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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:

  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

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.
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

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:

The v4 client requires Weaviate 1.23.7 or higher.

pip install -U weaviate-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.

import weaviate, os
import weaviate.classes as wvc

# Set these environment variables
URL = os.getenv("WCS_URL")
APIKEY = os.getenv("WCS_API_KEY")

# Connect to Weaviate Cloud
client = weaviate.connect_to_wcs(
cluster_url=URL,
auth_credentials=weaviate.auth.AuthApiKey(APIKEY),
)

# Check connection
client.is_ready()

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

    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(),
)

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.

    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
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".

    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)