Workflows

[Interpreted]

flow ProcessImage {
  node fetch(url: str) -> bytes = http.get(url)
  node classify(data: bytes) -> label = ai.classify(data, model:"clip")
  node store(label: str) = dstore.ipfs.put(label)
  
  wire fetch -> classify -> store
}

node fetch(url: str) -> bytes = http.get(url)
node post(url: str, data: bytes) -> response = http.post(url, data)

node classify(image: bytes) -> label = ai.classify(image, model:"clip")
node generate(prompt: str) -> text = ai.generate(prompt, model:"gpt-4")

node save(data: bytes) = dstore.ipfs.put(data)
node load(cid: str) -> bytes = dstore.ipfs.get(cid)

flow DataPipeline {
  node fetch(url) = http.get(url)
  node parse(data) = json.parse(data)
  node transform(parsed) = process(parsed)
  node save(result) = dstore.ipfs.put(result)
  
  wire fetch -> parse -> transform -> save
}

flow ConditionalFlow {
  node check(condition) -> bool = validate(condition)
  node if_true(data) = process_a(data)
  node if_false(data) = process_b(data)
  
  wire check -> if_true (when: true)
  wire check -> if_false (when: false)
}

flow ProcessImage {
  # ... node definitions ...
}

# Run with input
ProcessImage.run({ url: "https://example.com/image.jpg" })

[Interpreted]

flow ProcessImage {
  node fetch(url: str) -> bytes = http.get(url)
  node resize(image: bytes) -> bytes = image.resize(800, 600)
  node classify(image: bytes) -> label = ai.classify(image)
  node store(image: bytes, label: str) = dstore.ipfs.put({
    "image": image,
    "label": label
  })
  
  wire fetch -> resize -> classify
  wire resize -> store
  wire classify -> store
}

ProcessImage.run({ url: "https://example.com/cat.jpg" })

flow DataPipeline {
  node fetch(url) = http.get(url)
  node parse(data) = json.parse(data)
  node validate(parsed) -> bool = check_valid(parsed)
  node save(valid_data) = database.save(valid_data)
  node notify(saved) = email.send("Data saved", saved)
  
  wire fetch -> parse -> validate -> save -> notify
}