REST - /v1/schema

Get the schema

Dumps the current Weaviate schema. The result contains an array of objects.

Method and URL

GET /v1/schema

Example request

Python
JavaScript/TypeScript
Go
Java
Curl

import weaviate

client = weaviate.Client("http://localhost:8080")

schema = client.schema.get()
print(schema)

import weaviate from 'weaviate-ts-client';

const client = weaviate.client({
  scheme: 'http',
  host: 'localhost:8080',
});

const response = await client.schema
  .getter()
  .do();
console.log(JSON.stringify(response, null, 2));

package main

import (
  "context"
  "fmt"

  "github.com/weaviate/weaviate-go-client/v4/weaviate"
)

func main() {
  cfg := weaviate.Config{
    Host:   "localhost:8080",
    Scheme: "http",
  }
  client, err := weaviate.NewClient(cfg)
  if err != nil {
    panic(err)
  }

  schema, err := client.Schema().Getter().Do(context.Background())
  if err != nil {
    panic(err)
  }
  fmt.Printf("%v", schema)
}

package io.weaviate;

import io.weaviate.client.Config;
import io.weaviate.client.WeaviateClient;
import io.weaviate.client.base.Result;
import io.weaviate.client.v1.schema.model.Schema;

public class App {
  public static void main(String[] args) {
    Config config = new Config("http", "localhost:8080");
    WeaviateClient client = new WeaviateClient(config);

    Result<Schema> result = client.schema().getter().run();

    if (result.hasErrors()) {
      System.out.println(result.getError());
      return;
    }
    System.out.println(result.getResult());
  }
}

curl http://localhost:8080/v1/schema

Example response

{
  "classes": [
    {
      "class": "Category",
      "description": "Category an article is a type off",
      "moduleConfig": {
        "text2vec-contextionary": {
          "vectorizeClassName": false
        }
      },
      "properties": [
        {
          "dataType": [
            "text"
          ],
          "description": "category name",
          "indexFilterable": true,
          "indexSearchable": true,
          "moduleConfig": {
            "text2vec-contextionary": {
              "vectorizePropertyName": false
            }
          },
          "name": "name"
        }
      ],
      "vectorIndexType": "hnsw",
      "vectorizer": "none"
    },
    {
      "class": "Publication",
      "description": "A publication with an online source",
      "moduleConfig": {
        "text2vec-contextionary": {
          "vectorizeClassName": false
        }
      },
      "properties": [
        {
          "dataType": [
            "text"
          ],
          "description": "Name of the publication",
          "name": "name"
        },
        {
          "dataType": [
            "geoCoordinates"
          ],
          "description": "Geo location of the HQ",
          "name": "headquartersGeoLocation"
        },
        {
          "dataType": [
            "Article"
          ],
          "description": "The articles this publication has",
          "name": "hasArticles"
        },
        {
          "dataType": [
            "Article"
          ],
          "description": "Articles this author wrote",
          "name": "wroteArticles"
        }
      ],
      "vectorIndexType": "hnsw",
      "vectorizer": "none"
    },
    {
      "class": "Author",
      "description": "Normalised types",
      "moduleConfig": {
        "text2vec-contextionary": {
          "vectorizeClassName": true
        }
      },
      "properties": [
        {
          "dataType": [
            "text"
          ],
          "description": "Name of the author",
          "name": "name"
        },
        {
          "dataType": [
            "Publication"
          ],
          "description": "The publication this author writes for",
          "name": "writesFor"
        }
      ],
      "vectorIndexType": "hnsw",
      "vectorizer": "none"
    },
    {
      "class": "Article",
      "description": "Normalised types",
      "moduleConfig": {
        "text2vec-contextionary": {
          "vectorizeClassName": false
        }
      },
      "properties": [
        {
          "dataType": [
            "text"
          ],
          "description": "title of the article",
          "indexFilterable": true,
          "indexSearchable": true,
          "moduleConfig": {
            "text2vec-contextionary": {
              "vectorizePropertyName": false
            }
          },
          "name": "title"
        },
        {
          "dataType": [
            "text"
          ],
          "description": "url of the article",
          "indexFilterable": true,
          "indexSearchable": false,
          "moduleConfig": {
            "text2vec-contextionary": {
              "vectorizePropertyName": false
            }
          },
          "name": "url"
        },
        {
          "dataType": [
            "text"
          ],
          "description": "summary of the article",
          "indexFilterable": true,
          "indexSearchable": true,
          "moduleConfig": {
            "text2vec-contextionary": {
              "vectorizePropertyName": false
            }
          },
          "name": "summary"
        },
        {
          "dataType": [
            "date"
          ],
          "description": "date of publication of the article",
          "name": "publicationDate"
        },
        {
          "dataType": [
            "int"
          ],
          "description": "Words in this article",
          "name": "wordCount"
        },
        {
          "dataType": [
            "Author",
            "Publication"
          ],
          "description": "authors this article has",
          "name": "hasAuthors"
        },
        {
          "dataType": [
            "Publication"
          ],
          "description": "publication this article is in",
          "name": "inPublication"
        },
        {
          "dataType": [
            "Category"
          ],
          "description": "category this article is of",
          "name": "ofCategory"
        },
        {
          "dataType": [
            "boolean"
          ],
          "description": "whether the article is currently accessible through the url",
          "name": "isAccessible"
        }
      ],
      "vectorIndexType": "hnsw",
      "vectorizer": "none"
    }
  ]
}

