meilisearch 0.16.0

Meilisearch Dart is the Meilisearch API client for Dart and Flutter developers.

Meilisearch Dart

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⚡ The Meilisearch API client written in Dart

Meilisearch Dart is the Meilisearch API client for Dart and Flutter developers.

Meilisearch is an open-source search engine. Learn more about Meilisearch.

Table of Contents

• 📖 Documentation
• ⚡ Supercharge your Meilisearch experience
• 🔧 Installation
• 🚀 Getting started
• Advanced Configuration
  • Customizing the dio instance
  • Using MeiliDocumentContainer
• 🤖 Compatibility with Meilisearch
• 💡 Learn more
• ⚙️ Contributing

📖 Documentation

This readme contains all the documentation you need to start using this Meilisearch SDK.

For general information on how to use Meilisearch—such as our API reference, tutorials, guides, and in-depth articles—refer to our main documentation website.

⚡ Supercharge your Meilisearch experience

Say goodbye to server deployment and manual updates with Meilisearch Cloud. Get started with a 14-day free trial! No credit card required.

🔧 Installation

You can install the meilisearch package by adding a few lines into pubspec.yaml file.

dependencies:
  meilisearch: ^0.16.0

Then open your terminal and update dart packages.

pub get

Run Meilisearch

There are many easy ways to download and run a Meilisearch instance.

For example, using the curl command in your Terminal:

#Install Meilisearch
curl -L https://install.meilisearch.com | sh

# Launch Meilisearch
./meilisearch --master-key=masterKey

NB: you can also download Meilisearch from Homebrew or APT or even run it using Docker.

🚀 Getting started

Add Documents

import 'package:meilisearch/meilisearch.dart';

void main() async {
  var client = MeiliSearchClient('http://127.0.0.1:7700', 'masterKey');

  // An index is where the documents are stored.
  var index = client.index('movies');

  const documents = [
    { 'id': 1, 'title': 'Carol', 'genres': ['Romance', 'Drama'] },
    { 'id': 2, 'title': 'Wonder Woman', 'genres': ['Action', 'Adventure'] },
    { 'id': 3, 'title': 'Life of Pi', 'genres': ['Adventure', 'Drama'] },
    { 'id': 4, 'title': 'Mad Max: Fury Road', 'genres': ['Adventure', 'Science Fiction'] },
    { 'id': 5, 'title': 'Moana', 'genres': ['Fantasy', 'Action']},
    { 'id': 6, 'title': 'Philadelphia', 'genres': ['Drama'] },
  ]

  // If the index 'movies' does not exist, Meilisearch creates it when you first add the documents.
  var task = await index.addDocuments(documents); // => { "uid": 0 }
}

With the uid, you can check the status (enqueued, canceled, processing, succeeded or failed) of your documents addition using the task.

Basic Search

// Meilisearch is typo-tolerant:
var result = await index.search('carlo');

print(result.hits);

JSON Output:

[
  {
    "id": 1,
    "title": "Carol",
    "genres": ["Romance", "Drama"]
  }
]

Custom Search

All the supported options are described in the search parameters section of the documentation.

var result = await index.search(
  'carol',
  attributesToHighlight: ['title'],
);

JSON output:

{
    "hits": [
        {
            "id": 1,
            "title": "Carol",
            "_formatted": {
                "id": 1,
                "title": "<em>Carol</em>"
            }
        }
    ],
    "offset": 0,
    "limit": 20,
    "processingTimeMs": 0,
    "query": "carol"
}

Custom Search With Filters

If you want to enable filtering, you must add your attributes to the filterableAttributes index setting.

await index.updateFilterableAttributes(['id', 'genres']);

You only need to perform this operation once.

Note that MeiliSearch will rebuild your index whenever you update filterableAttributes. Depending on the size of your dataset, this might take time. You can track the process using the task status.

Then, you can perform the search:

await index.search('wonder', filter: ['id > 1 AND genres = Action']);

{
  "hits": [
    {
      "id": 2,
      "title": "Wonder Woman",
      "genres": ["Action","Adventure"]
    }
  ],
  "offset": 0,
  "limit": 20,
  "estimatedTotalHits": 1,
  "processingTimeMs": 0,
  "query": "wonder"
}

Advanced Configuration

Customizing the dio instance

Meilisearch uses dio internally to send requests, you can provide it with your own interceptors or adapter using the MeiliSearchClient.withCustomDio constructor.

Using MeiliDocumentContainer

The MeiliDocumentContainer<T> class contains meilisearch-specific fields (e.g. rankingScoreDetails, _formatted, matchesPosition, etc...).

We define the mapToContainer() extension to help you quickly opt-in to this class, example:

final res = await index 
      .search("hello world") 
      .asSearchResult() //or .asPaginatedResult() if using page parameters
      .mapToContainer(); 

🤖 Compatibility with Meilisearch

This package guarantees compatibility with version v1.x of Meilisearch, but some features may not be present. Please check the issues for more info.

⚠️ This package is not compatible with the vectoreStore experimental feature of Meilisearch v1.6.0 and later. More information on this issue.

💡 Learn more

The following sections in our main documentation website may interest you:

• Manipulate documents: see the API references or read more about documents.
• Search: see the API references or follow our guide on search parameters.
• Manage the indexes: see the API references or read more about indexes.
• Configure the index settings: see the API references or follow our guide on settings parameters.

⚙️ Contributing

Any new contribution is more than welcome in this project!

If you want to know more about the development workflow or want to contribute, please visit our contributing guidelines for detailed instructions!

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Meilisearch provides and maintains many SDKs and Integration tools like this one. We want to provide everyone with an amazing search experience for any kind of project. If you want to contribute, make suggestions, or just know what's going on right now, visit us in the integration-guides repository.