Get the stored fields for a document from the document number
stored_fields = searcher.stored_fields(docnum)
Find every document
myquery = query.Every()
Use the :class:`whoosh.analysis.NgramWordAnalyzer` as the analyzer for the field you want to search as the user types. You can save space in the index by turning off positions in the field using phrase=False, since phrase searching on N-gram fields usually doesn't make much sense:
# For example, to search the "title" field as the user types analyzer = analysis.NgramWordAnalyzer() title_field = fields.TEXT(analyzer=analyzer, phrase=False) schema = fields.Schema(title=title_field)
See the documentation for the :class:`~whoosh.analysis.NgramWordAnalyzer` class for information on the available options.
Look up documents by a field value
# Single document (unique field value) stored_fields = searcher.document(id="bacon") # Multiple documents for stored_fields in searcher.documents(tag="cake"): ...
How many hits were there?
The number of scored hits:
found = results.scored_length()
Depending on the arguments to the search, the exact total number of hits may be known:
if results.has_exact_length(): print("Scored", found, "of exactly", len(results), "documents")
Usually, however, the exact number of documents that match the query is not known, because the searcher can skip over blocks of documents it knows won't show up in the "top N" list. If you call len(results) on a query where the exact length is unknown, Whoosh will run an unscored version of the original query to get the exact number. This is faster than the scored search, but may still be noticeably slow on very large indexes or complex queries.
As an alternative, you might display the estimated total hits:
if results.has_exact_length(): print("Scored", found, "of exactly", len(results), "documents") else: low = results.estimated_min_length() high = results.estimated_length() print("Scored", found, "of between", low, "and", "high", "documents")
Which terms matched in each hit?
# Use terms=True to record term matches for each hit results = searcher.search(myquery, terms=True) for hit in results: # Which terms matched in this hit? print("Matched:", hit.matched_terms()) # Which terms from the query didn't match in this hit? print("Didn't match:", myquery.all_terms() - hit.matched_terms())
How many documents are in the index?
# Including documents that are deleted but not yet optimized away numdocs = searcher.doc_count_all() # Not including deleted documents numdocs = searcher.doc_count()
What fields are in the index?
Is term X in the index?
return ("content", "wobble") in searcher
How many times does term X occur in the index?
# Number of times content:wobble appears in all documents freq = searcher.frequency("content", "wobble") # Number of documents containing content:wobble docfreq = searcher.doc_frequency("content", "wobble")
Is term X in document Y?
# Check if the "content" field of document 500 contains the term "wobble" # Without term vectors, skipping through list... postings = searcher.postings("content", "wobble") postings.skip_to(500) return postings.id() == 500 # ...or the slower but easier way docset = set(searcher.postings("content", "wobble").all_ids()) return 500 in docset # If field has term vectors, skipping through list... vector = searcher.vector(500, "content") vector.skip_to("wobble") return vector.id() == "wobble" # ...or the slower but easier way wordset = set(searcher.vector(500, "content").all_ids()) return "wobble" in wordset