Create a class

Create a new data object class in the schema.

note

From v1.5.0 onwards, creating a schema is optional. Learn more about Auto Schema.

Method and URL

POST /v1/schema

Parameters

Learn more about the schema configuration here.

Name	Location	Type	Description
`class`	body	string	The name of the class. Multiple words should be concatenated in CamelCase, e.g. `ArticleAuthor`.
`description`	body	string	Description of the class.
`vectorIndexType`	body	string	Defaults to hnsw. Can be omitted in schema definition since this is the only available type for now.
`vectorIndexConfig`	body	object	Vector index type specific settings.
`vectorizer`	body	string	Vectorizer to use for data objects added to this class. Default can be set via Weaviate environment variables.
`moduleConfig` > `text2vec-contextionary` > `vectorizeClassName`	body	object	Include the class name in vector calculation (default true). Learn more about semantic indexing in Weaviate.
`properties`	body	array	An array of property objects.
`properties` > `dataType`	body	array	See the available data types.
`properties` > `description`	body	string	Description of the property.
`properties` > `moduleConfig` > `text2vec-contextionary` > `skip`	body	boolean	If true, the whole property will NOT be included in vectorization. Default is false, meaning that the object will be NOT be skipped.
`properties` > `moduleConfig` > `text2vec-contextionary` > `vectorizePropertyName`	body	boolean	Whether the name of the property is used in the calculation for the vector position of data objects. Default is true. Learn more about semantic indexing in Weaviate.
`properties` > `name`	body	string	The name of the property. Multiple words should be concatenated in camelCase, e.g. `nameOfAuthor`.
`properties` > `indexFilterable` (available from `v1.19`)	body	boolean	Should the data stored in this property be indexed with the filterable, Roaring Bitmap index? Read more about indexing in Weaviate.
`properties` > `indexSearchable` (available from `v1.19`)	body	boolean	Should the data stored in this property be indexed to allow BM25/hybrid-search index? Read more on how to configure indexing in Weaviate.
`properties` > `indexInverted` (deprecated)	body	boolean	Should the data stored in this property be indexed? Learn more about indexing in Weaviate.
`properties` > `tokenization`	body	string	Only for `string`/`text` props. Introduced in `v1.12.0`. Control how a field is tokenized in the inverted index. Defaults to `"word"`, can be set to `"field"`. Learn more about property tokenization.
`invertedIndexConfig` > `stopwords`	body	object	Configure which words should be treated as stopwords and therefore be ignored on querying (stopwords are still indexed). Since `v1.18`, stopwords can be configured at runtime. See more details here.
`invertedIndexConfig` > `indexTimestamps`	body	boolean	Maintain an inverted index for each object by its internal timestamps, currently including `creationTimeUnix` and `lastUpdateTimeUnix`. See more details here.
`replicationConfig` > `factor`	body	int	The replication factor, aka the number of copies in a replicated Weaviate setup.
`multiTenancyConfig` > `enabled`	body	Boolean	Whether to enable multi-tenancy for this class. (Defaults to `false`.)

