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Build Your Own Search Engine: Python Programming Series




Python is a versatile and powerful programming language that can be used for various applications, including web development, data analysis, machine learning, and natural language processing. One of the most interesting and challenging projects that you can do with Python is to build your own search engine. A search engine is a software system that allows users to find information on the web by entering keywords or phrases in natural language. In this article, we will show you how to build your own search engine using Python and some of its popular libraries and modules. You will learn the basic concepts and algorithms behind search engines, and how to implement them in Python. By the end of this article, you will have a working prototype of a simple search engine that can retrieve documents from a collection of documents based on a user query.




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What are the Components of a Search Engine?




A search engine consists of three main components: a crawler, an indexer, and a query processor.


  • A crawler is a program that scans the web and collects documents from different websites. A crawler follows the links on each webpage and downloads the content of each document.



  • An indexer is a program that processes the documents collected by the crawler and creates an index that maps each term (word) to the documents that contain it. An index is a data structure that allows fast and efficient retrieval of documents based on terms.



  • A query processor is a program that receives a user query and returns a list of relevant documents from the index. A query processor uses various algorithms and techniques to rank the documents according to their relevance to the query.



How to Build a Crawler using Python?




To build a crawler using Python, we need to use some existing modules that can help us to download and parse HTML content from websites. We will use requests module to make HTTP requests to websites, and BeautifulSoup module to extract text from HTML tags. Here are the steps we will follow:


  • Define URLs of websites: We will use a list of URLs of Wikipedia articles as our data source. We will download and extract the text from these articles using requests and BeautifulSoup modules.



  • Download and parse HTML content: For each URL, we will make a request using requests module and get the HTML content as a response. Then, we will create a BeautifulSoup object to parse the HTML content and extract text from tags.



  • Save text as documents: We will save the text extracted from each URL as a document in a list of documents. These documents will be our collection of documents that we want to search.



Code Example




Here is an example of Python code that implements a simple crawler based on the steps above. You can run this code on your local machine or on an online platform like Google Colab or Repl.it.


```python


# Import modules


import requests


from bs4 import BeautifulSoup


# Define URLs of Wikipedia articles


urls = [


"https://en.wikipedia.org/wiki/Python_(programming_language)",


"https://en.wikipedia.org/wiki/Java_(programming_language)",


"https://en.wikipedia.org/wiki/C_(programming_language)",


"https://en.wikipedia.org/wiki/C%2B%2B",


"https://en.wikipedia.org/wiki/C_Sharp_(programming_language)",


"https://en.wikipedia.org/wiki/JavaScript",


"https://en.wikipedia.org/wiki/PHP",


"https://en.wikipedia.org/wiki/Ruby_(programming_language)",


"https://en.wikipedia.org/wiki/Perl",


"https://en.wikipedia.org/wiki/SQL"


]


# Download and parse HTML content of each URL


docs = [] # List of documents (strings)


for url in urls:


# Make a request to the URL


r = requests.get(url)


# Create a BeautifulSoup object to parse the HTML content


soup = BeautifulSoup(r.content, 'html.parser')


# Extract text from tags


text = ' '.join([p.text for p in soup.find_all('p')])


# Save text as a document


docs.append(text)


```


How to Build an Indexer using Python?




To build an indexer using Python, we need to use some existing modules that can help us to preprocess and analyze the text from the documents. We will use nltk module to tokenize, lowercase, remove stopwords, and stem the words in each document. We will also use math and numpy modules to calculate term frequency-inverse document frequency (TF-IDF) weighting scheme for each term in each document. TF-IDF is a measure that reflects how important a term is in a document relative to the whole collection. Here are the steps we will follow:


  • Define stopwords and stemmer: We will use a set of stopwords from nltk module to remove common words that have little meaning or relevance in the documents. We will also use a PorterStemmer from nltk module to reduce words to their root form.



  • Preprocess each document: For each document, we will tokenize it into words, lowercase them, remove stopwords, and stem them using nltk module.



