Example of raising an error

Example

error_output.py Example

In this example, we will use the funcchain library to build a system that extracts user information from text. Most importantly we will be able to raise an error thats programmatically usable. You can adapt this for your own usage.

The main functionality is to take a string of text and attempt to extract user information, such as name and email, and return a User object. If the information is insufficient, an Error is returned instead.

Full Code Example

from funcchain import BaseModel, Error, chain
from rich import print

class User(BaseModel):
    name: str
    email: str | None

def extract_user_info(text: str) -> User | Error:
    """
    Extract the user information from the given text.
    In case you do not have enough infos, raise.
    """
    return chain()

print(extract_user_info("hey"))
# => returns Error

print(extract_user_info("I'm John and my mail is john@gmail.com"))
# => returns a User object

Demo

$ extract_user_info("hey")

Error(
    title='Invalid Input',
    description='The input text does not contain user information.'
)

$ extract_user_info("I'm John and my mail is john@gmail.com")

User(
    name='John',
    email='john@gmail.com'
)

Instructions

Step-by-step

Necessary Imports

from funcchain import BaseModel, Error, chain
from rich import print

Define the User Model

class User(BaseModel):
    name: str
    email: str | None

The User class is a Pydantic model that defines the structure of the user information to be extracted, with fields for name and an email. Change the fields to experiment and alignment with your project.

Implement the Extraction Function

The extract_user_info function is intended to process the input text and return either a User object with extracted information or an Error if the information is not sufficient.

def extract_user_info(text: str) -> User | Error:
    """
    Extract the user information from the given text.
    In case you do not have enough infos, raise.
    """
    return chain()

For experiments and adoptions also change the str that will be used in chain() to identify what you defined earlier in the User(BaseModel)

Run the Extraction System

This conditional block is used to execute the extraction function and print the results when the script is run directly.

print(extract_user_info("hey"))
# => returns Error

print(extract_user_info("I'm John and my mail is john@gmail.com"))
# => returns a User object