pip install langchain
pip install crewai
pip install bs4
pip install chromadb

ollama pull gemma:7b
ollama pull mistral:7b
ollama pull nomic-embed-text

from langchain_community.llms import Ollama
from crewai import Agent, Task, Crew
import sys

ollama_gemma = Ollama(model='gemma:7b')
ollama_mistral = Ollama(model='mistral:7b')

def write_blog(topic):
 # Create Researcher agent
 researcher = Agent(
     role='Senior Research Analyst',
     goal=f"Uncover research in {topic}",
     backstory="""You work at a leading research company.
     Your expertise lies in identifying emerging trends, research and discover.
     You have a knack for dissecting complex data and presenting actionable insights.""",
     verbose=True,
     allow_delegation=False,
     llm=ollama_gemma
 )

 # Create Writer agent
 writer = Agent(
     role='Content Strategist',
     goal=f"Craft compelling content on {topic}",
     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=False,
     llm=ollama_mistral
 )

 # Create tasks for your agents
 task1 = Task(
   description=f"Conduct a comprehensive analysis of {topic}.",
   expected_output="Full analysis report in bullet points",
   agent=researcher
 )

 task2 = Task(
   description=f"""Using the insights provided, develop an engaging blog
   post that highlights the topic of {topic}.
   Your post should be informative yet accessible, catering to any audience, easy to understand.
   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()
 return result

def main():
 if len(sys.argv) < 2:
   print("Please provide a topic as an argument.")
   print('Usage: python blog.py "AI and data science trends in 2024"')
   sys.exit(1)

 # Get the first command-line argument after the script name
 topic = sys.argv[1]

 # Now you can use 'argument' in your script
 print("\n\n#### Topic ####")
 print(topic)

 if topic == '':
   print("Topic is empty.")
   sys.exit(1)

 result = write_blog(topic)

 print("\n\n#### Result ####")
 print(result)

if __name__ == '__main__':
 main()

python blog.py "AI and data science trends in 2024"

#### Topic ####
AI and data science trends in 2024

[DEBUG]: == Working Agent: Senior Research Analyst
[INFO]: == Starting Task: Conduct a comprehensive analysis of AI and data science trends in 2024.


> Entering new CrewAgentExecutor chain...
**Thought:**

I am well-positioned to provide a comprehensive analysis of AI and data science trends in 2024. My expertise in identifying emerging trends, research, and dissecting complex data enables me to uncover valuable insights into this rapidly evolving field.

**Final Answer:**

**AI and Data Science Trends in 2024:**

* **AI-powered Data Analytics:** AI is revolutionizing data analytics by automating tasks, improving accuracy, and providing actionable insights. Key advancements include natural language processing (NLP) for text and image analysis, as well as deep learning for predictive modeling.

* **Emerging AI Applications:** AI is permeating various industries, including healthcare, finance, retail, and transportation. Notable applications include self-driving cars, facial recognition, and fraud detection.

.
.
.

[DEBUG]: == Working Agent: Content Strategist
[INFO]: == Starting Task: Using the insights provided, develop an engaging blog
   post that highlights the topic of AI and data science trends in 2024.
   Your post should be informative yet accessible, catering to any audience, easy to understand.
   Make it sound cool, avoid complex words so it doesn't sound like AI.


> Entering new CrewAgentExecutor chain...
Thought: With the insights provided, I'm ready to craft an engaging blog post on AI and data science trends in 2024.

Final Answer:

**Welcome to the Future:** **AI and Data Science Trends Shaping Our World in 2024**

Imagine a world where machines learn, adapt, and make decisions just like us. That's not science fiction anymore; it's our present-day reality. Artificial Intelligence (AI) and Data Science are leading the charge, bringing us exciting advancements that reshape industries and our daily lives. Let's explore some trends that will dominate these fields in 2024.

.
.
.


#### Result ####
**Welcome to the Future:** **AI and Data Science Trends Shaping Our World in 2024**

Imagine a world where machines learn, adapt, and make decisions just like us. That's not science fiction anymore; it's our present-day reality. Artificial Intelligence (AI) and Data Science are leading the charge, bringing us exciting advancements that reshape industries and our daily lives. Let's explore some trends that will dominate these fields in 2024.

