Weaviate's integration with Jina AI's APIs allows you to access their models' capabilities directly from Weaviate. [Configure a Weaviate vector index](#configure-the-vectorizer) to use a Jina AI embedding model, and Weaviate will generate embeddings for various operations using the specified model and your Jina AI 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_jinaai_embedding.png) ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the Jina AI vectorizer integration (`text2vec-jinaai`) 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 Jina AI API key to Weaviate for this integration. Go to [Jina AI](https://jina.ai/embeddings/) to sign up and obtain an API key. Provide the API key to Weaviate using one of the following methods: - Set the `JINAAI_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 jinaai_key = os.getenv("JINAAI_API_KEY") ``` ```typescript title="JavaScript/TypeScript" const jinaaiApiKey = process.env.JINAAI_API_KEY || ''; // Replace with your inference API key ``` ```goraw title="Go" "X-JinaAI-Api-Key": os.Getenv("JINAAI_API_KEY"), ``` ::: ## Configure the vectorizer [Configure a Weaviate index](../how-to-manage-collections/vector-config.md#specify-a-vectorizer) as follows to use a Jina AI embedding model: :::code-group{sync="languages"} ```python title="Python" {5-10} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", vector_config=[ Configure.Vectors.text2vec_jinaai( name="title_vector", source_properties=["title"] ) ], # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" {9-14} await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, ], vectorizers: [ weaviate.configure.vectors.text2VecJinaAI({ name: 'title_vector', sourceProperties: ['title'], }), ], // Additional parameters not shown }); ``` ```goraw title="Go" {1-19} // Define the collection basicJinaVectorizerDef := &models.Class{ Class: "DemoCollection", VectorConfig: map[string]models.VectorConfig{ "title_vector": { Vectorizer: map[string]interface{}{ "text2vec-jinaai": map[string]interface{}{ "properties": []string{"title"}, }, }, }, }, } // add the collection err = client.Schema().ClassCreator().WithClass(basicJinaVectorizerDef).Do(ctx) if err != nil { panic(err) } ``` ::: ### 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" {5-11} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", vector_config=[ Configure.Vectors.text2vec_jinaai( name="title_vector", source_properties=["title"], model="jina-embeddings-v3", ) ], ) ``` ```typescript title="JavaScript/TypeScript" {9-15} await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, ], vectorizers: [ weaviate.configure.vectors.text2VecJinaAI({ name: 'title_vector', sourceProperties: ['title'], model: 'jina-embeddings-v3' }), ], }); ``` ```goraw title="Go" {1-20} // Define the collection jinaVectorizerWithModelDef := &models.Class{ Class: "DemoCollection", VectorConfig: map[string]models.VectorConfig{ "title_vector": { Vectorizer: map[string]interface{}{ "text2vec-jinaai": map[string]interface{}{ "properties": []string{"title"}, "model": "jina-embeddings-v3", }, }, }, }, } // add the collection err = client.Schema().ClassCreator().WithClass(jinaVectorizerWithModelDef).Do(ctx) if err != nil { panic(err) } ``` ::: 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 The following examples show how to configure Jina AI-specific options. Note that `dimensions` is not applicable for the `jina-embeddings-v2` models. :::code-group{sync="languages"} ```python title="Python" {5-13} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", vector_config=[ Configure.Vectors.text2vec_jinaai( name="title_vector", source_properties=["title"], # Further options # model="jina-embeddings-v3", # dimensions=512, # e.g. 1024, 256, 64 (only applicable for some models) ) ], # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" {9-17} await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, ], vectorizers: [ weaviate.configure.vectors.text2VecJinaAI({ name: 'title_vector', sourceProperties: ['title'], // model: 'jina-embeddings-v3-small-en' // dimensions: 512, // e.g. 1024, 