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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

import { serve } from "@upstash/workflow/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 { body: dataset } = await context.call("download-dataset", {
      url: datasetUrl,
      method: "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 { body: processedChunk } = await context.api.openai.call(
        `process-chunk-${i}`,
        {
          token: process.env.OPENAI_API_KEY,
          operation: "chat.completions.create",
          body: {
            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_completion_tokens: 150,
          },
        }
      )

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

const { body: dataset } = await context.call("download-dataset", {
  url: datasetUrl,
  method: "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: 
for (let i = 0; i < chunks.length; i++) {
  const { body: processedChunk } = await context.api.openai.call<OpenAiResponse>(
    `process-chunk-${i}`,
    {
      token: process.env.OPENAI_API_KEY!,
      operation: "chat.completions.create",
      body: {
        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_completion_tokens: 150,
      },
    }
  )
}

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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: 
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: 
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.