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OpenAI (Deprecated)

This Integration is part of the OpenAI Pack.#

Deprecated

Use OpenAI GPT instead.

The OpenAI API can be applied to virtually any task that involves understanding or generating natural language or code. We offer a spectrum of models with different levels of power suitable for different tasks, as well as the ability to fine-tune your own custom models. These models can be used for everything from content generation to semantic search and classification. This integration was integrated and tested with version 1 of OpenAI

Configure OpenAI on Cortex XSOAR#

  1. Navigate to Settings > Integrations > Servers & Services.

  2. Search for OpenAI.

  3. Click Add instance to create and configure a new integration instance.

    ParameterRequired
    OpenAI API URL(e.g. https://api.openai.com/)True
    API KeyTrue
    Trust any certificate (not secure)False
    Use system proxy settingsFalse
  4. Click Test to validate the URLs, token, and connection.

Commands#

You can execute these commands from the Cortex XSOAR CLI, as part of an automation, or in a playbook. After you successfully execute a command, a DBot message appears in the War Room with the command details.

openai-completions#


Enter an instruction and watch the API respond with a completion that attempts to match the context or pattern you provided.

Base Command#

openai-completions

Input#

Argument NameDescriptionRequired
promptInstruction.Required
modelThe model which will generate the completion. Some models are suitable for natural language tasks, others specialize in code. Possible values are: text-davinci-003, text-curie-001, text-babbage-001, text-ada-001, code-davinci-002, code-cushman-001. Default is text-davinci-003.Optional
temperatureControls randomness: Lowering results in less random completions. Default is 0.7.Optional
max_tokensThe maximum number of token to generate. Default is 256.Optional
top_pControls Diversity via nucleus sampling: 0.5 means half of all likihood-weighted options are considered. Default is 1.Optional
frequency_penaltyHow much to penalize new tokens based on their existing frequency in the text so far. Decreases the model's likelihood to repeat the same line verbatim. Default is 0.Optional
presence_penaltyHow much to penalize new tokens based on whether they appear in the text so far. Increases the model's likelihood to talk about new topics. Default is 0.Optional

Context Output#

PathTypeDescription
OpenAI.Completions.idStringId of the returned completion.
OpenAI.Completions.modelStringThe model which will generate the completion.
OpenAI.Completions.textStringCompleted text generated by OpenAI?

Command example#

!openai-completions prompt="Give me some characteristics of a phishing email" model="text-davinci-003" temperature="0.7" max_tokens="256" top_p="1" frequency_penalty="0" presence_penalty="0"

Context Example#

{
"OpenAI": {
"Completions": {
"id": "cmpl-6K7q5vYUr6SzEbAOb6LoQRF7rp3KN",
"model": "text-davinci-003",
"text": "1. Unsolicited email from an unknown source2. Asks for confidential information such as passwords, bank account details, or credit card numbers3. Contains spelling and grammar errors4. Contains urgent language, threats, or a false sense of urgency5. Uses generic greetings like \"Dear Customer\" instead of your name6. Links to a suspicious website that looks legitimate7. Uses a spoofed email address that appears to be from a trusted source"
}
}
}