maihem 1.0.1

LLM evaluations and synthetic data generation with the MAIHEM models

maihem

Introduction

The maihem python package allows you to generate synthetic text data for training and evaluating your LLMs.

Getting Started

Installation

To install the API, run the following command:

pip install maihem

Obtaining your maihem API key

Get a free API key by subscribing to our product here: www.maihem.ai.

Setting API key

Before using the maihem package, you need to set your maihem API key as an environment variable. You can add it directly in your python code, or to your local bash script.

Alternative 1: In python code

In the beggining of your code add the following lines with your corresponding API key:

import os

os.environ['MAIHEM_API_KEY'] = '<your_maihem_api_key>'

Alternative 2: In local bash script

For Linux:

Open the .bashrc file in your home directory with a text editor

vim ~/.bashrc

add the following line to the file with your corresponding API key:

export MAIHEM_API_KEY = '<your_maihem_api_key>'

Run the following command in the terminal to apply the changes

source ~/.bashrc

For Mac:

Open the .bash_profile file in your home directory with a text editor

vim ~/.bash_profile

add the following line to the file with your corresponding API key:

export MAIHEM_API_KEY = '<your_maihem_api_key>'

Run the following command in the terminal to apply the changes

source ~/.bash_profile

Generate synthetic data

Persona prompts

See run_examply.py for an example python script for persona prompt generation. The example code is also below

import os
import maihem as mh


os.environ['MAIHEM_API_KEY'] = 'a923c14d881247a7bad58b93d9595494'

# Parameter dictionary for intent
intent = {
    'intent': "unblock credit card",
    'context': "credit card got blocked when traveling abroad",
    'category': "retail banking"
}

# Parameter dictionary for persona
persona = {
    'mood': "angry",
    'age': "30-40",
    'gender': "male",
    'ethnicity': "white",
    'disability': "none",
    'income': "high",
    'education': "college degree",
    'marital_status': "married",
    'children': "2",
    'employment': "employed",
    'housing': "rent",
    'occupation': "data scientist",
    'location': "New York",
    'customer_name': "John Doe",
  }

# Create data generator object
dg = mh.DataGenerator()

# Generate list of prompts for defined persona
data = dg.generate_prompts(intent, persona, model_temperature=0.8, n_calls=3, n_prompts_per_call=2)
print(data)