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Quickstart: Bring your own vectors

Weaviate is a vector database. Vector databases store data objects and vectors that represent those objects. The vector embedding is also called an "embedding."

When you import data, you can pass pre-existing vectors or generate new ones. "Bring your own vectors" means you supply the vector embeddings when you upload your data. The embeddings you provide can be custom vectors or vectors that are pre-generated using a model provider.

This guide discusses importing data that has pre-existing vectors.

Guide steps

Section titled “Guide steps”

To use this guide to import data that has pre-existing vectors, follow these steps:

  1. Review the setup steps.
  2. Connect to a Weaviate instance.
  3. Import data that specifies its own vectors.
  4. Perform a vector search.

Setup

Section titled “Setup”

This section introduces setup requirements and the example data that this guide uses.

Example Data

Section titled “Example Data”

The example data is based on a set of ten questions from the "Jeopardy!" television program. The import data file is a JSON formatted document that contains the vectors and the underlying data.

The sample data

The JSON file is based on this data. The vector embeddings are generated with the OpenAI API text-embedding-ada-002 model.

Your vectors can come from any source, including your own vectorizer model or another model provider such as Cohere or Hugging Face.

CategoryQuestionAnswerVector
SCIENCEThis organ removes excess glucose from the blood & stores it as glycogenLiver[ -0.006632288, -0.0042016874, ..., -0.020163147 ]
ANIMALSIt's the only living mammal in the order ProboseideaElephant[ -0.0166891, -0.00092290324, ..., -0.032253385 ]
ANIMALSThe gavial looks very much like a crocodile except for this bodily featurethe nose or snout[ -0.015592773, 0.019883318, ..., 0.0033349802 ]
ANIMALSWeighing around a ton, the eland is the largest species of this animal in AfricaAntelope[ 0.014535263, -0.016103541, ..., -0.025882969 ]
ANIMALSHeaviest of all poisonous snakes is this North American rattlesnakethe diamondback rattler[ -0.0030859283, 0.015239313, ..., -0.021798335 ]
SCIENCE2000 news: the Gunnison sage grouse isn't just another northern sage grouse, but a new one of this classificationspecies[ -0.0090561025, 0.011155112, ..., -0.023036297 ]
SCIENCEA metal that is "ductile" can be pulled into this while cold & under pressurewire[ -0.02735741, 0.01199829, ..., 0.010396339 ]
SCIENCEIn 1953 Watson & Crick built a model of the molecular structure of this, the gene-carrying substanceDNA[ -0.014227471, 0.020493254, ..., -0.0027445166 ]
SCIENCEChanges in the tropospheric layer of this are what gives us weatherthe atmosphere[ 0.009625228, 0.027518686, ..., -0.0068922946 ]
SCIENCEIn 70-degree air, a plane traveling at about 1,130 feet per second breaks itSound barrier[ -0.0013459147, 0.0018580769, ..., -0.033439033 ]

Weaviate Instance

Section titled “Weaviate Instance”

Weaviate is open source. You can run Weaviate locally, in the cloud, or as a service. If you want to follow this guide without setting up an instance of your own, consider using a free cluster in Weaviate Cloud.

Client library

Section titled “Client library”

Client libraries simplify working with Weaviate. Clients are available for multiple programming languages. This guide provides examples in Python, TypeScript, Java, C#, and cURL.

To install a client library, use the installer for the client language:

The Python client requires Weaviate v1.23.7 and later.

Bash
pip install -U weaviate-client
Bash
npm install weaviate-client
xml
<dependency>
  <groupId>io.weaviate</groupId>
  <artifactId>client6</artifactId>
  <version>6.2.0</version> <!-- Check latest version: https://github.com/weaviate/java-client  -->
</dependency>
Bash
dotnet add package Weaviate.Client --version 1.2.0

Connect to Weaviate

Section titled “Connect to Weaviate”

This guide uses a Weaviate Cloud instance to host the collection. To connect to a Weaviate Cloud instance, you need the following information:

  • The Weaviate URL
  • The Weaviate API key

If you are using a free cluster, the URL and API keys are listed in the details panel for your instance.

To connect to Weaviate, run the code for your language to create a client object. Re-use the client object in the later steps to connect to your instance and run the sample code.

