Using Sphinx I can rank document any way I want.
SELECT *
FROM someIndex
WHERE MATCH('foo bar')
OPTION ranker=expr('<any rank expression>')
How can I achieve same behavior with Solr? Is {!boost q=<some_boost_expression>} is the only way? For example, I need to documents with more number of words have higher score:
A: foo bar blah blah blah
B: foo bar
I need A to be more relevant for foo bar query. Right now B have higher score.
You can apply boost functions (bf attribute) to customize your scoring in a more complex way than a simple query term boost. This is available in the DisMax query parser, and, as you might expect, is further extended in the Extended dismax query parser
The norm is where you would normally expect to find information readily available about the length of the field, although it will be combined with any field level boost found, and you logic (to weigh more heavily the longer field) is the reverse of the default scoring. That will make supporting field boosts and that logic difficult, unless you create a custom Similarity. Norms, by the way, are stored at index time, not calculated at query time, if you decide to take that route.
Related
I'm attempting to query solr for documents, given a basic schema with the following field names, data types irrelevant:
I'm attempting to match documents that match at least one of the following:
occupation, name, age, gender but i want to OR them together
How do you OR together many terms, and enforce the document to match at least one?
This seems to be failing: +(name:Sarah age:24 occupation:doctor gender:male)
How do you convert a boolean expression into solr query syntax? I can't figure out the syntax with + and - and the default operator for OR.
Still I don't get your requirement but you just need to query like:
+(age:24 OR gender:male)
Or if you want data for multiple value in same field with OR condition like.
i.e. You get data of age:24 and age:25 both.
+(age:24 OR age:25 OR gender:male)
Then you can:
+(age:(24 25) OR gender:male)
If it is't your requirement, then let me know.
If you want to make it as simple as possible for the client, just go for the dismax[1] or edismax[2] query parser.
Specifically you can configure a request parameter called "qf" :
"The qf parameter introduces a list of fields, each of which is assigned a boost factor to increase or decrease that particular field’s importance in the query. For example, the query below:
qf=fieldOne^2.3 fieldTwo fieldThree^0.4
assigns fieldOne a boost of 2.3, leaves fieldTwo with the default boost (because no boost factor is specified), and fieldThree a boost of 0.4.
These boost factors make matches in fieldOne much more significant than matches in fieldTwo, which in turn are much more significant than matches in fieldThree." from the wiki
Then you can just pass a free text query, and it will be searched in the fields you specified, giving also different importance to each one, if necessary.
[1] https://lucene.apache.org/solr/guide/6_6/the-dismax-query-parser.html
[2] https://lucene.apache.org/solr/guide/6_6/the-extended-dismax-query-parser.html
I'm looking into the possibility of de-boosting a set of documents during
query time. In my application, when I search for e.g. "preferences", I want
to de-boost content tagged with ContentGroup:"Developer" or in other words,
push those content back in the order. Here's the catch. I've the following
weights on query fields and boost query on source
qf=text^6 title^15 IndexTerm^8
As you can see, title has a higher weight.
Now, a bunch of content tagged with ContentGroup:"Developer" consists of a
title like "Preferences.material" or "Preferences Property" or
"Preferences.graphics". The boost on title pushes these documents at the
top.
What I'm looking is to see if there's a way to deboost all documents that are
tagged with ContentGroup:"Developer" irrespective of the term occurrence is
text or title. I tried something like, but didn't make any difference.
Source:simplecontent^10 Source:Help^20 (-ContentGroup-local:("Developer"))^99
I'm using edismax query parser.
Any pointers will be appreciated.
Thanks,
Shamik
You're onto something with your last attempt, but you have to start with *:*, so that you actually have something to subtract the documents from. The resulting set of documents (those not matching your query) can then be boosted.
From the Solr Relevancy FAQ
How do I give a negative (or very low) boost to documents that match a query?
True negative boosts are not supported, but you can use a very "low" numeric boost value on query clauses. In general the problem that confuses people is that a "low" boost is still a boost, it can only improve the score of documents that match. For example, if you want to find all docs matching "foo" or "bar" but penalize the scores of documents matching "xxx" you might be tempted to try...
q = foo^100 bar^100 xxx^0.00001 # NOT WHAT YOU WANT
...but this will still help a document matching all three clauses score higher then a document matching only the first two. One way to fake a "negative boost" is to give a large boost to everything that does not match. For example...
q = foo^100 bar^100 (*:* -xxx)^999
NOTE: When using (e)dismax, people sometimes expect that specifying a pure negative query with a large boost in the "bq" param will work (since Solr automatically makes top level purely negative positive queries by adding an implicit ":" -- but this doesn't work with "bq", because of how queries specified via "bq" are added directly to the main query. You need to be explicit...
?defType=dismax&q=foo bar&bq=(*:* -xxx)^999
We're working on a plan to identify content tags our users are interested in. So, for instance, we may determine that User X consumes content tagged with "kermit" and "piggy" more often than other tags. These are their "favored tags."
When the users search, we'd like to favor/bias documents that contain these terms.
This means we can't boost the documents at index time, because every user will have different favored tags. Additionally, they may not be searching for the favored tags themselves. They may search for "gonzo," and so we absolutely want to give them documents with "gonzo," but we want to boost documents that also contain "kermit" or "piggy."
