ballerinax/weaviate Ballerina library

Overview

This is a generated connector for the Weaviate Vector Search Engine API OpenAPI specification. Weaviate is an open-source vector search engine, which allows storing data objects and vector embeddings from the ML models, including the LLMs offered by OpenAI, Hugging Face, and Cohere. Weaviate provides a powerful GraphQL API for querying the embeddings while looking at the similarity and can scale seamlessly into billions of data objects.

Key Features

• Programmatic access to create and manage resources via REST API
• Send and publish data through the API
• Secure authentication with API key or OAuth support

Prerequisites

Before using this connector in your Ballerina application, complete the following:

1. Create a Weaviate Cluster using the Weaviate Cloud Service or deploy using Docker/Kubernetes.
2. Obtain the OIDC Authentication key.

Quick start

To use the Weaviate connector in your Ballerina application, update the .bal file as follows:

Step 1: Import the connector

First, import the ballerinax/weaviate module into the Ballerina project.

import ballerinax/weaviate;

Step 2: Create a new connector instance

Create and initialize a weaviate:Client with your Service URL and the obtained Authentication key.

weaviate:Client weaviateClient = check new ({
    auth: {
        token: "sk-XXXXXXXXX"
    }
}, serviceURL: "https://weaviate-server:port/v1");

Step 3: Invoke the connector operation

1. Now, you can use the operations available within the connector. Following is an example of inserting new objects to the Weaviate vector storage as a batch operation.

   string className = "DocClass"; // weaviate class name
   string[] text; // list of text
   float[][] embeddings; // list of embbedings for the texts
   int len = text.length();
   
   // Creates the batch of Weaviate objects.
   weaviate:Object[] objArr = [];
   foreach int i in 0...len {
       objArr.push(
           {
               'class: className,
               vector: embeddings[i],
               properties: {
                   "docs": text[i]
               }
           });
   }
   
   weaviate:ObjectsGetResponse[] responseArray = check weaviateClient->/batch/objects.post({
       objects: objArr
   });

   Once the new records are inserted, you can query the Weaviate vector storage using the Weaviate GraphQL API, similar to the example below.

   float[] embeddings;  // This is the embedding for the text being searched for similar content.
   
   string graphQLQuery =  string`{
                               Get {
                                   DocStore (
                                   nearVector: {
                                       vector: ${embeddings.toString()}
                                       }
                                       limit: 5
                                   ){
                                   docs
                                   _additional {
                                       certainty,
                                       id
                                       }
                                   }
                               }
                           }`;
   
   weaviate:GraphQLResponse|error results = check weaviateClient->/graphql.post({
       query: graphQLQuery
   });

2. Use the bal run command to compile and run the Ballerina program.