We initiate the creation of our Information Retrieval System (IRS) by setting up the document store. Remember the analogy of the document store being akin to a library? If we extrapolate that analogy, the unique keys in our document store dictionary are like book titles in a library, serving as identifiers for the document entries.

Let's imagine we have three documents relevant to our fields of interest: stock markets, AI in finance, and programming for AI. In Python, we can initialize an empty dictionary to function as our document store, then populate it with these documents. We will assign a unique docID (like 'doc1', 'doc2', and so on) as keys, and the respective document contents as values.

In the provided Python code snippet, we first initialize an empty dictionary named document_store. Then, we create three entries representing our documents - 'doc1', 'doc2', and 'doc3'. Each has document content relevant to the described interest fields.

Try executing the attached Python code in your environment to set up the document store and print its content. Here, we have simplified the process, but in real world scenarios, document data would typically come from various sources and be much larger.

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if __name__ == '__main__':

    # Python logic here

    document_store = {}    # Initialize an empty dictionary to function as our document store

    document_store['doc1'] = 'The stock market had a significant dip today.'

    document_store['doc2'] = 'AI in finance is on the rise, with many companies adopting machine learning algorithms.'

    document_store['doc3'] = 'Programming for AI requires strong skills in languages such as Python.'

    print(document_store)