The schema specifies the fields of documents in an index.
Each document can have multiple fields, such as title, content, url, date, etc.
Some fields can be indexed, and some fields can be stored with the document so the contents of the field so the field value is available in search results. Some fields will be both indexed and stored.
The schema is the set of all possible fields in a document. Each individual document might only use a subset of the available fields in the schema.
For example, a simple schema for indexing emails might have fields like from_addr, to_addr, subject, body, and attachments, where the attachments field lists the names of attachments to the email. For emails without attachments, you would omit the attachments field.
Whoosh provides some useful predefined field types:
This type is for body text. It indexes (and optionally stores) the text and stores term positions to allow phrase searching.
TEXT fields use StandardAnalyzer? by default. To specify a different analyzer, use the analyzer keyword argument to the constructor, e.g. TEXT(analyzer=analysis.StemmingAnalyzer()). See TextAnalysis?.
By default, TEXT fields store position information for each indexed term, to allow you to search for phrases. If you don’t need to be able to search for phrases in a text field, you can turn off storing term positions to save space. Use TEXT(phrase=False).
By default, TEXT fields are not stored. Usually you will not want to store the body text in the search index. Usually you have the indexed documents themselves available to read or link to based on the search results, so you don’t need to store their text in the search index. However, in some circumstances it can be useful (see HighlightingResults?). Use TEXT(stored=True) to specify that the text should be stored in the index.
This field 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.
To store the value of the field in the index, use stored=True in the constructor. To automatically lowercase the keywords before indexing them, use lowercase=True.
By default, the keywords are space separated. To separate the keywords by commas instead (to allow keywords containing spaces), use commas=True.
If you users will use the keyword field for searching, use scorable=True.
The ID field 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 terms). This type of field does not store frequency information, so it’s quite compact, but not very useful for scoring.
Use ID for fields like url or path (the URL or file path of a document), date, category – fields where the value must be treated as a whole, and each document only has one value for the field.
By default, ID fields are not stored. Use ID(stored=True) to specify that the value of the field should be stored with the document for use in the search results. For example, you would want to store the value of a url field so you could provide links to the original in your search results.
Expert users can create their own field types.
To create a schema:
from whoosh.fields import Schema, TEXT, KEYWORD, ID, STORED from whoosh.analysis import StemmingAnalyzer schema = Schema(from_addr=ID(stored=True), to_addr=ID(stored=True), subject=TEXT(stored=True), body=TEXT(analyzer=StemmingAnalyzer()), tags=KEYWORD)
If you aren’t specifying any constructor keyword arguments to one of the predefined fields, you can leave off the brackets (e.g. fieldname=TEXT instead of fieldname=TEXT()). Whoosh will instantiate the class for you.
Alternatively you can create a schema declaratively using the SchemaClass base class:
from whoosh.fields import SchemaClass, TEXT, KEYWORD, ID, STORED class MySchema(SchemaClass): path = ID(stored=True) title = TEXT(stored=True) content = TEXT tags = KEYWORD
After you have created an index, you can add or remove fields to the schema using the add_field() and remove_field() methods. These methods are on the Writer object:
writer = ix.writer() writer.add_field("fieldname", fields.TEXT(stored=True)) writer.remove_field("content") writer.commit()
(If you’re going to modify the schema _and_ add documents using the same writer, you must call add_field() and/or remove_field _before_ you add any documents.)
These methods are also on the Index object as a convenience, but when you call them on an Index, the Index object simply creates the writer, calls the corresponding method on it, and commits, so if you want to add or remove more than one field, it’s much more efficient to create the writer yourself:
In the filedb backend, removing a field simply removes that field from the _schema_ – the index will not get smaller, data about that field will remain in the index until you optimize. Optimizing will compact the index, removing references to the deleted field as it goes:
writer = ix.writer() writer.add_field("uuid", fields.ID(stored=True)) writer.remove_field("path") writer.commit(optimize=True)
Because data is stored on disk with the field name, do not add a new field with the same name as a deleted field without optimizing the index in between:
writer = ix.writer() writer.delete_field("path") # Don't do this!!! writer.add_field("path", fields.KEYWORD)
(A future version of Whoosh may automatically prevent this error.)
Dynamic fields let you associate a field type with any field name that matches a given “glob” (a name pattern containing *, ?, and/or [abc] wildcards).
