Bring your own vectors

Overview

As a vector database, Weaviate make use of vector representations, also known as "embeddings", of data objects.

One way to generate these vectors is to use a "vectorizer" module, which will generate a vector at import and query time. This can be a convenient method if you are using a publicly available model and want to simplify your pipeline.

Alternatively, you can supply your own, "custom", vector embeddings at import time as well as for any vector-based queries. This is useful if you have a custom model, or if you want to use a model that is not available through a Weaviate module.

This tutorial will guide you through the process of using Weaviate with your own vectors. We will:

Create a Weaviate instance
Connect to the Weaviate instance
Import your data, specifying your own vectors
Perform a vector search with Weaviate

Data

We'll use as an example a tiny dataset consisting of 10 questions from the popular quiz show "Jeopardy!". Each object has an associated vector embedding.

In this example, these vectors were obtained through an OpenAI API, using the text-embedding-ada-002 model. But your vectors can come from any source, such as another inference service provider (e.g Cohere or Hugging Face) or your own fine-tuned model.

Take a look at the dataset

	Category	Question	Answer	Vector
0	SCIENCE	This organ removes excess glucose from the blood & stores it as glycogen	Liver	[ -0.006632288, -0.0042016874, ..., -0.020163147 ]
1	ANIMALS	It's the only living mammal in the order Proboseidea	Elephant	[ -0.0166891, -0.00092290324, ..., -0.032253385 ]
2	ANIMALS	The gavial looks very much like a crocodile except for this bodily feature	the nose or snout	[ -0.015592773, 0.019883318, ..., 0.0033349802 ]
3	ANIMALS	Weighing around a ton, the eland is the largest species of this animal in Africa	Antelope	[ 0.014535263, -0.016103541, ..., -0.025882969 ]
4	ANIMALS	Heaviest of all poisonous snakes is this North American rattlesnake	the diamondback rattler	[ -0.0030859283, 0.015239313, ..., -0.021798335 ]
5	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 ]
6	SCIENCE	A metal that is "ductile" can be pulled into this while cold & under pressure	wire	[ -0.02735741, 0.01199829, ..., 0.010396339 ]
7	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 ]
8	SCIENCE	Changes in the tropospheric layer of this are what gives us weather	the atmosphere	[ 0.009625228, 0.027518686, ..., -0.0068922946 ]
9	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 ]

Installation

Weaviate is open source and you can easily run the binary on Linux or run a local instance from a Docker image on any OS, or even easier - you can create a free sandbox instance with the Weaviate Cloud Services (WCS).

For this example, we will assume that you are using a WCS instance with authentication enabled.

Client library

For the best experience with Weaviate, we recommend installing a Weaviate client library for one of these languages:

Add weaviate-client to your Python environment with pip.

Please use the v4 client with Weaviate 1.23.7 or higher.

pip install -U weaviate-client

Add weaviate-client to your Python environment with pip:

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

Add weaviate-client to your project with npm:

npm install weaviate-client --tag next

Add weaviate-ts-client to your project with npm:

npm install weaviate-ts-client

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

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

Add this dependency to your project:

<dependency>
  <groupId>io.weaviate</groupId>
  <artifactId>client</artifactId>
  <version>4.0.0</version>  <!-- Check latest version -->
</dependency>

For JavaScript/TypeScript, we'll also need the node-fetch library to download the dataset, so make sure to npm install node-fetch.

Connect to Weaviate

To connect to your Weaviate instance, you need the following information:

The Weaviate URL (get it from WCS Details tab),
The Weaviate API key (if enabled - get it from WCS Details tab), and

Run the following example code to connect to Weaviate. You can re-use the resulting client object in the following steps.

import weaviate

# As of November 2023, WCS sandbox instances are not yet compatible with the new API introduced in the v4 Python client.
# This example connects to a local instance of Weaviate. You do not need to provide the Weaviate API key when local,
# anonymous, authentication is enabled.
client = weaviate.connect_to_local()

try:
    # Your code here
except:
    # Close the connection gracefully
    client.close()

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.connectToWCS(
    '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
});

package main

import (
  "context"

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

func main() {
  cfg := weaviate.Config{
    Host:   "WEAVIATE_INSTANCE_URL/",  // Replace with your Weaviate endpoint
    Scheme: "https",
    AuthConfig: auth.ApiKey{Value: "YOUR-WEAVIATE-API-KEY"}, // Replace with your Weaviate instance API key
  }

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

With 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 objects in collections, each of which is called a class. Each object has a set of properties, and a vector representation generated automatically by a vectorizer, or specified at import time.

Weaviate lets you define vectorizers at the class level, with further control (e.g. specific model, or vectorization behavior) at the property level. Since you're bringing your own vectors, a vectorizer is not needed, so we will set the vectorizer to none here.

Optional: Set a compatible vectorizer

If you're using "custom" vectors that utilize the same model available through a Weaviate module, you have the option to designate that specific model and module as the class vectorizer.

In this tutorial, the vectors were produced using the OpenAI model ada-002. As this model is also available through the text2vec-openai module, it may be set as the class vectorizer. Doing so will enable Weaviate to generate vectors if any objects are upserted without a vector, and enable the nearText operator.

