docproc

docproc

A document processing pipeline mostly used with search-index

docProc = require('docproc')
readableStream.pipe(docProc.pipeline(ops))

DocProc is a pumpify chain of transform streams that turns Plain Old JSON Objects into a format that can be indexed by search-index.

Each processed document must have the following fields:

• id - document id
• vector - vector, used for ranking
• stored - the document that will be cached
• raw - the unadulterated document
• normalised - the "cleaned up" document.
• tokenised - the tokenised document.

So

  {
    id: 'one',
    text: 'the first doc'
  }

becomes

  { id: 'one',
    normalised: { id: 'one', text: 'the first doc' },
    raw: { id: 'one', text: 'the first doc' },
    stored: { id: 'one', text: 'the first doc' },
    tokenised: { id: [ 'one' ], text: [ 'the', 'first', 'doc' ] },
    vector:
    { id: { one: 1, '*': 1 },
      text: { doc: 1, first: 1, the: 1, '*': 1 },
      '*': { one: 1, '*': 1, doc: 1, first: 1, the: 1 } } },

...after being passeds through docProc.

You can also compose document processing pipelines by reusing the stages provided, or by creating new ones using the node.js transform stream specification:

  docProc.customPipeline([
    new docProc.IngestDoc(),
    new docProc.CreateStoredDocument(),
    new docProc.NormaliseFields(),
    new docProc.Tokeniser({separator: ' '}),
    new docProc.RemoveStopWords({stopwords: []}),
    new docProc.CalculateTermFrequency(),
    new docProc.CreateCompositeVector(),
    new docProc.CreateSortVectors(),
    new docProc.FieldedSearch({fieldedSearch: false})
  ])

API

.defaultPipeline(options)

A function that returns a writable stream that contains a sensible default document processing pipeline

.customPipeline(pipeline)

A function that takes in an Array of pipeline stages where every stage is a transform stream and returns a writable stream.

CalculateTermFrequency

A transform stream that calculates term frequency.

CreateCompositeVector

A transform stream that calculates the composite vector- used for searching accross all fields.

CreateSortVectors

A transform stream that creates sort vectors.

CreateStoredDocument

A transform stream that defines the parts of each document that are to be cached in the index itself.

FieldedSearch

A transform stream that determines which fields can be searched on individually. In order to make indexes smaller, you can index fields that can be searched on.

IngestDoc

A transform stream that takes an unprocessed document and converts it into a structure that can be processed by search-index.

LowCase

A transform stream that converts text to lower case.

NormaliseFields

A transform stream that converts non-string fields into Strings.

RemoveStopWords

A transform stream that removes stopwords

Spy

A transform stream that will do nothing other than print out the state of the document to console.log. Use this when developing and debugging.

Tokeniser

A transform stream that splits fields down into their individual linguistic tokens

Options

See: https://github.com/fergiemcdowall/search-index/blob/master/doc/API.md#options-and-settings