Quickstart

Expected time: 30 minutes

What you will learn

This quickstart shows you how to combine Weaviate Cloud and the Weaviate Embeddings service to:

1. Set up a Weaviate Cloud instance. (10 minutes)
2. Add and vectorize your data using Weaviate Embeddings. (10 minutes)
3. Perform a semantic (vector) search and hybrid search. (10 minutes)

Notes:

• The code examples here are self-contained. You can copy and paste them into your own environment to try them out.

Requirements

To use Weaviate Embeddings, you will need:

• A Weaviate Cloud Sandbox running at least Weaviate 1.28.5
• A Weaviate client library that supports Weaviate Embeddings:
  • Python client version 4.9.5 or higher
  • JavaScript/TypeScript client version 3.2.5 or higher
  • Go/Java clients are not yet officially supported; you must pass the X-Weaviate-Api-Key and X-Weaviate-Cluster-Url headers manually upon instantiation as shown below.

Step 1: Set up Weaviate

1.1 Create a new cluster

To create a free Sandbox cluster in Weaviate Cloud, follow these instructions.

TIP: Use the latest Weaviate version!

When possible, try to use the latest Weaviate version. New releases include cutting-edge features, performance enhancements, and critical security updates to keep your application safe and up-to-date.

1.2 Install a client library

We recommend using a client library to work with Weaviate. Follow the instructions below to install one of the official client libraries, available in Python, JavaScript/TypeScript, Go, and Java.

Python

Install the latest, Python client v4, by adding weaviate-client to your Python environment with pip:

pip install -U weaviate-client

JS/TS

Install the latest, JS/TS client v3, by adding weaviate-client to your project with npm:

npm install weaviate-client

Go

Add weaviate-go-client to your project with go get:

go get github.com/weaviate/weaviate-go-client/v4

Java

Add this dependency to your project:

<dependency>
  <groupId>io.weaviate</groupId>
  <artifactId>client</artifactId>
  <version>4.8.3</version>  <!-- Check latest version: https://github.com/weaviate/java-client -->
</dependency>

1.3 Connect to Weaviate Cloud

Weaviate Embeddings is integrated with Weaviate Cloud. Your Weaviate Cloud credentials will be used to authorize your Weaviate Cloud instance's access for Weaviate Embeddings.

Python API v4

import weaviate
from weaviate.classes.init import Auth
import os

# Best practice: store your credentials in environment variables
wcd_url = os.getenv("WEAVIATE_URL")
wcd_key = os.getenv("WEAVIATE_API_KEY")

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=wcd_url,                     # Weaviate URL: "REST Endpoint" in Weaviate Cloud console
    auth_credentials=Auth.api_key(wcd_key),  # Weaviate API key: "ADMIN" API key in Weaviate Cloud console
)

print(client.is_ready())  # Should print: `True`

# Work with Weaviate

client.close()

  API docs

JS/TS API v3

import weaviate from 'weaviate-client'

// Best practice: store your credentials in environment variables
const wcdUrl = process.env.WEAVIATE_URL as string;        // Weaviate URL: "REST Endpoint" in Weaviate Cloud console
const wcdApiKey = process.env.WEAVIATE_API_KEY as string; // Weaviate API key: "ADMIN" API key in Weaviate Cloud console

const client = await weaviate.connectToWeaviateCloud(
  wcdUrl,  
  {
    authCredentials: new weaviate.ApiKey(wcdApiKey),  
  }
)

// Work with Weaviate

client.close()

  API docs

Go

// Best practice: store your credentials in environment variables
// WEAVIATE_URL            Weaviate URL: "REST Endpoint" in Weaviate Cloud console
// WEAVIATE_API_KEY        Weaviate API key: "ADMIN" API key in Weaviate Cloud console

package main

import (
    "context"
    "fmt"
    "os"

