Bring your own vectors

Weaviate is a vector database. Vector databases store data objects and vectors that represent those objects. The vector representation 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

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

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

Setup

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

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.

Category	Question	Answer	Vector
SCIENCE	This organ removes excess glucose from the blood & stores it as glycogen	Liver	[ -0.006632288, -0.0042016874, ..., -0.020163147 ]
ANIMALS	It's the only living mammal in the order Proboseidea	Elephant	[ -0.0166891, -0.00092290324, ..., -0.032253385 ]
ANIMALS	The gavial looks very much like a crocodile except for this bodily feature	the nose or snout	[ -0.015592773, 0.019883318, ..., 0.0033349802 ]
ANIMALS	Weighing around a ton, the eland is the largest species of this animal in Africa	Antelope	[ 0.014535263, -0.016103541, ..., -0.025882969 ]
ANIMALS	Heaviest of all poisonous snakes is this North American rattlesnake	the diamondback rattler	[ -0.0030859283, 0.015239313, ..., -0.021798335 ]
SCIENCE	2000 news: the Gunnison sage grouse isn't just another northern sage grouse, but a new one of this classification	species	[ -0.0090561025, 0.011155112, ..., -0.023036297 ]
SCIENCE	A metal that is "ductile" can be pulled into this while cold & under pressure	wire	[ -0.02735741, 0.01199829, ..., 0.010396339 ]
SCIENCE	In 1953 Watson & Crick built a model of the molecular structure of this, the gene-carrying substance	DNA	[ -0.014227471, 0.020493254, ..., -0.0027445166 ]
SCIENCE	Changes in the tropospheric layer of this are what gives us weather	the atmosphere	[ 0.009625228, 0.027518686, ..., -0.0068922946 ]
SCIENCE	In 70-degree air, a plane traveling at about 1,130 feet per second breaks it	Sound barrier	[ -0.0013459147, 0.0018580769, ..., -0.033439033 ]

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, Sandbox instance in Weaviate Cloud.

Client library

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

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

Python Client v4
Python Client v3
JS/TS Client v3
JS/TS Client v2

The v4 client requires Weaviate 1.23.7 or higher.

pip install -U weaviate-client

pip install "weaviate-client==3.*"

npm install weaviate-client

npm install weaviate-ts-client

Connect to Weaviate

This guide uses an 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 Sandbox instance, 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.

Python Client v4
Python Client v3
JS/TS Client v3
JS/TS Client v2
Curl

import weaviate, os
import weaviate.classes as wvc

# Set these environment variables
URL = os.getenv("WCD_URL")
APIKEY = os.getenv("WCD_API_KEY")

# Connect to Weaviate Cloud
client = weaviate.connect_to_weaviate_cloud(
    cluster_url=URL,
    auth_credentials=wvc.init.Auth.api_key(APIKEY),
)

# Check connection
client.is_ready()

import weaviate
import json

client = weaviate.Client(
    url = "https://WEAVIATE_INSTANCE_URL",  # Replace with your Weaviate endpoint
    auth_client_secret=weaviate.auth.AuthApiKey(api_key="YOUR-WEAVIATE-API-KEY"),  # Replace with your Weaviate instance API key
)

import weaviate, { WeaviateClient } from 'weaviate-client'

const client: WeaviateClient = await weaviate.connectToWeaviateCloud(
    'https://WEAVIATE_INSTANCE_URL',  // Replace with your Weaviate endpoint
  {
    authCredentials: new weaviate.ApiKey('YOUR-WEAVIATE-API-KEY'),  // Replace with your Weaviate instance API key
    headers: {
      'X-OpenAI-Api-Key': process.env.OPENAI_API_KEY || '',  // Replace with your inference API key
    }
  }
)

import weaviate, { WeaviateClient, ObjectsBatcher, ApiKey } from 'weaviate-ts-client';
import fetch from 'node-fetch';

const client: WeaviateClient = weaviate.client({
  scheme: 'https',
  host: 'WEAVIATE_INSTANCE_URL',  // Replace with your Weaviate endpoint
  apiKey: new ApiKey('YOUR-WEAVIATE-API-KEY'),  // Replace with your Weaviate instance API key
});

For curl, add the API key to the header as shown below:

echo '{
  "query": "<QUERY>"
}' | curl \
    -X POST \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer YOUR-WEAVIATE-API-KEY" \
    -d @- \
    https://WEAVIATE_INSTANCE_URL/v1/graphql  # Replace WEAVIATE_INSTANCE_URL with your instance URL

