adlib-client

adlib-client

A Python wrapper for monetizing LLM output with AdLib.

Install

Install from PyPI:

pip install adlib-client

Required API keys

AdLib requires two keys:

ADLIB_API_KEY=your_adlib_api_key_here
ADLIB_CHATBOT_PUBLIC_KEY=your_chatbot_public_key_here

You can provide them using environment variables (in a .env file) or directly when creating AdLib():

from adlib_client import AdLib

adlib = AdLib(
    adlib_api_key="your_adlib_api_key_here",
    adlib_chatbot_api_key="your_chatbot_public_key_here",
)

Authorization on Initialization

When you create an instance of AdLib(), it automatically attempts to validate your credentials against the AdLib service.

• Success: The object is initialized and ready for use.
• Missing Keys: If either key is missing (and not found in environment variables), an Exception is raised immediately.
• Invalid Keys: If the keys are provided but rejected by the server, a ValueError is raised with the specific error message from the service.

Basic usage

from adlib_client import AdLib

# Automatically loads keys from environment or .env
adlib = AdLib()

llm_output = "Here is the answer from your model."
adified_output = adlib.adify(llm_output)

print(adified_output)

adlib.adify() sends text to AdLib and returns the final transformed output string. If an ad is inserted, it's included in the string; otherwise, the original text is returned.

Use AdLib as a decorator

You can wrap your generation functions directly:

from adlib_client import AdLib

adlib = AdLib()

@adlib.adify
def generate_answer(prompt: str) -> str:
    # Your LLM logic here
    answer = ...
    return answer

# The returned value is now automatically adified
print(generate_answer("What is the weather?"))

Error Handling

• Initialization: Exception for missing keys, ValueError for invalid keys.
• Network Errors: Failed HTTP requests raise standard requests exceptions (e.g., connection issues).
• Service Errors: If the adify service fails during a call, adify() safely returns the original LLM output.

Notes

• adlib.adify() returns the transformed text string directly.
• Use adify when you want to wrap LLM outputs or decorate generation functions.