This fragment of code is self-describing – we just close all used object to release index and make it available for other parts of the app. More can be found here.įor each element on the list the document is created and then added to index writer.ģ. The tokens are indexed by running the field’s value through an Analyzer in the second one. In the first the field’s value is indexed without using an Analyzer. In the example I have used only two possible states (out of 5): NOT_ANALYZED and ANALYZED. Fourth and the last one specifies whether and how a field should be indexed.
Determines whether the value should be stored in the index or not.Second is the actual value of the property for the document.First one is the name of the value which we can later reference (this probably should be some constant value to simple re-usage and refactoring).
The constructor of Fieldused in the example takes 4 arguments: Each document should (typically) contain one of more stored fields which uniquely identify the document. Each Document is a set of fields, where every has a name and a textual value. As you can check in the API documentation, documents are the unit of search and indexing. As you can see there is a new concept – the Documentobject is created for each enumeration element. This iteration goes through the data enumeration (in this example this data is not important, so I have omitted it). ANALYZED)) ĭoc.Add( new Field ( "Description", sampleData.Description, Field. NOT_ANALYZED)) ĭoc.Add( new Field ( "Name", sampleData.Name, Field. This index has 20-30% the size of text indexed.ĭoc.Add( new Field ( "Id", sampleData.Id.ToString(), Field. In the third line we create IndexWriterwhich just simply creates index – we can think of this index as if it was an index on the Database. Used StandardAnalyzerfilters input sequence with StandardFilter (normalizes tokens), LowerCaseFilter (normalizes token text to lower case) and StopFilter (using a list of English stop words). In short words the analyzer is tokenizer, stemmer and stop-words filter. In the second the analyzer is instantiated. In (1) the directory on the C disk is opened – this line is straightforward. Var writer = new IndexWriter (dir, analyzer, IndexWriter. Var analyzer = new StandardAnalyzer ( Version. Create Writer which later will write down the Analyzer. Build indexįirst step is to create index for Lucene. You can decide what level of scores will be enough to recognize a document as the search result. Each time you will receive result it will contain not only info about the documents but also scores for each of them. In this case we need a way of scoring the return values – this is done for you by the library. But it is very important to understand that every file (document in Lucene’s language) can be more or less good as the search result. Base on that your search engine can use the power of Lucene.Įach query returns a set of data which fulfill your requirements. Lucene uses something called index which is a textual form of the data on which the search methods will work – there are two main forms: file and memory index. There are couple of aspects which needs to be introduced before we dig into the code. You just need to properly include and use Lucene.NET in your application. Thanks to that library you do not need to implement sophisticated search logic in your application or SQL queries you use. The current version of the core is stable and no major bugs were announced so far.
#LUCENE PDF SEARCH EXAMPLE FOR FREE#
Apache Lucene as well as Lucene.NET are open source projects available for free downloads (Lucene.NET also as NuGet package).Īs you can expect this port-library is under ongoing development and can cause potential problems. Fortunately there is a port version – called Lucene.NET ( ). One of the biggest disadvantages for C# developer is that Lucene is entirely written in Java. One of the most well known one is Lucene ( ) – full-text search engine library. One of the possible ones is to use third-party library.
This is where the developers need to find suitable solution. It is not easy to build a search tool which will be more than just simple SQL query with couple of LIKE clausules. In this post I will try to shortly present capabilities of Lucene. This way of providing searching is not very sophisticated and dedicated developer would like to provide his/her own search engine. Most of them are using most popular search engines which search something on the website – I think about Bing, Google etc. Nowadays it is common that you see search boxes on websites.
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