added agents

.python-version

@@ -0,0 +1 @@
+3.10

installer/client/cli/agents/.python-version

@@ -0,0 +1 @@
+3.10

installer/client/cli/agents/example.py

@@ -0,0 +1,81 @@
+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)

installer/client/cli/agents/trip_planner/main.py

@@ -0,0 +1,89 @@
+from crewai import Crew
+from textwrap import dedent
+from .trip_agents import TripAgents
+from .trip_tasks import TripTasks
+import os
+from dotenv import load_dotenv
+
+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'
+
+
+class TripCrew:
+
+    def __init__(self, origin, cities, date_range, interests):
+        self.cities = cities
+        self.origin = origin
+        self.interests = interests
+        self.date_range = date_range
+
+    def run(self):
+        agents = TripAgents()
+        tasks = TripTasks()
+
+        city_selector_agent = agents.city_selection_agent()
+        local_expert_agent = agents.local_expert()
+        travel_concierge_agent = agents.travel_concierge()
+
+        identify_task = tasks.identify_task(
+            city_selector_agent,
+            self.origin,
+            self.cities,
+            self.interests,
+            self.date_range
+        )
+        gather_task = tasks.gather_task(
+            local_expert_agent,
+            self.origin,
+            self.interests,
+            self.date_range
+        )
+        plan_task = tasks.plan_task(
+            travel_concierge_agent,
+            self.origin,
+            self.interests,
+            self.date_range
+        )
+
+        crew = Crew(
+            agents=[
+                city_selector_agent, local_expert_agent, travel_concierge_agent
+            ],
+            tasks=[identify_task, gather_task, plan_task],
+            verbose=True
+        )
+
+        result = crew.kickoff()
+        return result
+
+
+class planner_cli:
+    def ask(self):
+        print("## Welcome to Trip Planner Crew")
+        print('-------------------------------')
+        location = input(
+            dedent("""
+        From where will you be traveling from?
+        """))
+        cities = input(
+            dedent("""
+        What are the cities options you are interested in visiting?
+        """))
+        date_range = input(
+            dedent("""
+        What is the date range you are interested in traveling?
+        """))
+        interests = input(
+            dedent("""
+        What are some of your high level interests and hobbies?
+        """))
+
+        trip_crew = TripCrew(location, cities, date_range, interests)
+        result = trip_crew.run()
+        print("\n\n########################")
+        print("## Here is you Trip Plan")
+        print("########################\n")
+        print(result)

installer/client/cli/agents/trip_planner/tools/browser_tools.py

@@ -0,0 +1,38 @@
+import json
+import os
+
+import requests
+from crewai import Agent, Task
+from langchain.tools import tool
+from unstructured.partition.html import partition_html
+
+
+class BrowserTools():
+
+  @tool("Scrape website content")
+  def scrape_and_summarize_website(website):
+    """Useful to scrape and summarize a website content"""
+    url = f"https://chrome.browserless.io/content?token={os.environ['BROWSERLESS_API_KEY']}"
+    payload = json.dumps({"url": website})
+    headers = {'cache-control': 'no-cache', 'content-type': 'application/json'}
+    response = requests.request("POST", url, headers=headers, data=payload)
+    elements = partition_html(text=response.text)
+    content = "\n\n".join([str(el) for el in elements])
+    content = [content[i:i + 8000] for i in range(0, len(content), 8000)]
+    summaries = []
+    for chunk in content:
+      agent = Agent(
+          role='Principal Researcher',
+          goal=
+          'Do amazing researches and summaries based on the content you are working with',
+          backstory=
+          "You're a Principal Researcher at a big company and you need to do a research about a given topic.",
+          allow_delegation=False)
+      task = Task(
+          agent=agent,
+          description=
+          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}'
+      )
+      summary = task.execute()
+      summaries.append(summary)
+    return "\n\n".join(summaries)

installer/client/cli/agents/trip_planner/tools/calculator_tools.py

@@ -0,0 +1,15 @@
+from langchain.tools import tool
+
+class CalculatorTools():
+
+    @tool("Make a calculation")
+    def calculate(operation):
+        """Useful to perform any mathematical calculations, 
+        like sum, minus, multiplication, division, etc.
+        The input to this tool should be a mathematical 
+        expression, a couple examples are `200*7` or `5000/2*10`
+        """
+        try:
+            return eval(operation)
+        except SyntaxError:
+            return "Error: Invalid syntax in mathematical expression"