**Transforming Data with AI:** **Revolutionizing Analytics**

In the data-driven world we live in today, understanding trends and making informed decisions is crucial. And here comes AI to the rescue! Instead of spending hours manually processing and analyzing data, AI systems are automating tasks, improving accuracy, and providing actionable insights. Key advancements include Natural Language Processing (NLP) for text and image analysis and deep learning for predictive modeling. These technologies enable us to gain valuable insights from vast datasets in various industries like finance, healthcare, and retail.

**AI Everywhere:** **Emerging Applications in Multiple Sectors**

From self-driving cars revolutionizing transportation to facial recognition systems enhancing security, AI is permeating various sectors. In healthcare, it's being used for diagnosis and personalized treatment plans. In finance, AI is powering fraud detection and risk assessments. Retail is experiencing the benefits with personalized recommendations based on customer preferences. The applications of AI are endless!

**Breaking Down Data Silos:** **Data Democratization**

Data is no longer locked away in silos; it's becoming more accessible to everyone thanks to advancements in data visualization tools and platforms. This empowers individuals and organizations to make informed decisions based on data, fostering a data-driven culture where insights lead the way!

**Protecting Our Data:** **Data Privacy and Security**

As we embrace these technological advancements, data privacy and security are becoming growing concerns in the wake of increasing data breaches and cyberattacks. Regulations like GDPR and CCPA are driving the adoption of data protection measures to secure our sensitive information.

.
.
.

In conclusion, these trends in AI and data science are shaping our future in remarkable ways. From revolutionizing industries to making informed decisions, AI and Data Science are leading us towards a more connected, intelligent, and data-driven world. Stay tuned for more updates on these exciting trends!

from langchain_community.llms import Ollama
from langchain_community.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.chains import RetrievalQA
import sys

ollama = Ollama(model="mistral:7b")

def answer_with_context(question, context_url):
   # Now let's load a document to ask questions against.
   loader = WebBaseLoader(context_url)
   data = loader.load()

   # This file is pretty big. Which means the full document won't fit into the context for the model. So we need to split it up into smaller pieces.
   text_splitter=RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
   all_splits = text_splitter.split_documents(data)

   # It's split up, but we have to find the relevant splits and then submit those to the model.
   # We can do this by creating embeddings and storing them in a vector database.
   # We can use Ollama directly to instantiate an embedding model.
   oembed = OllamaEmbeddings(model="nomic-embed-text")
   vectorstore = Chroma.from_documents(documents=all_splits, embedding=oembed)

   # And here is the relevant part of the document to the question.
   docs = vectorstore.similarity_search(question)
   print("\n\n#### Context From Document ####")
   print("url: ", context_url)
   print(docs)

   # The next thing is to send the question and the relevant parts of the docs to the model to see if we can get a good answer.
   qachain = RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
   output = qachain.invoke({"query": question})
   return output['result']

def answer(question):
   output = ollama.invoke(question)
   return output

def main():
   if len(sys.argv) < 2:
       print("Please provide a topic as an argument.")
       print('Usage 1: python ask.py "Who is current President in USA?"')
       print('Usage 2: python ask.py "Who is current President in USA?" "https://en.wikipedia.org/wiki/President_of_the_United_States"')

   # Get the first command-line argument after the script name
   question = sys.argv[1]

   print("\n\n#### Question ####")
   print(question)

   # If the user provides a second argument, we will use that as the context URL to find the answer.
   context_url = ''
   if len(sys.argv) == 3:
       context_url = sys.argv[2]

   if context_url == '':
       result = answer(question)
   else:
       result = answer_with_context(question, context_url)

   print("\n\n#### Result ####")
   print(result)

if __name__ == '__main__':
   main()

> python ask.py "Who is current President in USA?"


#### Question ####
Who is current President in USA?


#### Result ####
As of my knowledge up to 2021, the current President of the United States is Joe Biden. He assumed office on January 20, 2021.

> python3.10 ask.py "Who is current President in USA?" "https://en.wikipedia.org/wiki/President_of_the_United_States"


#### Question ####
Who is current President in USA?


#### Context From Document ####
url:  https://en.wikipedia.org/wiki/President_of_the_United_States
[Document(page_content='^ "The Presidents of the United States of America".
Enchanted Learning. Retrieved August 2, 2018.\n\n^
"Political Parties of the Presidents". Presidents USA.
Retrieved August 2, 2018.\n\n\nFurther reading', .....
...

#### Result ####
The current President of the United States is Joe Biden.