We are going to apply very simple tokenization, by just splitting the text on whitespace.

The idea is that we first break up or tokenize the text into words, and then apply zero or more filters (such as lowercasing or stemming) on each token to improve the odds of matching queries to text. Note that in the example above the words in the dictionary are lowercased before building the index we are going to break down or analyze the raw text into a list of words or tokens. Practically, what this means is that we’re going to create a dictionary where we map all the words in our corpus to the IDs of the documents they occur in. Think of it as the index in the back of a book that has an alphabetized list of relevant words and concepts, and on what page number a reader can find them. We are going to store this in a data structure known as an “inverted index” or a “postings list”. Yield Abstract(ID =doc_id, title =title, url =url, abstract =abstract)ĭoc_id += 1 # the `element.clear()` call will explicitly free up the memory # used to store the element iterparse(f, events =( 'end',), tag = 'doc'):Ībstract = element. open( 'data/', 'rb') as f:ĭoc_id = 1 # iterparse will yield the entire `doc` element once it finds the # closing `` tag for _, element in etree. # open a filehandle to the gzipped Wikipedia dump with gzip. One abstract in this file is contained by a element, and looks roughly like this (I’ve omitted elements we’re not interested in): The file is one large XML file that contains all abstracts. I’ve written a simple function to download the gzipped XML, but you can also just manually download the file. We are going to be searching abstracts of articles from the English Wikipedia, which is currently a gzipped XML file of about 785mb and contains about 6.27 million abstracts 1.

This will download all the data and execute the example query with and without rankings.īefore we’re jumping into building a search engine, we first need some full-text, unstructured data to search. You can run the full example by installing the requirements ( pip install -r requirements.txt) and run python run.py. I’ll provide links with the code snippets here, so you can try running this yourself. Your browser does not support the audio elementĪll the code you in this blog post can be found on Github.

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