To connect, use the REST Endpoint and the Admin API key stored as environment variables:

Python
import weaviate
from weaviate.classes.init import Auth

# Best practice: store your credentials in environment variables
weaviate_url = os.environ["WEAVIATE_URL"]
weaviate_api_key = os.environ["WEAVIATE_API_KEY"]

# Connect to Weaviate Cloud
client = weaviate.connect_to_weaviate_cloud(
    cluster_url=weaviate_url,
    auth_credentials=Auth.api_key(weaviate_api_key),
)

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

client.close()  # Free up resources
TypeScript
// Set these environment variables
// WEAVIATE_URL      your WCD instance URL
// WEAVIATE_API_KEY  your WCD instance API key

const weaviateURL = process.env.WEAVIATE_URL as string
const weaviateKey = process.env.WEAVIATE_API_KEY as string

const client: WeaviateClient = await weaviate.connectToWeaviateCloud(weaviateURL, {
    authCredentials: new weaviate.ApiKey(weaviateKey),
  }
)
Python
// Set these environment variables
// WEAVIATE_HOSTNAME    Your Weaviate instance hostname
// WEAVIATE_API_KEY    Your Weaviate instance API key

package main

import (
  "context"
  "fmt"
  "os"

  "github.com/weaviate/weaviate-go-client/v5/weaviate"
  "github.com/weaviate/weaviate-go-client/v5/weaviate/auth"
)

// Create the client
func CreateClient() {
  cfg := weaviate.Config{
    Host:       os.Getenv("WEAVIATE_HOSTNAME"),
    Scheme:     "https",
    AuthConfig: auth.ApiKey{Value: os.Getenv("WEAVIATE_API_KEY")},
    Headers:    nil,
  }

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

  // Check the connection
  live, err := client.Misc().LiveChecker().Do(context.Background())
  if err != nil {
    panic(err)
  }
  fmt.Printf("%v", live)

}

func main() {
  CreateClient()
}
Java
// Best practice: store your credentials in environment variables
String weaviateUrl = System.getenv("WEAVIATE_URL");
String weaviateApiKey = System.getenv("WEAVIATE_API_KEY");

WeaviateClient client = WeaviateClient.connectToWeaviateCloud(weaviateUrl, // Replace with your Weaviate Cloud URL
    weaviateApiKey // Replace with your Weaviate Cloud key
);

System.out.println(client.isReady()); // Should print: `True`

client.close(); // Free up resources
C#
// Best practice: store your credentials in environment variables
string weaviateUrl = Environment.GetEnvironmentVariable("WEAVIATE_URL");
string weaviateApiKey = Environment.GetEnvironmentVariable("WEAVIATE_API_KEY");

WeaviateClient client = await Connect.Cloud(
    weaviateUrl, // Replace with your Weaviate Cloud URL
    weaviateApiKey // Replace with your Weaviate Cloud key
);

var isReady = await client.IsReady();
Console.WriteLine(isReady);
Bash
# Set these environment variables
# WEAVIATE_URL      your Weaviate instance URL
# WEAVIATE_API_KEY  your Weaviate instance API key

curl https://${WEAVIATE_URL}/v1/meta -H "Authorization: Bearer ${WEAVIATE_API_KEY}" | jq

Collection definition

Section titled “Collection definition”

Weaviate stores data in collections. Each data object in a collection has a set of properties and a vector embedding. Before you import data, you should create a collection definition to define the data properties for the collection.

The data property definitions are called a schema. Vectorizers are defined at the collection level. Additional details, such as the specific model or behavior, are sometimes defined at the property level.

This guide assumes you already have vector embeddings for your data. Since the vector embeddings are included with the import file, the sample configuration code sets the vectorizer to none on the collection level .

For more on configurations, see vectorizer settings.

Define the collection

Section titled “Define the collection”
Python
import weaviate.classes as wvc

# Create the collection. Weaviate's autoschema feature will infer properties when importing.
questions = client.collections.create(
    "Question",
    vector_config=wvc.config.Configure.Vectors.self_provided(),
)
JavaScript/TypeScript
const newCollection = await client.collections.create({
  name: 'JeopardyQuestion',
  vectorizers: vectors.selfProvided(),
});

console.log('We have a new collection!', newCollection['name']);
Java
// Create the collection.
client.collections.create(collectionName,
    col -> col
        .properties(Property.text("answer"), Property.text("question"),
            Property.text("category"))
        .vectorConfig(VectorConfig.selfProvided()));
C#
// Create the collection.
await client.Collections.Create(
    new CollectionCreateParams
    {
        Name = collectionName,
        Properties =
        [
            Property.Text("answer"),
            Property.Text("question"),
            Property.Text("category"),
        ],
        // Configure the "default" vector to be SelfProvided (BYOV)
        VectorConfig = Configure.Vector("default", v => v.SelfProvided()),
    }
);
Curl
# Requiring `bash` above enables process substitution support, used when redirecting the output of jq to the while loop.
echo "Downloading the data file..."
curl -O -L "https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/jeopardy_tiny+vectors.json" --no-progress-meter