Example request for creating a class

Python
JavaScript/TypeScript
Go
Java
Curl

import weaviate

client = weaviate.Client("http://localhost:8080")

class_obj = {
    "class": "Article",
    "description": "A written text, for example a news article or blog post",
    "properties": [
        {
            "dataType": ["text"],
            "description": "Title of the article",
            "name": "title",
        },
        {
            "dataType": ["text"],
            "description": "The content of the article",
            "name": "content"
        }
    ]
}

client.schema.create_class(class_obj)

import weaviate from 'weaviate-ts-client';

const client = weaviate.client({
  scheme: 'http',
  host: 'localhost:8080',
});

const classObj = {
  'class': 'Article',
  'description': 'A written text, for example a news article or blog post',
  'vectorizeClassName': true,
  'properties': [
    {
      'name': 'title',
      'dataType': ['string'],
      'description': 'Title of the article',
      'vectorizePropertyName': true,
      'index': true,
    },
    {
      'name': 'content',
      'dataType': ['text'],
      'description': 'The content of the article',
    },
  ],
};

const response = await client
  .schema
  .classCreator()
  .withClass(classObj)
  .do();
console.log(JSON.stringify(response, null, 2));

package main

import (
    "context"

    "github.com/weaviate/weaviate-go-client/v4/weaviate"
    "github.com/weaviate/weaviate/entities/models"
)

func main() {
    cfg := weaviate.Config{
        Host:   "localhost:8080",
        Scheme: "http",
    }
    client, err := weaviate.NewClient(cfg)
    if err != nil {
      panic(err)
    }

    classObj := &models.Class{
        Class:       "Article",
        Description: "A written text, for example a news article or blog post",
        Properties: []*models.Property{
            {
                DataType:    []string{"string"},
                Description: "Title of the article",
                Name:        "title",
            },
            {
                DataType:    []string{"text"},
                Description: "The content of the article",
                Name:        "content",
            },
        },
    }

    err := client.Schema().ClassCreator().WithClass(classObj).Do(context.Background())
    if err != nil {
        panic(err)
    }
}

package io.weaviate;

import java.util.ArrayList;
import io.weaviate.client.Config;
import io.weaviate.client.WeaviateClient;
import io.weaviate.client.base.Result;
import io.weaviate.client.v1.schema.model.DataType;
import io.weaviate.client.v1.schema.model.Property;
import io.weaviate.client.v1.schema.model.WeaviateClass;

public class App {
  public static void main(String[] args) {
    Config config = new Config("http", "localhost:8080");
    WeaviateClient client = new WeaviateClient(config);

    WeaviateClass clazz = WeaviateClass.builder()
      .className("Article")
      .description("A written text, for example a news article or blog post")
      .properties(new ArrayList() { {
        add(Property.builder()
          .dataType(new ArrayList(){ { add(DataType.STRING); } })
          .description("Title of the article")
          .name("title")
          .build());
        add(Property.builder()
          .dataType(new ArrayList(){ { add(DataType.TEXT); } })
          .description("The content of the article")
          .name("content")
          .build());
      } })
      .build();

    Result<Boolean> result = client.schema().classCreator().withClass(clazz).run();
    if (result.hasErrors()) {
      System.out.println(result.getError());
      return;
    }
    System.out.println(result.getResult());
  }
}

curl \
    -X POST \
    -H "Content-Type: application/json" \
    -d '{
        "class": "Article",
        "description": "A written text, for example a news article or blog post",
        "properties": [
            {
            "dataType": [
                "string"
            ],
            "description": "Title of the article",
            "name": "title"
            },
            {
            "dataType": [
                "text"
            ],
            "description": "The content of the article",
            "name": "content"
            }
        ]
    }' \
    http://localhost:8080/v1/schema

Or with all the possible parameters:

Python
JavaScript/TypeScript
Go
Java
Curl

import weaviate

client = weaviate.Client("http://localhost:8080")

