Building Your AI Q&A Bot for Webpages Using Open Source AI Models
In today& information-rich digital landscape, navigating extensive web content can be overwhelming. Whether you& 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& utilize open-source models from Hugging Face to create a solution that&
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& have a functional web Q&A system that can help you extract insights from online content more efficiently.
What We& Build
We& 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 knowledge required
Step 1: Setting Up the Environment
First, let& create a new Google Colab notebook. Go to and create a new notebook.
Let& start by installing the necessary libraries:
# Install required packages
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!pip install transformers torch beautifulsoup4 requests
This installs:
transformers: Hugging Face& library for state-of-the-art NLP models
torch: PyTorch 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& import all the necessary libraries and define some helper functions:
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import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
import requests
from bs4 import BeautifulSoup
import re
import textwrap
# Check if GPU is available
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device = e(‘cuda’ if _available() else ‘cpu’)
print(f»Using device: device»)
# Function to extract text from a webpage
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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 = (url, headers=headers)
_for_status()
soup = BeautifulSoup(, ‘r’)
for script_or_style in soup([‘script’, ‘style’, ‘header’, ‘footer’, ‘nav’]):
script_or_pose()
text = _text()
lines = (() for line in lines())
chunks = (() for line in lines for phrase in (» «))
text = ‘n’.join(chunk for chunk in chunks if chunk)
text = (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& load a pre-trained question-answering model from Hugging Face:
# Load pre-trained model and tokenizer
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model_name = «deepset/roberta-base-squad2″
print(f»Loading model: model_name»)
tokenizer = AutoT_pretrained(model_name)
model = AutoModelForQuestionA_pretrained(model_name).to(device)
print(«Model loaded successfully!»)
We& 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& implement the core functionality &; the ability to answer questions based on the extracted webpage content:
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def answer_question(question, context, max_length=512):
max_chunk_size = max_length — len(e(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 _grad():
outputs = model(**inputs)
answer_start = x(_logits)
answer_end = x(_logits)
start_score = _logits[0]
[answer_start].item()
end_score = _logits[0][answer_end].item()
score = start_score + end_score
input_ids = _t()[0]
tokens = rt_ids_to_tokens(input_ids)
answer = rt_tokens_to_string(tokens[answer_start:answer_end+1])
answer = ce(«[CLS]», «»).replace(«[SEP]», «»).strip()
if answer and len(answer) > 2:
all_d((answer, score))
if all_answers:
all_(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& test our system with some examples. Here& the complete code:
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url = »
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& 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& powerful models and the flexibility of Google Colab, you& created a practical application that demonstrates the capabilities of modern NLP. Feel free to customize and extend this project to meet your specific needs.
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