Quick start

Whoosh is a library of classes and functions for indexing text and then searching the index. It allows you to develop custom search engines for your content. For example, if you were creating blogging software, you could use Whoosh to add a search function to allow users to search blog entries.

A quick introduction

>>> from whoosh.index import create_in
>>> from whoosh.fields import *
>>> schema = Schema(title=TEXT(stored=True), path=ID(stored=True), content=TEXT)
>>> ix = create_in("indexdir", schema)
>>> writer = ix.writer()
>>> writer.add_document(title=u"First document", path=u"/a",
...                     content=u"This is the first document we've added!")
>>> writer.add_document(title=u"Second document", path=u"/b",
...                     content=u"The second one is even more interesting!")
>>> writer.commit()
>>> from whoosh.qparser import QueryParser
>>> with ix.searcher() as searcher:
...     query = QueryParser("content", ix.schema).parse("first")
...     results = searcher.search(query)
...     results[0]
{"title": u"First document", "path": u"/a"}

The Index and Schema objects

To begin using Whoosh, you need an index object. The first time you create an index, you must define the index’s schema. The schema lists the fields in the index. A field is a piece of information for each document in the index, such as its title or text content. A field can be indexed (meaning it can be searched) and/or stored (meaning the value that gets indexed is returned with the results; this is useful for fields such as the title).

This schema has two fields, “title” and “content”:

from whoosh.fields import Schema, TEXT

schema = Schema(title=TEXT, content=TEXT)

You only need to do create the schema once, when you create the index. The schema is pickled and stored with the index.

When you create the Schema object, you use keyword arguments to map field names to field types. The list of fields and their types defines what you are indexing and what’s searchable. Whoosh comes with some very useful predefined field types, and you can easily create your own.

This type simply indexes (and optionally stores) the entire value of the field as a single unit (that is, it doesn’t break it up into individual words). This is useful for fields such as a file path, URL, date, category, etc.
This field is stored with the document, but not indexed. This field type is not indexed and not searchable. This is useful for document information you want to display to the user in the search results.
This type is designed for space- or comma-separated keywords. This type is indexed and searchable (and optionally stored). To save space, it does not support phrase searching.
This type is for body text. It indexes (and optionally stores) the text and stores term positions to allow phrase searching.
This type is for numbers. You can store integers or floating point numbers.
This type is for boolean (true/false) values.
This type is for datetime objects. See Indexing and parsing dates/times for more information.
whoosh.fields.NGRAM and whoosh.fields.NGRAMWORDS
These types break the field text or individual terms into N-grams. See Indexing and searching N-grams for more information.

(As a shortcut, if you don’t need to pass any arguments to the field type, you can just give the class name and Whoosh will instantiate the object for you.)

from whoosh.fields import Schema, STORED, ID, KEYWORD, TEXT

schema = Schema(title=TEXT(stored=True), content=TEXT,
                path=ID(stored=True), tags=KEYWORD, icon=STORED)

See Designing a schema for more information.

Once you have the schema, you can create an index using the create_in function:

import os.path
from whoosh.index import create_in

if not os.path.exists("index"):
ix = create_in("index", schema)

(At a low level, this creates a Storage object to contain the index. A Storage object represents that medium in which the index will be stored. Usually this will be FileStorage, which stores the index as a set of files in a directory.)

After you’ve created an index, you can open it using the open_dir convenience function:

from whoosh.index import open_dir

ix = open_dir("index")

The IndexWriter object

OK, so we’ve got an Index object, now we can start adding documents. The writer() method of the Index object returns an IndexWriter object that lets you add documents to the index. The IndexWriter’s add_document(**kwargs) method accepts keyword arguments where the field name is mapped to a value:

writer = ix.writer()
writer.add_document(title=u"My document", content=u"This is my document!",
                    path=u"/a", tags=u"first short", icon=u"/icons/star.png")
writer.add_document(title=u"Second try", content=u"This is the second example.",
                    path=u"/b", tags=u"second short", icon=u"/icons/sheep.png")
writer.add_document(title=u"Third time's the charm", content=u"Examples are many.",
                    path=u"/c", tags=u"short", icon=u"/icons/book.png")

Two important notes:

  • You don’t have to fill in a value for every field. Whoosh doesn’t care if you leave out a field from a document.
  • Indexed text fields must be passed a unicode value. Fields that are stored but not indexed (STORED field type) can be passed any pickle-able object.

If you have a text field that is both indexed and stored, you can index a unicode value but store a different object if necessary (it’s usually not, but sometimes this is really useful) using this trick:

writer.add_document(title=u"Title to be indexed", _stored_title=u"Stored title")

Calling commit() on the IndexWriter saves the added documents to the index:


See How to index documents for more information.

Once your documents are committed to the index, you can search for them.

The Searcher object

To begin searching the index, we’ll need a Searcher object:

searcher = ix.searcher()

You’ll usually want to open the searcher using a with statement so the searcher is automatically closed when you’re done with it (searcher objects represent a number of open files, so if you don’t explicitly close them and the system is slow to collect them, you can run out of file handles):

with ix.searcher() as searcher:

This is of course equivalent to:

    searcher = ix.searcher()

The Searcher’s search() method takes a Query object. You can construct query objects directly or use a query parser to parse a query string.

For example, this query would match documents that contain both “apple” and “bear” in the “content” field:

# Construct query objects directly

from whoosh.query import *
myquery = And([Term("content", u"apple"), Term("content", "bear")])

To parse a query string, you can use the default query parser in the qparser module. The first argument to the QueryParser constructor is the default field to search. This is usually the “body text” field. The second optional argument is a schema to use to understand how to parse the fields:

# Parse a query string

from whoosh.qparser import QueryParser
parser = QueryParser("content", ix.schema)
myquery = parser.parse(querystring)

Once you have a Searcher and a query object, you can use the Searcher‘s search() method to run the query and get a Results object:

>>> results = searcher.search(myquery)
>>> print(len(results))
>>> print(results[0])
{"title": "Second try", "path": "/b", "icon": "/icons/sheep.png"}

The default QueryParser implements a query language very similar to Lucene’s. It lets you connect terms with AND or OR, eleminate terms with NOT, group terms together into clauses with parentheses, do range, prefix, and wilcard queries, and specify different fields to search. By default it joins clauses together with AND (so by default, all terms you specify must be in the document for the document to match):

>>> print(parser.parse(u"render shade animate"))
And([Term("content", "render"), Term("content", "shade"), Term("content", "animate")])

>>> print(parser.parse(u"render OR (title:shade keyword:animate)"))
Or([Term("content", "render"), And([Term("title", "shade"), Term("keyword", "animate")])])

>>> print(parser.parse(u"rend*"))
Prefix("content", "rend")

Whoosh includes extra features for dealing with search results, such as

  • Sorting results by the value of an indexed field, instead of by relelvance.
  • Highlighting the search terms in excerpts from the original documents.
  • Expanding the query terms based on the top few documents found.
  • Paginating the results (e.g. “Showing results 1-20, page 1 of 4”).

See How to search for more information.