class_obj = {
    "class": "Article",
    "description": "A written text, for example a news article or blog post",
    "vectorIndexType": "hnsw",
    "vectorIndexConfig": {
        "distance": "cosine",
        "efConstruction": 128,
        "maxConnections": 64
    },
    "vectorizer": "text2vec-contextionary",
    "moduleConfig": {
      "text2vec-contextionary": {
        "vectorizeClassName": True
      }
    },
    "properties": [
        {
            "dataType": [
                "text"
            ],
            "description": "Title of the article",
            "name": "title",
            "indexInverted": True,
            "moduleConfig": {
                "text2vec-contextionary": {
                  "skip": False,
                  "vectorizePropertyName": False
                }
              }
        },
        {
            "dataType": [
                "text"
            ],
            "description": "The content of the article",
            "name": "content",
            "indexInverted": True,
            "moduleConfig": {
                "text2vec-contextionary": {
                  "skip": False,
                  "vectorizePropertyName": False
                }
              }
        }
    ],
    "shardingConfig": {
      "virtualPerPhysical": 128,
      "desiredCount": 1,
      "desiredVirtualCount": 128,
      "key": "_id",
      "strategy": "hash",
      "function": "murmur3"
    },
    "invertedIndexConfig": {
      "stopwords": {
        "preset": "en",
        "additions": ["star", "nebula"],
        "removals": ["a", "the"]
      },
      "indexTimestamps": True
    },
    "replicationConfig": {
      "factor": 3
    }
}

client.schema.create_class(class_obj)

import weaviate from 'weaviate-ts-client';

const client = weaviate.client({
  scheme: 'http',
  host: 'localhost:8080',
});

const classObj = {
  class: 'Article',
  description: 'A written text, for example a news article or blog post',
  vectorIndexType: 'hnsw',
  vectorIndexConfig: {
    distance: 'cosine',
    efConstruction: 128,
    maxConnections: 64,
  },
  vectorizer: 'text2vec-contextionary',
  moduleConfig: {
    'text2vec-contextionary': {
      vectorizeClassName: true,
    },
  },
  properties: [
    {
      dataType: [
        'text',
      ],
      description: 'Title of the article',
      name: 'title',
      indexInverted: true,
      moduleConfig: {
        'text2vec-contextionary': {
          skip: false,
          vectorizePropertyName: false,
        },
      },
    },
    {
      dataType: [
        'text',
      ],
      description: 'The content of the article',
      name: 'content',
      indexInverted: true,
      moduleConfig: {
        'text2vec-contextionary': {
          skip: false,
          vectorizePropertyName: false,
        },
      },
    },
  ],
  shardingConfig: {
    virtualPerPhysical: 128,
    desiredCount: 1,
    desiredVirtualCount: 128,
    key: '_id',
    strategy: 'hash',
    function: 'murmur3',
  },
  invertedIndexConfig: {
    stopwords: {
      preset: 'en',
      additions: ['star', 'nebula'],
      removals: ['a', 'the'],
    },
    indexTimestamps: true,
  },
  replicationConfig: {
    factor: 3,
  },
};

const response = await client
  .schema
  .classCreator()
  .withClass(classObj)
  .do();
console.log(JSON.stringify(response, null, 2));

package main

import (
  "context"

  "github.com/weaviate/weaviate-go-client/v4/weaviate"
  "github.com/weaviate/weaviate/entities/models"
)

func main() {
  cfg := weaviate.Config{
    Host:   "localhost:8080",
    Scheme: "http",
  }
  client, err := weaviate.NewClient(cfg)
  if err != nil {
    panic(err)
  }

  classObj := &models.Class{
    Class:             "Article",
    Description:       "A written text, for example a news article or blog post",
    VectorIndexType:   "hnsw",
    Vectorizer:        "text2vec-contextionary",
    VectorIndexConfig: map[string]interface{}{
      "distance": "cosine",
      "ef": float64(128),
      "efConstruction": float64(128),
      "maxConnections": float64(32),
    },
    Properties: []*models.Property{
      {
        DataType:    []string{"text"},
        Description: "Title of the article",
        Name:        "title",
      },
      {
        DataType:    []string{"text"},
        Description: "The content of the article",
        Name:        "content",
      },
    },
    InvertedIndexConfig: &models.InvertedIndexConfig{
      Stopwords: &models.StopwordConfig{
        Preset:    "en",
        Additions: []string{"star", "nebula"},
        Removals:  []string{"a", "the"},
      },
      IndexTimestamps: true,
    },
    ReplicationConfig: &models.ReplicationConfig{
      Factor: 3,
    }
  }

  err := client.Schema().ClassCreator().WithClass(classObj).Do(context.Background())
  if err != nil {
    panic(err)
  }
}

package io.weaviate;

