Initial commit for working code for both math instructor and researcher

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Mahesh Kommareddi 2024-06-12 19:58:05 -04:00
parent e370775ad9
commit 07a4b80301

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local-agent.py Normal file
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from crewai import Agent, Task, Crew
from crewai_tools import SerperDevTool, ScrapeWebsiteTool, SeleniumScrapingTool
from langchain_community.chat_models import ChatOllama
from langchain_openai import ChatOpenAI
from flask import Flask, jsonify
import os
# export OPENAI_API_BASE=http://the.mk:11434/v1
# export OPENAI_MODEL_NAME=crewai-llama3
# export SERPER_API_KEY=
# class MyAgent(Agent):
# def __init__(self, role="My Math Professor", goal=None, backstory=None, allow_delegation=False, verbose=True, llm=None):
# super().__init__(role=role, goal=goal, backstory=backstory, allow_delegation=allow_delegation, verbose=verbose, llm=llm)
# def to_dict(self):
# return {
# 'role': self.role,
# 'goal': self.goal,
# 'backstory': self.backstory,
# 'allow_delegation': self.allow_delegation,
# 'verbose': self.verbose
# }
os.environ["OPENAI_API_KEY"] = "NA"
llm = ChatOpenAI(
model = "crewai-llama3",
base_url = "http://chat.the.mk:11434/v1",
temperature=0.1)
# general_agent = MyAgent(role = "Math Professor",
# goal = """Provide the solution to the students that are asking mathematical questions and give them the answer.""",
# backstory = """You are an excellent math professor that likes to solve math questions in a way that everyone can understand your solution""",
# allow_delegation = False,
# verbose = True,
# llm = llm)
# task = Task (description="""what is 3 + 5""",
# agent = general_agent,
# expected_output="A numerical answer.")
# crew = Crew(
# agents=[general_agent],
# tasks=[task],
# verbose=2
# )
# Loading Tools
search_tool = SerperDevTool()
scrape_tool = SeleniumScrapingTool()
# Define your agents with roles, goals, tools, and additional attributes
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI and data science',
backstory=(
"You are a Senior Research Analyst at a leading tech think tank."
"Your expertise lies in identifying emerging trends and technologies in AI and data science."
"You have a knack for dissecting complex data and presenting actionable insights."
"Always search the web first and make the determination for the best 4 Links"
"if you are going to use Read website content tool replace the search positional argument: to 'website_url'"
# "Don't include single or double quotes in any or the searches or parameters for the tools including website_url or css_element"
"For any web searches, be sure to scrape the website content from the Link in the search"
),
verbose=True,
allow_delegation=False,
tools=[search_tool, scrape_tool],
max_rpm=100
)
writer = Agent(
role='Tech Content Strategist',
goal='Craft compelling content on tech advancements',
backstory=(
"You are a renowned Tech Content Strategist, known for your insightful and engaging articles on technology and innovation."
"With a deep understanding of the tech industry, you transform complex concepts into compelling narratives."
"Always search the web first and make the determination for the best 4 Links"
"if you are going to use Read website content tool replace the search positional argument: to 'website_url'"
# "Don't include single or double quotes in any or the searches or parameters for the tools including website_url or css_element"
"For any web searches, be sure to scrape the website content from the Link in the search"
),
verbose=True,
allow_delegation=True,
tools=[search_tool, scrape_tool],
cache=False, # Disable cache for this agent
)
# Create tasks for your agents
task1 = Task(
description=(
"Conduct a comprehensive analysis of the latest advancements in AI in 2024."
"Identify key trends, breakthrough technologies, and potential industry impacts."
"Compile your findings in a detailed report."
"Make sure to check with a human if the draft is good before finalizing your answer."
),
expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
agent=researcher,
human_input=True,
)
task2 = Task(
description=(
"Using the insights from the researcher's report, develop an engaging blog post that highlights the most significant AI advancements."
"Your post should be informative yet accessible, catering to a tech-savvy audience."
"Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
),
expected_output='A compelling three paragraphs blog post formatted as markdown with headings, subheadings, and a main thesis about the latest AI advancements in 2024',
agent=writer
)
# Instantiate your crew with a sequential process
crew = Crew(
agents=[researcher, writer],
tasks=[task1, task2],
verbose=4
)
app = Flask(__name__)
def get_agents(cls):
return [agent.__dict__ for agent in cls.agents]
def get_tasks(cls):
return [{**task.__dict__, 'expected_output': task.expected_output} for task in cls.tasks]
@app.route('/agents', methods=['GET'])
def get_agents_route():
return jsonify({'agents': get_agents(crew)})
@app.route('/tasks', methods=['GET'])
def get_tasks_route():
return jsonify({'tasks': get_tasks(crew)})
@app.route('/kickoff', methods=['GET'])
def get_kickoff_route():
result = crew.kickoff()
print(result)
return result
if __name__ == '__main__':
app.run(debug=True, port=5001)