Adding search snippets (#298)

appengine/search/snippets/snippets.py

@@ -0,0 +1,287 @@
+# Copyright 2016 Google Inc. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from datetime import datetime
+
+from google.appengine.api import search
+
+
+def simple_search(index):
+ index.search('rose water')
+
+
+def search_date(index):
+ index.search('1776-07-04')
+
+
+def search_terms(index):
+ # search for documents with pianos that cost less than $5000
+ index.search("product = piano AND price < 5000")
+
+
+def create_document():
+ document = search.Document(
+ # Setting the doc_id is optional. If omitted, the search service will
+ # create an identifier.
+ doc_id='PA6-5000',
+ fields=[
+ search.TextField(name='customer', value='Joe Jackson'),
+ search.HtmlField(
+ name='comment', value='this is <em>marked up</em> text'),
+ search.NumberField(name='number_of_visits', value=7),
+ search.DateField(name='last_visit', value=datetime.now()),
+ search.DateField(
+ name='birthday', value=datetime(year=1960, month=6, day=19)),
+ search.GeoField(
+ name='home_location', value=search.GeoPoint(37.619, -122.37))
+ ])
+ return document
+
+
+def add_document_to_index(document):
+ index = search.Index('products')
+ index.put(document)
+
+
+def add_document_and_get_doc_id(documents):
+ index = search.Index('products')
+ results = index.put(documents)
+ document_ids = [document.id for document in results]
+ return document_ids
+
+
+def get_document_by_id():
+ index = search.Index('products')
+
+ # Get a single document by ID.
+ document = index.get("AZ125")
+
+ # Get a range of documents starting with a given ID.
+ documents = index.get_range(start_id="AZ125", limit=100)
+
+ return document, documents
+
+
+def query_index():
+ index = search.Index('products')
+ query_string = 'product: piano AND price < 5000'
+
+ results = index.search(query_string)
+
+ for scored_document in results:
+ print(scored_document)
+
+
+def delete_all_in_index(index):
+ # index.get_range by returns up to 100 documents at a time, so we must
+ # loop until we've deleted all items.
+ while True:
+ # Use ids_only to get the list of document IDs in the index without
+ # the overhead of getting the entire document.
+ document_ids = [
+ document.doc_id
+ for document
+ in index.get_range(ids_only=True)]
+
+ # If no IDs were returned, we've deleted everything.
+ if not document_ids:
+ break
+
+ # Delete the documents for the given IDs
+ index.delete(document_ids)
+
+
+def async_query(index):
+ futures = [index.search_async('foo'), index.search_async('bar')]
+ results = [future.get_result() for future in futures]
+ return results
+
+
+def query_options():
+ index = search.Index('products')
+ query_string = "product: piano AND price < 5000"
+
+ # Create sort options to sory on price and brand.
+ sort_price = search.SortExpression(
+ expression='price',
+ direction=search.SortExpression.DESCENDING,
+ default_value=0)
+ sort_brand = search.SortExpression(
+ expression='brand',
+ direction=search.SortExpression.DESCENDING,
+ default_value="")
+ sort_options = search.SortOptions(expressions=[sort_price, sort_brand])
+
+ # Create field expressions to add new fields to the scored documents.
+ price_per_note_expression = search.FieldExpression(
+ name='price_per_note', expression='price/88')
+ ivory_expression = search.FieldExpression(
+ name='ivory', expression='snippet("ivory", summary, 120)')
+
+ # Create query options using the sort options and expressions created
+ # above.
+ query_options = search.QueryOptions(
+ limit=25,
+ returned_fields=['model', 'price', 'description'],
+ returned_expressions=[price_per_note_expression, ivory_expression],
+ sort_options=sort_options)
+
+ # Build the Query and run the search
+ query = search.Query(query_string=query_string, options=query_options)
+ results = index.search(query)
+ for scored_document in results:
+ print(scored_document)
+
+
+def query_results(index, query_string):
+ result = index.search(query_string)
+ total_matches = result.number_found
+ list_of_docs = result.results
+ number_of_docs_returned = len(list_of_docs)
+ return total_matches, list_of_docs, number_of_docs_returned
+
+
+def query_offset(index, query_string):
+ offset = 0
+
+ while True:
+ # Build the query using the current offset.
