weaviate 0.0.1-dev.1

The Weaviate Dart project provides a Dart wrapper for the Weaviate REST API, enabling developers to interact with a Weaviate vector database.

Weaviate Dart Wrapper

A Dart wrapper for the Weaviate REST API, allowing you to easily integrate Weaviate into your Dart projects.

Table of Contents

• Installation
• Usage
• Examples
• Contributing
• License

Installation

Add weaviate as a dependency in your pubspec.yaml file:

dependencies:
  ...
  weaviate: ^0.0.1-dev.1

Then run flutter pub get to fetch the package.

Usage

Import the package in your Dart file:

import 'package:weaviate/weaviate.dart';

Create a new instance of the Weaviate client:

  final weaviate = Weaviate(
      weaviateUrl: '[your cloud instance or other host]',
      ));

Now you can use the client to interact with the Weaviate API.

Examples

Here are a few examples demonstrating the usage of the Weaviate Dart wrapper:

Creating an object

import 'package:weaviate/weaviate.dart';

void main() async {
  final weaviate = WeaviateClient('[your cloud instance or other host]');

  // delete schema if it exists
  await weaviate.deleteSchema('Question');
  
  // define the schema for for your objects
  final schema = SchemaClass(
    className: 'Question',
    vectorizer: 'text2vec-huggingface',
    moduleConfig: Text2vecHuggingFace(
      model: 'sentence-transformers/all-MiniLM-L6-v2',
    ).toJson(),
  );

  // add the schema to your weaviate instance
  await weaviate.addSchema(schema);
    
  try {
    // use a json file as input documents
    final inputData = json.decode(File('jeopardy_tiny.json').readAsStringSync())
      as List<dynamic>;

    // create the objects that will be uploaded
    final objects = inputData
      .map((element) => WeaviateObject(
            className: 'Question',
            properties: {
              'category': element['Category'],
              'question': element['Question'],
              'answer': element['Answer'],
            },
          ))
      .toList();

    // upload the docs into your instance as a batch
    await weaviate.batchObjects(BatchObjectRequest(objects: objects));
    
    print('Object created successfully!');
  } catch (e) {
    print('Error creating object: $e');
  }
}

Querying objects

import 'package:graphql/client.dart';
import 'package:weaviate/weaviate.dart';

void main() async {
  final weaviate = WeaviateClient('[your cloud instance or other host]');
  
  try {
    final QueryOptions options = QueryOptions(document: gql(r'''{
  Get{
    Question (
      limit: 2
      where: {
        path: ["category"],
        operator: Equal,
        valueText: "ANIMALS"
      }
      nearText: {
        concepts: ["biology"],
      }
    ){
      question
      answer
      category
    }
  }
}'''));
      
    print('querying...');

    final result = await weaviate.getGraphQLClient().query(options);

    print(result.data?['Get']['Question']);
  } catch (e) {
    print('Error querying objects: $e');
  }
}

Contributing

Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please create an issue on the GitHub repository.

To contribute code, please fork the repository and create a pull request with your changes.

License

This project is licensed under the MIT License.