t('year-month-day', engine.NestedField, sep='-') # set method supports custom field types inheriting their default settings add ( doc, location = location, incorporated = date ( * incorporated ), point = ) indexer. pop ( 'latitude' ) location = doc + '.' + doc incorporated = map ( int, doc. NestedField ( 'state.city' ) for doc in docs : doc = doc point = doc. SpatialField ) # assigned fields can have a different key from their underlying field name indexer. set ( 'population', dimensions = 1 ) indexer. set ( 'state', stored = True ) # set method supports custom field types inheriting their default settings indexer. Hits = arch(query, 10).scoreDocsįrom datetime import date docs = indexer = engine. # Parse a simple query that searches for "text": ![]() Isearcher = search.IndexSearcher(ireader) Iwriter = index.IndexWriter(directory, config)ĭoc.add(document.Field('fieldname', text, _STORED)) rmtree ( 'tempIndex' )įrom import analysis, document, index, queryparser, search, storeĪssert lucene.getVMEnv() or lucene.initVM()Īnalyzer = ()ĭirectory = (File('tempIndex').toPath())Ĭonfig = index.IndexWriterConfig(analyzer) scoreDocs assert len ( hits ) = 1 # Iterate through the results: for hit in hits : hitDoc = isearcher. QueryParser ( 'fieldname', analyzer ) query = parser. IndexSearcher ( ireader ) # Parse a simple query that searches for "text": parser = queryparser. close () # Now search the index: ireader = index. Document () text = "This is the text to be indexed." doc. IndexWriter ( directory, config ) doc = document. IndexWriterConfig ( analyzer ) iwriter = index. To add Term Vectors to your index see the Field constructors.ĭeleted Documents: An optional file indicating which documents are deleted.įig.Import shutil import lucene from java.io import File from import analysis, document, index, queryparser, search, store from lupyne import engine assert lucene. A term vector consists of term text and term frequency. ![]() Term Vectors: For each field in each document, the term vector (sometimes called document vector) may be stored. Normalization Factors: For each field in each document, a value is stored that is multiplied into the score for hits on that field. Note that this will not exist if all fields in all documents omit position data. Term Proximity Data: For each term in the dictionary, the positions that the term occurs in each document. Term Frequency Data: For each term in the dictionary, the numbers of all the documents that contain that term, and the frequency of the term in that document, unless frequencies are omitted (IndexOptions.DOCS_ONLY) The dictionary also contains the number of documents which contain the term and pointers to the term's frequency and proximity data. Term Dictionary: A dictionary containing all of the terms used in all of the indexed fields of all of the documents. The set of stored fields is what is returned for each hit when searching. These are used to store auxiliary information about the document, such as its title, URL, or an identifier to access a database. Stored Field Values: This contains, for each document, a list of attribute-value pairs, where the attributes are field names.
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