
+	result := buildMetaPrompt(in)
+	if strings.Contains(result, "补充说明") {
+		t.Error("buildMetaPrompt should not contain 补充说明 when UserNote is empty")
+	}
+}
+
+func TestParseTagsFromMeta(t *testing.T) {
+	meta := `用户选择标签: 像素、中世纪、战士
+素材类型: sprite`
+	tags, assetType := parseTagsFromMeta(meta)
+	if len(tags) != 3 {
+		t.Fatalf("expected 3 tags, got %d: %v", len(tags), tags)
+	}
+	if tags[0] != "像素" || tags[1] != "中世纪" || tags[2] != "战士" {
+		t.Errorf("unexpected tags: %v", tags)
+	}
+	if assetType != "sprite" {
+		t.Errorf("expected assetType=sprite, got %s", assetType)
+	}
+}
+
+func TestParseTagsFromMeta_SingleTag(t *testing.T) {
+	meta := `用户选择标签: 赛博朋克
+素材类型: background`
+	tags, _ := parseTagsFromMeta(meta)
+	if len(tags) != 1 || tags[0] != "赛博朋克" {
+		t.Errorf("expected [赛博朋克], got %v", tags)
+	}
+}
+
+func TestParseTagsFromMeta_Empty(t *testing.T) {
+	tags, assetType := parseTagsFromMeta("no tags here")
+	if len(tags) != 0 || assetType != "" {
+		t.Errorf("expected empty, got tags=%v assetType=%s", tags, assetType)
+	}
+}
+
+func TestGenerateStructuredPrompt(t *testing.T) {
+	prompt := generateStructuredPrompt([]string{"像素", "战士"}, "sprite")
+	if !strings.HasPrefix(prompt, "【主题】") {
+		t.Error("prompt should start with 【主题】")
+	}
+	themeIdx := strings.Index(prompt, "【主题】")
+	styleIdx := strings.Index(prompt, "【风格】")
+	techIdx := strings.Index(prompt, "【技术】")
+	if !(themeIdx < styleIdx && styleIdx < techIdx) {
+		t.Error("sections should be ordered: 主题 → 风格 → 技术")
+	}
+}
+
+func TestBuildSubject(t *testing.T) {
+	tags := []string{"像素", "战士"}
+	tests := []struct {
+		assetType, want string
+	}{
+		{"sprite", "精灵图"},
+		{"background", "场景背景"},
+		{"ui", "UI元素"},
+		{"animation", "动画帧序列"},
+		{"unknown", "游戏素材"},
+	}
+	for _, tt := range tests {
+		result := buildSubject(tags, tt.assetType)
+		if !strings.Contains(result, tt.want) {
+			t.Errorf("buildSubject(%q) = %s, want containing %q", tt.assetType, result, tt.want)
+		}
+	}
+}
+
+func TestBuildStyle(t *testing.T) {
+	result := buildStyle([]string{"像素", "暗黑"})
+	if !strings.Contains(result, "色彩鲜明") {
+		t.Error("style should contain default descriptions")
+	}
+	for _, tag := range []string{"像素", "暗黑"} {
+		if !strings.Contains(result, tag+"风格") {
+			t.Errorf("style missing %q", tag+"风格")
+		}
+	}
+}
+
+func TestBuildTechNotes(t *testing.T) {
+	tests := []struct {
+		assetType, want string
+	}{
+		{"sprite", "spritesheet"},
+		{"background", "1920x1080"},
+		{"ui", "九宫格"},
+		{"animation", "4方向x4帧"},
+		{"unknown", "PNG"},
+	}
+	for _, tt := range tests {
+		result := buildTechNotes(tt.assetType)
+		if !strings.Contains(result, tt.want) {
+			t.Errorf("buildTechNotes(%q) = %s, want containing %q", tt.assetType, result, tt.want)
+		}
+	}
+}