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Installing Ollama on a MAC or PC

There is really nothing to installing Ollama on your personal device. I have it running on several different devices and while it is slower on some it has worked with all of the smaller LLM’s available.

TypeCPURAM# of CoresYear
MacBook Proi9 – 2.3GHz16GB8 cores2018
MacBook ProM3 Max36GB30 cores2024
Ubuntu 24.04i5 – 2.40GHz32GB8 cores2020
table of computers I am running Ollama on

Of course the M3 is faster and can handle anything I throw at it, but the others although a bit slower still give a response on most tasks.

To install Ollama on your machine visit https://ollama.com/ and press the Download button.

From here it will take you too the download page where you can choose your operating system. The Windows version is still in Preview and I have not downloaded and installed it at this time.

For Linux:

There are additional instructions for installing on Linux here.

For MAC:

For Windows:

Following these instructions will get you up and running with Ollama. Keep in mind Ollama is a terminal application and you will probably want to use some kind of application to access the power of Ollama and we will cover that in the next installment of this series.

To use Ollama as it is installed just open your terminal and type :

ollama run llama3

This will start Ollama and give you a prompt to ask it a question.

Then ask it a question.

From there you can start your conversation. But that is just the beginning. We cover more as we go on. Good luck with your install and see you next time.

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Convert Web Pages to Text Files Using Python

Extracting text content from websites can be helpful for various tasks, such as content analysis, research, and SEO optimization. Python offers powerful tools for web scraping and text extraction using the BeautifulSoup library.

Installing BeautifulSoup:

pip install beautifulsoup4

Code Example:

from bs4 import BeautifulSoup
import requests

# Get the HTML content of the webpage
url = "example.com"
response = requests.get(url)

# Parse the HTML content
soup = BeautifulSoup(response.content, "html.parser")

# Extract the text content
text = soup.get_text()

# Write the text content to a text file
with open('output.txt', 'w') as f:
    f.write(text)

Explanation:

  • The code first imports the necessary libraries.
  • It then sends a request to the website URL and retrieves the HTML content.
  • The HTML content is parsed using BeautifulSoup.
  • The get_text() method extracts the text content from the parsed HTML.
  • Finally, the text is written to a text file named output.txt.

Additional Features:

  • BeautifulSoup offers various methods for navigating the HTML structure and extracting specific elements.
  • You can specify the encoding when writing the text file.

Benefits of Using BeautifulSoup:

  • Easy to use: Requires minimal setup and coding knowledge.
  • Fast: Extracts text content from websites quickly.
  • Flexible: Offers advanced functionalities for web scraping.

Conclusion:

Converting web pages to text files using BeautifulSoup is a powerful tool for various tasks, such as content analysis, research, and SEO optimization. Its ease of use, speed, and flexibility make it a valuable asset for anyone working with text data

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Convert Word Documents to Text Files Using Python

Converting Word documents to text files can be easily done using Python with the python-docx library. This library provides access to Word document structure and allows you to extract text content.

Installing python-docx:

pip install python-docx

Code Example:

from docx import Document

# Open the Word document
document = Document('example.docx')

# Extract the text content
text = ""
for paragraph in document.paragraphs:
    text += paragraph.text + "\n"

# Write the text content to a text file
with open('output.txt', 'w') as f:
    f.write(text)

Explanation:

  • The code first imports the docx library.
  • It then opens the Word document using the Document class.
  • The text content is extracted by iterating over each paragraph in the document.
  • Finally, the text is written to a new text file named output.txt.

Additional Features:

  • python-docx allows accessing other Word document elements, such as tables, headers, and footers.
  • You can also specify the encoding when writing the text file.

Benefits of Using python-docx:

  • Easy to use: Requires minimal setup and coding knowledge.
  • Fast: Converts Word documents into text quickly.
  • Flexible: Offers advanced functionalities for data extraction.

Conclusion:

Converting Word documents to text files using python-docx is a powerful tool for various tasks, such as data analysis, text mining, and content creation. Its ease of use, speed, and flexibility make it a valuable asset for anyone working with text data.

Next we will look at getting Web data with Python.

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Pros and Cons of a Private AI solution and a Cloud based (public) solution.

  1. Pros of Private AI Solution
  • Data security and privacy – The company has full control over its data, ensuring that it is kept safe from unauthorized access or misuse.
  • Customization – The solution can be customized to meet the specific needs of the organization, allowing for greater control over the AI system’s performance and accuracy.
  • Investment protection – The company can avoid significant investments in infrastructure and maintenance by using a private AI solution.
  • Faster implementation – The private solution can be implemented quickly without waiting on cloud availability or capacity issues.
  1. Cons of Private AI Solution
  • Higher cost – Private solutions require the company to invest in its own hardware and software, which may not be feasible for smaller organizations with limited resources.
  • Lack of flexibility – The company is locked into using their private solution and cannot easily switch to a cloud-based one if needed.
  • Limited access – Only authorized personnel can access the data and AI system, restricting collaboration opportunities with other teams or external partners.
  1. Pros of Cloud Based AI Solution
  • Scalability – The company can quickly scale up or down as their needs change without investing in additional hardware or software.
  • Collaboration – The cloud-based solution allows for easy collaboration between teams and partners, enabling cross-functional work to be completed more efficiently.
  • Cost-effective – Cloud solutions are typically more cost-effective than private solutions, making them a viable option for smaller organizations with limited budgets.
  1. Cons of Cloud Based AI Solution
  • Security risks – As with any cloud solution, the company’s data is vulnerable to hacking and other security threats if proper measures are not taken.
  • Lack of control – The company has less control over their data when using a cloud-based solution than they would with a private one.
  • Compatibility issues – As more organizations move to the cloud, compatibility issues can arise when collaborating with those who have different systems.
  1. Conclusion

The decision between a private and cloud-based AI solution ultimately comes down to the specific needs of the organization and its priorities. While each has its pros and cons, it is important for companies to carefully weigh their options before making a decision.

