wehub-resource-sync
144 lines
5.3 KiB
Python
144 lines
5.3 KiB
Python
"""
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This is an example for leveraging MLflow's auto tracing capabilities for CrewAI.
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Most codes are from https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner.
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For more information about MLflow Tracing, see: https://mlflow.org/docs/latest/llms/tracing/index.html
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Note that the following example works with crewai>=0.83.0.
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"""
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from textwrap import dedent
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from crewai import Agent, Crew, Task
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from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
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from crewai_tools import SerperDevTool, WebsiteSearchTool
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import mlflow
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mlflow.set_experiment("CrewAI")
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# Turn on auto tracing by calling mlflow.crewai.autolog()
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mlflow.crewai.autolog()
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content = "Users name is John. He is 30 years old and lives in San Francisco."
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string_source = StringKnowledgeSource(content=content, metadata={"preference": "personal"})
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search_tool = SerperDevTool()
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web_rag_tool = WebsiteSearchTool()
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class TripAgents:
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def city_selection_agent(self):
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return Agent(
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role="City Selection Expert",
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goal="Select the best city based on weather, season, and prices",
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backstory="An expert in analyzing travel data to pick ideal destinations",
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tools=[search_tool, web_rag_tool],
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verbose=True,
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)
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def local_expert(self):
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return Agent(
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role="Local Expert at this city",
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goal="Provide the BEST insights about the selected city",
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backstory="""A knowledgeable local guide with extensive information
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about the city, it's attractions and customs""",
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tools=[search_tool, web_rag_tool],
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verbose=True,
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)
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class TripTasks:
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def identify_task(self, agent, origin, cities, interests, range):
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return Task(
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description=dedent(f"""
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Analyze and select the best city for the trip based
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on specific criteria such as weather patterns, seasonal
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events, and travel costs. This task involves comparing
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multiple cities, considering factors like current weather
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conditions, upcoming cultural or seasonal events, and
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overall travel expenses.
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Your final answer must be a detailed
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report on the chosen city, and everything you found out
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about it, including the actual flight costs, weather
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forecast and attractions.
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Traveling from: {origin}
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City Options: {cities}
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Trip Date: {range}
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Traveler Interests: {interests}
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"""),
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agent=agent,
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expected_output="Detailed report on the chosen city including flight costs, weather forecast, and attractions",
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)
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def gather_task(self, agent, origin, interests, range):
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return Task(
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description=dedent(f"""
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As a local expert on this city you must compile an
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in-depth guide for someone traveling there and wanting
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to have THE BEST trip ever!
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Gather information about key attractions, local customs,
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special events, and daily activity recommendations.
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Find the best spots to go to, the kind of place only a
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local would know.
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This guide should provide a thorough overview of what
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the city has to offer, including hidden gems, cultural
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hotspots, must-visit landmarks, weather forecasts, and
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high level costs.
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The final answer must be a comprehensive city guide,
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rich in cultural insights and practical tips,
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tailored to enhance the travel experience.
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Trip Date: {range}
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Traveling from: {origin}
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Traveler Interests: {interests}
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"""),
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agent=agent,
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expected_output="Comprehensive city guide including hidden gems, cultural hotspots, and practical travel tips",
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)
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class TripCrew:
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def __init__(self, origin, cities, date_range, interests):
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self.cities = cities
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self.origin = origin
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self.interests = interests
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self.date_range = date_range
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def run(self):
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agents = TripAgents()
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tasks = TripTasks()
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city_selector_agent = agents.city_selection_agent()
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local_expert_agent = agents.local_expert()
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identify_task = tasks.identify_task(
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city_selector_agent, self.origin, self.cities, self.interests, self.date_range
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)
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gather_task = tasks.gather_task(
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local_expert_agent, self.origin, self.interests, self.date_range
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)
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crew = Crew(
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agents=[city_selector_agent, local_expert_agent],
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tasks=[identify_task, gather_task],
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verbose=True,
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memory=True,
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knowledge={
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"collection_name": "crewai_example",
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"sources": [string_source],
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"metadata": {"preference": "personal"},
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},
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)
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result = crew.kickoff()
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return result
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trip_crew = TripCrew("California", "Tokyo", "Dec 12 - Dec 20", "sports")
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result = trip_crew.run()
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print("\n\n########################")
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print("## Here is you Trip Plan")
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print("########################\n")
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print(result)
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