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Weaviate’s integration with other open-source projects

Published 18 June, 2023
– Last updated 18 June, 2023

Table of Contents


Weaviate is an open-source knowledge graph that allows you to build smart applications by connecting data from various sources. It provides powerful search capabilities and a flexible schema that enables you to define your own data model. One of the key strengths of Weaviate is its ability to integrate with other open-source projects, making it a versatile tool for application development.


Integration with other open-source projects

Weaviate can be easily integrated with other open-source projects, enabling you to leverage the power of multiple tools in building your applications. Here, we will explore a few popular projects that can be used in conjunction with Weaviate.

1. PyTorch:
PyTorch is an open-source machine learning library that provides support for deep learning algorithms. Weaviate can be integrated with PyTorch to perform advanced machine learning tasks on your data. By using PyTorch models in Weaviate, you can take advantage of its high-performance computing capabilities and build powerful intelligent applications.

2. TensorFlow:
TensorFlow, another popular machine learning library, can also be integrated with Weaviate. TensorFlow provides a vast array of pre-trained models for various tasks such as image recognition, natural language processing, and more. By incorporating TensorFlow models in Weaviate, you can enhance your application’s capabilities and provide intelligent features to your users.

3. Apache Kafka:
Apache Kafka is a distributed streaming platform that enables you to build real-time data pipelines and streaming applications. Weaviate can be integrated with Kafka to consume data from Kafka topics and update the knowledge graph in real-time. This allows you to build applications that can react to changes in the data stream, providing up-to-date and dynamic insights to your users.


PHP client integration example

To illustrate how Weaviate can be integrated with other open-source projects, let’s take a look at an example of integrating Weaviate with PHP. We will use the PHP client provided by Weaviate to interact with the Weaviate API.

require_once 'vendor/autoload.php';

use Weaviate\Client\Configuration;
use Weaviate\Client\ObjectApi;

$config = Configuration::getDefaultConfiguration();

$apiClient = new \Weaviate\Client\ApiClient($config);
$objectApi = new ObjectApi($apiClient);

try {
 $response = $objectApi->getObjects();
} catch (\Exception $e) {
 echo 'Exception when calling ObjectApi->getObjects: ', $e->getMessage(), PHP_EOL;

In this example, we first set up the Weaviate API client by specifying the API host URL. Then, we create an instance of the ObjectApi class to interact with the Weaviate objects. Finally, we call the `getObjects` method to retrieve all objects from Weaviate.


WPSOLR integration

WPSOLR is an open-source WordPress plugin that enhances the default WordPress search functionality by integrating with external search engines such as Elasticsearch and Solr. By integrating Weaviate with WPSOLR, you can provide advanced search capabilities to your WordPress website.

WPSOLR can be configured to index and search data from Weaviate, allowing you to combine the power of Weaviate’s knowledge graph with WPSOLR’s search features. This integration enables you to create highly customizable search experiences for your WordPress users, leveraging the search capabilities provided by both Weaviate and WPSOLR.



Weaviate’s integration with other open-source projects opens up a world of possibilities for building intelligent applications. By combining the strengths of different open-source tools, you can create powerful and innovative solutions that provide advanced search, machine learning, and real-time data processing capabilities. Whether you’re using Elasticsearch, PyTorch, TensorFlow, Apache Kafka, or other open-source projects, Weaviate’s flexibility allows you to seamlessly integrate with them and unlock the full potential of your applications.

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