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Complete example on how to retrieve search highlightings using RavenDB
using System;
using System.Diagnostics;
using System.Linq;
using System.Reflection;
using System.Threading.Tasks;
using Raven.Abstractions.Data;
using Raven.Abstractions.Indexing;
using Raven.Client;
using Raven.Client.Embedded;
using Raven.Client.Indexes;
namespace RavenDbTest
{
internal class Program
{
private readonly IDocumentStore store;
private Program()
{
store = new EmbeddableDocumentStore
{
RunInMemory = true
}.Initialize();
IndexCreation.CreateIndexes(Assembly.GetExecutingAssembly(), store);
}
private static void Main()
{
try
{
new Program().Run();
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
private void Run()
{
Transact(session =>
{
session.Store(new Document {Text = "The quick brown fox jumped over the lazy dog"});
});
Transact(session =>
{
FieldHighlightings highlightings = null;
var results = session.Query<Document, DocumentIndex>()
.Customize(x => x.Highlight("Text", 128, 1, out highlightings))
.Search(x => x.Text, "brown")
.ToArray();
foreach (var result in results)
{
Console.WriteLine("Found document {0}", result.Id);
}
Console.WriteLine("Highlights:");
foreach (var resultIndent in highlightings.ResultIndents)
{
var fragments = highlightings.GetFragments(resultIndent);
foreach (var fragment in fragments)
{
Console.WriteLine(fragment);
}
}
});
}
private void Transact(Action<IDocumentSession> action)
{
using (var session = store.OpenSession())
{
action.Invoke(session);
session.SaveChanges();
}
WaitForIndexing();
}
private void WaitForIndexing()
{
const int Timeout = 15000;
var indexingTask = Task.Factory.StartNew(
() =>
{
var sw = Stopwatch.StartNew();
while (sw.Elapsed.TotalMilliseconds < Timeout)
{
var s = store.DatabaseCommands.GetStatistics()
.StaleIndexes;
if (s.Length == 0)
{
break;
}
Console.WriteLine("Waiting for indexing to complete...");
Task.Delay(500).Wait();
}
});
indexingTask.Wait(Timeout);
}
}
public class Document
{
public string Text { get; set; }
public string Id { get; set; }
}
public class DocumentIndex : AbstractIndexCreationTask<Document>
{
public DocumentIndex()
{
Map = documents => from document in documents
select new
{
document.Text
};
/*
* this blog gives a decent conceptual idea of what is going on:
* http://blog.jpountz.net/post/41301889664/putting-term-vectors-on-a-diet
*
* TermVector.Yes = store an inverted index per document with terms and their frequencies within that document
* TermVector.WithPositions = also include the positions of the terms relative to other terms (tokens)
* TermVector.WithOffsets = also include the locations of the term in the original field text
* TermVector.WithPositionsAndOffsets = store an inverted term index per document including term frequencies, positions and offsets per document
*
* For Highlighting, you must use TermVector.WithPositionsAndOffsets because the FastVectorHighlighter needs to know
* the relative positions of tokens and where they are located in the original text to highlight fragments.
* Without the term vectors enabled, you will never get highlight results. I made sure that dynamic indexes
* using highlighting will throw if this is not set, but did not want to touch static indexes.
*
* For MoreLikeThis, you should be using TermVector.Yes since all you need are the term frequencies per document
* to know which ones relate. MoreLikeThis will fall back to reading the terms itself if you set Storage.Yes on
* the index field, but it is generally more efficient to use term vectors. Since MoreLikeThis has been in RavenDB
* for awhile and presumably was only working if you stored the field, I would recommend to anyone using MoreLikeThis
* to turn on TermVector.Yes for the relevant index field, and turn off Storage.Yes unless you have another reason to
* store the field.
*/
Indexes.Add(x => x.Text, FieldIndexing.Analyzed);
IndexSuggestions.Add(x => x.Text, new SuggestionOptions {Accuracy = 0.5f, Distance = StringDistanceTypes.Levenshtein});
TermVectors.Add(x => x.Text, FieldTermVector.WithPositionsAndOffsets);
Store(x => x.Text, FieldStorage.Yes);
}
}
}
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