Go

This guide provides a walk-through of how to use the Algorithmia Go client to call algorithms and manage your data through the Algorithmia platform.

Here you will learn how to install the Algorithmia Go Client, work with the Data API by uploading and downloading files, create and update directories and permission types and last, you’ll learn how to call an algorithm that summarizes text files.

To follow along you can create a new Go file in the IDE of your choice.

Getting Started with Algorithmia

The official Algorithmia Go Client is available on GitHub where you can find more documentation on the client.

To get started, first install the Algorithmia Go Client:

go get github.com/algorithmiaio/algorithmia-go

Authentication

Next, login to Algorithmia to get your API key:

Now import the Algorithmia library and create the Algorithmia client:

// Authenticate with your API key
import (
	algorithmia "github.com/algorithmiaio/algorithmia-go"
)

var apiKey = "YOUR_API_KEY"

// Create the Algorithmia client object
var client = algorithmia.NewClient(apiKey, "")

Now you’re ready to start working with Algorithmia in Go.

Specifying an On-Premises or Private Cloud Endpoint

This feature is available to Algorithmia Enterprise users only.

If you are running Algorithmia Enterprise, you can specify the API endpoint when you create the client object:

var client = algorithmia.NewClient(apiKey, "https://mylocalendpoint");

Working with Data Using the Data API

For application developers, Algorithmia’s Data Portal offers three different ways to store your data, all available via the Data API.

This guide will show you how to work with the Hosted Data option on the Algorithmia platform which is available to both algorithm and application developers.

Prerequisites

If you wish to follow along working through the example yourself, create a text file that contains any unstructured text such as a chapter from a public domain book or article. We used a chapter from Burning Daylight, by Jack London which you can copy and paste into a text file. Or copy and paste it from here: Chapter One Burning Daylight, by Jack London. This will be used throughout the guide.

Create a Data Collection

This section will show how to create a data collection which is essentially a folder of data files hosted on Algorithmia for free.

Now create a data collection called nlp_directory:

// Instantiate a DataDirectory object, set your data URI and call Create
nlp_directory := client.Dir("data://YOUR_USERNAME/nlp_directory")
dir_exists, dir_err := nlp_directory.Exists()

// Create your data collection if it does not exist
if dir_err != nil {
	fmt.Println(dir_err)
} else if dir_exists == false {
	nlp_directory.Create(nil)
	fmt.Println("nlp_directory created")
} else {
	fmt.Println("nlp_directory already exists")
}

A Data URI uniquely identifies files and directories and contains a protocol “data://” and path “your_username/data_collection”. For more information on the Data URI see the Data API Specification.

Instead of your username you can also use ‘.my’ when calling algorithms. For more information about the ‘.my’ pseudonym check out the Hosted Data Guide.

Work with Directory Permissions

When we created the data collection in the previous code snippet, the default setting is “MyAlgos” which is a permission type that allows other users on the platform to interact with your data through the algorithms you create if you decide to contribute to algorithm development. This means users can call your algorithm to perform an operation on your data stored in this collection, otherwise the algorithm you created would only work for you.

First check for the data collection’s permission type and update those permissions to private:

// Create the acl object and check if it's the MyAlgos default setting
acl, _ := nlp_directory.Permissions()
fmt.Println(acl.ReadAcl() == algorithmia.AclTypeMyAlgos) //true

// Update permissions to private
nlp_directory.UpdatePermissions(algorithmia.ReadAclPrivate)
fmt.Println(acl.ReadAcl() == algorithmia.AclTypePrivate) // true

Notice that we changed our data collection to private, which means that only you will be able to read and write to your data collection.

Note that read access that is set to the default DataMyAlgorithms allows any algorithm you call to have access to your data collection so most often, this is the setting you want when you are calling an algorithm and are an application developer.

For more information on collection-based Access Control Lists (ACLs) and other data collection permissions go to the Hosted Data Guide.

Upload Data to your Data Collection

So far you’ve created your data collection and checked and updated directory permissions. Now you’re ready to upload the text file that you created at the beginning of the guide to your data collection using the Data API.

First create a variable that holds the path to your data collection and the text file you will be uploading:

var text_file = "data://your_username/nlp_directory/jack_london.txt"

Next upload your local file to the data collection using the .putFile() method:

var local_file = "/your_local_path_to_file/jack_london.txt"
file_exists, file_err := client.File(text_file).Exists()

if file_err != nil {
	fmt.Println(file_err)
} else if file_exists == false {
	client.File(text_file).PutFile(local_file)
	fmt.Println("File has been uploaded")
} else {
	fmt.Println("File already exists in your data collection.")
}

This endpoint will replace a file if it already exists. If you wish to avoid replacing a file, check if the file exists before using this endpoint.

You can confirm that the file was created by navigating to Algorithmia’s Hosted Data Source and finding your data collection and file.

You can also upload your data through the UI on Algorithmia’s Hosted Data Source. For instructions on how to do this go to the Hosted Data Guide.

Downloading Data from a Data Collection

Next check if the file that you just uploaded to data collections exists, and try downloading it to a (new) local file:

if file_err != nil {
	fmt.Println(file_err)
} else if file_exists == true {
	// Download the file
	localfile, _ := foo.File("binary_file").File()
	defer localfile.Close()
	fmt.Println("File has been downloaded.")
} else {
	fmt.Println("File doesn't exist in your data collection.")
}

This copies the file from your data collection and saves it as a file on your local machine, storing the filename in the variable localfile.