# Replace with your Weaviate endpoint and Weaviate Admin API key
WEAVIATE_URL=https://WEAVIATE_INSTANCE_URL  # Replace WEAVIATE_INSTANCE_URL with your instance URL
WEAVIATE_API_KEY=YOUR_WEAVIATE_API_KEY

SCHEMA_API_URL="$WEAVIATE_URL/v1/schema"
BATCH_API_URL="$WEAVIATE_URL/v1/batch/objects"
GRAPHQL_URL="$WEAVIATE_URL/v1/graphql"
BATCH_SIZE=100

# Uncomment to delete all Question objects if you see a "Name 'Question' already used" error
# curl -X DELETE $SCHEMA_API_URL/Question -H "Authorization: Bearer $WEAVIATE_API_KEY"

echo "Creating the schema. Weaviate's autoschema feature will infer class properties when importing..."
echo '{
  "class": "Question",
  "vectorizer": "text2vec-openai"
}' | curl --no-progress-meter \
  -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WEAVIATE_API_KEY" \
  -d @- \
  -o /dev/null \
  $SCHEMA_API_URL

Optional: Set a compatible vectorizer

Section titled “Optional: Set a compatible vectorizer”

If Weaviate has an integration for the vectorizer that you use to generate your custom vectors, consider adding your vectorizer to the collection definition. If you specify a vectorizer, Weaviate can generate new vectors when it needs them.

In this example, the vectors are generated by the OpenAI ada-002 model. The Weaviate text2vec-openai integration can access the ada-002 model, so you would specify text2vec-openai in the collection definition.

At import time, Weaviate uses the vectors you provide even if the collection specifies a vectorizer.

Import data and vectors

Section titled “Import data and vectors”

For large datasets, batch import is more efficient than importing individual objects. This batch import code imports the question objects and their vectors.

Python
import requests

fname = "jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json"  # This file includes pre-generated vectors
url = f"https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/{fname}"
resp = requests.get(url)
data = json.loads(resp.text)  # Load data

question_objs = list()
for i, d in enumerate(data):
    question_objs.append(wvc.data.DataObject(
        properties={
            "answer": d["Answer"],
            "question": d["Question"],
            "category": d["Category"],
        },
        vector=d["vector"]
    ))

questions = client.collections.use("Question")
questions.data.insert_many(question_objs)
JavaScript/TypeScript
type JeopardyItem = {
  Answer: string;
  Question: string;
  Category: string;
  vector: number[],
}

async function getJsonData(): Promise<JeopardyItem[]> {
  const file = await fetch('https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json');
  return await file.json();
}

async function importQuestionsWithVectors() {
  // Get the questions directly from the URL
  const data = await getJsonData();
  const jeopardyCollection = client.collections.use('JeopardyQuestion');
  let itemsToInsert = new Array()

  for (const item of data) {
    // Construct the object to add to insert with insertMany()
    itemsToInsert.push({
      properties: {
        answer: item.Answer,
        question: item.Question,
        category: item.Category,
      },
      vectors: item.vector,
    })
  }

  const res = await jeopardyCollection.data.insertMany(itemsToInsert)
  console.log(`Finished importing ${res.allResponses.length} objects.`);
}

