Now, let's bring together what we've learned thus far, in the context of building our lightweight, feature-centric search engine. Assume we are familiar with data structures in Python, and are conceptually aware of how relevancy in search results works, akin to the way portfolio managers optimize their finance portfolio.
To reiterate, we've covered instantiating a Document Store for indexing and searching documents, implementing an Inverted Index for keyword-based mapping, incorporating Distributed and Faceted Search to broaden our search scope and filter category-wise, and a basic relevance algorithm to rank the search results.
- Instantiate the Document Store
- Initialize the Search Engine with the Document Store
- Ingest some documents in our Document Store
- Engage our powerful features i.e., the Inverted Index and Relevance Algorithm
- Perform the search!
And voila, we have a rudimentary search engine!
In the Python code snippet below, for each query term, we calculate its frequency in each document and add these frequencies up to derive a 'relevance score'. Higher the score, the more relevant the document is to the search query. Afterwards, we print the search results.
However, remember that real-world search engines employ much more sophisticated algorithms. Our simplified search engine, much like building a basic tree from scratch in computer science, provides an understanding of how the engine functions under the hood.
Congratulations on building your own search functionality!
xxxxxxxxxxif __name__ == "__main__": # Instantiate our document store doc_store = DocumentStore() # Instantiate our search engine with the document store search_engine = SearchEngine(document_store=doc_store) # Add documents to our document store documents = [...] for doc in documents: doc_store.add_document(doc) # Implementing inverted index and relevance engine search_engine.create_inverted_index() search_engine.relevance_algorithm() # Perform a search result = search_engine.search('example query') print('Search Results: ', result)