import java.util.ArrayList;
import io.weaviate.client.Config;
import io.weaviate.client.WeaviateClient;
import io.weaviate.client.base.Result;
import io.weaviate.client.v1.misc.model.InvertedIndexConfig;
import io.weaviate.client.v1.misc.model.ShardingConfig;
import io.weaviate.client.v1.misc.model.StopwordConfig;
import io.weaviate.client.v1.misc.model.VectorIndexConfig;
import io.weaviate.client.v1.schema.model.DataType;
import io.weaviate.client.v1.schema.model.Property;
import io.weaviate.client.v1.schema.model.WeaviateClass;
import io.weaviate.client.v1.misc.model.ReplicationConfig;

public class App {
  public static void main(String[] args) {
    Config config = new Config("http", "localhost:8080");
    WeaviateClient client = new WeaviateClient(config);

    StopwordConfig stopwordConfig = StopwordConfig.builder()
      .preset("en")
      .additions(new String[]{ "star", "nebula" })
      .removals(new String[]{ "a", "the" })
      .build();

    InvertedIndexConfig invertedIndexConfig = InvertedIndexConfig.builder()
      .stopwords(stopwordConfig)
      .indexTimestamps(true)
      .build();

    VectorIndexConfig vectorIndexConfig = VectorIndexConfig.builder()
      .distance("cosine")
      .cleanupIntervalSeconds(300)
      .efConstruction(128)
      .maxConnections(64)
      .vectorCacheMaxObjects(500000)
      .ef(-1)
      .skip(false)
      .dynamicEfFactor(8)
      .dynamicEfMax(500)
      .dynamicEfMin(100)
      .flatSearchCutoff(40000)
      .build();

    ShardingConfig shardingConfig = ShardingConfig.builder()
      .desiredCount(1)
      .desiredVirtualCount(128)
      .function("murmur3")
      .key("_id")
      .strategy("hash")
      .virtualPerPhysical(128)
      .build();

    ReplicationConfig replicationConfig = ReplicationConfig.builder()
      .factor(3)
      .build();


    WeaviateClass clazz = WeaviateClass.builder()
      .className("Article")
      .description("A written text, for example a news article or blog post")
      .vectorIndexType("hnsw")
      .vectorizer("text2vec-contextionary")
      .invertedIndexConfig(invertedIndexConfig)
      .shardingConfig(shardingConfig)
      .vectorIndexConfig(vectorIndexConfig)
      .replicationConfig(replicationConfig)
      .properties(new ArrayList() { {
        add(Property.builder()
          .dataType(new ArrayList(){ { add(DataType.TEXT); } })
          .description("Title of the article")
          .name("title")
          .build());
        add(Property.builder()
          .dataType(new ArrayList(){ { add(DataType.TEXT); } })
          .description("The content of the article")
          .name("content")
          .build());
      } })
      .build();

    Result<Boolean> result = client.schema().classCreator().withClass(clazz).run();
    if (result.hasErrors()) {
      System.out.println(result.getError());
      return;
    }
    System.out.println(result.getResult());
  }
}

curl \
-X POST \
-H "Content-Type: application/json" \
-d '{
    "class": "Article",
    "description": "A written text, for example a news article or blog post",
    "vectorIndexType": "hnsw",
    "vectorIndexConfig": {
        "distance": "cosine",
        "efConstruction": 128,
        "maxConnections": 64
    },
    "vectorizer": "text2vec-contextionary",
    "moduleConfig": {
      "text2vec-contextionary": {
        "vectorizeClassName": true
      }
    },
    "properties": [
        {
            "dataType": [
                "text"
            ],
            "description": "Title of the article",
            "name": "title",
            "indexInverted": true,
            "moduleConfig": {
                "text2vec-contextionary": {
                  "skip": false,
                  "vectorizePropertyName": true
                }
              }
        },
        {
            "dataType": [
                "text"
            ],
            "description": "The content of the article",
            "name": "content",
            "indexInverted": true,
            "moduleConfig": {
                "text2vec-contextionary": {
                  "skip": false,
                  "vectorizePropertyName": true
                }
              }
        }
    ],
    "shardingConfig": {
      "virtualPerPhysical": 128,
      "desiredCount": 1,
      "desiredVirtualCount": 128,
      "key": "_id",
      "strategy": "hash",
      "function": "murmur3"
    },
    "invertedIndexConfig": {
      "stopwords": {
        "preset": "en",
        "additions": ["star", "nebula"],
        "removals": ["a", "the"]
      },
      "indexTimestamps": true
    },
    "replicationConfig": {
      "factor": 3
    }
}' \
http://localhost:8080/v1/schema

Get a single class from the schema

Retrieves the configuration of a single class in the schema.