256, 64 Support for this parameter is coming soon (Only applicable for some models) }, ), ], // Additional parameters not shown }); ``` ```goraw title="Go" {1-21} // Define the collection jinaVectorizerFullDef := &models.Class{ Class: "DemoCollection", VectorConfig: map[string]models.VectorConfig{ "title_vector": { Vectorizer: map[string]interface{}{ "text2vec-jinaai": map[string]interface{}{ "properties": []string{"title"}, "model": "jina-embeddings-v3", "dimensions": 512, // e.g. 1024, 512, 256 (only applicable for some models) }, }, }, }, } // add the collection err = client.Schema().ClassCreator().WithClass(jinaVectorizerFullDef).Do(ctx) if err != nil { panic(err) } ``` ::: ## 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." } ]; ``` ```goraw title="Go" {9-44} var sourceObjects = []map[string]string{ {"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."}, } // Convert items into a slice of models.Object objects := []models.PropertySchema{} for i := range sourceObjects { objects = append(objects, map[string]interface{}{ // Populate the object with the data "title": sourceObjects[i]["title"], "description": sourceObjects[i]["description"], }) } // Batch write items batcher := client.Batch().ObjectsBatcher() for _, dataObj := range objects { batcher.WithObjects(&models.Object{ Class: "DemoCollection", Properties: dataObj, }) } // Flush batchRes, err := batcher.Do(ctx) // Error handling if err != nil { panic(err) } for _, res := range batchRes { if res.Result.Errors != nil { for _, err := range res.Result.Errors.Error { if err != nil { fmt.Printf("Error details: %v\n", *err) panic(err.Message) } } } } ``` ::: :::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 Jina AI model. ![Embedding integration at search illustration](/assets/docs/weaviate/model-providers/_includes/integration_jinaai_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) ``` ```goraw title="Go" {1-9} nearTextResponse, err := client.GraphQL().Get(). WithClassName("DemoCollection"). WithFields( graphql.Field{Name: "title"}, ). WithNearText(client.GraphQL().NearTextArgBuilder(). WithConcepts([]string{"A holiday film"})). WithLimit(2). Do(ctx) if err != nil { panic(err) } fmt.Printf("%v", nearTextResponse) ``` ::: ### 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) ``` ```goraw title="Go" {1-9} hybridResponse, err := client.GraphQL().Get(). WithClassName("DemoCollection"). WithFields( graphql.Field{Name: "title"}, ). WithHybrid(client.GraphQL().HybridArgumentBuilder(). WithQuery("A holiday film")). WithLimit(2). Do(ctx) if err != nil { panic(err) } fmt.Printf("%v", hybridResponse) ``` ::: ## References ### Available models The server default changed in `v1.32.0`, and was backported to `v1.31.6`. Earlier releases on each of those lines default to `jina-embeddings-v2-base-en`. - `jina-embeddings-v4` (server default) - When using this model, Weaviate will automatically use the appropriate `task` type, applying `retrieval.passage` for embedding entries and `retrieval.query` for queries. - `jina-embeddings-v3` - When using this model, Weaviate will automatically use the appropriate `task` type, applying `retrieval.passage` for embedding entries and `retrieval.query` for queries. - `jina-embeddings-v2-base-en` (previous server default) - `jina-embeddings-v2-small-en` - `jina-embeddings-v2-base-zh` - `jina-embeddings-v2-base-es` - `jina-embeddings-v2-base-code` If you do not set `dimensions`, Weaviate does not send a dimension count and the Jina AI API applies its own default for the model. Note that `dimensions` is not applicable for the `jina-embeddings-v2` models. ## Further resources ### Other integrations - [Jina AI ColBERT embedding models + Weaviate](jinaai-embeddings-colbert.md). - [Jina AI multimodal embedding models + Weaviate](jinaai-embeddings-multimodal.md) - [Jina AI reranker models + Weaviate](jinaai-reranker.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 - Jina AI [Embeddings API documentation](https://jina.ai/embeddings/) ## Questions and feedback 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=