To view the images, please visit https://github.com/bentleymi/ta-openai-api/blob/main/readme.md
1. Install using the latest tar.gz or .spl file
2. Add your OpenAI Org & API Key with the setup page:
(ref: https://beta.openai.com/account/org-settings & https://beta.openai.com/account/api-keys)
3. Use the search command: | openai prompt="your prompt" NOTE: org={yourORGID} is no longer supported as of version 2.1.0
The command will create a "ChatCompletion", "Completion", "Edit" or "Moderate" request to the OpenAI API depending on which model you specify:
ref: https://beta.openai.com/docs/api-reference/
The following options are supported by the command:
prompt - Explanation: Optional, your prompt for OpenAI
prompt_field - Explanation: Optional, if streaming data to openai, a field in your result set that you wish to use as a prompt for OpenAI
model - Default: gpt-3.5-turbo - Explanation: Optional, which GPT model to use (ref: https://beta.openai.com/docs/models/). As of Version 3.0.0, if you choose a completion model, code will genearate a completion task. If you choose a moderation model, code will generate a moderation task, and so on.
task - DEPRECATED in Version 3.0.0+ | Default: chat - Explanation: Optional, the task you wish to complete from this list (Chat,Complete,Edit,Moderate)
instruction - Default: None - Explanation: Optional, the instruction you want the Edit/Edits to follow. Note this is only valid when edit models are specified.
max_tokens - Default: None - Explanation: Optional, the maximum number of tokens to generate in the completion.
stop - Default: None - Explanation: Optional, up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
temperature - Default: None - Explanation: Optional, what sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or temperature but not both.
top_p - Default: None - Explanation: Optional, an alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
n - Default: None, max 10 - Explanation: Optional, how many completions to generate for each prompt. Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.
A simple completion example:
| openai prompt="When was GA, USA founded" model=text-davinci-003 task=completion
A simple edit example:
| openai prompt="Orenge" model=text-davinic-edit-001
A simple edit with instructions example:
| openai prompt="When was GA, USA founded" model=text-davinic-edit-001 instruction="expand the acronyms"
A simple moderation example:
| openai prompt="I want to kill" model=text-moderation-stable
Data cleaning examples:
Getting 5 incorrect spellings of a US City and then using AI to correct the spelling:
Chat examples:
| openai prompt="write a hello world js please"
Mapping Example:
`comment("Grab some data from an internal index and combine it into one field called raw")`
index=_internal sourcetype=splunk_web_access
| head 10
| rename _raw as raw
| fields raw
| mvcombine raw
`comment("Ask ChatGPT what's the best sourcetype to use for the data")`
| map [| openai model=gpt-4 prompt="What is the best Splunk sourcetype for this data? \n".$raw$]
`comment("Parse the reponse, dropping all but the value of the content field from the response message")`
| spath input=openai_response
| rename choices{}.message.content as response
| table response
Workflow Actions:
workflow actions example image 1
workflow actions example image 2
Streaming Example: