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82 lines
2.9 KiB
82 lines
2.9 KiB
1 year ago
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from langchain_community.tools import DuckDuckGoSearchRun
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import os
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from crewai import Agent, Task, Crew, Process
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from dotenv import load_dotenv
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import os
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current_directory = os.path.dirname(os.path.realpath(__file__))
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config_directory = os.path.expanduser("~/.config/fabric")
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env_file = os.path.join(config_directory, ".env")
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load_dotenv(env_file)
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os.environ['OPENAI_MODEL_NAME'] = 'gpt-4-0125-preview'
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# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.
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# osOPENAI_API_BASE='http://localhost:11434/v1'
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# OPENAI_MODEL_NAME='openhermes' # Adjust based on available model
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# OPENAI_API_KEY=''
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# Install duckduckgo-search for this example:
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# !pip install -U duckduckgo-search
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search_tool = DuckDuckGoSearchRun()
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# Define your agents with roles and goals
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researcher = Agent(
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role='Senior Research Analyst',
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goal='Uncover cutting-edge developments in AI and data science',
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backstory="""You work at a leading tech think tank.
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Your expertise lies in identifying emerging trends.
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You have a knack for dissecting complex data and presenting actionable insights.""",
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verbose=True,
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allow_delegation=False,
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tools=[search_tool]
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# You can pass an optional llm attribute specifying what mode you wanna use.
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# It can be a local model through Ollama / LM Studio or a remote
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# model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
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#
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# import os
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#
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# OR
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#
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# from langchain_openai import ChatOpenAI
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# llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
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)
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writer = Agent(
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role='Tech Content Strategist',
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goal='Craft compelling content on tech advancements',
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backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
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You transform complex concepts into compelling narratives.""",
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verbose=True,
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allow_delegation=True
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)
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# Create tasks for your agents
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task1 = Task(
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description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
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Identify key trends, breakthrough technologies, and potential industry impacts.""",
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expected_output="Full analysis report in bullet points",
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agent=researcher
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)
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task2 = Task(
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description="""Using the insights provided, develop an engaging blog
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post that highlights the most significant AI advancements.
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Your post should be informative yet accessible, catering to a tech-savvy audience.
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Make it sound cool, avoid complex words so it doesn't sound like AI.""",
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expected_output="Full blog post of at least 4 paragraphs",
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agent=writer
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)
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# Instantiate your crew with a sequential process
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crew = Crew(
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agents=[researcher, writer],
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tasks=[task1, task2],
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verbose=2, # You can set it to 1 or 2 to different logging levels
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)
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# Get your crew to work!
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result = crew.kickoff()
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print("######################")
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print(result)
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