Watch how search engines build an inverted index from raw documents. Follow the full pipeline from tokenization through index construction to query evaluation with boolean operators.
The cat sat on the mat. The cat is fluffy and warm.
A dog ran in the park. The dog is loyal and fast.
The garden has flowers and trees. A cat sleeps in the warm garden.
The park is big and green. Dogs and cats play in the park.
Raw text documents are the input. Each document has a unique ID and contains natural language text that needs to be searchable.
Text is split into tokens, lowercased, and stop words (common words like “the”, “is”) are removed. Remaining words are stemmed to their root form.
The inverted index maps each unique term to a posting list: the set of document IDs containing that term. This reverses the document-to-word relationship.
Queries look up terms in the index. AND queries intersect posting lists (documents with all terms), OR queries union them (documents with any term).