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This is the source code of the Weaviate vector store implementation for the RAG abstraction in Ballerina

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Ballerina Weaviate Vector Store Library

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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

    Sign Up
  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.

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

    Create Cluster
  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.

    Create Cluster
  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.

Issues and projects

Issues and Projects tabs are disabled for this repository as this is part of the Ballerina Library. To report bugs, request new features, start new discussions, view project boards, etc., go to the Ballerina Library parent repository. This repository only contains the source code for the module.

Build from the source

Prerequisites

  1. Download and install Java SE Development Kit (JDK) version 21 (from one of the following locations).

    • Oracle

    • OpenJDK

      Note: Set the JAVA_HOME environment variable to the path name of the directory into which you installed JDK.

  2. Generate a GitHub access token with read package permissions, then set the following env variables:

    export packageUser=<Your GitHub Username>
    export packagePAT=<GitHub Personal Access Token>

Build options

Execute the commands below to build from the source.

  1. To build the package:

    ./gradlew clean build
  2. To run the tests:

    ./gradlew clean test
  3. To run a group of tests

    ./gradlew clean test -Pgroups=<test_group_names>
  4. To build the without the tests:

    ./gradlew clean build -x test
  5. To debug the package with a remote debugger:

    ./gradlew clean build -Pdebug=<port>
  6. To debug with Ballerina language:

    ./gradlew clean build -PbalJavaDebug=<port>
  7. Publish the generated artifacts to the local Ballerina central repository:

    ./gradlew clean build -PpublishToLocalCentral=true
  8. Publish the generated artifacts to the Ballerina central repository:

    ./gradlew clean build -PpublishToCentral=true

Contribute to Ballerina

As an open-source project, Ballerina welcomes contributions from the community.

For more information, go to the contribution guidelines.

Code of conduct

All the contributors are encouraged to read the Ballerina Code of Conduct.

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This is the source code of the Weaviate vector store implementation for the RAG abstraction in Ballerina

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