Upload 5 files

article_evaluator.py

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+from crewai import Agent
+from llms.gpt import llm
+from tools.helpers import streamlit_callback
+from tools.scrape_website import scrape_tool
+
+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 evaluate articles based on the user's learning profile. "
+        "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=llm,
+    tools=[scrape_tool],
+    step_callback=streamlit_callback
+)

article_suggestion.py

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+from crewai import Crew
+from llms.gpt import llm
+from agents.learning_profiler import learning_profiler
+from agents.learning_curator import learning_curator
+from agents.article_evaluator import article_evaluator
+from agents.curiosity_catalyst import curiosity_catalyst
+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 tasks.create_article_pitch import article_pitch_task
+
+article_recommendation_crew = Crew(
+    agents=[learning_profiler, learning_curator,
+            article_evaluator, curiosity_catalyst],
+    tasks=[learning_profile_task, article_suggestion_task,
+           evaluation_task, article_pitch_task],
+    verbose=True,
+    # memory=True,
+    manager_llm=llm,
+    embedder={
+        "provider": "openai",
+        "config": {
+                    "model": 'text-embedding-3-small'
+        }
+    }
+)

curiosity_catalyst.py

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+from crewai import Agent
+from llms.gpt import llm
+from tools.helpers import streamlit_callback
+from tools.scrape_website import scrape_tool
+
+curiosity_catalyst = Agent(
+  role="Curiosity Catalyst",
+  goal="To pique the user's curiosity to read the article.",
+  verbose=True,
+  tools=[scrape_tool],
+  backstory=(
+    "As a Curiosity Catalyst, you know exactly how to pique the user's curiosity "
+    "for reading the articles."
+  ),
+  allow_delegation=False,
+  llm=llm,
+  step_callback=streamlit_callback
+)

learning_curator.py

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+from crewai import Agent
+from llms.gpt import llm
+from tools.helpers import streamlit_callback
+from tools.scrape_website import scrape_tool
+from tools.search_web import search_tool
+
+learning_curator = Agent(
+    role="Personal Learning Curator",
+    goal="Make sure you present 5 articles 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=llm,
+    step_callback=streamlit_callback
+)

learning_profiler.py

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+from crewai import Agent
+from llms.gpt import llm
+from tools.helpers import streamlit_callback
+from tools.scrape_website import scrape_tool
+
+learning_profiler = Agent(
+    role="Personal Learning Profiler",
+    goal="Make sure to create an excellent learning profile of the user based on his interests and previous reading history.",
+    verbose=True,
+    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 interests that the user has. "
+    ),
+    allow_delegation=False,
+    llm=llm,
+    step_callback=streamlit_callback
+)