Method and URL

GET /v1/schema/{ClassName}

Example request

Python
JavaScript/TypeScript
Go
Java
Curl

import weaviate

client = weaviate.Client("http://localhost:8080")

schema = client.schema.get("Article")
print(schema)

import weaviate from 'weaviate-ts-client';

const client = weaviate.client({
  scheme: 'http',
  host: 'localhost:8080',
});

const response = await client
  .schema
  .classGetter()
  .withClassName('Article')
  .do();
console.log(JSON.stringify(response, null, 2));

package main

import (
  "context"
  "fmt"

  "github.com/weaviate/weaviate-go-client/v4/weaviate"
)

func main() {
  cfg := weaviate.Config{
    Host:   "localhost:8080",
    Scheme: "http",
  }
  client, err := weaviate.NewClient(cfg)
  if err != nil {
    panic(err)
  }

  schema, err := client.Schema().ClassGetter().WithClassName("Article").Do(context.Background())
  if err != nil {
    panic(err)
  }
  fmt.Printf("%v", schema)
}

package io.weaviate;

import io.weaviate.client.Config;
import io.weaviate.client.WeaviateClient;
import io.weaviate.client.base.Result;
import io.weaviate.client.v1.schema.model.Schema;

public class App {
  public static void main(String[] args) {
    Config config = new Config("http", "localhost:8080");
    WeaviateClient client = new WeaviateClient(config);

    Result<Schema> result = client.schema().classGetter().withClassName("Article").run();

    if (result.hasErrors()) {
      System.out.println(result.getError());
      return;
    }
    System.out.println(result.getResult());
  }
}

curl http://localhost:8080/v1/schema/Article

Delete a class

Remove a class (and all data in the instances) from the schema.

Method and URL

DELETE v1/schema/{class_name}

URL parameters

Name	Location	Type	Description
`{class_name}`	path	string	The name of the class

Example request for deleting a class

Python
JavaScript/TypeScript
Go
Java
Curl

import weaviate

client = weaviate.Client("http://localhost:8080")

client.schema.delete_class("Article")  # deletes the class "Article" along with all data objects of class "Article"
# OR
client.schema.delete_all()  # deletes all classes along with all data

import weaviate from 'weaviate-ts-client';

const client = weaviate.client({
  scheme: 'http',
  host: 'localhost:8080',
});

await client.schema
  .classDeleter()
  .withClassName('Article')
  .do();

package main

import (
  "context"

  "github.com/weaviate/weaviate-go-client/v4/weaviate"
)

func main() {
  cfg := weaviate.Config{
    Host:   "localhost:8080",
    Scheme: "http",
  }
  client, err := weaviate.NewClient(cfg)
  if err != nil {
    panic(err)
  }

  err := client.Schema().ClassDeleter().
    WithClassName("Article").
    Do(context.Background())

  if err != nil {
    panic(err)
  }
}

package io.weaviate;

import io.weaviate.client.Config;
import io.weaviate.client.WeaviateClient;
import io.weaviate.client.base.Result;

public class App {
  public static void main(String[] args) {
    Config config = new Config("http", "localhost:8080");
    WeaviateClient client = new WeaviateClient(config);

    Result<Boolean> result = client.schema().classDeleter()
      .withClassName("Article")
      .run();

    if (result.hasErrors()) {
      System.out.println(result.getError());
      return;
    }
    System.out.println(result.getResult());
  }
}

curl -X DELETE http://localhost:8080/v1/schema/Article

Update a class

Update settings of an existing schema class.

Use this endpoint to alter an existing class in the schema. Note that not all settings are mutable. If an error about immutable fields is returned and you still need to update this particular setting, you will have to delete the class (and the underlying data) and recreate. This endpoint cannot be used to modify properties. Instead, use POST /v1/schema/{ClassName}/properties. A typical use case for this endpoint is to update configuration, such as the vectorIndexConfig. Note that even in mutable sections, such as vectorIndexConfig, some fields may be immutable.

You should attach a body to this PUT request with the entire new configuration of the class.

