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