OpenAI

Future Ideas

fine-turning

The openAI module can be used to understand and generate data specified by the user. For example extract contact information fron an email, correct the grammar from a research paper or get the most important keywords from a long boring text.

:: warning: The AI only understand english words.

Tokens and price calculation

Read more about pricing here.
View your API usage here.

Models

OpenAI comes with a variety of models with different capabilities and price points.

Davinci

Davinci is the most caple of all the models. It shines when the task is about performing a lot of understanding of the content, like summarization for a specific audience and creative content generation. It is also very good at solving many kinds of logic problems. However, the Davinci model is the most expensive of all the models.

Good at: Language translation, complex classification, text sentiment, summarization.

Price: $0.0200 / 1k tokens.

Curie

Curies is also very powerful. It can perform many of the same task as Davinci, however with a smaller cost. Curie shines more with nuanced task like sentiment classification and summarization.

Good at: Language translation, complex classification, text sentiment, summarization.

Price: $0.0020 / 1k tokens.

Babbage

Babbage is capable of solving more straight forward task, like semantic Search ranking and how well documents match up with search queries.

Good at: Moderate classification, semanic search classification.

Price: $0.0005 / 1k tokens.

Ada

Ada is the fastes of all the models. Ada is good at performing task that require parsing text, address correction and certain kind of cassification task that don’t require too much complexitiy. Ada’s capability can often be improved by providing more context.

Good at: Parsing text, simpole classification, address correction, keywords.

Price: $0.0004 / 1k tokens.

Finding the right model

If you don’t know what model to use, then you can use this tool, which lets you use different models side-by-side to compare outputs. Remeber that the tool still uses your token quota.

Setting up

Connect

The method connect initialises a connection to the openAI API.

Parameters

apiKey is your secret API key, which can be found here.
model is the model that you want to use. Currently it should be “text-davinci-002”, “text-Curie-001”, “text-babbage-001” or “text-ada-001”.

Example

javascript

const openAI = Module.load("OpenAI", {version: "1.0.0"})
openAI.connect("YOUR_SECRET_API_KEY", "text-davinci-002")

Methods

Grammar correction

The correctGrammar method corrects the grammar that the AI receives as input.

Parameters

token the text that the AI should correct.
options an optimal options object. Supported options are;
- max_tokens an integer. The maximum amount of tokens the AI should generate as output. (min: 10, max: 2048, differs from models).
- temperature a double. Determines randomness: Lowering results, the AI will be less creative and the answers will be more repetitive. (min: 0, max: 1).
- top_p a double. Determines diversity via nucleus sampling: 0.5 means halft of all likelihood weighted options are considered. (min: 0, max: 1).
- frequency_penalty a double. How much to penalize new tokens based on their existing frequency in the text so far. Decreses the model’s likelihood to repeat the same line verbatim. (min: 0, max 2).
- presence penalty a double. How much to penalize new tokens based on wheater they appear in the text so far. Increases the model’s likelihood tio talk about new topics. (min: 0, max: 2).
- num_outputs an integer. Generates multiple outputs. Use with caution, since it acts as a multiplayer on the number of completions, so this parameter can eat into your token quota very quickly (min: 1, max: 10).

Example

javascript

// load module to disk
const openAI = Module.load("OpenAI", {version: "1.0.0"})
// connect to the open AI API
openAI.connect("YOUR_SECRET_API_KEY", "text-davinci-002", {})
// the specified token which should be corrected
const tokenToBeCorrected = "Dis is vary bad gramma please you help me correct"
// the corrected token
const theCorrectedGrammar = openAI.correctGramma(tokenToBeCorrected)
// completed result
const output = theCorrectedGrammar["result"]
/*
    output: This is very bad grammar. Please help me correct it. 
*/

Extract TL;DR

The extractTLDR method tells the AI to generate a TL;DR (too long; didn’t read) string from what the AI receives as input.

