--- title: "Jina AI Embeddings with Weaviate" description: "Weaviate's integration with Jina AI's APIs allows you to access their models' capabilities directly from Weaviate." url: https://docs.weaviate.io/weaviate/model-providers/jinaai/embeddings --- # Jina AI Embeddings with Weaviate Weaviate's integration with Jina AI's APIs allows you to access their models' capabilities directly from Weaviate. [Configure a Weaviate vector index](https://docs.weaviate.io/weaviate/model-providers/jinaai/embeddings#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](https://docs.weaviate.io/weaviate/model-providers/jinaai/embeddings#data-import), Weaviate generates text object embeddings and saves them into the index. For [vector](https://docs.weaviate.io/weaviate/model-providers/jinaai/embeddings#vector-near-text-search) and [hybrid](https://docs.weaviate.io/weaviate/model-providers/jinaai/embeddings#hybrid-search) search operations, Weaviate converts text queries into embeddings. ![Embedding integration illustration](https://docs.weaviate.io/assets/images/integration_jinaai_embedding-2843eca6d973cbf4769c8464427f48be.png) ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the Jina AI vectorizer integration (`text2vec-jinaai`) 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](https://docs.weaviate.io/deploy/configuration/status#cluster-metadata) to verify if the module is enabled. - Follow the [how-to configure modules](https://docs.weaviate.io/weaviate/configuration/modules) 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. **Python** ```py import weaviate from weaviate.classes.init import Auth import os # Recommended: save sensitive data as environment variables jinaai_key = os.getenv("JINAAI_API_KEY") headers = { "X-JinaAI-Api-Key": jinaai_key, } client = weaviate.connect_to_weaviate_cloud( cluster_url=weaviate_url, # `weaviate_url`: your Weaviate URL auth_credentials=Auth.api_key(weaviate_key), # `weaviate_key`: your Weaviate API key headers=headers ) # Work with Weaviate client.close() ``` **JavaScript/TypeScript** ```ts import weaviate from '@weaviate/node' const jinaaiApiKey = process.env.JINAAI_API_KEY || ''; // Replace with your inference API key const client = await weaviate.connectToWeaviateCloud( 'WEAVIATE_INSTANCE_URL', // Replace with your instance URL { authCredentials: new weaviate.ApiKey('WEAVIATE_INSTANCE_API_KEY'), headers: { 'X-JinaAI-Api-Key': jinaaiApiKey, } } ) // Work with Weaviate client.close() ``` **Go (v5)** ```go // Set these environment variables // WEAVIATE_HOSTNAME your Weaviate instance hostname, excluding the scheme (https://) // WEAVIATE_API_KEY your Weaviate instance API key // _API_KEY your model provider API key (or token) package main import ( "context" "fmt" "os" "github.com/weaviate/weaviate-go-client/v5/weaviate" "github.com/weaviate/weaviate-go-client/v5/weaviate/auth" ) func main() { cfg := weaviate.Config{ Host: os.Getenv("WEAVIATE_HOSTNAME"), Scheme: "https", AuthConfig: auth.ApiKey{Value: os.Getenv("WEAVIATE_API_KEY")}, Headers: map[string]string{ "X-JinaAI-Api-Key": os.Getenv("JINAAI_API_KEY"), }, } client, err := weaviate.NewClient(cfg) if err != nil { fmt.Println(err) } // Work with Weaviate } ``` ## Configure the vectorizer [Configure a Weaviate index](https://docs.weaviate.io/weaviate/manage-collections/vector-config#specify-a-vectorizer) as follows to use a Jina AI embedding model: **Python** ```py 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 ) ``` **JavaScript/TypeScript** ```ts 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 }); ``` **Go (v5)** ```go // package, imports not shown func main() { // Instantiation not shown ctx := context.Background() // 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](https://docs.weaviate.io/weaviate/model-providers/jinaai/embeddings#available-models) for the vectorizer to use, as shown in the following configuration example. **Python** ```py 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", ) ], ) ``` **JavaScript/TypeScript** ```ts 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' }), ], }); ``` **Go (v5)** ```go // package, imports not shown func main() { // Instantiation not shown ctx := context.Background() // 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](https://docs.weaviate.io/weaviate/model-providers/jinaai/embeddings#vectorizer-parameters) one of the [available models](https://docs.weaviate.io/weaviate/model-providers/jinaai/embeddings#available-models) for Weaviate to use. The [default model](https://docs.weaviate.io/weaviate/model-providers/jinaai/embeddings#available-models) 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](https://docs.weaviate.io/weaviate/manage-collections/vector-config#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 collection name if `vectorizeClassName` is `true` (the default differs by integration — check this page's parameter table) Since `v1.27`, the string is sent to the model as is. Earlier versions lowercase it first, and you can restore that behavior with the [`LOWERCASE_VECTORIZATION_INPUT`](https://docs.weaviate.io/deploy/configuration/env-vars#module-specific) environment variable. ### 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. **Python** ```py 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 ) ``` **JavaScript/TypeScript** ```ts 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 }); ``` **Go (v5)** ```go // package, imports not shown func main() { // Instantiation not shown ctx := context.Background() // 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](https://docs.weaviate.io/weaviate/manage-objects/import) into Weaviate. Weaviate generates embeddings for text objects using the specified model. **Python** ```py 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]}") ``` **JavaScript/TypeScript** ```ts 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." } ]; const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) let dataObjects = new Array(); for (let srcObject of srcObjects) { dataObjects.push({ title: srcObject.title, description: srcObject.description, }); } const response = await myCollection.data.insertMany(dataObjects); console.log(response); ``` **Go (v5)** ```go // package, imports not shown func main() { // Instantiation not shown ctx := context.Background() 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) } } } } } ``` > [!TIP] > > **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](https://docs.weaviate.io/assets/images/integration_jinaai_embedding_search-20bbf72e7009131771abd68c5639efcc.png) ### Vector (near text) search When you perform a [vector search](https://docs.weaviate.io/weaviate/search/similarity#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`. **Python** ```py 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** ```ts const collectionName = 'DemoCollection' const myCollection = client.collections.use(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)); ``` **Go (v5)** ```go // package, imports not shown func main() { // Instantiation not shown ctx := context.Background() 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 > [!NOTE] > > **What is a hybrid search?** > > A hybrid search performs a vector search and a keyword (BM25) search, before [combining the results](https://docs.weaviate.io/weaviate/search/hybrid) to return the best matching objects from the database. When you perform a [hybrid search](https://docs.weaviate.io/weaviate/search/hybrid), 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** ```py 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** ```ts const collectionName = 'DemoCollection' const myCollection = client.collections.use(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)); ``` **Go (v5)** ```go // package, imports not shown func main() { // Instantiation not shown ctx := context.Background() 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](https://docs.weaviate.io/weaviate/model-providers/jinaai/embeddings-colbert). - [Jina AI multimodal embedding models + Weaviate](https://docs.weaviate.io/weaviate/model-providers/jinaai/embeddings-multimodal) - [Jina AI reranker models + Weaviate](https://docs.weaviate.io/weaviate/model-providers/jinaai/reranker) ### 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](https://docs.weaviate.io/weaviate/manage-collections) and [How-to: Manage objects](https://docs.weaviate.io/weaviate/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](https://docs.weaviate.io/weaviate/search) 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/)