crewai_with_groq_llama70b.py

# -*- coding: utf-8 -*-
"""CrewAI_with_Groq.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1gEfeGJd5_81o8zy5wa9I92u5Xq-M9891
"""

#!pip install crewai

# first, in a conda terminal, write te following command to install the packages: pip install crewai

from crewai import Agent, Task, Crew, Process

import os

os.environ["OPENAI_API_BASE"] = 'https://api.groq.com/openai/v1'  #OpenAI Compatibilty of Groq with OpenAI
os.environ["OPENAI_MODEL_NAME"] ='llama3-70b-8192'  # Adjust based on available model
os.environ["OPENAI_API_KEY"] ='<YOUR API KEY from GROQ>'

#model = Ollama(model = "llama3")

# email classification  with llama3
email = "Hey, Are you up for running?"

classifier = Agent(
    role = "email classifier",
    goal = "Accurately classify emails based on their level of importance. Give them one of the three ratings: Important, Casual, Spam",
    backstory = """You are an AI assistant whose job is to classify the emails accurately and honestly. Your job is to help the user manage his/her inbox.""",
    verbose = True,
    allow_delegation = False,  #We do not want this agent delegate its task to somebody else :)
)


responder = Agent(
    role = "email responder",
    goal = "Based on the importance of the email, write a concise and simple response. If the email is rated Important, write a formal response. If rated casual, write a casual response, and ignore the email if it is rated as Spam. In any case, be concise.",
    backstory = """You are an AI assistant whose job is to write short responses to emails based on their importance. The importance will be provided to you by the classifier agent.""",
    verbose = True,
    allow_delegation = False,  #We do not want this agent delegate its task to somebody else :)
)

classify_email = Task(
    description = f"Classify the following email: {email}",
    agent = classifier,
    expected_output = "One of these three options: 'Important', 'Casual', 'Spam'"
)


respond_to_email = Task(
    description = f"Respond to the following email: {email} based on the importance provided by 'classifier' agent.",
    agent = responder,
    expected_output = "short responses to emails based on their importance. The importance will be provided to you by the classifier agent."
)

crew = Crew(
    agents = [classifier, responder],
    tasks = [classify_email, respond_to_email],
    verbose = 2,
    process = Process.sequential
)

output = crew.kickoff()
print(output)