OpenAI

This Integration is part of the OpenAI Pack.#

Supported versions

Supported Cortex XSOAR versions: 6.5.0 and later.

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#

Navigate to Settings > Integrations > Servers & Services.
Search for OpenAI.
Click Add instance to create and configure a new integration instance.
Parameter Required
OpenAI API URL(e.g. https://api.openai.com/) True
API Key True
Trust any certificate (not secure) False
Use system proxy settings False
Click Test to validate the URLs, token, and connection.

Parameter	Required
OpenAI API URL(e.g. https://api.openai.com/)	True
API Key	True
Trust any certificate (not secure)	False
Use system proxy settings	False

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 Name	Description	Required
prompt	Instruction.	Required
model	The 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
temperature	Controls randomness: Lowering results in less random completions. Default is 0.7.	Optional
max_tokens	The maximum number of token to generate. Default is 256.	Optional
top_p	Controls Diversity via nucleus sampling: 0.5 means half of all likihood-weighted options are considered. Default is 1.	Optional
frequency_penalty	How 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_penalty	How 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#

Path	Type	Description
OpenAI.Completions.id	String	Id of the returned completion.
OpenAI.Completions.model	String	The model which will generate the completion.
OpenAI.Completions.text	String	Completed 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"
        }
    }
}