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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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