    "github.com/weaviate/weaviate-go-client/v4/weaviate"
    "github.com/weaviate/weaviate-go-client/v4/weaviate/auth"
)

func main() {
    cfg := weaviate.Config{
        Host:       os.Getenv("WEAVIATE_URL"),
        Scheme:     "https",
        AuthConfig: auth.ApiKey{Value: os.Getenv("WEAVIATE_API_KEY")},
        Headers: map[string]string{
            "X-Weaviate-Api-Key":     os.Getenv("WEAVIATE_API_KEY"),
            "X-Weaviate-Cluster-Url": fmt.Sprintf("https://%s", os.Getenv("WEAVIATE_URL")),
        },
    }

    client, err := weaviate.NewClient(cfg)
    if err != nil {
        fmt.Println(err)
    }

    // Work with Weaviate
}

  API docs

Java

import io.weaviate.client.Config;
import io.weaviate.client.WeaviateAuthClient;
import io.weaviate.client.WeaviateClient;
import io.weaviate.client.base.Result;

public class ConnectWeaviateEmbeddings {
  public static void main(String[] args) throws Exception {

    // Best practice: store your credentials in environment variables
    String wcdUrl = System.getenv("WEAVIATE_URL");      // Weaviate URL: "REST Endpoint" in Weaviate Cloud console
    String wcdKey = System.getenv("WEAVIATE_API_KEY");  // Weaviate API key: "ADMIN" API key in Weaviate Cloud console

    Map<String, String> headers = new HashMap<String, String>() { {
      put("X-Weaviate-Api-Key", wcdKey);
      put("X-Weaviate-Cluster-Url", "https://" + wcdUrl);
    } };

    Config config = new Config("https", wcdUrl, headers);
    WeaviateClient client = WeaviateAuthClient.apiKey(config, wcdKey);

    // check the result
    Result<Boolean> result = client.misc().readyChecker().run();
    System.out.println(result.getResult());

  }
}

  API docs

Step 2: Populate the database

2.1 Define a collection

Now we can define a collection that will store our data. When creating a collection, you need to specify one of the available models for the vectorizer to use. This model will be used to create vector embeddings from your data.

Python API v4

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vectorizer_config=[
        Configure.NamedVectors.text2vec_weaviate(
            name="title_vector",
            source_properties=["title"],
            model="Snowflake/snowflake-arctic-embed-l-v2.0",
            # Further options
            # dimensions=256
            # base_url="<custom_weaviate_embeddings_url>",
        )
    ],
    # Additional parameters not shown
)

  API docs

JS/TS API v3

await client.collections.create({
  name: 'DemoCollection',
  properties: [
    {
      name: 'title',
      dataType: 'text' as const,
    },
  ],
  vectorizers: [
    weaviate.configure.vectorizer.text2VecWeaviate({
        name: 'title_vector',
        sourceProperties: ['title'],
        model: 'Snowflake/snowflake-arctic-embed-l-v2.0',
        // // Further options
        // dimensions: 256,
        // baseUrl: '<custom_weaviate_embeddings_url>',
      },
    ),
  ],
  // Additional parameters not shown
});

  API docs

Go

// package, imports not shown

func main() {
    // Instantiation not shown

    ctx := context.Background()

    // Define the collection
    weaviateVectorizerArcticEmbedLV20 := &models.Class{
        Class: "DemoCollection",
        VectorConfig: map[string]models.VectorConfig{
            "title_vector": {
                VectorIndexType: `hnsw`,
                Vectorizer: map[string]interface{}{
                    "text2vec-weaviate": map[string]interface{}{
                        "model":      "Snowflake/snowflake-arctic-embed-l-v2.0",
                        "dimensions": 1024, // Or 256
                        // "base_url":   "<custom_weaviate_url>",
                    },
                },
            },
        },
    }

    // add the collection
    err = client.Schema().ClassCreator().WithClass(weaviateVectorizerArcticEmbedLV20).Do(ctx)
    if err != nil {
        panic(err)
    }
}

