In a search engine like Google or Elasticsearch, an Information Retrieval System (IRS) plays an important role in searching, storing, and retrieving the data efficiently. The basic structure of an IRS consists of a document store and an indexing system. Imagine the IRS as a librarian for a huge library, which in our case is the entire World Wide Web.

To get a grasp on these concepts, let's assume we are into finance, and we are pulling documents related to the world stock markets, and these documents are our data.

The first step in creating an IRS is to set up a document store. Let's take a simple Python dictionary as a document store. The keys to the dictionary could be unique identifiers for our stock market documents, and the values should be the content of those respective documents. In our Python script, we will create a dictionary for the document store and populate it with some basic document data using string values.

The next step is indexing. Indexing is a method by which we create an 'index' that maps each unique word to its appearance in the documents. For indexing data, we need to parse each document and map each word in the document to the document's unique identifier. Our 'index' can just be another Python dictionary, where the keys are unique words, and the values are lists of document identifiers where the word appears. Note that this is a basic example; in a real world scenario, the indexing process is much more complex and might involve techniques like stemming, lemmatizing, or removing stop words.

Lastly, with the index, we can easily search for documents that contain a certain word. We can take a term to search, look it up in our index, and our IRS should return all documents where the term appears.

So to summarize, in the IRS, we first store documents in a document store, then index these documents by mapping words to document identifiers, and finally, we can retrieve any documents based on a search term or keyword. All these concepts are demonstrated in accompanying Python code.

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  print(search_data(index_data(data_store), 'document'))

if __name__ == '__main__':

  # Start with a simple data store, for instance, a dict or hash map

  data_store = {}

​

  # Let's put some data into the data store

  data_store['document1'] = 'This is the first document.'

  data_store['document2'] = 'The second document lives here.'

  data_store['document3'] = 'Here lies the third document.'

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  # Create a simple function to index the data

  def index_data(store):

    index = {} # The index is just another dict

    for key, value in store.items():

      for word in value.split(' '):

        if word not in index:

          index[word] = [key]

        else:

          index[word].append(key)

    return index

  # Test indexing function

  print(index_data(data_store))

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  # With an index, we can then easily search the data

  def search_data(index, term):

    if term in index:

      return index[term]

    else:

      return 'Term not found.'

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