Collection definition

Weaviate stores data in collections. Each data object in a collection has a set of properties and a vector representation. 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

Python Client v4
Python Client v3
JS/TS Client v3
JS/TS Client v2
Curl

    import weaviate.classes as wvc

    # Create the collection. Weaviate's autoschema feature will infer properties when importing.
    questions = client.collections.create(
        "Question",
        vectorizer_config=wvc.config.Configure.Vectorizer.none(),
    )

# Class definition object. Weaviate's autoschema feature will infer properties when importing.
class_obj = {
    "class": "Question",
    "vectorizer": "none",
}

# Add the class to the schema
client.schema.create_class(class_obj)

const newCollection = await client.collections.create({
  name: 'Question',
  vectorizers: vectorizer.none(),
});

console.log('We have a new collection!', newCollection['name']);
// Uncomment to delete all Question objects if you see a "Name 'Question' already used" error
// await client.collections.deleteAll('Question');

const className = 'Question';
const classObj = {
  class: className,
  vectorizer: 'none',
};

// Uncomment to delete all Question objects if you see a "Name 'Question' already used" error
// await client.schema.classDeleter().withClassName(className).do();

// Add the class to the schema
await client.schema.classCreator().withClass(classObj).do();

# 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

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

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

Python Client v4
Python Client v3
JS/TS Client v3
JS/TS Client v2

    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.get("Question")
    questions.data.insert_many(question_objs)    # This uses batching under the hood

import requests

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

# Configure a batch process
client.batch.configure(batch_size=100)  # Configure batch
with client.batch as batch:
    # Batch import all Questions
    for i, d in enumerate(data):
        print(f"importing question: {i+1}")

        properties = {
            "answer": d["Answer"],
            "question": d["Question"],
            "category": d["Category"],
        }

        batch.add_data_object(properties, "Question", vector=d["Vector"])

The import uses the `node-fetch` library.

npm install node-fetch

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 file.json() as unknown as JeopardyItem[];
}

async function importQuestionsWithVectors() {
  // Get the questions directly from the URL
  const data = await getJsonData();
  const jeopardyCollection = client.collections.get('JeopardyQuestion');
  const res = await jeopardyCollection.data.insertMany(data)
  console.log(`Finished importing ${res.allResponses.length} objects.`);
}

await importQuestionsWithVectors();

The import uses the `node-fetch` library.

npm install node-fetch

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 file.json() as unknown as JeopardyItem[];
}

async function importQuestionsWithVectors() {
  // Get the questions directly from the URL
  const data = await getJsonData();

  // Prepare a batcher. Even though this dataset is tiny, this is the best practice for import.
  let batcher: ObjectsBatcher = client.batch.objectsBatcher();
  let counter: number = 0;
  let batchSize: number = 100;

  for (const item of data) {
    // Construct the object to add to the batch
    const obj = {
      class: "Question",
      properties: {
        answer: item.Answer,
        question: item.Question,
        category: item.Category,
      },
      vector: item.Vector,
    }

    // add the object to the batch queue
    batcher = batcher.withObject(obj);

    // When the batch counter reaches batchSize, push the objects to Weaviate
    if (counter++ % batchSize === 0) {
      // Flush the batch queue and restart it
      await batcher.do();
      batcher = client.batch.objectsBatcher();
    }
  }

  // Flush the remaining objects
  await batcher.do();
  console.log(`Finished importing ${counter} objects.`);
}

await importQuestionsWithVectors();

Vectors are not object properties

If a vector is listed in the properties section, Weaviate processes it as a regular property rather than as a vector embedding. Be careful not to mislabel vectors as properties when you specify the elements of your batch import.

Query

In order to query your stored vectors, Weaviate needs a vector representation 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 Client v4
Python Client v3
JS/TS Client v3
JS/TS Client v2
Curl

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

    print(response)

nearVector = {
    "vector": 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}

result = client.query.get(
        "Question", ["question", "answer", "category"]
    ).with_near_vector(
        nearVector
    ).with_limit(2).with_additional(['certainty']).do()

print(json.dumps(result, indent=4))

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

console.log(result)

const response = await client.graphql
  .get()
  .withClassName(className)
  .withFields('question answer category _additional {distance}')
  .withNearVector({ vector: 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Guide steps​

Setup​

Example Data​

Weaviate Instance​

Client library​

Connect to Weaviate​

Collection definition​

Define the collection​

Optional: Set a compatible vectorizer​

Import data and vectors​

Query​