Added in v1.31.0

Model2Vec Embeddings with Weaviate

Weaviate's integration with Model2Vec's models allows you to access their models' capabilities directly from Weaviate.

Configure a Weaviate vector index to use an Model2Vec embedding model, and Weaviate will generate embeddings for various operations using the specified model via the Model2Vec inference container. This feature is called the vectorizer.

At import time, Weaviate generates text object embeddings and saves them into the index. For vector and hybrid search operations, Weaviate converts text queries into embeddings.

Requirements

Weaviate configuration

Your Weaviate instance must be configured with the Model2Vec vectorizer integration (text2vec-model2vec) module.

For Weaviate Cloud (WCD) users

This integration is not available for Weaviate Cloud (WCD) serverless instances, as it requires a locally running Model2Vec instance.

For self-hosted users

• Check the cluster metadata to verify if the module is enabled.
• Follow the how-to configure modules guide to enable the module in Weaviate.

Configure the integration

To use this integration, you must configure the container image of the Model2Vec model, and the inference endpoint of the containerized model.

The following example shows how to configure the Model2Vec integration in Weaviate:

Docker

Docker Option 1: Use a pre-configured docker-compose.yml file

Follow the instructions on the Weaviate Docker installation configurator to download a pre-configured docker-compose.yml file with a selected model

Docker Option 2: Add the configuration manually

Alternatively, add the configuration to the docker-compose.yml file manually as in the example below.

services:
  weaviate:
    # Other Weaviate configuration
    environment:
      MODEL2VEC_INFERENCE_API: http://text2vec-model2vec:8080  # Set the inference API endpoint
  text2vec-model2vec:  # Set the name of the inference container
    image: cr.weaviate.io/semitechnologies/model2vec-inference:minishlab-potion-base-32M

• MODEL2VEC_INFERENCE_API environment variable sets the inference API endpoint
• text2vec-model2vec is the name of the inference container
• image is the container image

Kubernetes

Configure the Model2Vec integration in Weaviate by adding or updating the text2vec-model2vec module in the modules section of the Weaviate Helm chart values file. For example, modify the values.yaml file as follows:

modules:

  text2vec-model2vec:

    enabled: true
    tag: minishlab-potion-base-8M
    repo: semitechnologies/model2vec-inference
    registry: cr.weaviate.io

See the Weaviate Helm chart for an example of the values.yaml file including more configuration options.

Credentials

As this integration connects to a local Model2Vec container, no additional credentials (e.g. API key) are required. Connect to Weaviate as usual, such as in the examples below.

Python API v4

import weaviate

client = weaviate.connect_to_local()

# Work with Weaviate

client.close()

  API docs

JS/TS API v3

import weaviate from 'weaviate-client'

const client = await weaviate.connectToLocal()

// Work with Weaviate

client.close()

  API docs

Configure the vectorizer

Configure a Weaviate index as follows to use a Model2Vec embedding model:

Python API v4

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vector_config=[
        Configure.Vectors.text2vec_model2vec(
            name="title_vector",
            source_properties=["title"],
        )
    ],
    # Additional parameters not shown
)

  API docs

JS/TS API v3

// Coming soon

  API docs

Note that for this integration, you specify the model to be used in the Weaviate configuration file.

Vectorization behavior

Weaviate follows the collection configuration and a set of predetermined rules to vectorize objects.

Unless specified otherwise in the collection definition, the default behavior is to:

• Only vectorize properties that use the text or text[] data type (unless skipped)
• Sort properties in alphabetical (a-z) order before concatenating values
• If vectorizePropertyName is true (false by default) prepend the property name to each property value
• Join the (prepended) property values with spaces
• Prepend the class name (unless vectorizeClassName is false)
• Convert the produced string to lowercase

Data import

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.fixed_size(batch_size=200) 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.use(collectionName)

let dataObjects = new Array();

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

Re-use existing vectors

If you already have a compatible model vector available, you can provide it directly to Weaviate. This can be useful if you have already generated embeddings using the same model and want to use them in Weaviate, such as when migrating data from another system.

Searches

Once the vectorizer is configured, Weaviate will perform vector and hybrid search operations using the specified Model2Vec 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.use(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

Hybrid search

What is a hybrid search?

A hybrid search performs a vector search and a keyword (BM25) search, before combining the results to return the best matching objects from the database.

When you perform a hybrid search, Weaviate converts the text query into an embedding using the specified model and returns the best scoring objects from the database.

The query below returns the n best scoring objects from the database, set by limit.

Python API v4

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

response = collection.query.hybrid(
    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.use(collectionName)

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

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

  API docs

References

Available models

For the latest list of available models, see the Docker Hub tags for the model2vec-inference container.

Further resources

Code examples

Once the integrations are configured at the collection, the data management and search operations in Weaviate work identically to any other collection. See the following model-agnostic examples:

• The How-to: Manage collections and How-to: Manage objects guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them).
• The How-to: Query & Search guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.

External resources

• Model2Vec documentation

Questions and feedback

If you have any questions or feedback, let us know in the user forum.

Technical questions

If you have questions feel free to post on our Community forum.

Documentation feedback

Leave feedback by opening a GitHub issue.