Besides expressing the shape of the data with zod, it's also possible to convert from existing schemas to zod. For example, if an OpenAPI/SmartAPI spec was already created, the schema of those parameters can be used with the JSON Schema to Zod converter. Alternatively, if you just have raw JSON, you can get a head start using the JSON to Zod converter.
To avoid storing large files in the database or in memory we pass around pointers to those files in the form of URLs. To interact with files you must first resolve it before you can manipulate it and potentially produce a new file which needs to be uploaded and turned back into a pointer. Since how this is done is subject to change (i.e. storing on the filesystem, in s3, or other mechanisms), helper functions are available for managing files.
# turning a file object into content:
# when reading sequentially, you can read it as a stream directly from
# wherever it is. it behaves like a file object
from components.core.file import file_as_stream
with file_as_stream(file) as fh: # you get something like a file handle here
for line in fh:
print(line)
# if your file needs seek support (like some database formats, i.e. h5)
# you'll need to use file_as_path which ensures its on the disk
from components.core.file import file_as_path
with file_as_path(file['url']) as path: # you get an actual path rather than a handle
with open(path, 'r+') as fh:
print(fh.read())
# turning content into a file object
from components.core.file import upsert_file
with upsert_file('.ext') as f:
# f.file is a python file handler
f.file.write('test')
# after the context manager, f.url contains the uploaded file url
return f
Please note only resolvers should be operating on files, views and prompts should operate on prepared datatypes which resolvers could set up. That-is picking options in a way that depends on information in a file would need to prepare those options in a resolver. This is because potentially large files should not be downloaded to an end user's browser which is where views and prompts run.
import { fileAsStream } from '@/components/core/file/api/download'
import { fileFromStream, uploadFile } from '@/components/core/file/api/upload'
import FormData from 'form-data'
export const SomeFileOp = MetaNode('SomeFileOp')
.meta({ label: 'Some File Operation', description: 'Perform an operation' })
.inputs({ file: FileURL })
.output(FileURL)
.resolve(async (props) => {
// read the file as a stream
const fileReader: any = await fileAsStream(props.inputs.file)
// send that file somewhere for processing
const formData = new FormData()
formData.append('file', fileReader, props.inputs.file.filename)
const { default: axios } = await import('axios')
const res = await axios.post('https://example.com/upload', formData, {
headers: { ...formData.getHeaders() },
responseType: 'stream',
})
// create a new file object from a stream
const file = await uploadFile(await fileFromStream(res.data, `derived.${props.inputs.file.filename}`))
return file
})
.story(props => ({ abstract: `The performed some operation on the file${props.inputs && props.inputs.file.description ? ` containing ${props.inputs.file.description}` : ''}.` }))
.build()
The current way this can be done is through a service account token which can be passed as an environment variable to your component by including the token in the .env
file and accessing it with process.env.MY_SECRET_TOKEN
. Ultimately, this token will need to be shared with the current deployment maintainer to add to the deployment environment.
.env
#...
MY_SECRET_TOKEN=supersecrettoken
components/[component]/index.ts
MetaNode('MyProcess')
//...
.resolve(async (props) => {
const req = await fetch(`mypublic.api/route`, {
headers: {
// MY_SECRET_TOKEN is accessible here, only sever side code will have access to it
Authorization: `Bearer ${process.env.MY_SECRET_TOKEN}`,
},
})
})
//...
Icons are exported from the icons/index.tsx
, two main types of icons are currently in use:
- icons from Material Design Icons, or from BlueprintJS Icons. these are imported from their respective libraries and exported from
icons/mdi/index.ts
with apath
(the svg path in the icon) andtitle
a human readable label for that icon. By convention, they should end with_icon
.// example material design icon for "gene" import { mdiDna } from '@mdi/js'; export const gene_icon = { path: mdiDna, title: 'Gene' } // example blueprintjs icon for "fork" export const fork_icon = { path: IconSvgPaths20.Fork.join(' '), title: 'Expand From this Step' }
- icons from some raster like a favicon. These icons can be added to
icons/services/src/*.png
andnpm run codegen:icons
can be used to trace those pngs into an svg path which gets inlined intoicons/services/index.ts
Once registered, icons can be imported in your component like
import { some_icon } from '@/icons'
Note that the Icon
type can be a list, the <Icon>
react component in app/components/icon.tsx
will display up to 4 different icons as one.
We've Dockerized our dev environment, so if all else fails you should be able to use that given that you have Docker
and docker-compose
installed. It can be used with docker-compose run dev
, in that shell you can execute any commands that weren't working.
Find other topics in the Playbook Workflow Builder Developer Guide.