Author: Shubh Tripathi

Imagine you’re browsing online and companies keep prompting you to rate and review your experiences. Have you ever wondered how these companies manage to process and make sense of the deluge of feedback they receive? Don’t worry! They don’t do it manually. This is where sentiment analysis steps in—a technology that analyzes text to understand the emotions and opinions expressed within.

Companies like Amazon, Airbnb, and others harness sentiment analysis to extract valuable insights. For example, Amazon refines product recommendations based on customer sentiments, while Airbnb analyzes reviews to enhance accommodations and experiences for future guests. Sentiment analysis silently powers these platforms, empowering businesses to better understand and cater to their customers’ needs.

Traditionally, companies like Amazon had to train complex models specifically for sentiment analysis. These models required significant time and resources to build and fine-tune. However, the game changed with Large Language Models like OpenAI’s ChatGPT, Google’s Gemini, Meta’s Llama, etc. which have revolutionized the landscape of natural language processing.

Now, with Large Language Models (LLMs), sentiment analysis becomes remarkably easier. LLMs are exceptionally skilled at understanding the sentiment of text because they have been trained on vast amounts of language data, enabling them to understand the subtleties of human expression.

Continue reading “Understanding Embeddings and Matrices with the help of Sentiment Analysis and LLMs (Hands-On)”

Welcome to the kickoff of our new blog series dedicated to demystifying common misconceptions surrounding Language Models (LLMs) and generative Artificial Intelligence (AI). In this series, we aim to explore prevalent myths, clarify misunderstandings, and shed light on the nuanced realities of working with these cutting-edge technologies.

In recent years, LLMs like GPT-3, Gemini, LLama 3 have garnered significant attention for their impressive capabilities in natural language processing. However, with this growing interest comes a wave of misconceptions about what LLMs can and cannot do, often overlooking the vital role of traditional machine learning techniques in AI development.

Myth 1: LLMs Can Do Everything – We do not need Machine Learning.

In the rapidly evolving landscape of artificial intelligence (AI), there’s a prevalent myth that Large Language Models (LLMs) can autonomously handle all tasks, rendering traditional machine learning irrelevant. This oversimplified view is akin to saying, “If I have a hammer, everything must be a nail.” Let’s delve deeper into why this myth needs debunking.

Continue reading “Myth 1: LLMs Can Do Everything – We do not need Machine Learning.”

The world of Generative AI and Large Language Models (LLMs) is booming, offering groundbreaking possibilities for creative text formats, intelligent chatbots, and more. But for those new to AI development, the technical hurdles can be daunting. Setting up complex environments with libraries and frameworks can slow down the learning process.

CloudxLab is here to break down those barriers. We offer a unique platform where you can build Generative AI applications entirely within our cloud lab. This means you can:

• Focus on Creativity, Not Configuration: No more wrestling with installations or environment setups. Our cloud lab provides everything you need to start building right away.
• Seamless Learning Experience: Dive straight into the exciting world of Generative AI and LLMs. Our platform streamlines the process, letting you concentrate on understanding and applying these powerful technologies.
• Accessible for All: Whether you’re a seasoned developer or a curious beginner, CloudxLab’s cloud environment makes Gen AI and LLM development approachable.

Continue reading “Building Generative AI and LLMs with CloudxLab”

Imagine a tireless assistant on your website, ready to answer customer questions 24/7. That’s the power of a chatbot! In this post, we’ll guide you through building a custom chatbot specifically trained on your website’s data using OpenAI and Langchain. Let’s dive in and create this helpful conversational AI!

If you want to perform the steps along with the project in parallel, rather than just reading, check out our project on the same at Building a RAG Chatbot from Your Website Data using OpenAI and Langchain. You will also receive a project completion certificate which you can use to showcase your Generative AI skills.

Step 1: Grabbing Valuable Content from Your Website

We first need the gold mine of information – the content from your website! To achieve this, we’ll build a web crawler using Python’s requests library and Beautiful Soup. This script will act like a smart visitor, fetching the text content from each webpage on your website.

Here’s what our web_crawler.py script will do:

1. Fetch the Webpage: It’ll send a request to retrieve the HTML content of a given website URL.
2. Check for Success: The script will ensure the server responds positively (think status code 200) before proceeding.
3. Parse the HTML Structure: Using Beautiful Soup, it will analyze the downloaded HTML to understand how the webpage is built.
4. Clean Up the Mess: It will discard unnecessary elements like scripts and styles that don’t contribute to the core content you want for the chatbot.
5. Extract the Text: After that, it will convert the cleaned HTML into plain text format, making it easier to process later.
6. Grab Extra Info (Optional): The script can optionally extract metadata like page titles and descriptions for better organization.

Imagine this script as a virtual visitor browsing your website and collecting the text content, leaving behind the fancy formatting for now.

Let’s code!

import requests
from bs4 import BeautifulSoup
import html2text


def get_data_from_website(url):
    """
    Retrieve text content and metadata from a given URL.

    Args:
        url (str): The URL to fetch content from.

    Returns:
        tuple: A tuple containing the text content (str) and metadata (dict).
    """
    # Get response from the server
    response = requests.get(url)
    if response.status_code == 500:
        print("Server error")
        return
    # Parse the HTML content using BeautifulSoup
    soup = BeautifulSoup(response.content, 'html.parser')

    # Removing js and css code
    for script in soup(["script", "style"]):
        script.extract()

    # Extract text in markdown format
    html = str(soup)
    html2text_instance = html2text.HTML2Text()
    html2text_instance.images_to_alt = True
    html2text_instance.body_width = 0
    html2text_instance.single_line_break = True
    text = html2text_instance.handle(html)

    # Extract page metadata
    try:
        page_title = soup.title.string.strip()
    except:
        page_title = url.path[1:].replace("/", "-")
    meta_description = soup.find("meta", attrs={"name": "description"})
    meta_keywords = soup.find("meta", attrs={"name": "keywords"})
    if meta_description:
        description = meta_description.get("content")
    else:
        description = page_title
    if meta_keywords:
        meta_keywords = meta_description.get("content")
    else:
        meta_keywords = ""

    metadata = {'title': page_title,
                'url': url,
                'description': description,
                'keywords': meta_keywords}

    return text, metadata

Explanation:

The get_data_from_website function takes a website URL and returns the extracted text content along with any optional metadata. Explore the code further to see how it performs each step mentioned!

Step 2: Cleaning Up the Raw Text

Continue reading “Building a RAG Chatbot from Your Website Data using OpenAI and Langchain (Hands-On)”