Note that if a class has a specified vectorizer but you provide a vector at the time of import, Weaviate will use your supplied vector.

Python (v4)
Python (v3)
JS/TS v3
JS/TS 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(),
        vector_index_config=wvc.config.Configure.VectorIndex.hnsw(
            distance_metric=wvc.config.VectorDistances.COSINE # select prefered distance metric
        ),
    )

# 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',
  vectorizer: weaviate.configure.vectorizer.none(),
  vectorIndex: weaviate.configure.vectorIndex.hnsw({
    distanceMetric: 'cosine',
  })
});

console.log('We have a new class!', 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

Import data with vectors

Here is the set of 10 pre-vectorized Jeopardy questions in JSON format.

The following will load the question objects with vectors, and import them to Weaviate.

Note that we use a batch import process, so that each request to the inference API contains multiple objects. You should use batch imports unless you have a good reason not to, as it will significantly improve the speed of data ingestion.

Python (v4)
Python (v3)
JS/TS v3
JS/TS v2
Curl

    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
url = 'https://raw.githubusercontent.com/weaviate-tutorials/quickstart/main/data/jeopardy_tiny+vectors.json'
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"])

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+vectors.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();

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+vectors.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: className,
      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();

function send_data() {
  curl --no-progress-meter -X POST "$BATCH_API_URL" \
       -H "Content-Type: application/json" \
       -H "Authorization: Bearer $WEAVIATE_API_KEY" \
       -d "$1" \
       -o /dev/null  # suppress the output because vectors are long
}


echo "Importing data..."
lines_processed=0
batch_data='{"objects": ['

# Read the JSON file and loop through its entries
while read -r line; do
  # Create the class object out of the JSON data
  line=$(echo "$line" | jq '{class: "Question", properties: {answer: .Answer, question: .Question, category: .Category}, vector: .Vector}')

  if [ $lines_processed -eq 0 ]; then
    batch_data+=$line
  else
    batch_data+=",$line"
  fi

  lines_processed=$((lines_processed + 1))

  # If the batch is full, send it to the API
  if [ $lines_processed -eq $BATCH_SIZE ]; then
    batch_data+="]}"

    send_data "$batch_data"

    # Reset the batch data and counter
    lines_processed=0
    batch_data='{"objects": ['
  fi
done < <(jq -c '.[]' jeopardy_tiny+vectors.json)  # process substitution

echo "Sending the remaining data (if any) to the API..."
if [ $lines_processed -ne 0 ]; then
  batch_data+="]}"
  send_data "$batch_data"
fi

vector != object property

Do not specify object vectors as an object property. This will cause Weaviate to treat it as a regular property, rather than as a vector embedding.

Query

Let's say you want to find questions related to biology. We can do that by obtaining a vector embedding for "biology", and finding objects nearest to it. In this example, we've used the OpenAI API to generate it, with the same text-embedding-ada-002 model. Then, in the following query, we pass that vector to the nearVector operator:

    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 = await 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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})
  .withLimit(2)
  .do();

console.log(response['data']['Get'][className]);

className := "Question"
content := graphql.Field{Name: "question"}
_additional := graphql.Field{Name: "_additional", Fields: []graphql.Field{
  {Name: "certainty"}, // only supported if distance==cosine
  {Name: "distance"},  // always supported
}}

nearVector := client.GraphQL().NearVectorArgBuilder().
  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ctx := context.Background()
result, err := client.GraphQL().Get().
  WithClassName(className).
  WithFields(content, _additional).
  WithNearVector(nearVector).
  Do(ctx)

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

String className = "Question";
Field title = Field.builder().name("question").build();
Field _additional = Field.builder()
  .name("_additional")
  .fields(new Field[]{
    Field.builder().name("certainty").build(), // only supported if distance==cosine
    Field.builder().name("distance").build(),  // always supported)
  }).build();

Float[] vector = new 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NearVectorArgument nearVector = NearVectorArgument.builder()
  .vector(vector)
  .build();

Result<GraphQLResponse> result = client.graphQL().get()
  .withClassName(className)
  .withFields(title, _additional)
  .withNearVector(nearVector)
  .run();

if (result.hasErrors()) {
  System.out.println(result.getError());
  return;
}
System.out.println(result.getResult());

{
  Get {
    Question (
      nearVector: {
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      },
      limit: 2
    ) {
      question
      answer
      category
      _additional {
        distance
      }
    }
  }
}

echo '{
  "query": "{
    Get {
      Question(
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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

You should see something like this:

{
    "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"
                }
            ]
        }
    }
}

Recap

If you made it here - well done. We have covered a lot, and you've successfully built a fully functioning vector database! 🥳

You have:

Populated your Weaviate instance with your own vectors, and
Performed text similarity searches.

Of course, there is a lot more to Weaviate that we have not yet covered, and probably a lot that you wish to know about. So we include a few links below that might help you to get started in your journey with us.

Also, please feel free to reach out to us on our community Slack. We love to hear from our users.

Overview​

Data​

Installation​

Client library​

Connect to Weaviate​

Collection definition​

Import data with vectors​

Query​

Recap​