Text Embeddings (Deprecated)

Deprecated integrations

OctoAI integrations are deprecated

OctoAI announced that they are winding down the commercial availability of its services by 31 October 2024. Accordingly, the Weaviate OctoAI integrations are deprecated. Do not use these integrations for new projects.

From Weaviate v1.25.22, v1.26.8, and v1.27.1, the text2vec-octoai and generative-octoai modules are inactive. Every vectorization and generation request they receive fails server-side with the error OctoAI is permanently shut down. The configuration, import, search, and RAG examples on these pages therefore no longer run against OctoAI, and are kept only as a record of how the integrations used to be configured. Of the options below, only "bring your own vectors" (Option 1) keeps an existing OctoAI collection usable.

If you have a collection that is using an OctoAI integration, consider your options depending on whether you are using OctoAI's embedding models (your options) or generative models (your options).

For collections with OctoAI embedding integrations

OctoAI provided thenlper/gte-large as the embedding model. This model is also available through the Hugging Face API.

After the shutdown date, this model will no longer be available through OctoAI. If you are using this integration, you have the following options:

Option 1: Use the existing collection, and provide your own vectors

You can continue to use the existing collection, provided that you rely on some other method to generate the required embeddings yourself for any new data, and for queries. If you are unfamiliar with the "bring your own vectors" approach, refer to this starter guide.

Option 2: Migrate to a new collection with another model provider

Alternatively, you can migrate your data to a new collection (read how). At this point, you can re-use the existing embeddings or choose a new model.

Re-using the existing embeddings will save on time and inference costs.
Choosing a new model will allow you to explore new models and potentially improve the performance of your application.

If you would like to re-use the existing embeddings, you must select a model provider (e.g. Hugging Face API) that offers the same embedding model.

You can also select a new model with any embedding model provider. This will require you to re-generate the embeddings for your data, as the existing embeddings will not be compatible with the new model.

For collections with OctoAI generative AI integrations

If you are only using the generative AI integration, you do not need to migrate your data to a new collection.

Follow this how-to to re-configure your collection with a new generative AI model provider. Note this requires Weaviate v1.25.23, v1.26.8, v1.27.1, or later.

You can select any model provider that offers generative AI models.

If you would like to continue to use the same model that you used with OctoAI, providers such as Anyscale, FriendliAI, Mistral or local models with Ollama each offer some of the suite of models that OctoAI provided.

How to migrate

An outline of the migration process is as follows:

Create a new collection with the desired model provider integration(s).
Export the data from the existing collection.
- (Optional) To re-use the existing embeddings, export the data with the existing embeddings.
Import the data into the new collection.
- (Optional) To re-use the existing embeddings, import the data with the existing embeddings.
Update your application to use the new collection.

See How-to manage data: migrate data for examples on migrating data objects between collections.

OctoAI Embeddings with Weaviate

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

Configure a Weaviate vector index to use an OctoAI embedding model, and Weaviate will generate embeddings for various operations using the specified model and your OctoAI API key. 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.

Embedding integration illustration

Requirements

Weaviate configuration

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

For Weaviate Cloud (WCD) users

This integration is enabled by default on Weaviate Cloud (WCD) instances.

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.

API credentials

You must provide a valid OctoAI API key to Weaviate for this integration. Go to OctoAI to sign up and obtain an API key.

Provide the API key to Weaviate using one of the following methods:

Set the OCTOAI_APIKEY environment variable that is available to Weaviate.
Provide the API key at runtime, as shown in the examples below.

Python

# Recommended: save sensitive data as environment variables
octoai_key = os.getenv("OCTOAI_API_KEY")

JavaScript/TypeScript

const octoaiApiKey = process.env.OCTOAI_API_KEY || '';  // Replace with your inference API key

Configure the vectorizer

Configure a Weaviate index as follows to use an OctoAI embedding model:

Python

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vector_config=[
        Configure.Vectors.text2vec_octoai(
            name="title_vector",
            source_properties=["title"]
        )
    ],
    # Additional parameters not shown
)

JavaScript/TypeScript

await client.collections.create({  name: 'DemoCollection',  properties: [    {      name: 'title',      dataType: 'text' as const,    },  ],  vectorizers: [    weaviate.configure.vectors.text2VecOctoAI({      name: 'title_vector',      sourceProperties: ['title'],    },    ),  ],  // Additional parameters not shown

Select a model

You can specify one of the available models for the vectorizer to use, as shown in the following configuration example.

Python

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vector_config=[
        Configure.Vectors.text2vec_octoai(
            name="title_vector",
            source_properties=["title"],
            model="thenlper/gte-large"
        )
    ],
    # Additional parameters not shown
)

JavaScript/TypeScript

await client.collections.create({  name: 'DemoCollection',  properties: [    {      name: 'title',      dataType: 'text' as const,    },  ],  vectorizers: [    weaviate.configure.vectors.text2VecOctoAI({      name: 'title_vector',      sourceProperties: ['title'],      model: "thenlper/gte-large",    },    ),  ],  // Additional parameters not shown});

You can specify one of the available models for Weaviate to use. The default model is used if no model is specified.

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

Vectorizer parameters

model: Model name, default - "thenlper/gte-large".
vectorize_collection_name: If the Collection name should be vectorized, default - True.
base_url: The URL to use (e.g. a proxy) instead of the default OctoAI URL - "https://text.octoai.run".

The following examples show how to configure OctoAI-specific options.

Python

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vector_config=[
        Configure.Vectors.text2vec_octoai(
            name="title_vector",
            source_properties=["title"],
            # # Further options
            # model="thenlper/gte-large",
            # vectorize_collection_name=True
            # base_url="https://text.octoai.run",
        )
    ],
)

JavaScript/TypeScript

await client.collections.create({  name: 'DemoCollection',  properties: [    {      name: 'title',      dataType: 'text' as const,    },  ],  vectorizers: [    weaviate.configure.vectors.text2VecOctoAI({      name: 'title_vector',      sourceProperties: ['title'],      // model: "thenlper/gte-large",      // vectorizeCollectionName: true,      // baseURL: "https://text.octoai.run",    },    ),  ],  // Additional parameters not shown});

For further details on model parameters, see the OctoAI API documentation.

Data import

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

Python

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]}")

JavaScript/TypeScript

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

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

Embedding integration at search illustration

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

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"])

JavaScript/TypeScript

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

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.

Python

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"])

JavaScript/TypeScript

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

References

Available models

You can use any embedding model hosted by OctoAI with text2vec-octoai.

Currently the embedding models OctoAI has made available are:

thenlper/gte-large

Further resources

Other integrations

OctoAI 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 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

OctoAI Embed API documentation

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