101 lines
2.7 KiB
Markdown
101 lines
2.7 KiB
Markdown
# 构建并行工作流
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现在您将创建一个以最大性能并行运行您分析步骤的工作流。
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## 创建并行工作流
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将此工作流添加到您的文件中：
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```typescript
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export const parallelAnalysisWorkflow = createWorkflow({
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id: "parallel-analysis-workflow",
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description: "Run multiple content analyses in parallel",
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inputSchema: z.object({
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content: z.string(),
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type: z.enum(["article", "blog", "social"]).default("article"),
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}),
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outputSchema: z.object({
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results: z.object({
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seo: z.object({
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seoScore: z.number(),
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keywords: z.array(z.string()),
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}),
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readability: z.object({
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readabilityScore: z.number(),
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gradeLevel: z.string(),
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}),
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sentiment: z.object({
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sentiment: z.enum(["positive", "neutral", "negative"]),
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confidence: z.number(),
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}),
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}),
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}),
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})
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.parallel([seoAnalysisStep, readabilityStep, sentimentStep])
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.then(
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createStep({
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id: "combine-results",
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description: "Combines parallel analysis results",
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inputSchema: z.object({
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"seo-analysis": z.object({
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seoScore: z.number(),
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keywords: z.array(z.string()),
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}),
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"readability-analysis": z.object({
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readabilityScore: z.number(),
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gradeLevel: z.string(),
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}),
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"sentiment-analysis": z.object({
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sentiment: z.enum(["positive", "neutral", "negative"]),
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confidence: z.number(),
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}),
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}),
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outputSchema: z.object({
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results: z.object({
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seo: z.object({
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seoScore: z.number(),
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keywords: z.array(z.string()),
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}),
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readability: z.object({
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readabilityScore: z.number(),
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gradeLevel: z.string(),
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}),
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sentiment: z.object({
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sentiment: z.enum(["positive", "neutral", "negative"]),
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confidence: z.number(),
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}),
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}),
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}),
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execute: async ({ inputData }) => {
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console.log("🔄 Combining parallel results...");
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return {
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results: {
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seo: inputData["seo-analysis"],
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readability: inputData["readability-analysis"],
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sentiment: inputData["sentiment-analysis"],
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},
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};
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},
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}),
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)
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.commit();
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```
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## 理解并行数据流
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当步骤并行运行时：
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1. 每个步骤接收相同的输入数据
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2. 步骤同时执行
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3. 结果被收集到以步骤ID为键的对象中
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4. 下一步接收所有并行结果
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## 关键点
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- **`.parallel([step1, step2, step3])`**：同时运行所有步骤
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- **结果对象键**：使用步骤ID（例如"seo-analysis"）
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- **合并步骤**：一起处理所有并行结果
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接下来，您将测试此并行工作流并看到性能提升！
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