Now that we've discussed the importance and utility of faceted search, let's dive into how we can implement this in our search engine. To do this, we need a way to categorize our documents and filter them according to a user's search criteria.

In the world of finance, for instance, we could categorize documents according to different 'facets' such as 'stocks', 'bonds', 'real estate', among others. When a user searches with a specific facet in mind, our engine should first perform a regular search using the query, and then filter the results by the provided category.

One way to achieve this in Python is by using dictionaries. We can create a dictionary where the keys are categories or 'facets' and the values are lists of document names or IDs. We can also add to this by having sub-dictionaries for each category, calling them 'sub-facets'. This method will allow us to efficiently categorize and retrieve documents.

Remember, creating and maintaining a good faceted search system is a complex task and requires upfront planning and testing. The categories or 'facets' must be meaningful and add real value to the users of the system.

It is a balance that requires careful consideration of system performance, search precision, and the flexibility of our system to add new categories or facets.

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if __name__ == "__main__":

    # Initialize document store

    document_store = {"stocks": [], "bonds": [], "real estate": []}

    # Add documents to store

    document_store["stocks"].extend(["doc1", "doc2", "doc3"])

    document_store["bonds"].extend(["doc4", "doc5"])

    document_store["real estate"].extend(["doc6", "doc7", "doc8", "doc9"])

    # Search functionality

    def search(category, query):

        # Initial search logic here

        ...

        # Filter search results by category

        if category in document_store:

            return [doc for doc in search_results if doc in document_store[category]]

        else:

            return "Invalid category. Please choose from: " + ", ".join(document_store.keys())

    # Executing a search

    print(search('stocks', 'AI'))