await importQuestionsWithVectors();
Java
String fname = "jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json";String url =    "https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/"        + fname;HttpClient httpClient = HttpClient.newHttpClient();HttpRequest request =    HttpRequest.newBuilder().uri(URI.create(url)).build();HttpResponse<String> responseHttp =    httpClient.send(request, HttpResponse.BodyHandlers.ofString());String responseBody = responseHttp.body();ObjectMapper objectMapper = new ObjectMapper();List<JeopardyQuestionWithVector> data =    objectMapper.readValue(responseBody, new TypeReference<>() {});// Get a handle to the collectionCollectionHandle<Map<String, Object>> questions =    client.collections.use(collectionName);// Declare the list with the specific generic signature required by insertManyList<WeaviateObject<Map<String, Object>>> questionObjs =    new ArrayList<>();for (JeopardyQuestionWithVector d : data) {  Map<String, Object> properties = Map.of("answer", d.answer, "question",      d.question, "category", d.category);  // Use the explicit Builder to construct the object with a custom vector  questionObjs.add(WeaviateObject.of(v -> v.properties(properties)      .uuid(UUID.randomUUID().toString())      .vectors(Vectors.of(d.vector))));}// Pass the correctly typed list directly to insertManyvar insertManyResponse = questions.data.insertMany(questionObjs);if (!insertManyResponse.errors().isEmpty()) {  System.err.printf("Number of failed imports: %d\n",      insertManyResponse.errors().size());  System.err.printf("First failed object error: %s\n",      insertManyResponse.errors().get(0));}
C#
var fname = "jeopardy_tiny_with_vectors_all-OpenAI-ada-002.json";var url =    $"https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/{fname}";using var httpClient = new HttpClient();var responseBody = await httpClient.GetStringAsync(url);var data = JsonSerializer.Deserialize<List<JeopardyQuestionWithVector>>(responseBody);// Get a handle to the collectionvar questions = client.Collections.Use(collectionName);// Using Insert in a loop allows explicit vector assignment per object.// Using Task.WhenAll creates a parallel batch-like effect.var insertTasks = data.Select(d =>{    return questions.Data.Insert(        // Pass properties as an Anonymous Type        properties: new        {            answer = d.Answer,            question = d.Question,            category = d.Category,        },        // Explicitly pass the vector        vectors: d.Vector    );});await Task.WhenAll(insertTasks);

Query

Section titled “Query”

In order to query your stored vectors, Weaviate needs a vector embedding of the query.

  • If the collection definition specifies a vectorizer, Weaviate uses that vectorizer to generate a vector embedding.
  • If the collection doesn't specify a vectorizer, provide a query vector when you search.

When you create a query vector, use the same vectorizer that you use to create the data embeddings.

This nearVector query supplies a query vector. The query vector is an embedding of the query string, "biology".

Python
query_vector = 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import time
time.sleep(2)  # Sleep so we don't query before async indexing finishes

response = questions.query.near_vector(
    near_vector=query_vector,
    limit=2,
    return_metadata=wvc.query.MetadataQuery(certainty=True)
)

print(response)
JavaScript/TypeScript
const jeopardyCollection = client.collections.use('JeopardyQuestion');
const weaviateVector = 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const result = await jeopardyCollection.query.nearVector(weaviateVector, {
  limit: 2,
  returnProperties: ['question', 'answer', 'category'],
})

console.log(result.objects)
Java
float[] queryVector = data.get(0).vector; // Use a vector from the dataset for a reliable query

// Added a small sleep to ensure indexing is complete
Thread.sleep(2000);

var response = questions.query.nearVector(queryVector,
    q -> q.limit(2).returnMetadata(Metadata.CERTAINTY));

System.out.println(response);
C#
var queryVector = data[0].Vector; // Use a vector from the dataset for a reliable query

// Added a small delay to ensure indexing is complete
await Task.Delay(2000);

var response = await questions.Query.NearVector(
    queryVector,
    limit: 2,
    returnMetadata: MetadataOptions.Certainty
);

Console.WriteLine(JsonSerializer.Serialize(response.Objects));
Curl
echo '{
  "query": "{
    Get {
      Question(
        nearVector: {
          vector: 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        }
        limit: 2
      ) {
        question
        answer
        category
        _additional {
          certainty
          distance
        }
      }
    }
  }"
}' | curl --no-progress-meter \
    -X POST \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer $WEAVIATE_API_KEY" \
    -d @- \
    $GRAPHQL_URL \
    | jq
Query response

The response is like this:

JSON
{
    "data": {
        "Get": {
            "Question": [
                {
                    "answer": "DNA",
                    "category": "SCIENCE",
                    "question": "In 1953 Watson & Crick built a model of the molecular structure of this, the gene-carrying substance"
                },
                {
                    "answer": "Liver",
                    "category": "SCIENCE",
                    "question": "This organ removes excess glucose from the blood & stores it as glycogen"
                }
            ]
        }
    }
}
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