text_indexing 0.6.0-2

Dart library for creating an inverted index on a collection of text documents.

text_indexing

Dart library for creating an inverted index on a collection of text documents.

THIS PACKAGE IS PRE-RELEASE, IN ACTIVE DEVELOPMENT AND SUBJECT TO DAILY BREAKING CHANGES.

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• Overview
• Usage
• API
• Definitions
• References
• Issues

Overview

This library provides an interface and implementation classes that build and maintain an (inverted, positional, zoned) index for a collection of documents or corpus (see definitions).

The TextIndexer constructs two artifacts:

• a dictionary that holds the vocabulary of terms and the frequency of occurrence for each term in the corpus; and
• a postings map that holds a list of references to the documents for each term (the postings list).

In this implementation, our postings list is a hashmap of the document id (docId) to maps that point to positions of the term in the document's fields (zones). This allows query algorithms to score and rank search results based on the position(s) of a term in document fields, applying different weights to the zones.

Refer to the references to learn more about information retrieval systems and the theory behind this library.

Usage

In the pubspec.yaml of your flutter project, add the text_indexing dependency.

dependencies:
  text_indexing: <latest version>

In your code file add the text_indexing import.

import 'package:text_indexing/text_indexing.dart';

For small collections, instantiate a TextIndexer.inMemory, (optionally passing empty Dictionary and Postings hashmaps), then iterate over a collection of documents to add them to the index.

  // - initialize a in-memory [TextIndexer]
  final indexer =TextIndexer.inMemory();

  // - iterate through the sample data
  await Future.forEach(documents.entries, (MapEntry<String, String> doc) async {
    // - index each document
    await indexer.index(doc.key, doc.value);
  });

The examples demonstrate the use of the TextIndexer.inMemory and TextIndexer.async factories.

API

The API exposes the TextIndexer interface that builds and maintain an index for a collection of documents.

Three implementations of the TextIndexer interface are provided:

• the TextIndexerBase abstract base class implements the TextIndexer.index, TextIndexer.indexJson and TextIndexer.emit methods;
• the InMemoryIndexer class is for fast indexing of a smaller corpus using in-memory dictionary and postings hashmaps; and
• the AsyncIndexer class, aimed at working with a larger corpus and asynchronous dictionaries and postings.

To maximise performance of the indexers the API manipulates nested hashmaps of DART core types int and String rather than defining strongly typed object models. To improve code legibility and maintainability the API makes use of type aliases throughout.

Type Aliases

• Dictionary is an alias for Map<Term, Ft>, a hashmap of Term to Ft.
• DictionaryEntry is an alias for MapEntry<Term, Ft>, an entry in a Dictionary.
• DocId is an alias for String, used whenever a document id is referenced.
• DocumentPostings is an alias for Map<DocId, FieldPostings>, a hashmap of document ids to FieldPostings.
• DocumentPostingsEntry is an alias for MapEntry<DocId, FieldPostings>, an entry in a DocumentPostings hashmap.
• FieldPostings is an alias for Map<FieldName, TermPositions>, a hashmap of FieldNames to TermPositions in the field with FieldName.
• FieldPostingsEntry is an alias for MapEntry<FieldName, TermPositions>, an entry in a FieldPostings hashmap.
• Ft is an lias for int and denotes the frequency of a Term in an index or indexed object (the term frequency).
• JSON is an alias for Map<String, dynamic>, a hashmap known as "Java Script Object Notation" (JSON), a common format for persisting data.
• JsonCollection is an alias for Map<String, Map<String, dynamic>>, a hashmap of DocId to JSON documents.
• Pt is an alias for int, used to denote the position of a Term in SourceText indexed object (the term position).
• TermPositions is an alias for List<Pt>, an ordered Set of unique zero-based Term positions in SourceText, sorted in ascending order.

InvertedPositionalZoneIndex Interface

The `` is an interface that exposes methods for working with an inverted, positional zoned index on a collection of documents:

• getDictionary Asynchronously retrieves a Dictionary for a collection of Terms from a Dictionary repository;
• upsertDictionary inserts entries into a Dictionary repository, overwriting any existing entries;
• getPostings asynchronously retrieves Postings for a collection of Terms from a Postings repository; and
• upsertPostings inserts entries into a Postings repository, overwriting any existing entries.

TextIndexer Interface

The text indexing classes (indexers) in this library implement TextIndexer, an interface intended for information retrieval software applications. The design of the TextIndexer interface is consistent with information retrieval theory and is intended to construct and/or maintain two artifacts:

• a hashmap with the vocabulary as key and the document frequency as the values (the dictionary); and
• another hashmap with the vocabulary as key and the postings lists for the linked documents as values (the postings).

The dictionary and postings can be asynchronous data sources or in-memory hashmaps. The TextIndexer reads and writes to/from these artifacts using the TextIndexer.index.

Text or documents can be indexed by calling the following methods:

• TextIndexer.indexJson indexes the fields in a JSON document;
• TextIndexer.indexText indexes text from a text document.
• The TextIndexer.indexCollection method indexes text from a collection of JSON documents, emitting the Postings for each document in the TextIndexer.postingsStream.

The TextIndexer.emit method is called by TextIndexer.index, and adds an event to the postingsStream.

Listen to TextIndexer.postingsStream to handle the postings list emitted whenever a document is indexed.

