This article is a followup to the ranking introduction. Some use cases in this guide are better solved using tensors.
wand (aka Parallel Wand) is a search operator that can be used for efficient top-k retrieval. It implements the Weak AND/Weighted AND algorithm as described by Broder et al in Efficient query evaluation using a two-level retrieval process. See using Wand with Vespa for details.
dotProduct is the brute force equivalent to wand. They are both used to search for documents where weighted tokens in a field matches a subset of weighted tokens in the query. The raw scores produced by dotProduct are equivalent to those produced by wand.
The difference is that wand will perform local optimizations in order to retrieve the top-k targetHits results that would be returned by inner maximum dotproduct. Which one of these are most cost-efficient is complex as it depends on the size of the vocabulary (features) and:
It is easy to compare the two approaches. One can run benchmarks using either and compare latency and total number of hits, if on average, total number of hits approaches the total number of documents matching the other filters in the query, it is cheaper to use tensor dot product.
Refer to the dotProduct reference. dotProduct calculates the dot product of a weighted set in the query and a weighted set in a field - and stores the result in raw scores, which is used in ranking expressions.
Use a weighted set field (use attribute with fast-search for higher performance) in the document to store the tokens:
field features type weightedset<string> {
indexing: summary | attribute
attribute: fast-search
}
The query needs to be prepared by a custom searcher or sent using YQL. The code below shows the relevant part. If using multiple dot products in the same query it is a good idea to label them. This enables us to use individual dot product scores when ranking results later.
Item makeDotProduct(String label, String field, Map<String, Integer> token_map) {
DotProductItem item = new DotProductItem(field);
item.setLabel(label);
for (Map.Entry<String, Integer> entry : token_map.entrySet()) {
item.addToken(entry.getKey(), entry.getValue());
}
return item;
}
dotProduct produces raw scores that can be used in a ranking expression. The simplest approach is to use the sum of all raw scores for the field containing the tokens:
rank-profile default {
first-phase {
expression: rawScore(features)
}
}
For better control, label each dot product in the query and use their scores individually:
rank-profile default {
first-phase {
expression: itemRawScore(dp1) + itemRawScore(dp2)
}
}
Refer to the in operator reference. The use cases for the in operator are for limiting the search result to documents with specific properties that can have a large number of distinct values, like:
Using the in operator is more performant than a big OR expression:
select * from data where category in ('cat1', 'cat2', 'cat3')
select * from data where category = 'cat1' OR category = 'cat2' OR category = 'cat3' ...
See multi-lookup set filtering for details.
Note that in most actual use cases,
the field we are searching in is some sort of user ID, topic ID, group ID, or document ID
and can often be modeled as a number -
usually in a field of type long
(or array<long> if multiple values are needed).
If a string field is used, it will usually also be some sort of ID;
if you have data in a string field intended for searching with the in operator,
then using match: word (default for attribute string fields) is recommended.
The following example shows how to use the in operator programmatically in a Java Searcher to search a category field:
field category type string {
indexing: attribute | summary
attribute: fast-search
rank: filter
}
The Searcher will typically do the following:
StringInItem (or NumericInIterm) for the field you want to use as filter.StringInItem with the original query by using an AndItem.A simple code example adding a hardcoded filter containing 10 tokens:
private Result hardCoded(Query query, Execution execution) {
var filter = new StringInItem("category");
filter.addToken("magazine1");
filter.addToken("magazine2");
filter.addToken("magazine3");
filter.addToken("tv");
filter.addToken("tabloid1");
filter.addToken("tabloid2");
filter.addToken("tabloid3");
filter.addToken("tabloid4");
filter.addToken("tabloid5");
filter.addToken("tabloid6");
var tree = query.getModel().getQueryTree();
var oldRoot = tree.getRoot();
var newRoot = new AndItem();
newRoot.addItem(oldRoot);
newRoot.addItem(filter);
tree.setRoot(newRoot);
query.trace("MyCustomFilterSearcher added hardcoded filter: ", true, 2);
return execution.search(query);
}
The biggest challenge here is finding the tokens to insert; normally the incoming search request URL might not contain all the tokens directly. For example, the search request could contain the user id, and a lookup (in a database or a Vespa index) would fetch the relevant categories for this user.
Refer to javadoc for more details: NumericInItem and StringInItem.
Vespa ranking is flexible and relatively decoupled from document matching. The output from the matching pipeline typically indicates how the different words in the query matches a specific document and lets the ranking framework figure out how this translates to match quality.
However, some of the more complex match operators will produce scores directly,
rather than expose underlying match information.
A good example is the wand operator.
During ranking, a wand will look like a single word that has no detailed match information,
but rather a numeric score attached to it.
This is called a raw score,
and can be included in ranking expressions using the rawScore feature.
The rawScore feature takes a field name as parameter
and gives the sum of all raw scores produced by the query for that field.
If more fine-grained control is needed
(the query contains multiple operators producing raw scores for the same field,
but we want to handle those scores separately in the ranking expression),
the itemRawScore feature may be used.
This feature takes a query item label as parameter
and gives the raw score produced by that item only.
Query item labeling is a generic mechanism that can be used to attach symbolic names to query items.
A query item is labeled by using the setLabel method on a query item
in the search container query API.