Building Your AI Q&A Bot for Webpages Using Open Source AI Models

In today’s information-rich digital landscape, navigating extensive web content can be overwhelming. Whether you’re researching for a project, studying complex material, or trying to extract specific information from lengthy articles, the process can be time-consuming and inefficient. This is where an AI-powered Question-Answering (Q&A) bot becomes invaluable.

This tutorial will guide you through building a practical AI Q&A system that can analyze webpage content and answer specific questions. Instead of relying on expensive API services, we’ll utilize open-source models from Hugging Face to create a solution that’s:

Completely free to use
Runs in Google Colab (no local setup required)
Customizable to your specific needs
Built on cutting-edge NLP technology

By the end of this tutorial, you’ll have a functional web Q&A system that can help you extract insights from online content more efficiently.

What We’ll Build

We’ll create a system that:

Takes a URL as input

Extracts and processes the webpage content

Accepts natural language questions about the content

Provides accurate, contextual answers based on the webpage

Prerequisites

A Google account to access Google Colab
Basic understanding of Python
No prior machine learning knowledge required

Step 1: Setting Up the Environment

First, let’s create a new Google Colab notebook. Go to Google Colab and create a new notebook.

Let’s start by installing the necessary libraries:

# Install required packages

!pip install transformers torch beautifulsoup4 requests

This installs:

transformers: Hugging Face’s library for state-of-the-art NLP models
torch: PyTorch deep learning framework
beautifulsoup4: For parsing HTML and extracting web content
requests: For making HTTP requests to webpages

Step 2: Import Libraries and Set Up Basic Functions

Now let’s import all the necessary libraries and define some helper functions:

import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
import requests
from bs4 import BeautifulSoup
import re
import textwrap

# Check if GPU is available

device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)
print(f”Using device: {device}”)

# Function to extract text from a webpage

def extract_text_from_url(url):
try:
headers = {
‘User-Agent’: ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36’
}
response = requests.get(url, headers=headers)
response.raise_for_status()
soup = BeautifulSoup(response.text, ‘html.parser’)

for script_or_style in soup([‘script’, ‘style’, ‘header’, ‘footer’, ‘nav’]):
script_or_style.decompose()

text = soup.get_text()

lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(” “))
text=”n”.join(chunk for chunk in chunks if chunk)

text = re.sub(r’s+’, ‘ ‘, text).strip()

return text

except Exception as e:
print(f”Error extracting text from URL: {e}”)
return None

This code:

Imports all necessary libraries

Sets up our device (GPU if available, otherwise CPU)

Creates a function to extract readable text content from a webpage URL

Step 3: Load the Question-Answering Model

Now let’s load a pre-trained question-answering model from Hugging Face:

# Load pre-trained model and tokenizer

model_name = “deepset/roberta-base-squad2″

print(f”Loading model: {model_name}”)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name).to(device)
print(“Model loaded successfully!”)

We’re using deepset/roberta-base-squad2, which is:

Based on RoBERTa architecture (a robustly optimized BERT approach)
Fine-tuned on SQuAD 2.0 (Stanford Question Answering Dataset)
A good balance between accuracy and speed for our task

Step 4: Implement the Question-Answering Function

Now, let’s implement the core functionality – the ability to answer questions based on the extracted webpage content:

def answer_question(question, context, max_length=512):
max_chunk_size = max_length – len(tokenizer.encode(question)) – 5
all_answers = []

for i in range(0, len(context), max_chunk_size):
chunk = context[i:i + max_chunk_size]

inputs = tokenizer(
question,
chunk,
add_special_tokens=True,
return_tensors=”pt”,
max_length=max_length,
truncation=True
).to(device)

with torch.no_grad():
outputs = model(**inputs)

answer_start = torch.argmax(outputs.start_logits)
answer_end = torch.argmax(outputs.end_logits)

start_score = outputs.start_logits[0][answer_start].item()
end_score = outputs.end_logits[0][answer_end].item()
score = start_score + end_score

input_ids = inputs.input_ids.tolist()[0]
tokens = tokenizer.convert_ids_to_tokens(input_ids)

answer = tokenizer.convert_tokens_to_string(tokens[answer_start:answer_end+1])

answer = answer.replace(“[CLS]”, “”).replace(“[SEP]”, “”).strip()

if answer and len(answer) > 2:
all_answers.append((answer, score))

if all_answers:
all_answers.sort(key=lambda x: x[1], reverse=True)
return all_answers[0][0]
else:
return “I couldn’t find an answer in the provided content.”

This function:

Takes a question and the webpage content as input

Handles long content by processing it in chunks

Uses the model to predict the answer span (start and end positions)

Processes multiple chunks and returns the answer with the highest confidence score

Step 5: Testing and Examples

Let’s test our system with some examples. Here’s the complete code:

url = “https://en.wikipedia.org/wiki/Artificial_intelligence”
webpage_text = extract_text_from_url(url)

print(“Sample of extracted text:”)
print(webpage_text[:500] + “…”)

questions = [
“When was the term artificial intelligence first used?”,
“What are the main goals of AI research?”,
“What ethical concerns are associated with AI?”
]

for question in questions:
print(f”nQuestion: {question}”)
answer = answer_question(question, webpage_text)
print(f”Answer: {answer}”)

This will demonstrate how the system works with real examples.

Output of the above code

Limitations and Future Improvements

Our current implementation has some limitations:

It can struggle with very long webpages due to context length limitations

The model may not understand complex or ambiguous questions

It works best with factual content rather than opinions or subjective material

Future improvements could include:

Implementing semantic search to better handle long documents
Adding document summarization capabilities
Supporting multiple languages
Implementing memory of previous questions and answers
Fine-tuning the model on specific domains (e.g., medical, legal, technical)

Conclusion

Now you’ve successfully built your AI-powered Q&A system for webpages using open-source models. This tool can help you:

Extract specific information from lengthy articles
Research more efficiently
Get quick answers from complex documents

By utilizing Hugging Face’s powerful models and the flexibility of Google Colab, you’ve created a practical application that demonstrates the capabilities of modern NLP. Feel free to customize and extend this project to meet your specific needs.

Useful Resources

Here is the Colab Notebook. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 85k+ ML SubReddit.

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Asjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.

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