Method and URL

PUT v1/schema/{class_name}

Parameters

The URL must contain the following parameter:

Name	Location	Type	Description
`{class_name}`	path	string	The name of the class

Parameters in the PUT body:

Name	Location	Type	Description
`class`	body	string	The name of the class. Multiple words should be concatenated in CamelCase, e.g. `ArticleAuthor`.
`description`	body	string	Description of the class.
`vectorIndexType`	body	string	Defaults to hnsw. Can be omitted in schema definition since this is the only available type for now.
`vectorIndexConfig`	body	object	Vector index type specific settings.
`vectorizer`	body	string	Vectorizer to use for data objects added to this class. Default can be set via Weaviate environment variables.
`moduleConfig` > `text2vec-contextionary` > `vectorizeClassName`	body	object	Include the class name in vector calculation (default true). Learn more about how to configure indexing in Weaviate.
`properties`	body	array	An array of property objects.
`properties` > `dataType`	body	array	See the available data types
`properties` > `description`	body	string	Description of the property.
`properties` > `moduleConfig` > `text2vec-contextionary` > `skip`	body	boolean	If true, the whole property will NOT be included in vectorization. Default is false, meaning that the object will be NOT be skipped.
`properties` > `moduleConfig` > `text2vec-contextionary` > `vectorizePropertyName`	body	boolean	Whether the name of the property is used in the calculation for the vector position of data objects. Default is true. Learn more about how to configure indexing in Weaviate.
`properties` > `name`	body	string	The name of the property. Multiple words should be concatenated in camelCase, e.g. `nameOfAuthor`.
`properties` > `indexFilterable` (available from `v1.19`)	body	boolean	Should the data stored in this property be indexed with the filterable, Roaring Bitmap index? Read more about indexing in Weaviate.
`properties` > `indexSearchable` (available from `v1.19`)	body	boolean	Should the data stored in this property be indexed to allow BM25/hybrid-search index? Read more about indexing in Weaviate.
`properties` > `indexInverted` (deprecated)	body	boolean	Should the data stored in this property be indexed? Learn more about indexing in Weaviate.
`properties` > `tokenization`	body	string	Only for `string`/`text` props. Introduced in `v1.12.0`. Control how a field is tokenized in the inverted index. Defaults to `"word"`. If `string` is used, can be set to `"field"`. Learn more about property tokenization.
`invertedIndexConfig` > `stopwords`	body	object	Configure which words should be treated as stopwords and therefore be ignored when querying (stopwords are still indexed). Since`v1.18`, stopwords can be configured at runtime. See more details here.
`invertedIndexConfig` > `indexTimestamps`	body	boolean	Maintain an inverted index for each object by its internal timestamps, currently including `creationTimeUnix` and `lastUpdateTimeUnix` See more details here.

Example request for updating a class

Python
Curl

import weaviate

client = weaviate.Client("http://localhost:8080")

class_obj = {
    "class": "Article",  # immutable - must be kept the same, or omitted
    "description": "A written text, for example a news article or blog post",
    "properties": [
        {
        "dataType": [
            "text"
        ],
        "description": "Title of the article",
        "name": "title",
        },
        {
        "dataType": [
            "text"
        ],
        "description": "The content of the article",
        "name": "content"
        }
    ]
}

client.schema.update_config("Article", class_obj)

curl \
    -X PUT \
    -H "Content-Type: application/json" \
    -d '{
        "class": "Article",
        "description": "A written text, for example a news article or blog post",
        "properties": [
            {
            "dataType": [
                "text"
            ],
            "description": "Title of the article",
            "name": "title"
            },
            {
            "dataType": [
                "text"
            ],
            "description": "The content of the article",
            "name": "content"
            }
        ]
    }' \
    http://localhost:8080/v1/schema/Article

Add a property

Method and URL

POST v1/schema/{class_name}/properties

Parameters

Name	Location	Type	Description
`dataType`	body	array	An available data type.
`description`	body	string	Description of the property.
`moduleConfig` > `text2vec-contextionary` > `skip`	body	boolean	If true, the whole property will NOT be included in vectorization. Default is false, meaning that the object will be NOT be skipped.
`moduleConfig` > `text2vec-contextionary` > `vectorizePropertyName`	body	boolean	Whether the name of the property is used in the calculation for the vector position of data objects. Default is true. Learn more about how to configure indexing in Weaviate.
`name`	body	string	The name of the property. Multiple words should be concatenated in camelCase like `nameOfAuthor`.
`indexFilterable` (available from `v1.19`)	body	boolean	Should the data stored in this property be indexed with the filterable, Roaring Bitmap index? Read more about indexing in Weaviate.
`indexSearchable` (available from `v1.19`)	body	boolean	Should the data stored in this property be indexed to allow BM25/hybrid-search index? Read more about indexing in Weaviate.
`indexInverted` (deprecated)	body	boolean	Should the data stored in this property be indexed? Learn more about indexing in Weaviate.