  • Create term-document matrix with TF-IDF weighting: For each term in each document, we will calculate its term frequency (tf) and inverse document frequency (idf) using math and numpy modules. Then, we will multiply tf and idf to get TF-IDF value for each term in each document. We will store these values in a term-document matrix, which is a data structure that maps each term to the documents that contain it.



  • Reduce dimensionality: To improve the efficiency and accuracy of our search engine, we will reduce the dimensionality of our term-document matrix by keeping only top K terms with highest TF-IDF in each document. We will use heapq module to find the top K terms for each document.



Code Example




Here is an example of Python code that implements a simple indexer based on the steps above. You can run this code on your local machine or on an online platform like Google Colab or Repl.it.


```python


# Import modules


import nltk


from nltk.corpus import stopwords


from nltk.stem import PorterStemmer


import math


import numpy as np


import heapq


# Download nltk data


nltk.download('punkt')


nltk.download('stopwords')


# Define constants


N = 10 # Number of documents to retrieve


K = 10 # Number of terms to keep in index


# Step 1: Define stopwords and stemmer


# Define stopwords from nltk module


stop_words = set(stopwords.words('english'))


# Define stemmer from nltk module


stemmer = PorterStemmer()


# Step 2: Preprocess each document


# Tokenize, lowercase, remove stopwords, and stem each word in each document using nltk module


tokens = [] # List of lists of tokens (strings)


for doc in docs:


# Tokenize document into words


words = nltk.word_tokenize(doc)


# Lowercase words


words = [w.lower() for w in words]


# Remove stopwords


words = [w for w in words if w not in stop_words]


# Stem words


words = [stemmer.stem(w) for w in words]


# Append words to tokens list


tokens.append(words)


# Step 3: Create term-document matrix with TF-IDF weighting


# Calculate document frequency (df) for each term using math module


df = # Dictionary of terms (strings) and their df (integers)


for words in tokens:


# Use set to remove duplicates


for term in set(words):


# Increment df count


df[term] = df.get(term, 0) + 1


# Calculate inverse document frequency (idf) for each term using math module


idf = # Dictionary of terms (strings) and their idf (floats)


for term in df:


idf[term] = math.log(len(docs) / df[term])


# Calculate term frequency (tf) for each term in each document using math module


tf = [] # List of dictionaries of terms (strings) and their tf (integers)


for words in tokens:


# Create a dictionary for each document


tf_dict =


for term in words:


# Increment tf count


tf_dict[term] = tf_dict.get(term, 0) + 1


# Append dictionary to tf list


tf.append(tf_dict)


# Calculate TF-IDF for each term in each document using numpy module


tfidf = [] # List of dictionaries of terms (strings) and their tfidf (floats)


for i in range(len(docs)):


# Create a dictionary for each document


tfidf_dict =


for term in tf[i]:


# Multiply tf and idf using numpy module


tfidf_dict[term] = np.multiply(tf[i][term], idf[term])


# Append dictionary to tfidf list


tfidf.append(tfidf_dict)


# Step 4: Reduce dimensionality by keeping only top K terms with highest TF-IDF in each document using heapq module


index = [] # List of dictionaries of terms (strings) and their tfidf (floats)


for i in range(len(docs)):


# Find top K terms with highest TF-IDF using heapq module


top_k_terms = heapq.nlargest(K, tfidf[i], key=tfidf[i].get)


# Create a dictionary for each document with only top K terms


index_dict =


for term in top_k_terms:


index_dict[term] = tfidf[i][term]


# Append dictionary to index list


index.append(index_dict)


```


How to Build a Query Processor using Python?




To build a query processor using Python, we need to use some existing modules that can help us to preprocess and analyze the user query and compare it with the documents in the index. We will use nltk module to tokenize, lowercase, remove stopwords, and stem the words in the query. We will also use numpy module to calculate cosine similarity between the query and each document in the index. Cosine similarity is a metric that measures the angle between two vectors in a high-dimensional space. Here are the steps we will follow:


  • Preprocess the query: We will ask the user to enter a query in natural language, and then we will tokenize it into words, lowercase them, remove stopwords, and stem them using nltk module.



