ballerinax/ai.weaviate Ballerina library

Overview

Weaviate is an open-source vector database that stores both objects and vectors, allowing for combining vector search with structured filtering with the scalability of a cloud-native database.

The Ballerina Weaviate vector store module provides a comprehensive API for integrating with Weaviate vector databases, enabling efficient storage, retrieval, and management of high-dimensional vectors. This implementation allows being used as a Ballerina ai:VectorStore, providing smooth integration with the Ballerina AI module.

Set up guide

Before using the Ballerina Weaviate vector store module, you need to set up a Weaviate instance and obtain the necessary credentials.

Step 1: Create a Weaviate account

You can create an account for free if you don't already have one.

1. Visit weaviate.io and click Try Now to sign up for a free account

   

2. Complete the registration process and verify your email address if required

3. Log in to your new Weaviate account

For more details, refer to the official documentation on creating a new account.

Step 2: Set up a Weaviate cluster

1. Access the Weaviate Console and click Create Cluster to create a new Weaviate instance.

   

2. Provide the required details (e.g., Cluster name) and preferred configuration options and confirm.

   

3. Click Create and wait for the cluster to be provisioned (this typically takes 2-3 minutes)

4. Once the cluster is ready, locate and copy the REST endpoint URL from your cluster dashboard. You'll use this URL as the serviceUrl in your weaviate:Client configuration

For more details, refer to the official documentation on creating clusters.

Step 3: Generate API credentials

1. In the Weaviate Console, navigate to your cluster dashboard and go to the API Keys section

2. Click Create API Key and provide a name for the key and create the API key.

   

3. Securely save the generated API key, which you'll use as the token in your weaviate:Client configuration.

For more details, refer to the official documentation on authentication.

Quick Start

Step 1: Import the module

import ballerina/ai;
import ballerinax/ai.weaviate;

Step 2: Initialize the Weaviate vector store

ai:VectorStore vectorStore = check new weaviate:VectorStore(
    serviceUrl = "add-weaviate-service-url", 
    config = {
        collectionName: "add-collection-name"
    }, 
    apiKey = "mock-token"
);

Step 3: Add vectors

ai:Error? result = vectorStore.add(
    [
        {
            id: "1",
            embedding: [1.0, 2.0, 3.0],
            chunk: {
                'type: "text", 
                content: "This is a chunk"
            }
        }
    ]
);

Examples

The Ballerina Weaviate vector store module provides practical examples illustrating usage in various scenarios. Explore these examples.

1. Book recommendation system This example shows how to use Weaviate vector store APIs to implement a book recommendation system that stores book embeddings and queries them to find similar books based on vector similarity and metadata filtering.