Build reliable AI Workflows
with humans in the loop

1import { Inferable } from "inferable";
2
3const inferable = new Inferable({
4  apiSecret: require("./cluster.json").apiKey,
5});
6
7const workflow = inferable.workflows.create({
8  name: "customerDataProcessor",
9  inputSchema: z.object({
10    executionId: z.string(),
11    customerId: z.string(),
12  }),
13});
14
15// Initial version of the workflow
16workflow.version(1).define(async (ctx, input) => {
17  const customerData = await fetchCustomerData(input.customerId);
18  
19  // Process the data with a simple analysis
20  const analysis = await ctx.llm.structured({
21    input: JSON.stringify(customerData),
22    schema: z.object({
23      riskLevel: z.enum(["low", "medium", "high"]),
24      summary: z.string(),
25    }),
26  });
27  
28  return { analysis };
29});
30
31// Enhanced version with more detailed analysis
32workflow.version(2).define(async (ctx, input) => {
33  const customerData = await fetchCustomerData(input.customerId);
34  const transactionHistory = 
35    await fetchTransactionHistory(input.customerId);
36  
37  // Process the data with more advanced analysis
38  const analysis = await ctx.llm.structured({
39    input: JSON.stringify({ customerData, transactionHistory }),
40    schema: z.object({
41      riskLevel: z.enum(["low", "medium", "high"]),
42      summary: z.string(),
43      recommendations: z.array(z.string()),
44      factors: z.array(z.object({
45        name: z.string(),
46        impact: z.enum(["positive", "negative", "neutral"]),
47        weight: z.number(),
48      })),
49    }),
50  });
51  
52  return { 
53    analysis,
54    version: 2,
55    processedAt: new Date().toISOString()
56  };
57});