  API docs

Java

Map<String, Object> text2vecWeaviate = new HashMap<>();
Map<String, Object> text2vecWeaviateSettings = new HashMap<>();

text2vecWeaviateSettings.put("properties", new String[]{"title"});
text2vecWeaviateSettings.put("model", new String[]{"Snowflake/snowflake-arctic-embed-l-v2.0"});
text2vecWeaviateSettings.put("dimensions", new Integer[]{1024});  // 1024, 256
text2vecWeaviateSettings.put("base_url", new String[]{"<custom_weaviate_url>"});
text2vecWeaviate.put("text2vec-weaviate", text2vecWeaviateSettings);

// Define the vector configurations
Map<String, WeaviateClass.VectorConfig> vectorConfig = new HashMap<>();
vectorConfig.put("title_vector", WeaviateClass.VectorConfig.builder()
  .vectorIndexType("hnsw")
  .vectorizer(text2vecWeaviate)
  .build());

// Create the collection "DemoCollection"
WeaviateClass clazz = WeaviateClass.builder()
  .className("DemoCollection")
  .vectorConfig(vectorConfig)
  .build();

Result<Boolean> result = client.schema().classCreator().withClass(clazz).run();

  API docs

For more information about the available model options visit the Choose a model page.

2.2 Import objects

After configuring the vectorizer, import data into Weaviate. Weaviate generates embeddings for text objects using the specified model.

Python API v4

source_objects = [
    {"title": "The Shawshank Redemption", "description": "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places."},
    {"title": "The Godfather", "description": "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga."},
    {"title": "The Dark Knight", "description": "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City."},
    {"title": "Jingle All the Way", "description": "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve."},
    {"title": "A Christmas Carol", "description": "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption."}
]

collection = client.collections.get("DemoCollection")

with collection.batch.dynamic() as batch:
    for src_obj in source_objects:
        # The model provider integration will automatically vectorize the object
        batch.add_object(
            properties={
                "title": src_obj["title"],
                "description": src_obj["description"],
            },
            # vector=vector  # Optionally provide a pre-obtained vector
        )
        if batch.number_errors > 10:
            print("Batch import stopped due to excessive errors.")
            break

failed_objects = collection.batch.failed_objects
if failed_objects:
    print(f"Number of failed imports: {len(failed_objects)}")
    print(f"First failed object: {failed_objects[0]}")

  API docs

JS/TS API v3

let srcObjects = [
  { title: "The Shawshank Redemption", description: "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places." },
  { title: "The Godfather", description: "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga." },
  { title: "The Dark Knight", description: "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City." },
  { title: "Jingle All the Way", description: "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve." },
  { title: "A Christmas Carol", description: "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption." }
];

const collectionName = 'DemoCollection'
const myCollection = client.collections.get(collectionName)

let dataObjects = []

for (let srcObject of srcObjects) {
  dataObjects.push({
    title: srcObject.title,
    description: srcObject.description,
  });
}

const response = await myCollection.data.insertMany(dataObjects);

console.log(response);

  API docs

Go

// package, imports not shown

func main() {
    // Instantiation not shown

    ctx := context.Background()

    var sourceObjects = []map[string]string{
        {"title": "The Shawshank Redemption", "description": "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places."},
        {"title": "The Godfather", "description": "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga."},
        {"title": "The Dark Knight", "description": "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City."},
        {"title": "Jingle All the Way", "description": "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve."},
        {"title": "A Christmas Carol", "description": "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption."},
    }

    // Convert items into a slice of models.Object
    objects := []models.PropertySchema{}
    for i := range sourceObjects {
        objects = append(objects, map[string]interface{}{
            // Populate the object with the data
            "title":       sourceObjects[i]["title"],
            "description": sourceObjects[i]["description"],
        })
    }

    // Batch write items
    batcher := client.Batch().ObjectsBatcher()
    for _, dataObj := range objects {
        batcher.WithObjects(&models.Object{
            Class:      "DemoCollection",
            Properties: dataObj,
        })
    }

