Text Embeddings

from weaviate.classes.config import Configureclient.collections.create(    "DemoCollection",    vector_config=Configure.Vectors.text2vec_google_gemini(        name="title_vector",        source_properties=["title"],        # (Optional) To manually set the model ID        model="gemini-embedding-2"    ),    # Additional parameters not shown)

await client.collections.create({  name: 'DemoCollection',  properties: [    {      name: 'title',      dataType: 'text' as const,    },  ],  vectorizers: [    weaviate.configure.vectors.text2VecGoogleGemini({      name: 'title_vector',      sourceProperties: ['title'],      // (Optional) To manually set the model ID      model: 'gemini-embedding-2'    }),  ],  // Additional parameters not shown});

// Define the collectionbasicGoogleStudioVectorizerDef := &models.Class{  Class: "DemoCollection",  VectorConfig: map[string]models.VectorConfig{    "title_vector": {      Vectorizer: map[string]interface{}{        "text2vec-google": map[string]interface{}{          "properties":  []string{"title"},          "apiEndpoint": "generativelanguage.googleapis.com",          "modelId":     "gemini-embedding-001", // (Optional) To manually set the model ID        },      },    },  },}// add the collectionerr = client.Schema().ClassCreator().WithClass(basicGoogleStudioVectorizerDef).Do(ctx)if err != nil {  panic(err)}

from weaviate.classes.config import Configureclient.collections.create(    "DemoCollection",    vector_config=Configure.Vectors.text2vec_google(        name="title_vector",        source_properties=["title"],        project_id="<google-cloud-project-id>",  # Required for Vertex AI        # (Optional) To manually set the model ID        model="gemini-embedding-2"    ),    # Additional parameters not shown)

await client.collections.create({  name: 'DemoCollection',  properties: [    {      name: 'title',      dataType: 'text' as const,    },  ],  vectorizers: [    weaviate.configure.vectors.text2VecGoogle({      name: 'title_vector',      sourceProperties: ['title'],      projectId: '<google-cloud-project-id>',      // (Optional) To manually set the model ID      model: 'gemini-embedding-2'    }),  ],  // Additional parameters not shown});

// Define the collectionbasicGoogleVertexVectorizerDef := &models.Class{  Class: "DemoCollection",  VectorConfig: map[string]models.VectorConfig{    "title_vector": {      Vectorizer: map[string]interface{}{        "text2vec-google": map[string]interface{}{          "projectId": "<google-cloud-project-id>",          "modelId":   "gemini-embedding-001", // (Optional) To manually set the model ID        },      },    },  },}// add the collectionerr = client.Schema().ClassCreator().WithClass(basicGoogleVertexVectorizerDef).Do(ctx)if err != nil {  panic(err)}

from weaviate.classes.config import Configure# For Vertex AIclient.collections.create(    "DemoCollection",    vector_config=Configure.Vectors.text2vec_google_vertex(        name="title_vector",        source_properties=["title"],        project_id="<google-cloud-project-id>",  # Required for Vertex AI        # Further options        # model="<google-model-id>",        # location="<google-cloud-region>",        # api_endpoint="<google-api-endpoint>",    ),    # Additional parameters not shown)# clean upclient.collections.delete("DemoCollection")# For Google AI Studio (Gemini API)client.collections.create(    "DemoCollection",    vector_config=Configure.Vectors.text2vec_google_gemini(        name="title_vector",        source_properties=["title"],        # Further options        model="gemini-embedding-2",    ),    # Additional parameters not shown)

await client.collections.create({  name: 'DemoCollection',  properties: [    {      name: 'title',      dataType: 'text' as const,    },  ],  vectorizers: [    weaviate.configure.vectors.text2VecGoogle({      name: 'title_vector',      sourceProperties: ['title'],      projectId: '<google-cloud-project-id>', // Required for Vertex AI      // modelId: '<google-model-id>',      // apiEndpoint: '<google-api_endpoint>',      // vectorizeClassName: false,    }),  ],  // Additional parameters not shown});

// Define the collectiongoogleVectorizerFullDef := &models.Class{  Class: "DemoCollection",  VectorConfig: map[string]models.VectorConfig{    "title_vector": {      Vectorizer: map[string]interface{}{        "text2vec-google": map[string]interface{}{          "properties":  []string{"title"},          "projectId":   "<google-cloud-project-id>",  // Required for Vertex AI          "modelId":     "textembedding-gecko@latest", // (Optional) To manually set the model ID          "apiEndpoint": "<google-api-endpoint>",      // (Optional) To manually set the API endpoint        },      },    },  },}// add the collectionerr = client.Schema().ClassCreator().WithClass(googleVectorizerFullDef).Do(ctx)if err != nil {  panic(err)}

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]}")

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.Objectobjects := []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 itemsbatcher := client.Batch().ObjectsBatcher()for _, dataObj := range objects {  batcher.WithObjects(&models.Object{    Class:      "DemoCollection",    Properties: dataObj,  })}// FlushbatchRes, err := batcher.Do(ctx)// Error handlingif 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)      }    }  }}

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"])

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)

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"])

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)