Parameters

-token the text that the AI will extract TL;DR from.

options an optimal options object. Supported options are;
- max_tokens an integer. The maximum amount of tokens the AI should generate as output. (min: 10, max: 2048, differs from models).
- temperature a double. Determines randomness: Lowering results, the AI will be less creative and the answers will be more repetitive. (min: 0, max: 1).
- top_p a double. Determines diversity via nucleus sampling: 0.5 means halft of all likelihood weighted options are considered. (min: 0, max: 1).
- frequency_penalty a double. How much to penalize new tokens based on their existing frequency in the text so far. Decreses the model’s likelihood to repeat the same line verbatim. (min: 0, max 2).
- presence penalty a double. How much to penalize new tokens based on wheater they appear in the text so far. Increases the model’s likelihood tio talk about new topics. (min: 0, max: 2).
- num_outputs an integer. Generates multiple outputs. Use with caution, since it acts as a multiplayer on the number of completions, so this parameter can eat into your token quota very quickly (min: 1, max: 10).

Example

javascript

// load module to disk
const openAI = Module.load("OpenAI", {version: "1.0.0"})
// connect to the open AI API
openAI.connect("YOUR_SECRET_API_KEY", "text-davinci-002")
// the token that  
const javaToken = "One design goal of Java is portability, " +
        "which means that programs written for the Java platform must run similarly on any combination of hardware" +
        " and operating system with adequate run time support. " +
        "This is achieved by compiling the Java language code to an intermediate representation called Java bytecode, " +
        "instead of directly to architecture-specific machine code. Java bytecode instructions are analogous to machine code, " +
        "but they are intended to be executed by a virtual machine (VM) written specifically for the host hardware. " +
        "End-users commonly use a Java Runtime Environment (JRE) installed on their device for standalone Java applications " +
        "or a web browser for Java applets."
const tldr = openAI.extractTLDR(javaToken)
const output = tldr["result"]
/*
    output: Java is a portable language that can run on any platform with a Java VM. 
*/

// example with temperature set to 1
const tldr = openAI.extractTLDR(javatoken, {temperature: 1})
const output = tldr["result"]
/*
    output: Java code is compiled to an intermediate representation called Java bytecode, which is then executed by a virtual machine.
    This makes java progams protable, meaning they can run on any combination of hardware and operationg system with a Java Virtual Machine. 
*/

Extract keywords

The extractKeywords method tells the AI to generate keywords from what the AI receives as input.

Parameters

token the text that the AI will extract keywords from.
options an optimal options object. Supported options are;
- max_tokens an integer. The maximum amount of tokens the AI should generate as output. (min: 10, max: 2048, differs from models).
- temperature a double. Determines randomness: Lowering results, the AI will be less creative and the answers will be more repetitive. (min: 0, max: 1).
- top_p a double. Determines diversity via nucleus sampling: 0.5 means halft of all likelihood weighted options are considered. (min: 0, max: 1).
- frequency_penalty a double. How much to penalize new tokens based on their existing frequency in the text so far. Decreses the model’s likelihood to repeat the same line verbatim. (min: 0, max 2).
- presence penalty a double. How much to penalize new tokens based on wheater they appear in the text so far. Increases the model’s likelihood tio talk about new topics. (min: 0, max: 2).
- num_outputs an integer. Generates multiple outputs. Use with caution, since it acts as a multiplayer on the number of completions, so this parameter can eat into your token quota very quickly (min: 1, max: 10).

Example

javascript

// load module to disk
const openAI = Module.load("OpenAI", {version: "1.0.0"})
// connect to the open AI API
openAI.connect("YOUR_SECRET_API_KEY", "text-davinci-002", {})
// the token which the AI will extract keywords from
const javaToken = "One design goal of Java is portability, " +
        "which means that programs written for the Java platform must run similarly on any combination of hardware" +
        " and operating system with adequate run time support. " +
        "This is achieved by compiling the Java language code to an intermediate representation called Java bytecode, " +
        "instead of directly to architecture-specific machine code. Java bytecode instructions are analogous to machine code, " +
        "but they are intended to be executed by a virtual machine (VM) written specifically for the host hardware. " +
        "End-users commonly use a Java Runtime Environment (JRE) installed on their device for standalone Java applications " +
        "or a web browser for Java applets."

const keywords = openAI.extractKeywords(javaToken) 
const output = keywords["result"]
/*
    output: Java, portability, operating system, bytecode, virtual machine, JRE
*/

Extract contact information

The extractContactInformation method generates information that is specified by the user.