installer/client/cli/agents/trip_planner/tools/search_tools.py

@@ -0,0 +1,37 @@
+import json
+import os
+
+import requests
+from langchain.tools import tool
+
+
+class SearchTools():
+
+  @tool("Search the internet")
+  def search_internet(query):
+    """Useful to search the internet
+    about a a given topic and return relevant results"""
+    top_result_to_return = 4
+    url = "https://google.serper.dev/search"
+    payload = json.dumps({"q": query})
+    headers = {
+        'X-API-KEY': os.environ['SERPER_API_KEY'],
+        'content-type': 'application/json'
+    }
+    response = requests.request("POST", url, headers=headers, data=payload)
+    # check if there is an organic key
+    if 'organic' not in response.json():
+      return "Sorry, I couldn't find anything about that, there could be an error with you serper api key."
+    else:
+      results = response.json()['organic']
+      string = []
+      for result in results[:top_result_to_return]:
+        try:
+          string.append('\n'.join([
+              f"Title: {result['title']}", f"Link: {result['link']}",
+              f"Snippet: {result['snippet']}", "\n-----------------"
+          ]))
+        except KeyError:
+          next
+
+      return '\n'.join(string)

installer/client/cli/agents/trip_planner/trip_agents.py

@@ -0,0 +1,45 @@
+from crewai import Agent
+
+from .tools.browser_tools import BrowserTools
+from .tools.calculator_tools import CalculatorTools
+from .tools.search_tools import SearchTools
+
+
+class TripAgents():
+
+    def city_selection_agent(self):
+        return Agent(
+            role='City Selection Expert',
+            goal='Select the best city based on weather, season, and prices',
+            backstory='An expert in analyzing travel data to pick ideal destinations',
+            tools=[
+                SearchTools.search_internet,
+                BrowserTools.scrape_and_summarize_website,
+            ],
+            verbose=True)
+
+    def local_expert(self):
+        return Agent(
+            role='Local Expert at this city',
+            goal='Provide the BEST insights about the selected city',
+            backstory="""A knowledgeable local guide with extensive information
+        about the city, it's attractions and customs""",
+            tools=[
+                SearchTools.search_internet,
+                BrowserTools.scrape_and_summarize_website,
+            ],
+            verbose=True)
+
+    def travel_concierge(self):
+        return Agent(
+            role='Amazing Travel Concierge',
+            goal="""Create the most amazing travel itineraries with budget and 
+        packing suggestions for the city""",
+            backstory="""Specialist in travel planning and logistics with 
+        decades of experience""",
+            tools=[
+                SearchTools.search_internet,
+                BrowserTools.scrape_and_summarize_website,
+                CalculatorTools.calculate,
+            ],
+            verbose=True)

installer/client/cli/agents/trip_planner/trip_tasks.py

@@ -0,0 +1,83 @@
+from crewai import Task
+from textwrap import dedent
+from datetime import date
+
+
+class TripTasks():
+
+  def identify_task(self, agent, origin, cities, interests, range):
+    return Task(description=dedent(f"""
+        Analyze and select the best city for the trip based 
+        on specific criteria such as weather patterns, seasonal
+        events, and travel costs. This task involves comparing
+        multiple cities, considering factors like current weather
+        conditions, upcoming cultural or seasonal events, and
+        overall travel expenses. 
+        
+        Your final answer must be a detailed
+        report on the chosen city, and everything you found out
+        about it, including the actual flight costs, weather 
+        forecast and attractions.
+        {self.__tip_section()}
+
+        Traveling from: {origin}
+        City Options: {cities}
+        Trip Date: {range}
+        Traveler Interests: {interests}
+      """),
+                agent=agent)
+
+  def gather_task(self, agent, origin, interests, range):
+    return Task(description=dedent(f"""
+        As a local expert on this city you must compile an 
+        in-depth guide for someone traveling there and wanting 
+        to have THE BEST trip ever!
+        Gather information about  key attractions, local customs,
+        special events, and daily activity recommendations.
+        Find the best spots to go to, the kind of place only a
+        local would know.
+        This guide should provide a thorough overview of what 
+        the city has to offer, including hidden gems, cultural
+        hotspots, must-visit landmarks, weather forecasts, and
+        high level costs.
+        
+        The final answer must be a comprehensive city guide, 
+        rich in cultural insights and practical tips, 
+        tailored to enhance the travel experience.
+        {self.__tip_section()}
+
+        Trip Date: {range}
+        Traveling from: {origin}
+        Traveler Interests: {interests}
+      """),
+                agent=agent)
+
+  def plan_task(self, agent, origin, interests, range):
+    return Task(description=dedent(f"""
+        Expand this guide into a a full 7-day travel 
+        itinerary with detailed per-day plans, including 
+        weather forecasts, places to eat, packing suggestions, 
+        and a budget breakdown.
+        
+        You MUST suggest actual places to visit, actual hotels 
+        to stay and actual restaurants to go to.
+        
+        This itinerary should cover all aspects of the trip, 
+        from arrival to departure, integrating the city guide
+        information with practical travel logistics.
+        
+        Your final answer MUST be a complete expanded travel plan,
+        formatted as markdown, encompassing a daily schedule,
+        anticipated weather conditions, recommended clothing and
+        items to pack, and a detailed budget, ensuring THE BEST
+        TRIP EVER, Be specific and give it a reason why you picked
+        # up each place, what make them special! {self.__tip_section()}
+
+        Trip Date: {range}
+        Traveling from: {origin}
+        Traveler Interests: {interests}
+      """),
+                agent=agent)
+
+  def __tip_section(self):
+    return "If you do your BEST WORK, I'll tip you $100!"

