wink-bm25-text-search

Fast Full Text Search based on BM25

Add fast in-memory semantic search to your application using wink-bm25-text-search. It is based on state-of-the-art text search algorithm — BM25 — a Probabilistic Relevance Framework for document retrieval. It's API offers a rich set of features:

1. Scalable Design allows easy addition/customization of features like geolocation and more.

2. Search on exact values of pre-defined fields, makes search results more relevant.

3. Index optimized for size and speed can be exported (and imported) from the added documents in a JSON format.

4. Full control over BM25 configuration — while default values work well for most situations, there is an option to control them.

5. Add semantic flavor to the search by:

   1. Assigning different numerical weights to the fields. A negative field weight will pull down the document's score whenever a match with that field occurs.
   2. Using amplifyNegation() and propagateNegations() from wink-nlp-utils will ensure different search results for query texts containing phrases like "good" and "not good".
   3. Defining different text preparation tasks separately for the fields and query text.

6. Complete flexibility in text preparation — perform tasks such as tokenization and stemming using wink-nlp-utils or any other package of your choice.

Installation

Use npm to install:

npm install wink-bm25-text-search --save

Example

// Load wink-bm25-text-search
var bm25 = require( '../src/wink-bm25-text-search' );
// Create search engine's instance
var engine = bm25();
// Load NLP utilities
var nlp = require( 'wink-nlp-utils' );
// Load sample data (load any other JSON data instead of sample)
var docs = require( '../sample-data/data-for-wink-bm25.json' );

// Define preparatory task pipe!
var pipe = [
  nlp.string.lowerCase,
  nlp.string.tokenize0,
  nlp.tokens.removeWords,
  nlp.tokens.stem,
  nlp.tokens.propagateNegations
];
// Contains search query.
var query;

// Step I: Define config
// Only field weights are required in this example.
engine.defineConfig( { fldWeights: { title: 1, body: 2 } } );
// Step II: Define PrepTasks pipe.
// Set up 'default' preparatory tasks i.e. for everything else
engine.definePrepTasks( pipe );

// Step III: Add Docs
// Add documents now...
docs.forEach( function ( doc, i ) {
  // Note, 'i' becomes the unique id for 'doc'
  engine.addDoc( doc, i );
} );

// Step IV: Consolidate
// Consolidate before searching
engine.consolidate();

// All set, start searching!
query = 'not studied law';
// `results` is an array of [ doc-id, score ], sorted by score
var results = engine.search( query );
// Print number of results.
console.log( '%d entries found.', results.length );
// -> 1 entries found.
// results[ 0 ][ 0 ] i.e. the top result is:
console.log( docs[ results[ 0 ][ 0 ] ].body );
// -> George Walker Bush (born July 6, 1946) is an...
// -> ... He never studied Law...

// Whereas if you search for `law` then multiple entries will be
// found except the above entry!

API

defineConfig( config )

Defines the configuration from the config object. This object defines following 3 properties:

1. The fldWeights (mandatory) is an object where each key is the document's field name and the value is the numerical weight i.e. the importance of that field.

2. The bm25Params (optional) is also an object that defines upto 3 keys viz. k1, b, and k. Their default values are respectively 1.2, 0.75, and 1. Note: k1 controls TF saturation; b controls degree of normalization, and k manages IDF.

3. The ovFldNames (optional) is an array containing the names of the fields, whose original value must be retained. This is useful in reducing the search space using filter in search() api call.

definePrepTasks( tasks [, field ] )

Defines the text preparation tasks to transform raw incoming text into an array of tokens required during addDoc(), and search() operations. It returns the count of tasks.

The tasks should be an array of functions. The first function in this array must accept a string as input; and the last function must return an array of tokens as JavaScript Strings. Each function must accept one input argument and return a single value.

The second argument — field is optional. It defines the field of the document for which the tasks will be defined; in absence of this argument, the tasks become the default for everything else. The configuration must be defined via defineConfig() prior to this call.

As illustrated in the example above, wink-nlp-utils offers a rich set of such functions.

addDoc( doc, uniqueId )

Adds the doc with the uniqueId to the BM25 model. Prior to adding docs, defineConfig() and definePrepTasks() must be called. It accepts structured JSON documents as input for creating the model. Following is an example document structure of the sample data JSON contained in this package:

{
  title: 'Barack Obama',
  body: 'Barack Hussein Obama II born August 4, 1961 is an American politician...'
  tags: 'democratic nobel peace prize columbia michelle...'
}

The sample data is created using excerpts from Wikipedia articles such as one on Barack Obama.

It has an alias learn( doc, uniqueId ) to maintain API level uniformity across various wink packages such as wink-naive-bayes-text-classifier.

consolidate( fp )

Consolidates the BM25 model for all the added documents. The fp defines the precision at which term frequency values are stored. The default value is 4 and is good enough for most situations. It is a prerequisite for search() and documents cannot be added post consolidation.

search( text [, limit, filter, params ] )

Searches for the text and returns upto the limit number of results. The filter should be a function that must return true or false based on params. Think of it as Javascript Array's filter function. It receives two arguments viz. (a) an object containing field name/value pairs as defined via ovFldNames in defineConfig(), and (b) the params.

The last three arguments limit, filter and params are optional. The default value of limit is 10.

The result is an array of [ uniqueId, relevanceScore ], sorted on the relevanceScore.

Like addDoc(), it also has an alias predict( doc, uniqueId ) to maintain API level uniformity across various wink packages such as wink-naive-bayes-text-classifier.

exportJSON()

The BM25 model can be exported as JSON text that may be saved in a file. It is a good idea to export JSON prior to consolidation and use the same whenever more documents need to be added; whereas JSON exported after consolidation is only good for search operation.

importJSON( json )

An existing JSON BM25 model can be imported for search. It is essential to call definePrepTasks() before attempting to search.

reset()

It completely resets the BM25 model by re-initializing all the variables, except the preparatory tasks.

Need Help?

If you spot a bug and the same has not yet been reported, raise a new issue or consider fixing it and sending a pull request.

About wink

Wink is a family of open source packages for Statistical Analysis, Natural Language Processing and Machine Learning in NodeJS. The code is thoroughly documented for easy human comprehension and has a test coverage of ~100% for reliability to build production grade solutions.

Copyright & License

wink-bm25-text-search is copyright 2017-19 GRAYPE Systems Private Limited.

It is licensed under the terms of the MIT License.