Now that we have implemented an inverted index in our document store, we can build a basic search functionality. As experienced Python coders, we can create a very basic search engine using the inverted index to improve the speed and efficiency of our search.
The process is rather simple. For any given search term:
1. We check if the term exists in our inverted index.
2. If it does, we fetch all the docIDs that the term maps to, i.e., all documents that contain the term.
3. We then output these documents as the search results.
Consider a scenario where a user is searching for the term 'Python'. We check if 'Python' exists in our inverted index. If it does, we fetch all docIDs that 'Python' maps to and output these as search results.
Similar to how AI models are driven by data, our search functionality will be as good as the documents and the indexing we have. Also, remember how in finance, managing complex portfolios and predicting market trends depend heavily on how effectively we can retrieve and analyze information? This is where our retrieval system comes into play for our search engine, standing tall as the backbone that supports all query operations.
Keep in mind, this is a very basic implementation. In the real world, search engines deal with complex queries involving multiple terms, special characters, case sensitivity, relevancy of results and much more. We will discuss these complexities and their solutions in later sections.
xxxxxxxxxxif __name__ == '__main__': # we've already set up the document store with several documents and built our inverted index search_term = 'Python' results = set() # fetch all docIDs associated with the search term from inverted index if search_term in inverted_index: for docID in inverted_index[search_term]: results.add(docID) # print out all document IDs that contain the search term print('Documents containing term:', search_term) for docID in results: print('DocID:', docID, 'Document Content:', document_store[docID]) print('Search finalized')