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.

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

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:

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

modules:

  text2vec-model2vec:

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

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.

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

from weaviate.classes.config import Configureclient.collections.create(    "DemoCollection",    vector_config=[        Configure.Vectors.text2vec_model2vec(            name="title_vector",            source_properties=["title"],        )    ],    # Additional parameters not shown)

// Coming soon

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

After configuring the vectorizer, import data into Weaviate. Weaviate generates embeddings for text objects using the specified model.

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.use("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.")            breakfailed_objects = collection.batch.failed_objectsif failed_objects:    print(f"Number of failed imports: {len(failed_objects)}")    print(f"First failed object: {failed_objects[0]}")

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." }
];

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

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.

collection = client.collections.use("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"])

const collectionName = 'DemoCollection'
const myCollection = client.collections.use(collectionName)

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.

collection = client.collections.use("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"])

const collectionName = 'DemoCollection'
const myCollection = client.collections.use(collectionName)

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

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.

• Model2Vec documentation

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