+ options = search.QueryOptions(offset=offset)
+ query = search.Query(query_string=query_string, options=options)
+
+ # Get the results
+ results = index.search(query)
+
+ number_retrieved = len(results.results)
+ if number_retrieved == 0:
+ break
+
+ # Add the number of documents found to the offset, so that the next
+ # iteration will grab the next page of documents.
+ offset += number_retrieved
+
+ # Process the matched documents
+ for document in results:
+ print(document)
+
+
+def query_cursor(index, query_string):
+ cursor = search.Cursor()
+
+ while cursor:
+ # Build the query using the cursor.
+ options = search.QueryOptions(cursor=cursor)
+ query = search.Query(query_string=query_string, options=options)
+
+ # Get the results and the next cursor
+ results = index.search(query)
+ cursor = results.cursor
+
+ for document in results:
+ print(document)
+
+
+def query_per_document_cursor(index, query_string):
+ cursor = search.Cursor(per_result=True)
+
+ # Build the query using the cursor.
+ options = search.QueryOptions(cursor=cursor)
+ query = search.Query(query_string=query_string, options=options)
+
+ # Get the results.
+ results = index.search(query)
+
+ document_cursor = None
+ for document in results:
+ # discover some document of interest and grab its cursor, for this
+ # sample we'll just use the first document.
+ document_cursor = document.cursor
+ break
+
+ # Start the next search from the document of interest.
+ if document_cursor is None:
+ return
+
+ options = search.QueryOptions(cursor=document_cursor)
+ query = search.Query(query_string=query_string, options=options)
+ results = index.search(query)
+
+ for document in results:
+ print(document)
+
+
+def saving_and_restoring_cursor(cursor):
+ # Convert the cursor to a web-safe string.
+ cursor_string = cursor.web_safe_string
+ # Restore the cursor from a web-safe string.
+ cursor = search.Cursor(web_safe_string=cursor_string)
+
+
+def add_faceted_document(index):
+ document = search.Document(
+ doc_id='doc1',
+ fields=[
+ search.AtomField(name='name', value='x86')],
+ facets=[
+ search.AtomFacet(name='type', value='computer'),
+ search.NumberFacet(name='ram_size_gb', value=8)])
+
+ index.put(document)
+
+
+def facet_discovery(index):
+ # Create the query and enable facet discovery.
+ query = search.Query('name:x86', enable_facet_discovery=True)
+ results = index.search(query)
+
+ for facet in results.facets:
+ print('facet {}.'.format(facet.name))
+ for value in facet.values:
+ print('{}: count={}, refinement_token={}'.format(
+ value.label, value.count, value.refinement_token))
+
+
+def facet_by_name(index):
+ # Create the query and specify to only return the "type" and "ram_size_gb"
+ # facets.
+ query = search.Query('name:x86', return_facets=['type', 'ram_size_gb'])
+ results = index.search(query)
+
+ for facet in results.facets:
+ print('facet {}'.format(facet.name))
+ for value in facet.values:
+ print('{}: count={}, refinement_token={}'.format(
+ value.label, value.count, value.refinement_token))
+
+
+def facet_by_name_and_value(index):
+ # Create the query and specify to return the "type" facet with values
+ # "computer" and "printer" and the "ram_size_gb" facet with value in the
+ # ranges [0,4), [4, 8), and [8, max].
+ query = search.Query(
+ 'name:x86',
+ return_facets=[
+ search.FacetRequest('type', values=['computer', 'printer']),
+ search.FacetRequest('ram_size_gb', ranges=[
+ search.FacetRange(end=4),
+ search.FacetRange(start=4, end=8),
+ search.FacetRange(start=8)])
+ ])
+
+ results = index.search(query)
+ for facet in results.facets:
+ print('facet {}'.format(facet.name))
+ for value in facet.values:
+ print('{}: count={}, refinement_token={}'.format(
+ value.label, value.count, value.refinement_token))