ChatCompletion

id: openai_chat
namespace: company.team

inputs:
  - id: prompt
    type: STRING
    defaults: What is data orchestration?

tasks:
  - id: completion
    type: io.kestra.plugin.openai.ChatCompletion
    apiKey: "{{ secret('OPENAI_API_KEY') }}"
    model: gpt-4o
    prompt: "{{ inputs.prompt }}"

  - id: log_output
    type: io.kestra.plugin.core.log.Log
    message: "{{ outputs.completion.choices[0].message.content }}"

id: openai_chat
namespace: company.team

tasks:
  - id: prompt
    type: io.kestra.plugin.openai.ChatCompletion
    apiKey: "{{ secret('OPENAI_API_KEY') }}"
    model: gpt-4o
    prompt: Explain in one sentence why data engineers build data pipelines

  - id: use_output
    type: io.kestra.plugin.core.log.Log
    message: "{{ outputs.prompt.choices | jq('.[].message.content') | first }}"

id: openai_chat
namespace: company.team

inputs:
  - id: prompt
    type: STRING
    defaults: I love your product and would purchase it again!

tasks:
  - id: prioritize_response
    type: io.kestra.plugin.openai.ChatCompletion
    apiKey: "yourOpenAIapiKey"
    model: gpt-4o
    messages:
      - role: user
        content: "{{ inputs.prompt }}"
    functions:
      - name: respond_to_review
        description: Given the customer product review provided as input, determines how urgently a reply is required and then provides suggested response text.
        parameters:
          - name: response_urgency
            type: string
            description: How urgently this customer review needs a reply. Bad reviews
                         must be addressed immediately before anyone sees them. Good reviews can
                         wait until later.
            required: true
            enumValues:
              - reply_immediately
              - reply_later
          - name: response_text
            type: string
            description: The text to post online in response to this review.
            required: true

  - id: response_urgency
    type: io.kestra.plugin.core.debug.Return
    format: "{{ outputs.prioritize_response | jq('.choices[0].message.tool_calls[0].function.arguments | fromjson | .response_urgency') | first }}"

  - id: response_text
    type: io.kestra.plugin.core.debug.Return
    format: "{{ outputs.prioritize_response | jq('.choices[0].message.tool_calls[0].function.arguments | fromjson | .response_text') | first }}"