  • Calculate TF-IDF for the query: We will use the same idf values that we calculated for the documents to calculate TF-IDF for each term in the query using numpy module.



  • Calculate cosine similarity: We will convert the query and each document into vectors of TF-IDF values using numpy module. Then, we will calculate the cosine similarity between the query vector and each document vector using numpy module.



  • Rank and retrieve documents: We will use heapq module to find the top N documents with highest cosine similarity to the query and display them to the user.



Code Example




Here is an example of Python code that implements a simple query processor based on the steps above. You can run this code on your local machine or on an online platform like Google Colab or Repl.it.


```python


# Import modules


import nltk


from nltk.corpus import stopwords


from nltk.stem import PorterStemmer


import numpy as np


import heapq


# Define constants


N = 10 # Number of documents to retrieve


K = 10 # Number of terms to keep in index


# Step 1: Preprocess the query


# Ask user to enter a query in natural language


query = input("Enter your query: ")


# Tokenize, lowercase, remove stopwords, and stem each word in the query using nltk module


words = nltk.word_tokenize(query)


words = [w.lower() for w in words]


words = [w for w in words if w not in stop_words]


words = [stemmer.stem(w) for w in words]


# Step 2: Calculate TF-IDF for the query


# Calculate term frequency (tf) for each term in the query using math module


tf_query = # Dictionary of terms (strings) and their tf (integers)


for term in words:


# Increment tf count


tf_query[term] = tf_query.get(term, 0) + 1


# Calculate TF-IDF for each term in the query using numpy module


tfidf_query = # Dictionary of terms (strings) and their tfidf (floats)


for term in tf_query:


# Multiply tf and idf using numpy module


tfidf_query[term] = np.multiply(tf_query[term], idf.get(term, 0))


# Step 3: Calculate cosine similarity


# Convert query and documents into vectors of TF-IDF values using numpy module


query_vector = [] # List of TF-IDF values (floats) for query terms


doc_vectors = [] # List of lists of TF-IDF values (floats) for document terms


for i in range(len(docs)):


# Create a list for each document vector


doc_vector = []


for term in index[i]:


# Append TF-IDF value for each term in document vector


doc_vector.append(index[i][term])


# Append TF-IDF value for each term in query vector only once


if i == 0:


query_vector.append(tfidf_query.get(term, 0))


# Append document vector to doc_vectors list


doc_vectors.append(doc_vector)


# Calculate cosine similarity between query vector and each document vector using numpy module


cosine_similarities = [] # List of cosine similarities (floats) between query and documents


for i in range(len(docs)):


# Calculate dot product between query vector and document vector using numpy module


dot_product = np.dot(query_vector, doc_vectors[i])


# Calculate magnitude of query vector and document vector using numpy module


query_magnitude = np.linalg.norm(query_vector)


doc_magnitude = np.linalg.norm(doc_vectors[i])


# Calculate cosine similarity using numpy module


cosine_similarity = np.divide(dot_product, np.multiply(query_magnitude, doc_magnitude))


# Append cosine similarity to cosine_similarities list


cosine_similarities.append(cosine_similarity)


# Step 4: Rank and retrieve documents


# Find top N documents with highest cosine similarity to query using heapq module


top_n_docs = heapq.nlargest(N, range(len(cosine_similarities)), key=cosine_similarities.__getitem__)


# Display top N documents to user


print(f"Top N documents for your query:")


for i in top_n_docs:


print(f"Document i+1: urls[i]")


```


Conclusion




In this article, we have shown you how to build your own search engine using Python and some of its popular libraries and modules. You have learned the basic concepts and algorithms behind search engines, and how to implement them in Python. You have also created a working prototype of a simple search engine that can retrieve documents from a collection of documents based on a user query. We hope that this article has inspired you to explore more about search engines and Python programming. Happy coding! 4e3182286b


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