added intial version of article_suggestion crew

README.MD

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+### Initial Setup
+* Install python using conda by running the following command. `conda create -p venv python==3.11 -y`
+* Activate conda profile `conda activate ./venv`
+* Install required packages `./venv/bin/pip install -r requirements.txt`
+* Copy .env.example to .env and enter the required values.

agents/article_evaluator.py

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+from crewai import Agent
+from llms.gemini import gemini
+
+article_evaluator = Agent(
+    role="Recommended Article Evaluator",
+    goal="Verify if the articles suggested for the user align with his "
+    "interests or the articles he has read in the past. The artiles should "
+    "provide incremental learning to the user.",
+    verbose=True,
+    backstory=(
+        "You are a Recommended Article Evaluator, you are strict in your evaluation. "
+        "You provide personalised evaluation based on user's interests and previous readings. "
+        "The articles you approve spark the interests in the user and the user has "
+        "incremental learning once the user completes reading the articles."
+    ),
+    allow_delegation=False,
+    # llm=gemini
+)

agents/curiosity_catalyst.py

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+from crewai import Agent
+from llms.gemini import gemini
+
+curiosity_catalyst = Agent(
+  role="Curiosity Catalyst",
+  goal="To pique the user's curiosity to read the article.",
+  verbose=True,
+  backstory=(
+    "As a Curiosity Catalyst, you know exactly how to pique the user's curiosity "
+    "for reading the articles."
+  ),
+  allow_delegation=False,
+  # llm=gemini
+)

agents/learning_curator.py

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+from crewai import Agent
+from tools.scrape_website import scrape_tool
+from tools.search_web import search_tool
+from llms.gemini import gemini
+
+learning_curator = Agent(
+    role="Personal Learning Curator",
+    goal="Make sure you present an article on the topics that the user is interested in, "
+    "the article should provide the user with an incremental learning.",
+    verbose=True,
+    backstory=(
+        "As an Learning Strategist, the articles you suggest motivate and "
+        "provide incremental learning for the user."
+    ),
+    allow_delegation=False,
+    tools=[scrape_tool, search_tool],
+    # llm=gemini
+)

agents/learning_profiler.py

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+from crewai import Agent
+from tools.scrape_website import scrape_tool
+from llms.gemini import gemini
+
+learning_profiler = Agent(
+    role="Personal Learning Profiler",
+    goal="Make sure to create an excellent learning profile of the user.",
+    verbose=True,
+    tools=[scrape_tool],
+    backstory=(
+      "As a Personal Learning Profiler, you excel at building a learning profile of a user. "
+      "The profile you build gives a high level overview of what technical skills and interests that the user has. "
+    ),
+    allow_delegation=False,
+    # llm=gemini
+)

app.py

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+from dotenv import load_dotenv
+load_dotenv()
+
+from crew.article_suggestion import article_recommendation_crew
+
+result = article_recommendation_crew.kickoff(inputs={
+  "interests": "GenAI, Architecture, Agentic Programming",
+  "previous_article_insights": "Agentic Design Patterns (https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/)\n"
+  "Reflection: The LLM examines its own work to come up with ways to improve it. "
+  "Tool Use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data. "
+  "Planning: The LLM comes up with, and executes, a multistep plan to achieve a goal "
+  "Multi-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would."
+})
+
+print(result)

crew/article_suggestion.py

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+from crewai import Crew
+from agents.learning_profiler import learning_profiler
+from agents.learning_curator import learning_curator
+from agents.article_evaluator import article_evaluator
+from tasks.create_learning_profile import learning_profile_task
+from tasks.new_article_suggestion import article_suggestion_task
+from tasks.evaluate_articles import evaluation_task
+from llms.gemini import gemini
+
+article_recommendation_crew = Crew(
+    agents=[learning_profiler, learning_curator, article_evaluator],
+    tasks=[learning_profile_task, article_suggestion_task, evaluation_task],
+    verbose=True,
+    # manager_llm=gemini
+)

llms/gemini.py

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+from langchain_google_genai import ChatGoogleGenerativeAI
+
+gemini=ChatGoogleGenerativeAI(model="gemini-1.5-flash",verbose=True)

requirements.txt

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 python-dotenv
 crewai
 crewai_tools
+langchain_community
+langchain-google-genai

tasks/create_learning_profile.py

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+from crewai import Task
+from agents.learning_profiler import learning_profiler
+
+learning_profile_task = Task(
+    description=(
+        "Create a Learning profile of the user based on his following interests {interests} "
+        "and based on the following articles and insights he has read in the past: \n"
+        "{previous_article_insights}"
+    ),
+    expected_output=(
+        "A structured learning profile of the user with his interests, topics he has read about "
+        "and any other information you feel is relavant."
+    ),
+    agent=learning_profiler,
+    async_execution=False
+)

tasks/evaluate_articles.py

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+from crewai import Task
+from agents.article_evaluator import article_evaluator
+
+evaluation_task = Task(
+  description=(
+    "Evaluate articles to ensure that they provide incremental learning for the user "
+    "based on his interests and previous article summaries he has captured."
+  ),
+  expected_output=(
+    "List of article titles along with their links and evaluation reason of why you "
+    "think the article is a good recommendation for the user."
+  ),
+  agent=article_evaluator,
+  async_execution=False
+)

tasks/new_article_suggestion.py

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+from crewai import Task
+from agents.learning_curator import learning_curator
+
+article_suggestion_task = Task(
+  description=(
+    "Suggest 4 articles to the user based on his learning profile. "
+    "The articles should provide incremental learning to the user."
+  ),
+  expected_output=(
+    "List of article titles along with their links"
+  ),
+  agent=learning_curator,
+  async_execution=False
+)

tools/scrape_website.py

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+from crewai_tools import ScrapeWebsiteTool
+
+scrape_tool = ScrapeWebsiteTool()

tools/search_web.py

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+from langchain_community.tools.tavily_search import TavilySearchResults
+
+search_tool = TavilySearchResults(max_results=5)