Text Embeddings
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
Section titled “Requirements”Ollama
Section titled “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
Section titled “Weaviate configuration”Your Weaviate instance must be configured with the Ollama vectorizer integration (text2vec-ollama) module.
For Weaviate Cloud (WCD) users
This integration is enabled by default on Weaviate Cloud (WCD) instances.
To use Ollama with Weaviate Cloud, make sure your Ollama server is running and accessible from the Weaviate Cloud instance. If you are running Ollama on your own machine, you may need to expose it to the internet. Carefully consider the security implications of exposing your Ollama server to the internet.
For use cases such as this, consider using a self-hosted Weaviate instance, or another API-based integration method.
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
Section titled “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.
Configure the vectorizer
Section titled “Configure the vectorizer”Configure a Weaviate index as follows to use an Ollama embedding model:
from weaviate.classes.config import Configureclient.collections.create( "DemoCollection", vector_config=[ Configure.Vectors.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)await client.collections.create({ name: 'DemoCollection', vectorizers: [ weaviate.configure.vectors.text2VecOllama({ name: 'title_vector', sourceProperties: ['title'], apiEndpoint: 'http://ollama:11434', // If using Docker you might need: http://host.docker.internal:11434 model: 'snowflake-arctic-embed', // The model to use, e.g. "nomic-embed-text" }), ],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
textortext[]data type (unless skipped) - Sort properties in alphabetical (a-z) order before concatenating values
- If
vectorizePropertyNameistrue(falseby default) prepend the property name to each property value - Join the (prepended) property values with spaces
- Prepend the class name (unless
vectorizeClassNameisfalse) - Convert the produced string to lowercase
Data import
Section titled “Data import”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." }
];Searches
Section titled “Searches”Once the vectorizer is configured, Weaviate will perform vector and hybrid search operations using the specified Ollama model.
Vector (near text) search
Section titled “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.
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)Hybrid search
Section titled “Hybrid search”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)References
Section titled “References”Available models
Section titled “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
Section titled “Further resources”Other integrations
Section titled “Other integrations”Code examples
Section titled “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
Section titled “External resources”Have a question or feedback? Here's how to reach us.