Schemas

What are schemas?

An AI feature has at least one schema. Each schema define:

• an input structure

• an output structure

For example, a feature that answer question about a PDF is represented:

An example of a schema

Python

WorkflowAI uses Pydantic to define schemas.

class PdfQuestionAnsweringTaskInput(BaseModel):
    pdf_document: Optional[File] = None
    question: Optional[str] = None

class SupportingQuote(BaseModel):
    quote: Optional[str] = None
    page_number: Optional[float] = None

class PdfQuestionAnsweringTaskOutput(BaseModel):
    answer: Optional[str] = None
    supporting_quotes: Optional[list[SupportingQuote]] = None

TypeScript

interface PdfQuestionAnsweringTaskInput {
    pdf_document?: File;
    question?: string;
}

interface SupportingQuote {
    quote?: string;
    page_number?: number;
}

interface PdfQuestionAnsweringTaskOutput {
    answer?: string;
    supporting_quotes?: SupportingQuote[];
}

Examples

For example, if you want an agent to summarize a text, the input is a text, and the output is a summary. If you want this agent to summarize a text in a specific language, you'll need to add a language parameter to the input. The inputs are like all the variables the LLM will have access to. The outputs are the different variables the LLM will generate.

When using WorkflowAI web-app, you can write what you want the agent to do, and the web-app will generate a schema for you.

Why are schemas a good idea?

Clear input and output structures (=schemas) have a few benefits:

1. simplify integration with a backend by providing a clear interface

2. provide output consistency

3. increase the quality of LLM outputs by structuring the task

Technical details

WorkflowAI leverages structured generation, also called structured output, or controlled generation. Structured generation is currently enabled for all supported OpenAI models, and for all models on Fireworks. When structured generation is not available, WorkflowAI automatically falls back to JSON mode, and always guarantees the output will follow the output schema.

How to create a schema?

WorkflowAI supports two ways to create a schema:

• using our web-app, using AI or manually.

• writing code directly (for Python).

Field data types

When creating a schema, you will notice that each field is assigned a data type. In most cases, the AI schema generator will choose the correct data type. However, you can also manually choose the data type.

Here are the different data types you can choose from:

• string: a sequence of characters, like letters, numbers, and symbols.

• boolean: the value can only be true or false.

• number: can be both whole numbers (integers) and numbers with decimal points (floats).

• list: contains 1 or more entries of another data types (ex. a list of strings). The number of entries can vary in different task runs of the same task.

• enum: the value can only be one of a predetermined, finite number of options. Similar to a boolean, but the options can be customized.

• object: a single instance of a defined structure. The structure can contain multiple properties within it.

• date: includes a year, a month, and a day.

• date-time: includes a year, a month, a day, an hour, a minute, and a second.

• time: includes an hour, a minute, and a second.

• timezone: is a region that has the same standard time, defined by the difference in hours from Coordinated Universal Time (UTC).

• html: content formatted as an HTML page.

• image: an image. Supported formats are PNG, JPG, and WEBP.

• audio: an audio file. Supported formats are MP3, WAV, AIFF, AAC, OGG Vorbis, and FLAC.

• document: an uploaded document. Supported formats are PDF, CSV, TXT, and supported image formats.

Edit a schema

Finding the right schema takes a few iterations, so we try to make editing a schema as easy as possible.

When possible, we recommend to edit the schema using the agent via the playground. If you need more control, you can manually edit the schema.

Archiving and restoring a schema

When building a new task, it's very likely you'll need multiple iterations to get the right schema. To clean up unused schemas, you can archive them.

To archive a schema, navigate to the "Schemas" section from the menu, and click on the "Archive" button in the schema's detail view.

To restore a schema, navigate to the "Schemas" section from the menu, and click on the "Restore" button in the schema's detail view.

Archived schemas are not deleted, but hidden from the UI. Any deployment or version using an archived schema will continue to work, to avoid breaking changes.