These favored tags are not used to actually query the index, but rather are used to bias the result ordering. The favored tags become something of a tie-breaker -- all else being equal, documents containing these terms will rank higher.
This is new/planned development, so we can use whatever version and parser stack is optimal to solve this problem.
Solution in SolrNet
The question was correctly answered below, but here's the code for SolrNet just in case someone else is using it.
var localParams = new LocalParams();
localParams.Add("bq", "kermit^10000); //numeric value is the degree of boost
var solr = ServiceLocator.Current.GetInstance<ISolrOperations<MySolrDocumentClass>>();
solr.Query(new SolrQuery("whatever") + localParams);
You didn't specify which query parser you're using, but if you are using the Dismax or Extended Dismax query parser, the bq argument should do exactly what you're looking for. bq adds search criteria to a search solely for the purpose of affecting the relevancy, but not to limit the result set.
From the Dismax documentation:
The bq (Boost Query) Parameter
The bq parameter specifies an additional, optional, query clause that
will be added to the user's main query to influence the score. For
example, if you wanted to add a relevancy boost for recent documents:
q=cheese
bq=date:[NOW/DAY-1YEAR TO NOW/DAY]
You can specify multiple bq parameters. If you want your query to be
parsed as separate clauses with separate boosts, use multiple bq
parameters.
In this case, you may want to add &bq=kermit&bq=piggy to the end of your Solr query. If you aren't using one of these query parsers, this need may be exactly the motivation you need to switch.
We're having issues with non relevant results being returned as the highest results in our search and we're trying to improve that behavior, but not really sure how.
We have SearchIndex with about a dozen fields. The document=True field is a template backed field that we have placed the majority of the content into. Some of the stuff found in there is much less relevant than other stuff, even if it's still useful.
To give a concrete example: if a user searches for "red rose", we want to return red roses as the top results...even better if lower results are just roses or just red, or even are described as being "rose red" in color.
The issue is our document=True field has a ton of items that are described as being "rose red". Worse the actual red roses don't have "red" and "rose" particularly close to each other as those values would come from disparate fields. As a result we get the top few hundred results that are completely irrelevant.
What we would like to do is either:
A. Search the primary document and then search each of our other fields and boost (but not hard filter) accordingly. If the term "rose" appears in one of the items names and "red" appears as one of it's attribute values than that result should have a higher score. This gives us the optimal results in theory sorted by relevancy.
B. Search all fields at once and boost if the value is any of the "boosted" fields.
It seems like using field boost should be the answer, but we can't figure out how to express it since filtering based on a field is a harsh exclude and we want it to only impact the relevance scoring.
The result of both of these is effectively the same. We just can't figure out how to do either of them with Haystack. Or if we'd have to fall back to raw queries how to write a solr query that accomplishes this.
I can give you some pointers, as I did not get the exact use case :-
You can check on Solr edismax query parser to configure:-
Fields you want to search on - Mainly to select the results
Variable boost on fields for relevancy - To determine the importance on fields
Variable boost for different words combination e.g. single words, phrase match, shingle match with slop to determine relevancy
Provide additional boost on other fields
This will help you to filter the results and order them accordingly as per the field and word combination matches
Let's say I have documents with two fields, A and B.
I'd like to use SOLR's MoreLikeThis, but with a twist: I'm most interested in boosting documents whose A field is like my model document's B field. (That is, extract MLT's 'interesting terms' from the model B field, but only collect MLT results based on the A field.)
I don't see a way to use the mlt.fl fields or mlt.qf boosts to achieve this effect in a single query. (It seems mlt.fl specifies fields used for both discovery of 'interesting terms' and matching to those terms.) Am I missing some option?
Or will I have to extract the 'interesting terms' myself and swap the 'field:term' details?
(Other ideas in this same vein appreciated as well.)
Two options I see are:
Use a copyField - index your original document with a copy of field A named B, and then query using B.
Extend MoreLikeThisHandler and change the fields you query.
The first option costs a bit of programming (mostly configuration changes) and some memory consumption. The second involves more programming but no memory footprint increase. Hope one of them suits your needs.
I now think there are two ways to achieve the desired effect (without customizing the MLT source code).
First option: Do an initial MLT query with the MLT handler, adding the parameter &mlt.interestingTerms=details. This includes the list of terms that were deemed interesting, ranked with their relative boosts. The usual behavior uses those discovered terms against the same mlt.fl fields to find similar documents. For example, the response will include something like:
"interestingTerms":
["field_b:foo",5.0,"field_b:bar",2.9085307,"field_b:baz",1.67070794]
(Since the only thing about this initial query that's interesting is the interestingTerms, throwing in an fq that rules out all docs could help it skip unnecessary scoring work.)
Explicitly re-composing that interestingTerms info into a new OR query field_a:foo^5.0 field_a:bar^2.9085307 field_a:baz^1.67070794 amounts to using the B field example text to find documents that are similar in field A, and may be mimicking exactly the kind of query default MLT does on its usual model field.
Second option: Grab the model document's actual field B text, and feed it directly as a ContentStream body, to be used in lieu of a query, for specifying the model document. Then target mlt.fl at field A for the sake of collecting similar results. For example, a fragment of the parameters might be …&stream.body=foo bar baz&mlt.fl=field_a&…. Again, the net effect being that model text originally from field_b is finding documents similar only in field_a.