I'd like to store in SOLR some items with addresses (City, State, ...) and I'd like to change how similarity is computed. The thing is that when comparing for example city I'm only interested if they are same and not if those strings are similar. Is there a way how to that? Is it through the custom similarity?
If so, can somebody please point me to how it can be done in Solr 6.2?
Thank you very much.
If you're only interested if something matches exactly, use a StrField (a StrField is case sensitive, so the case has to match as well). As you're only getting exact matches, the scoring will be the same for all documents.
The only time you need to implement a custom similarity class is if you want to score documents in a different way than what the built in similarities (or function queries) allow.
Matching exactly would be a regular query: city:Frankfurt. As long as the field is a StrField, only documents with exactly Frankfurt in that field will be returned (and unless you've added an index time boost for one of them, they'll all score identical).
Also, if you're sorting by a field (such as city), any score calculation will be thrown out.
Related
I implementing Solr search using an API. When I call it using the parameters as, "Chillout Lounge", it returns me the collection which are same/similar to the string "Chillout Lounge".
But when I search for "Chillout Lounge Box", it returns me results which don't have any of these three words.(in the DB there are values which have these 3 values, but they are not returned.)
According to me, Solr uses Fuzzy search, but when it is done it should return me some values, which will have at least one these value.
Or what could be the possible changes I should to my schema.XML, such that is would give me proper values.
First of all - "Fuzzy search" is a feature you'll have to ask for (by using ~ in standard Lucene query syntax).
If you're talking about regular searches, you can use q.op to select which operator to use. q.op=AND will make sure that all the terms match, while q.op=OR will make any document that contain at least one of the terms be returned. As long as you aren't using fq for this, the documents that match more terms should be scored higher (as the score will add up across multiple terms), and thus, be shown higher in the result set.
You can use the debug query feature in the web interface to see scores for each term for a document, and find out why the document was returned at all. If the document doesn't match any terms, it shouldn't be returned, unless you're asking for all documents to be returned.
Be aware that the analyzer chain defined for the field you're searching might affect what's considered a match and not.
You'll have to add a proper example to get a more detailed answer.
Suppose I want to create a recommendation system to suggest people you should connect with based off of certain attributes that I know about you and attributes I have about other people that are stored in a Solr index. Is it possible to query the index with a list of attributes (along with boosts for each attribute) and have Solr return scored results even if some of my fields return no matches? The way that I understand that Solr works is that if one of your fields doesn't contain a match in any documents found in your index, you get zero results for the entire query (even if other fields in the query matched) - is that right? What I would hope is that I could query the index and get a list of results back in order of a score given based on how many (and which) fields matched to something, even if some fields have no matches, for example:
Say that there are 2 people documents stored in the index as follows (figuratively):
Person 1:
Industry: Manufacturing
City: Oakland
Person 2:
Industry: Manufacturing
City: San Jose
And say that I perform a pseudo-Solr query that basically says "Search for everyone whose industry is equal to manufacturing and whose city is equal to Oakland". What I would like is to receive both results back in the result set, even though one of the "Persons" does not reside in Oakland. I just want that person to come back as a result with a lower score than Person1. Is this possible? What might a solr query look like to handle this? Assume that I have many more than 2 attributes for each person (so saying that I can use "And" and "Or" in my solr query isn't really feasible.. or is it?) Thanks in advance for your helpful input! (PS I'm using Solr 3.6)
You mention using the AND operator, which is likely your problem.
The default behavior of Lucene, and Solr, query syntax is exactly what you are asking for. A query like:
industry:manufacturing city:oakland
Will match either, with scoring preference on those that match both. See the lucene query syntax documentation
You can use the bq parameter (boost query) does not affect matching, but affects the scores only.
http://localhost:8983/solr/persons/select?q=industry:manufacturing&bq=City:Oakland^2
play with the boosting factor at the end to get the correct balance between matching score, and boosting score.
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
Is it possible to boost a document on the indexing stage depending on the field value?
I'm indexing a text field pulled from the database. I would like to boost results that are shorter over the longer ones. So the value of boost should depend on the length of the text field.
This is needed to alter the standard SOLR behavior that in my case tends to return documents with multiple matches first.
Considering I have a field that stores the length of the document, the equivalent in the query of what I need at indexing would be:
q={!boost b=sqrt(length)}text:abcd
Example:
I have two items in the DB:
ABCDEBCE
ABCD
I always want to get ABCD first for the 'BC' query even though the other item contains the search query twice.
The other solution to the problem would be ability to 'switch off' the feature that scores multiple matches higher at query time. Don't know if that is possible either...
Doing this at index time is important as the hardware I run the SOLR on is not too powerful and trying to boost on query time returns with OutOfMemory Exception. (Even If I could work around that increasing memory for java I prefer to be on the safe side and implement the index the most efficient way possible.)
Yes and no - but how you do it depends on how you're indexing your documents.
As far as I know there's no way of resolving this only on the solr server side at the moment.
If you're using the regular XML based interface to submit documents, let the code that generates the submitted XML add boost=".." values to the field or to the document depending on the length of the text field.
You can check upon DIH Special Commands which has a $docBoost command
$docBoost : Boost the current doc. The value can be a number or the
toString of a number
However, there seems no $fieldBoost Command.
For you case though, if you are using DefaultSimilarity, shorter fields are boosted higher then longer fields in the Score calculation.
You can surely implement your own Simiarity class with a changed TF (Term Frequency) and LengthNorm Calculation as your needs.
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.