Jonathan Dunn
9 months ago
14 changed files with 2275 additions and 3 deletions
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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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@ -0,0 +1,89 @@ |
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from crewai import Crew |
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from textwrap import dedent |
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from .trip_agents import TripAgents |
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from .trip_tasks import TripTasks |
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import os |
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from dotenv import load_dotenv |
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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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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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travel_concierge_agent = agents.travel_concierge() |
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identify_task = tasks.identify_task( |
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city_selector_agent, |
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self.origin, |
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self.cities, |
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self.interests, |
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self.date_range |
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) |
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gather_task = tasks.gather_task( |
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local_expert_agent, |
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self.origin, |
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self.interests, |
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self.date_range |
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) |
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plan_task = tasks.plan_task( |
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travel_concierge_agent, |
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self.origin, |
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self.interests, |
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self.date_range |
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) |
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crew = Crew( |
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agents=[ |
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city_selector_agent, local_expert_agent, travel_concierge_agent |
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], |
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tasks=[identify_task, gather_task, plan_task], |
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verbose=True |
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) |
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result = crew.kickoff() |
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return result |
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class planner_cli: |
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def ask(self): |
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print("## Welcome to Trip Planner Crew") |
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print('-------------------------------') |
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location = input( |
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dedent(""" |
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From where will you be traveling from? |
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""")) |
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cities = input( |
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dedent(""" |
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What are the cities options you are interested in visiting? |
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""")) |
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date_range = input( |
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dedent(""" |
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What is the date range you are interested in traveling? |
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""")) |
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interests = input( |
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dedent(""" |
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What are some of your high level interests and hobbies? |
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""")) |
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trip_crew = TripCrew(location, cities, date_range, interests) |
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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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@ -0,0 +1,38 @@ |
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import json |
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import os |
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import requests |
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from crewai import Agent, Task |
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from langchain.tools import tool |
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from unstructured.partition.html import partition_html |
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class BrowserTools(): |
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@tool("Scrape website content") |
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def scrape_and_summarize_website(website): |
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"""Useful to scrape and summarize a website content""" |
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url = f"https://chrome.browserless.io/content?token={os.environ['BROWSERLESS_API_KEY']}" |
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payload = json.dumps({"url": website}) |
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headers = {'cache-control': 'no-cache', 'content-type': 'application/json'} |
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response = requests.request("POST", url, headers=headers, data=payload) |
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elements = partition_html(text=response.text) |
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content = "\n\n".join([str(el) for el in elements]) |
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content = [content[i:i + 8000] for i in range(0, len(content), 8000)] |
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summaries = [] |
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for chunk in content: |
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agent = Agent( |
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role='Principal Researcher', |
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goal= |
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'Do amazing researches and summaries based on the content you are working with', |
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backstory= |
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"You're a Principal Researcher at a big company and you need to do a research about a given topic.", |
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allow_delegation=False) |
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task = Task( |
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agent=agent, |
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description= |
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f'Analyze and summarize the content bellow, make sure to include the most relevant information in the summary, return only the summary nothing else.\n\nCONTENT\n----------\n{chunk}' |
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) |
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summary = task.execute() |
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summaries.append(summary) |
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return "\n\n".join(summaries) |
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@ -0,0 +1,15 @@ |
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from langchain.tools import tool |
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class CalculatorTools(): |
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@tool("Make a calculation") |
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def calculate(operation): |
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"""Useful to perform any mathematical calculations, |
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like sum, minus, multiplication, division, etc. |
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The input to this tool should be a mathematical |
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expression, a couple examples are `200*7` or `5000/2*10` |
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""" |
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try: |
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return eval(operation) |
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except SyntaxError: |
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return "Error: Invalid syntax in mathematical expression" |
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@ -0,0 +1,37 @@ |
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import json |
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import os |
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import requests |
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from langchain.tools import tool |
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class SearchTools(): |
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@tool("Search the internet") |
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def search_internet(query): |
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"""Useful to search the internet |
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about a a given topic and return relevant results""" |
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top_result_to_return = 4 |
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url = "https://google.serper.dev/search" |
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payload = json.dumps({"q": query}) |
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headers = { |
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'X-API-KEY': os.environ['SERPER_API_KEY'], |
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'content-type': 'application/json' |
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} |
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response = requests.request("POST", url, headers=headers, data=payload) |
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# check if there is an organic key |
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if 'organic' not in response.json(): |
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return "Sorry, I couldn't find anything about that, there could be an error with you serper api key." |
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else: |
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results = response.json()['organic'] |
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string = [] |
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for result in results[:top_result_to_return]: |
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try: |
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string.append('\n'.join([ |
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f"Title: {result['title']}", f"Link: {result['link']}", |
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f"Snippet: {result['snippet']}", "\n-----------------" |
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])) |
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except KeyError: |
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next |
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return '\n'.join(string) |
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@ -0,0 +1,45 @@ |
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from crewai import Agent |
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from .tools.browser_tools import BrowserTools |
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from .tools.calculator_tools import CalculatorTools |
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from .tools.search_tools import SearchTools |
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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=[ |
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SearchTools.search_internet, |
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BrowserTools.scrape_and_summarize_website, |
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], |
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verbose=True) |
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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=[ |
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SearchTools.search_internet, |
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BrowserTools.scrape_and_summarize_website, |
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], |
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verbose=True) |
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def travel_concierge(self): |
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return Agent( |
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role='Amazing Travel Concierge', |
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goal="""Create the most amazing travel itineraries with budget and |
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packing suggestions for the city""", |
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backstory="""Specialist in travel planning and logistics with |
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decades of experience""", |
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tools=[ |
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SearchTools.search_internet, |
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BrowserTools.scrape_and_summarize_website, |
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CalculatorTools.calculate, |
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], |
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verbose=True) |
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@ -0,0 +1,83 @@ |
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from crewai import Task |
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from textwrap import dedent |
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from datetime import date |
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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(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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{self.__tip_section()} |
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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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def gather_task(self, agent, origin, interests, range): |
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return Task(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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{self.__tip_section()} |
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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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def plan_task(self, agent, origin, interests, range): |
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return Task(description=dedent(f""" |
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Expand this guide into a a full 7-day travel |
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itinerary with detailed per-day plans, including |
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weather forecasts, places to eat, packing suggestions, |
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and a budget breakdown. |
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You MUST suggest actual places to visit, actual hotels |
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to stay and actual restaurants to go to. |
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This itinerary should cover all aspects of the trip, |
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from arrival to departure, integrating the city guide |
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information with practical travel logistics. |
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Your final answer MUST be a complete expanded travel plan, |
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formatted as markdown, encompassing a daily schedule, |
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anticipated weather conditions, recommended clothing and |
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items to pack, and a detailed budget, ensuring THE BEST |
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TRIP EVER, Be specific and give it a reason why you picked |
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# up each place, what make them special! {self.__tip_section()} |
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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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def __tip_section(self): |
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return "If you do your BEST WORK, I'll tip you $100!" |
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