Elasticlunr.js

项目地址：http://elasticlunr.com/
代码地址：https://github.com/weixsong/elasticlunr.js
文档地址：http://elasticlunr.com/docs/index.html

Elasticlurn.js is a lightweight full-text search engine in Javascript for browser search and offline search.
Elasticlunr.js is developed based on Lunr.js, but more flexible than lunr.js. Elasticlunr.js provides Query-Time boosting and field search.
Elasticlunr.js is a bit like Solr, but much smaller and not as bright, but also provide flexible configuration and query-time boosting.

Key Features Comparing with Lunr.js

• Query-Time boosting, you don’t need to setup boosting weight in index building procedure, this make it more flexible that you could try different boosting scheme.
• More rational scoring mechanism, Elasticlunr.js use quite the same scoring mechanism as Elasticsearch, and also this scoring mechanism is used by lucene.
• Field-search, you could choose which field to index and which field to search.
• Boolean Model, you could set which field to search and the boolean model for each query token, such as “OR”, “AND”.
• Combined Boolean Model, TF/IDF Model and the Vector Space Model, make the results ranking more reliable.
• Fast, Elasticlunr.js removed TokenCorpus and Vector from lunr.js, by using combined model there is no need to compute the vector of a document and query string to compute similarity of query and matched document, this improve the search speed significantly.
• Small index file, Elasticlunr.js did not store TokenCorpus because there is no need to compute query vector and document vector, then the index file is very small, this is especially helpful when elasticlurn.js is used as offline search.

Example

A very simple search index can be created using the following scripts:

var index = elasticlunr(function () {
    this.addField('title');
    this.addField('body');
    this.setRef('id');
});

Adding documents to the index is as simple as:

var doc1 = {
    "id": 1,
    "title": "Oracle released its latest database Oracle 12g",
    "body": "Yestaday Oracle has released its new database Oracle 12g, this would make more money for this company and lead to a nice profit report of annual year."
}

var doc2 = {
    "id": 2,
    "title": "Oracle released its profit report of 2015",
    "body": "As expected, Oracle released its profit report of 2015, during the good sales of database and hardware, Oracle's profit of 2015 reached 12.5 Billion."
}

index.addDoc(doc1);
index.addDoc(doc2);

Then searching is as simple:

index.search("Oracle database profit");

Also, you could do query-time boosting by passing in a configuration.

index.search("Oracle database profit", {
    fields: {
        title: {boost: 2},
        body: {boost: 1}
    }
});

This returns a list of matching documents with a score of how closely they match the search query:

[{
    "ref": 1,
    "score": 0.5376053707962494
},
{
    "ref": 2,
    "score": 0.5237481076838757
}]

API documentation is available, as well as a full working example.

Description

Elasticlunr.js is developed based on Lunr.js, but more flexible than lunr.js. Elasticlunr.js provides Query-Time boosting and field search.
A bit like Solr, but much smaller and not as bright, but also provide flexible configuration and query-time boosting.

Why

1. In some system, you don’t want to deploy any Web Server(such as Apache, Nginx, etc.), you only provide some static web pages and provide search function in client side. Then you could build index in previous and load index in client side.
2. Provide offline search functionality. For some documents, user usually download these documents, you could build index and put index in the documents package, then provide offline search functionality.
3. For some limited or restricted network, such WAN or LAN, offline search is a better choice.
4. For mobile device, Iphone or Android phone, network traffic maybe very expensive, then provide offline search is a good choice.

Installation

Simply include the elasticlunr.js source file in the page that you want to use it. Elasticlunr.js is supported in all modern browsers.

Browsers that do not support ES5 will require a JavaScript shim for Elasticlunr.js to work. You can either use Augment.js, ES5-Shim or any library that patches old browsers to provide an ES5 compatible JavaScript environment.

Documentation

This part only contain important apects of elasticlunr.js, for the whole documentation, please go to API documentation.

1. Build Index

When you first create a index instance, you need to specify which field you want to index. If you did not specify which field to index, then no field will be searchable for your documents.
You could specify fields by:

var index = elasticlunr(function () {
    this.addField('title');
    this.addField('body');
    this.setRef('id');
});

You could also set the document reference by this.setRef('id'), if you did not set document ref, elasticlunr.js will use ‘id’ as default.

