:::callout{intent="warning" title="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](#for-collections-with-octoai-embedding-integrations)) or generative models ([your options](#for-collections-with-octoai-generative-ai-integrations)). #### 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](huggingface-embeddings.md). 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](../starter-guides/custom-vectors.md). **Option 2: Migrate to a new collection with another model provider** Alternatively, you can migrate your data to a new collection ([read how](#how-to-migrate)). 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](huggingface-embeddings.md)) 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](../how-to-manage-collections/generative-reranker-models.md#update-the-generative-model-integration) 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](anyscale-generative.md), [FriendliAI](friendliai-generative.md), [Mistral](mistral-generative.md) or local models with [Ollama](ollama-generative.md) 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](../how-to-manage-collections/migrate.md) 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](#configure-the-vectorizer) 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](#data-import), Weaviate generates text object embeddings and saves them into the index. For [vector](#vector-near-text-search) and [hybrid](#hybrid-search) search operations, Weaviate converts text queries into embeddings. ![Embedding integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_octoai_embedding.png) ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the OctoAI vectorizer integration (`text2vec-octoai`) module. :::accordion{title="For Weaviate Cloud (WCD) users"} This integration is enabled by default on Weaviate Cloud (WCD) instances. ::: :::accordion{title="For self-hosted users"} - Check the [cluster metadata](../monitoring-and-logging/status.md#cluster-metadata) to verify if the module is enabled. - Follow the [how-to configure modules](../how-to-configure-weaviate/modules.md) 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](https://octo.ai/) 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. :::code-group{sync="languages"} ```python title="Python" # Recommended: save sensitive data as environment variables octoai_key = os.getenv("OCTOAI_API_KEY") ``` ```typescript title="JavaScript/TypeScript" const octoaiApiKey = process.env.OCTOAI_API_KEY || ''; // Replace with your inference API key ``` ::: ## Configure the vectorizer [Configure a Weaviate index](../how-to-manage-collections/vector-config.md#specify-a-vectorizer) as follows to use an OctoAI embedding model: :::code-group{sync="languages"} ```python title="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 ) ``` ```typescript title="JavaScript/TypeScript" {9-15} 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](#available-models) for the vectorizer to use, as shown in the following configuration example. :::code-group{sync="languages"} ```python title="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 ) ``` ```typescript title="JavaScript/TypeScript" {9-16} 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](#vectorizer-parameters) one of the [available models](#available-models) for Weaviate to use. The [default model](#available-models) is used if no model is specified. :::accordion{title="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](../how-to-manage-collections/vector-config.md#property-level-settings)) - 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. :::code-group{sync="languages"} ```python title="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", ) ], ) ``` ```typescript title="JavaScript/TypeScript" {9-18} 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](https://octo.ai/docs/text-gen-solution/getting-started). ## Data import After configuring the vectorizer, [import data](../how-to-manage-objects/import.md) into Weaviate. Weaviate generates embeddings for text objects using the specified model. :::code-group{sync="languages"} ```python title="Python" {13-20} 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.") 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]}") ``` ```typescript title="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." } ]; ``` ::: :::callout{intent="tip" title="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 OctoAI model. ![Embedding integration at search illustration](/assets/docs/weaviate/model-providers/_includes/integration_octoai_embedding_search.png) ### Vector (near text) search When you perform a [vector search](../how-to-query-search/similarity.md#search-with-text), 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`. :::code-group{sync="languages"} ```python title="Python" {3-6} 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"]) ``` ```typescript title="JavaScript/TypeScript" const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) ``` ::: ### Hybrid search :::callout{intent="info" title="What is a hybrid search?"} A hybrid search performs a vector search and a keyword (BM25) search, before [combining the results](../how-to-query-search/hybrid.md) to return the best matching objects from the database. ::: When you perform a [hybrid search](../how-to-query-search/hybrid.md), 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`. :::code-group{sync="languages"} ```python title="Python" {3-6} 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"]) ``` ```typescript 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](https://octo.ai/docs/text-gen-solution/getting-started) are: - `thenlper/gte-large` ## Further resources ### Other integrations - [OctoAI generative models + Weaviate](octoai-generative.md). ### 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](../how-to-manage-collections/index.md) and [How-to: Manage objects](../how-to-manage-objects/index.md) guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them). - The [How-to: Query & Search](../how-to-query-search/index.md) guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation. ### External resources - OctoAI [Embed API documentation](https://octo.ai/docs/text-gen-solution/getting-started) Have a question or feedback? Here's how to reach us. ::::card-grid :::card{title="Community Forum" href="https://forum.weaviate.io/c/support" icon="messages-square"} Ask questions and connect with other developers on our **Community forum**. ::: :::card{title="Support" href="/guides/support-overview" icon="life-buoy"} Weaviate Cloud user or customer? Find the right channel on the **Support page**. ::: :::: ## Related pages - [Agents](./agents-index.md) - [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md) - [APIs](./apis-index.md) - [Authorization and authentication](./authorization-and-authentication-index.md) - [Benchmarks](./benchmarks-index.md) - [Best practices](./best-practices-index.md) - [Client libraries](./clients-index.md) - [Client Libraries / SDKs](./client-libraries-index.md) - [Cloud](./cloud-index.md) - [Cloud account management](./cloud-account-management-index.md) # Agent Instructions This portal answers questions programmatically. To receive a synthesized, source-cited answer instead of crawling page by page, append the `?ask=` query parameter to any page URL on this site: /guides/quickstart?ask=how+do+I+authenticate Optional parameters: - `&goal=` steers the answer toward your objective (e.g. `&goal=write+a+python+client`). - `&version=