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Introduction

This example demonstrates advanced AI data processing using Upstash Workflow. The following example workflow downloads a large dataset, processes it in chunks using OpenAI’s GPT-4 model, aggregates the results and generates a report.

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Use Case

Our workflow will:

  1. Receive a request to process a dataset
  2. Download the dataset from a remote source
  3. Process the data in chunks using OpenAI
  4. Aggregate results
  5. Generate and send a final report

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Code Example

api/workflow/route.ts
import { serve } from "@upstash/qstash/nextjs"
import {
  downloadData,
  aggregateResults,
  generateReport,
  sendReport,
  getDatasetUrl,
  splitIntoChunks,
} from "./utils"

type OpenAiResponse = {
  choices: {
    message: {
      role: string,
      content: string
    }
  }[]
}

export const POST = serve<{ datasetId: string; userId: string }>(
  async (context) => {
    const request = context.requestPayload

    // Step 1: Download the dataset
    const datasetUrl = await context.run("get-dataset-url", async () => {
      return await getDatasetUrl(request.datasetId)
    })

    // HTTP request with much longer timeout (2hrs)
    const dataset = await context.call("download-dataset", datasetUrl, "GET")

    // Step 2: Process data in chunks using OpenAI
    const chunkSize = 1000
    const chunks = splitIntoChunks(dataset, chunkSize)
    const processedChunks: string[] = []

    for (let i = 0; i < chunks.length; i++) {
      const processedChunk = await context.call<OpenAiResponse>(
        `process-chunk-${i}`,
        "https://api.openai.com/v1/chat/completions",
        "POST",
        {
          model: "gpt-4",
          messages: [
            {
              role: "system",
              content:
                "You are an AI assistant tasked with analyzing data chunks. Provide a brief summary and key insights for the given data.",
            },
            {
              role: "user",
              content: `Analyze this data chunk: ${JSON.stringify(chunks[i])}`,
            },
          ],
          max_tokens: 150,
        },
        { authorization: `Bearer ${process.env.OPENAI_API_KEY}` }
      )

      processedChunks.push(processedChunk.choices[0].message.content)

      // Every 10 chunks, we'll aggregate intermediate results
      if (i % 10 === 9 || i === chunks.length - 1) {
        await context.run(`aggregate-results${i}`, async () => {
          await aggregateResults(processedChunks)
          processedChunks.length = 0
        })
      }
    }

    // Step 3: Generate and send data report
    const report = await context.run("generate-report", async () => {
      return await generateReport(request.datasetId)
    })

    await context.run("send-report", async () => {
      await sendReport(report, request.userId)
    })
  }
)

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Code Breakdown

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1. Preparing our data

We start by retrieving the dataset URL and then downloading the dataset:

api/workflow/route.ts
const datasetUrl = await context.run("get-dataset-url", async () => {
  return await getDatasetUrl(request.datasetId)
})

const dataset = await context.call("download-dataset", datasetUrl, "GET")

Note that we use context-call for the download, a way to make HTTP requests that run for much longer than your serverless execution limit would normally allow.

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2. Processing our data

We split the dataset into chunks and process each one using OpenAI’s GPT-4 model:

api/workflow/route.ts
for (let i = 0; i < chunks.length; i++) {
  const processedChunk = await context.call(
    `process-chunk-${i}`,
    "https://api.openai.com/v1/chat/completions",
    "POST",
    {
      model: "gpt-4",
      messages: [
        {
          role: "system",
          content:
            "You are an AI assistant tasked with analyzing data chunks. Provide a brief summary and key insights for the given data.",
        },
        {
          role: "user",
          content: `Analyze this data chunk: ${JSON.stringify(chunks[i])}`,
        },
      ],
      max_tokens: 150,
    },
    { authorization: `Bearer ${process.env.OPENAI_API_KEY}` }
  )

  processedChunks.push(processedChunk.choices[0].message.content)
}

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3. Aggregating our data

After processing our data in smaller chunks to avoid any function timeouts, we aggregate results every 10 chunks:

api/workflow/route.ts
if (i % 10 === 9 || i === chunks.length - 1) {
  await context.run(`aggregate-results${i}`, async () => {
    await aggregateResults(processedChunks)
    processedChunks.length = 0
  })
}

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4. Sending a report

Finally, we generate a report based on the aggregated results and send it to the user:

api/workflow/route.ts
const report = await context.run("generate-report", async () => {
  return await generateReport(request.datasetId)
})

await context.run("send-report", async () => {
  await sendReport(report, request.userId)
})

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Key Features

  1. Non-blocking HTTP Calls: We use context.call for API requests so they don’t consume the endpoint’s execution time (great for optimizing serverless cost).

  2. Long-running tasks: The dataset download can take up to 2 hours, though is realistically limited by function memory.

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