You could do the above index setup as followings:

var index = elasticlunr();
index.addField('title');
index.addField('body');
index.setRef('id');

Default supported language of elasticlunr.js is English, if you want to use elasticlunr.js to index other language documents, then you need to use elasticlunr.js combined with lunr-languages.
Assume you’re using lunr-language in Node.js envrionment, you could import lunr-language as followings:

var lunr = require('./lib/lunr.js');
require('./lunr.stemmer.support.js')(lunr);
require('./lunr.de.js')(lunr);

var idx = lunr(function () {
    // use the language (de)
    this.use(lunr.de);
    // then, the normal lunr index initialization
    this.field('title')
    this.field('body')
});

For more details, please go to lunr-languages.

2. Add document to index

Add document to index is very simple, just prepare you document in JSON format, then add it to index.

var doc1 = {
    "id": 1,
    "title": "Oracle released its latest database Oracle 12g",
    "body": "Yestaday Oracle has released its new database Oracle 12g, this would make more money for this company and lead to a nice profit report of annual year."
}

var doc2 = {
    "id": 2,
    "title": "Oracle released its profit report of 2015",
    "body": "As expected, Oracle released its profit report of 2015, during the good sales of database and hardware, Oracle's profit of 2015 reached 12.5 Billion."
}

index.addDoc(doc1);
index.addDoc(doc2);

If your JSON document contains field that not configured in index, then that field will not be indexed, which means that field is not searchable.

3. Remove document from index

Elasticlunr.js support remove a document from index, just provide JSON document to elasticlunr.Index.prototype.removeDoc() function.

For example:

var doc = {
    "id": 1,
    "title": "Oracle released its latest database Oracle 12g",
    "body": "Yestaday Oracle has released its new database Oracle 12g, this would make more money for this company and lead to a nice profit report of annual year."
}

index.removeDoc(doc);

Remove a document will remove each token of that document’s each field from field-specified inverted index.

4. Update a document in index

Elasticlunr.js support update a document in index, just provide JSON document to elasticlunr.Index.prototype.update() function.

For example:

var doc = {
    "id": 1,
    "title": "Oracle released its latest database Oracle 12g",
    "body": "Yestaday Oracle has released its new database Oracle 12g, this would make more money for this company and lead to a nice profit report of annual year."
}

index.update(doc);

5. Query from Index

Elasticlunr.js provides flexible query configuration, supports query-time boosting and Boolean logic setting.
You could setup a configuration tell elasticlunr.js how to do query-time boosting, which field to search in, how to do the boolean logic.
Or you could just use it by simply provide a query string, this will aslo works perfectly because the scoring mechanism is very efficient.

5.1 Simple Query

Because elasticlunr.js has a very perfect scoring mechanism, so for most of your requirement, simple search would be easy to meet your requirement.

index.search("Oracle database profit");

Output is a results array, each element of results array is an Object contain a ref field and a score field.
ref is the document reference.
score is the similarity measurement.

Results array is sorted descent by score.

5.2 Configuration Query

5.2.1 Query-Time Boosting

Setup which fields to search in by passing in a JSON configuration, and setup boosting for each search field.
If you setup this configuration, then elasticlunr.js will only search the query string in the specified fields with boosting weight.

The scoring mechanism used in elasticlunr.js is very complex, please goto details for more information.

index.search("Oracle database profit", {
    fields: {
        title: {boost: 2},
        body: {boost: 1}
    }
});

5.2.2 Boolean Model

Elasticlunr.js also support boolean logic setting, if no boolean logic is setted, elasticlunr.js use “OR” logic defaulty. By “OR” default logic, elasticlunr.js could reach a high Recall.

index.search("Oracle database profit", {
    fields: {
        title: {boost: 2},
        body: {boost: 1}
    },
    boolean: "OR"
});

Boolean operation is performed based on field. This means that if you choose “AND” logic, documents with all the query tokens in the query field will be returned as a field results. If you query in multiple fields, different field results will be merged together to give a final query results.