Alternately, if you just need the text content of the file to be stored in a variable, you can retrieve the remote file’s content without saving the actual file:

if file_err != nil {
	fmt.Println(file_err)
} else if file_exists == true {
	// Download contents of file as a string
	input, _ := nlp_directory.File("jack_london.txt").StringContents()
	fmt.Println("File has been downloaded.")
} else {
	fmt.Println("File doesn't exist in your data collection.")
}

This will get your file as a string, saving it to the variable input. If the file was binary (an image, etc), you could instead use the function .Bytes() to retrieve the file’s content as a byte array.

Now you’ve seen how to upload a local data file, check if a file exists in a data collection, and download the file contents.

For more methods on how to get a file using the Data API from a data collection go to the API Specification.

Call an Algorithm

Finally we are ready to call an algorithm. In this guide we’ll use the natural language processing algorithm called Summarizer. This algorithm results in a string that is the summary of the text content you pass in as the algorithm’s input.

A single algorithm may have different input and output types, or accept multiple types of input, so consult the algorithm’s description for usage examples specific to that algorithm.

This example shows the summary of the text file which we downloaded from our data collection and set as the variable called “input” in the previous code sample.

Create the algorithm object and pass in the variable “input” into algo.pipe:

if file_err != nil {
	fmt.Println(file_err)
} else if file_exists == true {
	// Download contents of file as a string
	input, _ := nlp_directory.File("jack_london.txt").StringContents()
	fmt.Println("File has been downloaded.")
	// Create the algorithm object using the Summarizer algorithm
	algo, _ := client.Algo("algo://nlp/Summarizer/0.1.3")
	resp, _ := algo.Pipe(input)
	response := resp.(*algorithmia.AlgoResponse).Result
	fmt.Println(response)
} else {
	fmt.Println("File doesn't exist in your data collection.")
}

This guide used the the first chapter of Jack London’s Burning Daylight and the Summarizer algorithm outputs:

“It was a quiet night in the Shovel. The miners were in from Moseyed Creek and the other diggings to the west, the summer washing had been good, and the men’s pouches were heavy with dust and nuggets. MacDonald grinned and nodded, and opened his mouth to speak, when the front door swung wide and a man appeared in the light.”

If you are interested in learning more about working with unstructured text data check out our guide Introduction to Natural Language Processing.

Limits

Your account can make up to 80 Algorithmia requests at the same time (this limit can be raised if needed).

Algorithm requests have a payload size limit of 10MB for input and 15MB for output. If you need to work with larger amounts of data, you can make use of the Algorithmia Data API.

Conclusion

This guide covered installing the Algorithmia client, uploading and downloading data to and from a user created data collection, checking if a file exists using the Data API, calling an algorithm, and handling errors.

For more information on the methods available using the Data API check out the Data API documentation or the Go Client Docs.

For convenience, here is the whole script available to run:

// Authenticate with your API key
import (
	algorithmia "github.com/algorithmiaio/algorithmia-go"
)

var apiKey = "YOUR_API_KEY"

// Create the Algorithmia client object
var client = algorithmia.NewClient(apiKey, "")

// Instantiate a DataDirectory object, set your data URI and call Create
// Set your Data URI
nlp_directory := client.Dir("data://YOUR_USERNAME/nlp_directory")
dir_exists, dir_err := nlp_directory.Exists()

// Create your data collection if it does not exist
if dir_err != nil {
	fmt.Println(dir_err)
} else if dir_exists == false {
	nlp_directory.Create(nil)
	fmt.Println("nlp_directory created")
} else {
	fmt.Println("nlp_directory already exists")
}

// Create the acl object and check if it's the .my_algos default setting
acl, _ := nlp_directory.Permissions()
fmt.Println(acl.ReadAcl() == algorithmia.AclTypeMyAlgos) //true

// Update permissions to private
nlp_directory.UpdatePermissions(algorithmia.ReadAclPrivate)
fmt.Println(acl.ReadAcl() == algorithmia.AclTypePrivate) // true

var text_file = "data://YOUR_USERNAME/nlp_directory/jack_london.txt"
var local_file = "/your_local_path_to_file/jack_london.txt"
file_exists, file_err := client.File(text_file).Exists()

if file_err != nil {
	fmt.Println(file_err)
} else if file_exists == false {
	client.File(text_file).PutFile(local_file)
	fmt.Println("File has been uploaded")
} else {
	fmt.Println("File already exists in your data collection.")
}

if file_err != nil {
	fmt.Println(file_err)
} else if file_exists == true {
	// Download contents of file as a string
	input, _ := nlp_directory.File("jack_london.txt").StringContents()
	fmt.Println("File has been downloaded.")
	// Create the algorithm object using the Summarizer algorithm
	algo, _ := client.Algo("algo://nlp/Summarizer/0.1.3")
	resp, _ := algo.Pipe(input)
	response := resp.(*algorithmia.AlgoResponse).Result
	fmt.Println(response)
} else {
	fmt.Println("File doesn't exist in your data collection.")
}