AI Actions

Use the AI Actions channel to generate responses from a Large Language Model (LLM) based on your defined instructions and context.

You can include custom instructions, outputs from previous pipeline steps, and reference files stored in Quickbase. The AI then generates a response as the step output.  

Note

Each AI Actions step in a pipeline works independently. If your pipeline includes multiple AI Actions steps, the AI model does not retain context from previous steps, unless you explicitly include the output from those steps.

Steps

Limits

• Attached files must be stored in Quickbase, and the File transfer handle must be referenced

• Maximum file size: 2 MB

• The AI can extract text, but cannot recognize images

• The AI doesn’t process personally identifiable information, like names, emails, phone numbers, or social security numbers

• Each realm has daily limits on the amount of information sent to and received from the AI

Tip

To stay within the limits, instruct the AI to give you shorter responses

Example

Write a blog post based on the instructions in a file

Create a pipeline with these steps:

1. Look up a record step from the Quickbase channel to fetch the file

2. Custom Action step from the AI Actions channel to provide the AI instructions and the file

In the Custom Action step:

• Select Custom JSON as the response format to describe the AI output using a JSON schema.

Tip

Use AI suggestions for field mappings to quickly generate a JSON schema. Learn more

• Write your request. In this example, we ask to generate a blog post following the guidelines provided in the attached file.

• Describe the persona for the AI response. You can include details like the role, tone, or any specific requirements the response should follow. In this example, we include the AI is a blog post generator that will receive instructions from a file, and should generate a blog post based on those instructions.

• Reference the file transfer handle of the file you looked up in the first step.

Example YAML

# New pipeline 
# 

# Account slugs: 
#  - quickbase[REDACTED]: Realm Default Account <None> 
--- 
- META: 
    name: New pipeline 
    enabled: true 
- LOOKUP quickbase[REDACTED] record look_up -> a: 
    inputs-meta: 
      table: '"AI Document Scanning Test App: files" <bu7wh3rbq>' 
      export_fields: '"file" <7>' 
    inputs: 
      id: '24' 
    name: Find file 
- ACTION qb-ai-actions custom_action create -> b: 
    inputs-meta: 
      json_schema: "{\n  \"type\": \"object\",\n  \"properties\": {\n    \"title\"\ 
        : {\n      \"type\": \"string\",\n      \"description\": \"The title of the\ 
        \ blog post\"\n    },\n    \"body\": {\n      \"type\": \"string\",\n    \ 
        \  \"description\": \"The main content of the blog post\"\n    },\n    \"\ 
        summary\": {\n      \"type\": \"string\",\n      \"description\": \"A brief\ 
        \ summary of the blog post\"\n    },\n    \"word_count\": {\n      \"type\"\ 
        : \"integer\",\n      \"description\": \"The number of words in the blog post\"\ 
        \n    }\n  },\n  \"required\": [\n    \"title\",\n    \"body\",\n    \"summary\"\ 
        ,\n    \"word_count\"\n  ]\n}" 
      response_format: json_schema 
    inputs: 
      file_url: '{{a.file.file_transfer_handle}}' 
      system_message: 'You are a blog post generator. You will receive instructions 
        from a file and generate a blog post based on those instructions. ' 
      user_message: "Generate a blog post following the guidelines in the provided\ 
        \ file. \nThe blogpost should be about the new Quickbase capability called\ 
        \ AI Actions that allows users to connect with a LLM via Pipelines to send\ 
        \ instructions and the AI to generate a response" 
    name: Generate blogpost 
    note: 'generate a blog post based on instructions from a file. The response should 
      be a custom JSON that has the title, body, summary, number of words. 

      ' 
...