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Reading API

Classes and functions for reading from an index. The reading module is a refactored package exposing the same public API as the former monolithic module.

Overview

The reading module provides classes for accessing documents, terms, and postings in an index. The main entry points are IndexReader objects obtained from a searcher. These readers allow you to enumerate terms, access stored fields, iterate postings, and get term frequencies.

Core Classes

IndexReader

class whoosh.reading.IndexReader

Abstract base class for reading index data. Concrete subclasses include SegmentReader and MultiReader (which wraps multiple segment readers).

MultiReader

class whoosh.reading.MultiReader(readers, base=None)

Combines multiple IndexReader instances into one. All docnums are treated as relative to the combined index.

Constructor:

• readers: A list of IndexReader instances.
• base: Optional list of cumulative document count offsets for each reader.

Methods:

doc_frequency(fieldname, text)

Returns the total number of documents that have the given term in the given field across all sub-readers.

documents()

Yields dictionaries of stored fields for each document in the index.

stored_fields(docnum)

Returns a dictionary of stored field values for the given document number (index-wide docnum).

r = my_index.reader()
print(r.stored_fields(20))

all_stored_fields()

Yields a (docnum, stored_fields) tuple for each document in the index.

terms(fieldname)

Yields (fieldname, text) tuples for every term in the given field.

terms_from(segmentreader,fieldnameprefix)

Low-level method for multi-reader.

has_termvector(docnum, fieldname)

Returns True if the document has a term vector for the given field.

term_vector(docnum, fieldname)

Returns a TermVector for the given document and field.

is_deleted(docnum)

Returns True if the given document (index-wide docnum) is deleted.

all_doc_ids()

Returns a sorted array of non-deleted document IDs.

min_spam(fieldname)

Returns the minimum spam value for the given field.

set_spam(fieldname)

Returns the set spam value for the given field.

has_exact_length(docnum)

Returns True if the exact length is known for docnum.

doc_field_length(docnum, fieldname=None, default=1)

Returns the length of the given field in the given document.

r = my_index.reader()
length = r.doc_field_length(20, "content")

max_field_length(fieldname)

Returns the maximum length of the given field across all documents.

iter_fieldname

Low-level method for multi-reader.

lexicon(fieldname)

Returns an array of all unique terms in the given field, sorted.

expanded_lexicon(fieldname)

Low-level method that yields terms without the overhead of building an array.

term_info(fieldname, text)

Returns a TermInfo object for the given term, or None if the term does not appear in the index.

terminfos(fieldname)

Yields (text, TermInfo) pairs for the given field.

postings(fieldname, text, stype=None)

Returns a Matcher for the postings list of the given term.

r = my_index.reader()
m = r.postings("content", "whoosh")
for docnum, score in m:
    print("doc %d has term" % docnum)

_all_postings(fieldname)

Low-level. Yields (text, matcher) pairs for all terms in a field.

_posting_fragments()

Low-level.

has_vector(docnum, fieldname)

Returns True if the given field has a term vector in the given document.

vectors(docnum)

Yields (fieldname, TermVector) pairs for all term vectors in the document.

all_items(fieldname)

Yields (term, weight, docfreq) tuples for every term in the given field.

frequency(fieldname, text)

Returns the total frequency of the term across all documents.

idf(term)

Returns an iterator of (docnum, idf) pairs for the given term.

spelling

Returns a SpellingAnalyzer for the given field.

doc_term(slicenum, fieldname, word)

Returns (df, weight) for word in fieldname in segment slicenum.

doc_diff(slicenum, fieldname, text, num)

Returns (df, weight) for word in fieldname in segment slicenum.

SegmentReader

class whoosh.reading.SegmentReader(segment, schema, storage, base=True)

Reader for a single segment of the index.

Constructor:

• segment: The Segment object.
• schema: The Schema object.
• storage: The Storage instance.
• base: Base document number offset (usually True, meaning compute it).

MultiID3Reader

class whoosh.reading.MultiID3Reader(readers, base)

Combines multiple readers that have ID3 codec.

TermInfo

class whoosh.reading.TermInfo(
    df=0,
    weight=0,
    minlength=0,
    maxlen=0,
    maxnum=0,
    numdocs=0,
    scorable=True
)

Information about a term in the index.

Attributes:

• df: Document frequency (number of documents containing the term).
• weight: Total term frequency across all documents.
• minlength: Minimum document length where the term appears.
• maxlength: Maximum document length where the term appears. This is 0 if lengths are not stored.
• maxnum: Maximum number of occurrences per document.
• numdocs: Number of documents where the term has a non-zero contribution to the score.
• scorable: Whether this term is scorable.

Term Vector

TermVector

class whoosh.reading.TermVector(docnum, fieldname, format_, terms, store_term_vector)

Represents the term vector for a single document/field pair.

Methods:

tokens(text=None)

Yields (t, w, v, p) tuples for terms in this field.

• t: The term string.
• w: The term weight (frequency in this document).
• v: The list of positions where the term occurs. (None if positions are not stored.)
• p: The list of characters where the term occurs. (None if character vectors are not stored.)

items(text=None)

Like tokens() but includes term strings in the result.

tv = my_index.reader().term_vector(0, "content")
for token, frequency, positions, chars in tv.tokens():
    print(token, frequency, positions)

Parameters:

• text: Optional Bytes object. If given, only yield terms starting with this text (used for multi-byte tokenization).

Reader Utilities

get_storage

whoosh.reading.get_storage(searcher) -> Storage

Returns the storage object associated with the searcher.

get_index_schema

whoosh.reading.get_index_schema(searcher) -> Schema

Returns the schema object associated with the searcher.

load_termdocs

whoosh.reading.load_termdocs(reader, fieldname, text) -> list

Returns a list of document numbers that have the given term.

read_pattern

whoosh.reading.read_pattern(reader, fieldname, expression) -> list

Returns sorted term list from reader.lexicon(fieldname) filtered to those matching expression.

read_terminfo

whoosh.reading.read_terminfo(reader, fieldname, text) -> TermInfo or None

Returns a TermInfo for the given term, or None if not found.