92 lines
2.4 KiB
Markdown
92 lines
2.4 KiB
Markdown
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# 在工作流中使用代理
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现在您将创建一个使用您的AI代理提供智能内容分析的工作流步骤。
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在每个步骤中，在执行函数中，您可以访问 `mastra` 类，该类为您提供访问代理、工具甚至其他工作流的能力。在这种情况下，我们使用 `mastra` 类来获取我们的代理并调用该代理的 `generate()` 函数。
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## 创建AI分析步骤
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将此步骤添加到您的工作流文件中：
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```typescript
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const aiAnalysisStep = createStep({
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id: "ai-analysis",
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description: "AI-powered content analysis",
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inputSchema: z.object({
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content: z.string(),
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type: z.string(),
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wordCount: z.number(),
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metadata: z.object({
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readingTime: z.number(),
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difficulty: z.enum(["easy", "medium", "hard"]),
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processedAt: z.string(),
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}),
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summary: z.string(),
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}),
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outputSchema: z.object({
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content: z.string(),
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type: z.string(),
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wordCount: z.number(),
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metadata: z.object({
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readingTime: z.number(),
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difficulty: z.enum(["easy", "medium", "hard"]),
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processedAt: z.string(),
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}),
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summary: z.string(),
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aiAnalysis: z.object({
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score: z.number(),
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feedback: z.string(),
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}),
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}),
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execute: async ({ inputData, mastra }) => {
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const { content, type, wordCount, metadata, summary } = inputData;
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// Create prompt for the AI agent
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const prompt = `
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Analyze this ${type} content:
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Content: "${content}"
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Word count: ${wordCount}
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Reading time: ${metadata.readingTime} minutes
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Difficulty: ${metadata.difficulty}
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Please provide:
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1. A quality score from 1-10
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2. Brief feedback on strengths and areas for improvement
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Format as JSON: {"score": number, "feedback": "your feedback here"}
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`;
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// Get the contentAgent from the mastra instance.
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const contentAgent = mastra.getAgent("contentAgent");
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const { text } = await contentAgent.generate([
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{ role: "user", content: prompt },
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]);
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// Parse AI response (with fallback)
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let aiAnalysis;
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try {
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aiAnalysis = JSON.parse(text);
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} catch {
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aiAnalysis = {
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score: 7,
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feedback: "AI analysis completed. " + text,
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};
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}
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console.log(`🤖 AI Score: ${aiAnalysis.score}/10`);
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return {
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content,
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type,
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wordCount,
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metadata,
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summary,
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aiAnalysis,
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};
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},
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});
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```
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您的代理驱动步骤已准备就绪！接下来，您将将其添加到您的工作流中以实现完整的AI增强内容处理。
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