You can add dynamic fields to a new schema using the add() method with the glob keyword set to True:
schema = fields.Schema(...) # Any name ending in "_d" will be treated as a stored # DATETIME field schema.add("*_d", fields.DATETIME(stored=True), glob=True)
To set up a dynamic field on an existing index, use the same IndexWriter.add_field method as if you were adding a regular field, but with the glob keyword argument set to True:
writer = ix.writer() writer.add_field("*_d", fields.DATETIME(stored=True), glob=True) writer.commit()
To remove a dynamic field, use the IndexWriter.remove_field() method with the glob as the name:
writer = ix.writer() writer.remove_field("*_d") writer.commit()
For example, to allow documents to contain any field name that ends in _id and associate it with the ID field type:
schema = fields.Schema(path=fields.ID) schema.add("*_id", fields.ID, glob=True) ix = index.create_in("myindex", schema) w = ix.writer() w.add_document(path=u"/a", test_id=u"alfa") w.add_document(path=u"/b", class_id=u"MyClass") # ... w.commit() qp = qparser.QueryParser("path", schema=schema) q = qp.parse(u"test_id:alfa") with ix.searcher() as s: results = s.search(q)
You can specify a field boost for a field. This is a multiplier applied to the score of any term found in the field. For example, to make terms found in the title field score twice as high as terms in the body field:
schema = Schema(title=TEXT(field_boost=2.0), body=TEXT)
The predefined field types listed above are subclasses of fields.FieldType. FieldType is a pretty simple class. Its attributes contain information that define the behavior of a field.
|format||fields.Format||Defines what kind of information a field records about each term, and how the information is stored on disk.|
|vector||fields.Format||Optional: if defined, the format in which to store per-document forward-index information for this field.|
|scorable||bool||If True, the length of (number of terms in)the field in each document is stored in the index. Slightly misnamed, since field lengths are not required for all scoring. However, field lengths are required to get proper results from BM25F.|
|stored||bool||If True, the value of this field is stored in the index.|
|unique||bool||If True, the value of this field may be used to replace documents with the same value when the user calls document_update() on an IndexWriter.|
The constructors for most of the predefined field types have parameters that let you customize these parts. For example:
A Format object defines what kind of information a field records about each term, and how the information is stored on disk.
For example, the Existence format would store postings like this:
Whereas the Positions format would store postings like this:
The indexing code passes the unicode string for a field to the field’s Format object. The Format object calls its analyzer (see text analysis) to break the string into tokens, then encodes information about each token.
Whoosh ships with the following pre-defined formats.
|Stored||A “null” format for fields that are stored but not indexed.|
|Existence||Records only whether a term is in a document or not, i.e. it does not store term frequency. Useful for identifier fields (e.g. path or id) and “tag”-type fields, where the frequency is expected to always be 0 or 1.|
|Frequency||Stores the number of times each term appears in each document.|
|Positions||Stores the number of times each term appears in each document, and at what positions.|
The STORED field type uses the Stored format (which does nothing, so STORED fields are not indexed). The ID type uses the Existence format. The KEYWORD type uses the Frequency format. The TEXT type uses the Positions format if it is instantiated with phrase=True (the default), or Frequency if phrase=False.
In addition, the following formats are implemented for the possible convenience of expert users, but are not currently used in Whoosh:
|DocBoosts||Like Existence, but also stores per-document boosts|
|Characters||Like Positions, but also stores the start and end character indices of each term|
|PositionBoosts||Like Positions, but also stores per-position boosts|
|CharacterBoosts||Like Positions, but also stores the start and end character indices of each term and per-position boosts|
The main index is an inverted index. It maps terms to the documents they appear in. It is also sometimes useful to store a forward index, also known as a term vector, that maps documents to the terms that appear in them.
For example, imagine an inverted index like this for a field:
|apple||[(doc=1, freq=2), (doc=2, freq=5), (doc=3, freq=1)]|
The corresponding forward index, or term vector, would be:
|2||[(text=apple, freq=5), (text='bear', freq=7)]|
If you set FieldType.vector to a Format object, the indexing code will use the Format object to store information about the terms in each document. Currently by default Whoosh does not make use of term vectors at all, but they are available to expert users who want to implement their own field types.