Parameters

token the text that the AI will extract contact information from.
options an optimal options object. Supported options are;
- max_tokens an integer. The maximum amount of tokens the AI should generate as output. (min: 10, max: 2048, differs from models).
- temperature a double. Determines randomness: Lowering results, the AI will be less creative and the answers will be more repetitive. (min: 0, max: 1).
- top_p a double. Determines diversity via nucleus sampling: 0.5 means halft of all likelihood weighted options are considered. (min: 0, max: 1).
- frequency_penalty a double. How much to penalize new tokens based on their existing frequency in the text so far. Decreses the model’s likelihood to repeat the same line verbatim. (min: 0, max 2).
- presence penalty a double. How much to penalize new tokens based on wheater they appear in the text so far. Increases the model’s likelihood tio talk about new topics. (min: 0, max: 2).
- num_outputs an integer. Generates multiple outputs. Use with caution, since it acts as a multiplayer on the number of completions, so this parameter can eat into your token quota very quickly (min: 1, max: 10).

Example

javascript

// load module to disk
const openAI = Module.load("OpenAI", {version: "1.0.0"})
// connect to the open AI API
openAI.connect("YOUR_SECRET_API_KEY", "text-davinci-002", {})
// the token which the AI will look through information
const emailToken = "Good morning Mr.Sheehan, I would like to formally introduce " +
        "myself. My name is Ethan and I am from Secure Shield, a company focused on protecting your home " +
        "with security cameras and alarms. We understand the importance of keeping your family safe, " +
        "and we want to ensure you have the best security system to meet your needs and budget. " +
        "If you are interested in our services, please contact me at ccrenshaw@secureshield.com or " +
        "call me at 12-34-56-78. Im looking forward to hearing from you!";

// the AI will find the information that corresponds to name, company name, services, email and phonenumber
const information = openAI.extractKeywords(javaToken, "name", "company name", "services", "email", "phonenumber") 
const output = information["result"]
/*
    output: returns a JS Object which contains the information
*/
const name = output["name"] // Ethan
const company = output["company name"] // Secure Shield  
const services = output["services"] // Security cameras and alarms
const email = output["email"] // ccrenshaw@secureshield.com
const phonenumber = output["phonenumber"] // 12-34-56-78

Get Models

The getModels method return a JS object of the available models.

Example

javascript

// load module to disk
const openAI = Module.load("OpenAI", {version: "1.0.0"})
// gets all models object
const models = openAI.getModels()["models"]

const davinci = models["davinci"]   // text-davinci-002
const curie = models["curie"]       // text-curie-001
const babbage = models["babbage"]   // text-babbage-001
const ada = models["ada"]           // text-ada-001

// could be used as follows
openAI.connect("YOUR_SECRET_API_KEY", davinci)

Future Ideas ​

OpenAI ​

Tokens and price calculation ​

Models ​

Davinci ​

Curie ​

Babbage ​

Ada ​

Finding the right model ​

Setting up ​

Connect ​

Parameters ​

Example ​

Methods ​

Grammar correction ​

Parameters ​

Example ​

Extract TL;DR ​

Parameters ​

Example ​

Extract keywords ​

Parameters ​

Example ​

Extract contact information ​

Parameters ​

Example ​

Get Models ​

Example ​

Future Ideas

OpenAI

Tokens and price calculation

Models

Davinci

Curie

Babbage

Ada

Finding the right model

Setting up

Connect

Parameters

Example

Methods

Grammar correction

Parameters

Example

Extract TL;DR

Parameters

Example

Extract keywords

Parameters

Example

Extract contact information

Parameters

Example

Get Models

Example