installer/client/cli/fabric.py

@@ -1,4 +1,4 @@
-from .utils import Standalone, Update, Setup, Alias
+from .utils import Standalone, Update, Setup, Alias, AgentSetup
 import argparse
 import sys
 import time
@@ -16,6 +16,12 @@ def main():
     parser.add_argument(
         "--copy", "-C", help="Copy the response to the clipboard", action="store_true"
     )
+    subparsers = parser.add_subparsers(dest='command', help='Sub-command help')
+    agents_parser = subparsers.add_parser('agents', help='Crew command help')
+    agents_parser.add_argument(
+        "trip_planner", help="The origin city for the trip")
+    agents_parser.add_argument(
+        'ApiKeys', help="enter API keys for tools", action="store_true")
     parser.add_argument(
         "--output",
         "-o",
@@ -67,6 +73,20 @@ def main():
         Update()
         Alias()
         sys.exit()
+    if args.command == "agents":
+        from .agents.trip_planner.main import planner_cli
+        if args.ApiKeys:
+            AgentSetup().apiKeys()
+            sys.exit()
+        if not args.trip_planner:
+            print("Please provide an agent")
+            print(f"Available Agents:")
+            for agent in tripcrew.agents:
+                print(agent)
+        else:
+            tripcrew = planner_cli()
+            tripcrew.ask()
+        sys.exit()
     if args.update:
         Update()
         Alias()

installer/client/cli/utils.py

@@ -400,3 +400,32 @@ class Transcribe:
         except Exception as e:
             print("Error:", e)
             return None
+
+
+class AgentSetup:
+    def apiKeys(self):
+        """Method to set the API keys in the environment file.
+
+        Returns:
+            None
+        """
+
+        print("Welcome to Fabric. Let's get started.")
+        browserless = input("Please enter your Browserless API key\n")
+        serper = input("Please enter your Serper API key\n")
+
+        # Entries to be added
+        browserless_entry = f"BROWSERLESS_API_KEY={browserless}"
+        serper_entry = f"SERPER_API_KEY={serper}"
+
+        # Check and write to the file
+        with open(env_file, "r+") as f:
+            content = f.read()
+
+            # Determine if the file ends with a newline
+            if content.endswith('\n'):
+                # If it ends with a newline, we directly write the new entries
+                f.write(f"{browserless_entry}\n{serper_entry}\n")
+            else:
+                # If it does not end with a newline, add one before the new entries
+                f.write(f"\n{browserless_entry}\n{serper_entry}\n")

pyproject.toml

@@ -13,6 +13,11 @@ packages = [
 
 [tool.poetry.dependencies]
 python = "^3.10"
+crewai = "^0.11.0"
+unstructured = "0.10.25"
+pyowm = "3.3.0"
+tools = "^0.1.9"
+langchain-community = "^0.0.24"
 
 [tool.poetry.group.cli.dependencies]
 pyyaml = "^6.0.1"
@@ -33,7 +38,6 @@ gevent = "^23.9.1"
 httpx = "^0.26.0"
 tqdm = "^4.66.1"
 
-
 [tool.poetry.group.server.dependencies]
 requests = "^2.31.0"
 openai = "^1.12.0"