    // Flush
    batchRes, err := batcher.Do(ctx)

    // Error handling
    if err != nil {
        panic(err)
    }
    for _, res := range batchRes {
        if res.Result.Errors != nil {
            for _, err := range res.Result.Errors.Error {
                if err != nil {
                    fmt.Printf("Error details: %v\n", *err)
                    panic(err.Message)
                }
            }
        }
    }
}

  API docs

Java

List<HashMap<String, Object>> objects = new ArrayList<>();
for (Map<String, String> sourceObject : sourceObjects) {
  HashMap<String, Object> schema = new HashMap<>();
  schema.put("title", sourceObject.get("title"));
  schema.put("description", sourceObject.get("description"));
  objects.add(schema);
}

// Batch write items
ObjectsBatcher batcher = client.batch().objectsBatcher();
for (Map<String, Object> properties : objects) {
  batcher.withObject(WeaviateObject.builder()
    .className("DemoCollection")
    .properties(properties)
    // .tenant("tenantA")  // If multi-tenancy is enabled, specify the tenant to which the object will be added.
    .build()
  );
}

// Flush
batcher.run();

  API docs

Step 3: Query your data

Once the vectorizer is configured, Weaviate will perform vector search operations using the specified model.

Vector (near text) search

When you perform a vector search, Weaviate converts the text query into an embedding using the specified model and returns the most similar objects from the database.

The query below returns the n most similar objects from the database, set by limit.

Python API v4

collection = client.collections.get("DemoCollection")

response = collection.query.near_text(
    query="A holiday film",  # The model provider integration will automatically vectorize the query
    limit=2
)

for obj in response.objects:
    print(obj.properties["title"])

  API docs

JS/TS API v3

const collectionName = 'DemoCollection'
const myCollection = client.collections.get(collectionName)

let result;

result = await myCollection.query.nearText(
  'A holiday film',  // The model provider integration will automatically vectorize the query
  {
    limit: 2,
  }
)

console.log(JSON.stringify(result.objects, null, 2));

  API docs

Go

// package, imports not shown

func main() {
    // Instantiation not shown

    ctx := context.Background()

    nearTextResponse, err := client.GraphQL().Get().
        WithClassName("DemoCollection").
        WithFields(
            graphql.Field{Name: "title"},
        ).
        WithNearText(client.GraphQL().NearTextArgBuilder().
            WithConcepts([]string{"A holiday film"})).
        WithLimit(2).
        Do(ctx)

    if err != nil {
        panic(err)
    }
    fmt.Printf("%v", nearTextResponse)
}

  API docs

Java

Fields returnFields = Fields.builder()
  .fields(new Field[]{
    Field.builder().name("title").build(),
  })
  .build();

NearTextArgument nearText = NearTextArgument.builder()
  .concepts(new String[]{"A holiday film"})
  .build();

String nearTextQuery = GetBuilder.builder()
  .className("DemoCollection")
  .fields(returnFields)
  .withNearTextFilter(nearText)
  .limit(2)
  .build()
  .buildQuery();

Result<GraphQLResponse> nearTextResult = client.graphQL().raw().withQuery(nearTextQuery).run();

if (nearTextResult.hasErrors()) {
  System.err.println(nearTextResult.getError());
} else {
  System.out.println("Near Text Results: " + nearTextResult.getResult().getData());
}

  API docs

Next steps

Choose a model

Check out which additional models are available through Weaviate Embeddings.

Explore hybrid search

Discover how hybrid search combines keyword matching and semantic search.

Join the community

Connect with other developers and contribute to our forums.

Support

For help with Serverless Cloud, Enterprise Cloud, and Bring Your Own Cloud accounts, contact Weaviate support directly to open a support ticket.

For questions and support from the Weaviate community, try these resources:

• Community forum
• GitHub
• Slack

To add a support plan, contact Weaviate sales.