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Convert PDF to Text File Using Python

Converting scanned documents or PDF files to text can be a tedious and time-consuming task. Thankfully, Python offers a powerful and efficient solution with the pdftotext library.

What is pdftotext?

pdftotext is a Python module that allows you to extract text from PDF files. It uses a technique called Optical Character Recognition (OCR) to convert scanned text into machine-readable text.

Installing pdftotext:

If you are using a MAC make sure you run this command before doing the pip install


brew install pkg-config poppler
pip install pdftotext

Code Example:

from pdftotext import PDF

# Open the PDF file
with open('example.pdf', 'rb') as f:
    # Load the PDF document
    pdf_document = PDF(f)

# Extract the text content
text = ""
for page in pdf_document:
    text += page.strip()

# Write the text content to a text file
with open('output.txt', 'w') as f:
    f.write(text)

Explanation:

  • The code first imports the pdftotext library.
  • It then opens the PDF file in binary read mode (rb).
  • The PDF object is created by passing the file object to the PDF class.
  • The text content is extracted by iterating over each page in the PDF document.
  • Finally, the text is written to a new text file named output.txt.

Additional Features:

  • pdftotext supports various options, such as specifying the language, page range, and layout mode.
  • You can also use the get_layout() method to obtain information about the page layout, including tables, figures, and headers.

Benefits of Using pdftotext:

  • Easy to use: Requires minimal setup and coding knowledge.
  • Fast: Converts PDF files into text quickly.
  • Flexible: Offers various options for customization and data extraction.

Conclusion:

Converting PDF files to text using Python with the pdftotext library is a powerful tool for extracting information from scanned documents or PDF files. Its ease of use, speed, and flexibility make it a valuable asset for various tasks, such as research, document analysis, and accessibility.

Next we will look at getting text from a Word document. Stay Tuned.

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The Power of Text for Large Language Models (LLMs)

Large Language Models (LLMs) are revolutionizing various fields, including content creation, translation, and chatbots. But before these models can understand your data, it needs to be converted into a format they can process: text.

Why Convert Data to Text?

  • LLMs are text-based: They are trained on massive amounts of text data, so converting data to text unlocks their capabilities.
  • Text is searchable: Text formats allow for easy search and retrieval of information.
  • Text is shareable: Text files are widely compatible and easily accessible across various platforms.

Benefits of Text Conversion:

  • Improved understanding: Text data provides LLMs with a clearer understanding of your information.
  • Enhanced creativity: LLMs can generate new content based on the provided text input.
  • Increased accessibility: Text formats are more accessible for people with disabilities.

Data Conversion Methods:

  • Manual conversion: Manually transcribing data into text can be time-consuming and prone to errors.
  • Automated conversion: Using software tools or APIs can automate the conversion process.

Popular Data Types for Text Conversion:

  • Documents: Word processing files, presentations, and reports.
  • Web pages: Extracting text from websites can provide valuable insights.
  • Emails: Converting email threads into text allows for analysis and organization.
  • Audio recordings: Transcription services can convert audio recordings into text.

Using Text for LLM Applications:

  • Content creation: Generate different content formats like articles, social media posts, or product descriptions.
  • Translation: Translate text between multiple languages.
  • Chatbots: Create chatbots that understand and respond to user queries.
  • Data analysis: Analyze text data to extract insights and trends.

Conclusion:

Converting your data to text is crucial for unlocking the potential of LLMs. It provides these models with the necessary input to understand, analyze, and generate insights from your data. By leveraging text-based data, you can unlock a new world of possibilities and enhance your productivity and efficiency.

In the next post we will start converting the Popular Data Types into Text with Python.

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Welcome to my new blog!

I’m thrilled to have you here, and I’m excited to share my knowledge and passion for Python, machine learning, API data integration, and other computer programming-related topics with you.

As an experienced blogger and software developer, I’ve had the opportunity to work on a wide range of projects, from developing complex algorithms to building robust web applications. Through this blog, I aim to share my experiences, insights, and tips on these topics, as well as keep you up-to-date with the latest developments in the field.

Whether you’re a seasoned programmer or just starting out, I hope you find something useful here. My posts will cover a range of topics, including:

  • Python tutorials and projects for beginners and advanced users alike
  • Machine learning concepts and practical applications
  • API data integration techniques and best practices
  • Computer programming tips and tricks to help you improve your coding skills
  • Latest developments and trends in the field of computer programming

I’ll also be sharing my own projects, case studies, and experiences working with clients on various projects. My goal is to provide valuable insights and practical advice that can help you in your own projects and career.

In addition to the blog posts, I may also share some resources, such as eBooks, videos, and podcasts, that I find useful or relevant to the topics covered on the blog.

Thank you for visiting, and I hope you enjoy your time here! If you have any questions or feedback, please don’t hesitate to reach out. I’m always happy to hear from readers and fellow programmers.