Modeling with Archetypes

Archetype Tutorial

Step 1: Setup

First, let's set up our environment and import the necessary libraries:

from agent_torch.core.llm.archetype import Archetype
from agent_torch.core.llm.behavior import Behavior
from agent_torch.populations import NYC
from agent_torch.core.llm.backend import LangchainLLM
OPENAI_API_KEY = None

Setup : Covid Cases Data and Unemployment Rate

from utils import get_covid_cases_data
csv_path = '/models/covid/data/county_data.csv'
monthly_cases_kings = get_covid_cases_data(csv_path=csv_path,county_name='Kings County')

Step 2: Initialise LLM Instance

We can use either of the Langchain and Dspy backends to initialise a LLM instance. While these are the frameworks we are supporting currently, you may choose to use your own framework of choice by extending the LLMBackend class provided with AgentTorch.

Let's see how we can use Langchain to initialise an LLM instance

GPT 3.5 Turbo

agent_profile = "You are an helpful agent who is trying to help the user make a decision. Give answer as a single number between 0 and 1, only."
llm_langchain_35 = LangchainLLM(
    openai_api_key=OPENAI_API_KEY, agent_profile=agent_profile, model="gpt-3.5-turbo"
)

Step 3: Define an Archetype

# Create an object of the Archetype class
# n_arch is the number of archetypes to be created. This is used to calculate a distribution from which the outputs are then sampled.
archetype_n_2 = Archetype(n_arch=2) 
archetype_n_12 = Archetype(n_arch=12)

Create an object of the Behavior class

# Define a prompt template
# Age,Gender and other attributes which are part of the population data, will be replaced by the actual values of specified region, during the simulation.
# Other variables like Unemployment Rate and COVID cases should be passed as kwargs to the behavior model.
user_prompt_template = "Your age is {age}, gender is {gender}, ethnicity is {ethnicity}, and the number of COVID cases is {covid_cases}.Current month is {month} and year is {year}."

# Create a behavior model
# You have options to pass any of the above created llm objects to the behavior class
# Specify the region for which the behavior is to be sampled. This should be the name of any of the regions available in the populations folder.
earning_behavior_n_2 = Behavior(
    archetype=archetype_n_2.llm(llm=llm_langchain_35, user_prompt=user_prompt_template),
    region=NYC
)
earning_behavior_n_12 = Behavior(
    archetype=archetype_n_12.llm(llm=llm_langchain_35, user_prompt=user_prompt_template),
    region=NYC
)

# Define arguments to be used for creating a query for the LLM Instance
kwargs = {
    "month": "January",
    "year": "2020",
    "covid_cases": 1200,
    "device": "cpu",
    "current_memory_dir": "/populations/astoria/conversation_history",
    "unemployment_rate": 0.05,
}

Step 4: Compare performance between different Configurations of Archetype

from utils import get_labor_data, get_labor_force_correlation

labor_force_df_n_2, observed_labor_force_n_2, correlation_n_2 = get_labor_force_correlation(
    monthly_cases_kings, 
    earning_behavior_n_2, 
    'agent_torch/models/macro_economics/data/unemployment_rate_csvs/Brooklyn-Table.csv',
    kwargs
)
labor_force_df_n_12, observed_labor_force_n_12, correlation_n_12 = get_labor_force_correlation(
    monthly_cases_kings, 
    earning_behavior_n_12, 
    'agent_torch/models/macro_economics/data/unemployment_rate_csvs/Brooklyn-Table.csv',
    kwargs
)
print(f"Correlation with 2 Archetypes is {correlation_n_2} and 12 Archetypes is {correlation_n_12}")