I've got a basic search working, and I'm highlighting using FastVectorHighlighter. When you ask the highlighter for a "best fragment" you have a few overloads of getBestFragment(s) to choose from, documented here. I'm now using the simplest one, like this:
highlightedText = highlighter.getBestFragment(fieldQuery, searcher.getIndexReader(),
scoreDoc.doc, "description", 100)
So I'm highlighting the match from the "description" field. My query however searches another field, "notes". How do I include that in the highlighting? There is an overload that takes a Set<String> matchedFields and one String storedField, but I don't understand the docs. The doc for the method says:
it is advisable that all matchedFields share the same source as storedField or are at least a prefix of it.
What does that mean? How do I index the "notes" and "description" Strings, and what do I pass for matchedFields and storedField?
That call, I believe, is intended to highlight against multiple indexed forms of the same content. That is, if you have one stored full-text content field, but you have indexed it in a number of different ways to expand how you can search it. Perhaps you have one indexed field that uses standard analysis, another with language-specific stemming, another that uses ngrams, and another indexing metaphones.
If you want to highlight two different stored fields, two calls to getBestFragment would be called for. Or you could use a different highlighter that allows multiple stored fields to be highlighted at the same time, PostingsHighlighter, for instance.
Related
I've read a lot about Lucene indexing and searching and still can't understand what Term is?What is the difference between term and fields?
A very rough analogy would be that fields are like columns in a database table, and terms are like the contents in each database column.
More specifically to Lucene:
Terms
Terms are indexed tokens. See here:
Lucene Analyzers are processing pipelines that break up text into indexed tokens, a.k.a. terms
So, for example, if you have the following sentence in a document...
"This is a list of terms"
...and you pass it through a whitespace tokenizer, this will generate the following terms:
This
is
a
list
of
terms
Terms are therefore also what you place into queries, when performing searches. See here for a definition of how they are used in the classic query parser.
Fields
A field is a section of a document.
A simple example is the title of a document versus the body (the remaining text/content) of the document. These can be defined as two separate Lucene fields within a Lucene index.
(You obviously need to be able to parse the source document so that you can separate the title from the body - otherwise you cannot populate each separate field correctly, while building your Lucene index.)
You can then place all of the title's terms into the title field; and the body's terms into the body field.
Now you can search title data separately from body data.
You can read about fields here and here. There are various different types of fields, specific to the type of data (terms) they will be holding.
I have a solr index generated from a catalog of PDF files and correspoing metadata fields pertaining to the pdf files themselves. Still, I would like to provide my users an option to exclude in the query any text indexed from within a PDF. This is so the query results would be based on the metadata fields instead and not biased by the vast text within the pdf files.
I have thought of maybe having two indexes (cores) - one with the indexed pdf files and one without.
Is there another way?
Sounds like you are doing a general search against a default field. Which means you have a lot of copyField instructions (or just one copyField * -> text), which include the PDF content field.
You can create a second destination and copyField everything but the PDF content field into that as well. This way, users can search against or another combined field.
However, remember that this parses all content according to the analysis chain of the destination field. So, eDisMax with a list of source fields may be a better approach there. And, remember, you can use several request handlers (like 'select') and define different default parameters there. That usually makes the client code a bit easier.
You do not need to use 2 separate indexes. You can use the edismax parser and specify the qf parameter at query time. That will help determine what fields are searched.
You can look at field aliases
If you have 3 index fields
pdfmeta
pdftext
Then you can create two field aliases
quicksearch : pdfmeta
fullsearch : pdfmeta, pdftext
One advantage of using a field alias over qf is if your users have bookmarks like q=quicksearch:value, you can change the alias for quicksearch without affecting the user's bookmark.
I have developed a search application with Lucene. I have created the basic search. Basically, my app works as follows:
My index has many fields. (Around 40)
User can enter query to multiple fields i.e: +NAME:John +SURNAME:Doe
Queries can contain wildcards such as ? and * i.e: +NAME:J?hn +SURNAME:Do*
Queries can also contain fuzzy i.e: +NAME:Jahn~0.5
Now, I want to find, which field(s) contains my search term(s). As I am using wildcard and fuzzy, I cannot just make string comparison. How can I do it?
If you need it for debugging purposes, you could use IndexSearcher.explain.
Otherwise, this problem looks like highlighting, so you should be able to find out the fields that matched by:
re-analyzing your document,
or using its term vectors.
Lets use emails for an example as a document. You have your subject, body, the person who its from and lets say we can also tag them (as gmail does)
From my understanding of QueryParser i give it ONE field and the parser type. If a user enter text the user only searches whatever i set. I notice it will look in the subject or body field if i wrote fieldName: text to search however how do i make a regular query such as "funny SO question unicorn" find result(s) with some of those strings in the subject, the others in the body? ATM because i knew it would be easy i made a field called ALL and combined all the other fields into that but i would like to know how i can do it in a proper way. Especially since my next app is text search dependent
Use MultiFieldQueryParser. You can specify list of fields to be searched using following constructor.
MultiFieldQueryParser(Version matchVersion, String[] fields, Analyzer analyzer)
This will generate a query as if you have created multiple queries on different fields. This partially addresses your problem. This, still, will not match one term matching in field1 and another matching in field2. For this, as you have rightly pointed out, you will need to combine all the fields in one single field and search in that field. Nevertheless, you will find MultiFieldQueryParser useful when query terms do not cross the field boundaries.
In Lucene, using a Standard Analyzer, I want to make fields with space searchable.
I set Field.Index.NOT_ANALYZED and Field.Store.YES using the StandardAnalyzer
When I look at my index in LUKE, the fields are as I expected, a field and a value such as:
location -> 'New York'.
Here I found that I can use the KeywordAnalyzer to find this value using the query:
location:"New York".
But I want to add another term to the query. Let's say a have a body field which contains the normalized and analyzed terms created by the StandardAnalyzer. Using the KeywordAnalyzer for this field I get different results than when I use the StandardAnalyzer.
How do I combine two Analyzers in one QueryParser, where one Analyzer works for some fields and another one for another fields. I though of creating my own Analyzer which could behave differently depending on the field, but I have no clue how to do it.
PerFieldAnalyzerWrapper lets you apply different analyzers for different fields.