Purpose of the guide

In a world where information is abundant on the web, ChatGPT’s new web parsing features are a game-changer. This latest update allows the AI model to not only understand and generate text but also extract valuable data from websites, making it a versatile tool for a wide range of tasks. In this blog post, we’ll dive into the exciting world of ChatGPT’s web parsing capabilities with some code examples.
openai API reference

What Can ChatGPT Do with Web Parsing?

ChatGPT’s web parsing features enable it to extract information from websites, such as:

  1. Fetching Content: It can retrieve text content, links, and other information from a given webpage.
  2. Summarizing Articles: ChatGPT can summarize articles or blog posts by analyzing the content and generating concise summaries.
  3. Answering Questions: You can ask questions about the information extracted from a webpage, and ChatGPT will provide answers based on its understanding of the content.
  4. Comparing Products: It can compare products based on their specifications, prices, and user reviews from e-commerce websites.
  5. Providing Recommendations: ChatGPT can suggest products, services, or articles based on the content it extracts.

Let’s explore these capabilities with some code examples.

Example 1: Fetching Content from a Webpage

import openai

# Your OpenAI API key
api_key = 'YOUR_API_KEY'

# URL of the webpage to parse
url = 'https://example.com'

# Prompt for web content extraction
prompt = f"Extract the main content from {url}"

# Call the ChatGPT API
response = openai.Completion.create(
    engine="davinci",
    prompt=prompt,
    max_tokens=150,
    api_key=api_key
)

# Extracted content
extracted_content = response.choices[0].text.strip()
print(extracted_content)

Example 2: Summarizing an Article

# Prompt for summarizing an article
prompt = "Summarize the following article: [Paste the article here]"

# Call the ChatGPT API with the article text
response = openai.Completion.create(
    engine="davinci",
    prompt=prompt,
    max_tokens=150,
    api_key=api_key
)

# Article summary
summary = response.choices[0].text.strip()
print(summary)

Example 3: Answering Questions about Web Content

# Web content extracted earlier
web_content = extracted_content

# Question about the content
question = "What is the main topic discussed in the article?"

# Combine the web content and question
prompt = f"Web Content: {web_content}\nQuestion: {question}"

# Call the ChatGPT API
response = openai.Completion.create(
    engine="davinci",
    prompt=prompt,
    max_tokens=50,
    api_key=api_key
)

# Answer to the question
answer = response.choices[0].text.strip()
print(answer)

These code examples illustrate how ChatGPT can be used to extract, summarize, and interact with web content. With these capabilities, ChatGPT becomes a powerful tool for automating tasks involving web data extraction and analysis. Whether you’re a researcher, content creator, or just looking to gather information from the web, ChatGPT’s web parsing features are here to help you streamline your workflow and make data retrieval a breeze.

P.S. Written with a help of GPT-3.5