Ollama Embeddings with Weaviate

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

Configure a Weaviate vector index to use an Ollama embedding model, and Weaviate will generate embeddings for various operations using the specified model via your local Ollama instance. 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

Ollama

This integration requires a locally running Ollama instance with your selected model available. Refer to the Ollama documentation for installation and model download instructions.

Weaviate configuration

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

For Weaviate Cloud (WCD) users

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

Credentials

As this integration connects to a local Ollama 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 an Ollama embedding model:

Python API v4

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vectorizer_config=[
        Configure.NamedVectors.text2vec_ollama(
            name="title_vector",
            source_properties=["title"],
            api_endpoint="http://host.docker.internal:11434",  # If using Docker, use this to contact your local Ollama instance
            model="snowflake-arctic-embed",  # The model to use, e.g. "nomic-embed-text"
        )
    ],
    # Additional parameters not shown
)

  API docs

JS/TS API v3

await client.collections.create({
  name: 'DemoCollection',
  vectorizers: [
    weaviate.configure.vectorizer.text2VecOllama({
      name: 'title_vector',
      sourceProperties: ['title'],
      apiEndpoint: 'http://host.docker.internal:11434',  // If using Docker, use this to contact your local Ollama instance
      model: 'snowflake-arctic-embed',  // The model to use, e.g. "nomic-embed-text"
    }),
  ],
  // Additional parameters not shown
});

  API docs

The default model is used if no model is specified.

The Weaviate server has to be able to reach the Ollama API endpoint. If Weaviate is running in a Docker container and Ollama is running locally, use host.docker.internal to redirect Weaviate from localhost inside the container to localhost on the host machine.

If your Weaviate instance and Ollama instance are hosted in a different way, adjust the API endpoint parameter so it points to your Ollama instance.

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

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

with collection.batch.dynamic() as batch:
    for src_obj in source_objects:
        weaviate_obj = {
            "title": src_obj["title"],
            "description": src_obj["description"],
        }

        # The model provider integration will automatically vectorize the object
        batch.add_object(
            properties=weaviate_obj,
            # vector=vector  # Optionally provide a pre-obtained vector
        )

  API docs

JS/TS API v3

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

let dataObjects = []

for (let srcObject of srcObjects) {
  dataObject.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 Ollama 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.get(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.get(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

See the Ollama documentation for a list of available models. This list includes both large language models and embedding models; look for the word embed in the name or description to identify embedding models.

Download the desired model with ollama pull <model-name>.

If no model is specified, the default model (nomic-embed-text) is used.

Further resources

Other integrations

• Ollama generative models + Weaviate.

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 data guides show how to perform data operations (i.e. create, update, delete).
• The how-to: search guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.

External resources

• Ollama models
• Ollama repository
• How to change the host and port of the Ollama server

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

• Technical questions
• Documentation feedback