Merge branch 'master' into xmpp

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))