Implementing classes override the following fields:

• TextIndexer.index is a InvertedPositionalZoneIndex that exposes the getDictionary, upsertDictionary, getPostings and upsertPostings asynchronous methods;
• TextIndexer.tokenizer is the Tokenizer instance used by the indexer to parse text to tokens;
• TextIndexer.jsonTokenizer is the JsonTokenizer instance used by the indexer to parse JSON documents to tokens; and
• TextIndexer.postingsStream emits a list of DocumentPostingsEntry instances whenever a document is indexed.

Implementing classes override the following asynchronous methods:

• TextIndexer.indexText indexes text, returning a list of DocumentPostingsEntry and adding it to the TextIndexer.postingsStream by calling TextIndexer.emit;
• TextIndexer.indexJson indexes text from a JSON document, returning a list of DocumentPostingsEntry and adding it to the TextIndexer.postingsStream by calling TextIndexer.emit; TextIndexer.indexCollection method indexes text from a collection of JSON documents, emitting the Postings for each document in the TextIndexer.postingsStream.
• emit is called by index, and adds an event to the postingsStream after updating the dictionary and postings data stores.

TextIndexerBase Class

The TextIndexerBase is an abstract base class that implements the TextIndexer.indexText, TextIndexer.indexJson, TextIndexer.indexCollection and TextIndexer.emit methods and the TextIndexer.postingsStream field.

The TextIndexerBase.index is updated whenever TextIndexerBase.emit is called at the end of the InMemoryIndexer.index method, so awaiting a call to TextIndexerBase.index will provide access to the updated TextIndexerBase.index.dictionary and TextIndexerBase.index.postings maps.

Subclasses of TextIndexerBase must implement:

• TextIndexer.index;
• TextIndexer.tokenizer
• TextIndexer.jsonTokenizer; as well as

• TextIndexerBase.controller, a BehaviorSubject<Postings> that controls the TextIndex.postingsStream.

InMemoryIndex Class

The InMemoryIndex is a InvertedPositionalZoneIndex interface implementation with in-memory Dictionary and Postings hashmaps:

• dictionary is the in-memory term dictionary for the indexer. Pass a dictionary instance at instantiation, otherwise an empty Dictionary will be initialized; and
• postings is the in-memory postings hashmap for the indexer. Pass a postings instance at instantiation, otherwise an empty Postings will be initialized.

InMemoryIndexer Class

The InMemoryIndexer is a subclass of TextIndexerBase that builds and maintains an in-memory Dictionary and Postings and can be initialized using the TextIndexer.inMemory factory.

The InMemoryIndexer is suitable for indexing a smaller corpus. The InMemoryIndexer may have latency and processing overhead for large indexes or queries with more than a few terms. Consider running InMemoryIndexer in an isolate to avoid slowing down the main thread.

An example of the use of the TextIndexer.inMemory factory is included in the examples.

AsyncCallbackIndex Class

The AsyncCallbackIndex is a InvertedPositionalZoneIndex implementation class that uses asynchronous callbacks to perform read and write operations on Dictionary and Postings repositories:

• termsLoader synchronously retrieves a Dictionary for a vocabulary from a data source;
• dictionaryUpdater is callback that passes a Dictionary subset for persisting to Dictionary repository;
• postingsLoader asynchronously retrieves a Postings for a vocabulary from a data source; and
• postingsUpdater passes a Postings subset for persisting to a Postings repository.

AsyncIndexer Class

The AsyncIndexer is a subclass of TextIndexerBase that asynchronously reads and writes from / to a Dictionary and Postings using asynchronous callbacks. A AsyncIndexer can be initialized using the TextIndexer.async factory.

The AsyncIndexer is suitable for indexing a large corpus but may have latency and processing overhead. Consider running AsyncIndexer in an isolate to avoid slowing down the main thread.

An example of the use of the TextIndexer.async factory is included in the examples.

Definitions

The following definitions are used throughout the documentation:

• corpus- the collection of documents for which an index is maintained.
• dictionary - is a hash of terms (vocabulary) to the frequency of occurence in the corpus documents.
• document - a record in the corpus, that has a unique identifier (docId) in the corpus's primary key and that contains one or more text fields that are indexed.
• index - an inverted index used to look up document references from the corpus against a vocabulary of terms. The implementation in this package builds and maintains a positional inverted index, that also includes the positions of the indexed term in each document.
• index-elimination - selecting a subset of the entries in an index where the term is in the collection of terms in a search phrase.
• postings - a separate index that records which documents the vocabulary occurs in. In this implementation we also record the positions of each term in the text to create a positional inverted index.
• postings list - a record of the positions of a term in a document. A position of a term refers to the index of the term in an array that contains all the terms in the text.
• term - a word or phrase that is indexed from the corpus. The term may differ from the actual word used in the corpus depending on the tokenizer used.
• text - the indexable content of a document.
• token - representation of a term in a text source returned by a tokenizer. The token may include information about the term such as its position(s) in the text or frequency of occurrence.
• tokenizer - a function that returns a collection of tokens from text, after applying a character filter, term filter, stemmer and / or lemmatizer.
• vocabulary - the collection of terms indexed from the corpus.

References

• Manning, Raghavan and Schütze, "Introduction to Information Retrieval", Cambridge University Press, 2008
• University of Cambridge, 2016 "Information Retrieval", course notes, Dr Ronan Cummins, 2016
• Wikipedia (1), "Inverted Index", from Wikipedia, the free encyclopedia
• Wikipedia (2), "Lemmatisation", from Wikipedia, the free encyclopedia
• Wikipedia (3), "Stemming", from Wikipedia, the free encyclopedia

Issues

If you find a bug please fill an issue.

This project is a supporting package for a revenue project that has priority call on resources, so please be patient if we don't respond immediately to issues or pull requests.