vania_elasticsearch 1.0.0

Production-ready Elasticsearch client, query builder, bulk indexing, and Vania integration.

Vania Elasticsearch

Full-text search for Vania: index your models, then search them with real relevance ranking.

vania_elasticsearch connects a Vania app to an Elasticsearch cluster. It gives you a typed client, a query builder, bulk indexing, and — the part most apps want — a Searchable mixin that turns any model into a searchable document. Add it to a model and Article().search('dart') returns relevance-ranked hits, not a clumsy LIKE '%dart%'.

Install

dependencies:
  vania_elasticsearch: ^1.0.0

Register the provider and point it at your cluster:

final providers = <ServiceProvider>[
  ElasticsearchServiceProvider(
    config: ElasticsearchConfig(
      hosts: [Uri.parse(env('ELASTICSEARCH_HOST', 'http://localhost:9200'))],
      defaultIndex: 'articles',
    ),
  ),
];

Make a model searchable

import 'package:vania/database.dart' show Model;
import 'package:vania_elasticsearch/vania_elasticsearch.dart';

class Article extends Model with Searchable {
  Article({int? id, String? title, String? body}) {
    tableName = 'articles'; // the table name doubles as the index
    if (id != null) attributes['id'] = id;
    if (title != null) attributes['title'] = title;
    if (body != null) attributes['body'] = body;
  }
}

By default the model's table name is the index and its toJson() is the indexed document — no separate mapping to maintain.

Index and search

// Index a document
await article.searchable();

// Full-text search across fields, ranked by relevance
final result = await Article().search('dart backend', fields: ['title', 'body']);
print(result.total);
print(result.hits);

What's included

• Searchable mixin — searchable() to index, unsearchable() to remove, search() to query.
• Query builder — multi-match, match-all, and custom queries with paging (from, size).
• Direct client — Elasticsearch.search(...), Elasticsearch.index(...), and bulk helpers when you need lower-level control.
• Bulk indexing for backfilling large datasets efficiently.

Links

• Documentation: vdart.dev/docs
• Source & issues: github.com/vania-dart/framework

License

MIT