Example request for adding a property

Python
JavaScript/TypeScript
Go
Java
Curl

import weaviate

client = weaviate.Client("http://localhost:8080")

add_prop = {
  "dataType": [
      "boolean"
  ],
  "name": "onHomepage"
}

client.schema.property.create("Article", add_prop)

import weaviate from 'weaviate-ts-client';

const client = weaviate.client({
  scheme: 'http',
  host: 'localhost:8080',
});

const className = 'Article';
const prop = {
  dataType: ['boolean'],
  name: 'onHomepage',
};

const response = await client.schema
  .propertyCreator()
  .withClassName(className)
  .withProperty(prop)
  .do();
console.log(JSON.stringify(response, null, 2));

package main

import (
  "context"

  "github.com/weaviate/weaviate-go-client/v4/weaviate"
  "github.com/weaviate/weaviate/entities/models"
)

func main() {
  cfg := weaviate.Config{
    Host:   "localhost:8080",
    Scheme: "http",
  }
  client, err := weaviate.NewClient(cfg)
  if err != nil {
    panic(err)
  }

  prop := &models.Property{
    DataType: []string{"boolean"},
    Name:     "onHomepage",
  }

  err := client.Schema().PropertyCreator().
    WithClassName("Article").
    WithProperty(prop).
    Do(context.Background())

  if err != nil {
    panic(err)
  }
}

package io.weaviate;

import java.util.Arrays;
import io.weaviate.client.Config;
import io.weaviate.client.WeaviateClient;
import io.weaviate.client.base.Result;
import io.weaviate.client.v1.schema.model.DataType;
import io.weaviate.client.v1.schema.model.Property;

public class App {
  public static void main(String[] args) {
    Config config = new Config("http", "localhost:8080");
    WeaviateClient client = new WeaviateClient(config);

    Property property = Property.builder()
      .dataType(Arrays.asList(DataType.BOOLEAN))
      .name("onHomepage")
      .build();

    Result<Boolean> result = client.schema().propertyCreator()
      .withClassName("Article")
      .withProperty(property)
      .run();

    if (result.hasErrors()) {
      System.out.println(result.getError());
      return;
    }
    System.out.println(result.getResult());
  }
}

curl \
    -X POST \
    -H "Content-Type: application/json" \
    -d '{
        "dataType": [
          "boolean"
        ],
        "name": "onHomepage"
    }' \
    http://localhost:8080/v1/schema/Article/properties

Inspect the shards of a class

As described in Architecture > Storage, creation of a class leads to creating an index which manages all the disk storage and vector indexing. An index itself can be comprised of multiple shards. If a class index is used on multiple nodes of a multi-node Weaviate cluster there must be at least one shard per node.

You can view a list of all shards for a particular class:

Method and URL

note

This API was added in v1.12.0.

GET v1/schema/{class_name}/shards

Parameters

Name	Location	Type	Description
`{class_name}`	URL path	string	The name of the class

Example request viewing shards of a class

Python
JavaScript/TypeScript
Go
Java
Curl

import weaviate

client = weaviate.Client("http://localhost:8080")

client.schema.get_class_shards("Article")

import weaviate from 'weaviate-ts-client';

const client = weaviate.client({
  scheme: 'http',
  host: 'localhost:8080',
});

const response = await client.schema
  .shardsGetter()
  .withClassName('Article')
  .do();
console.log(JSON.stringify(response, null, 2));

package main

import (
  "context"
  "fmt"

  "github.com/weaviate/weaviate-go-client/v4/weaviate"
)

func main() {
  cfg := weaviate.Config{
    Host:   "localhost:8080",
    Scheme: "http",
  }
  client, err := weaviate.NewClient(cfg)
  if err != nil {
    panic(err)
  }
  shards, err := client.Schema().
    ShardsGetter().
    WithClassName("Article").
    Do(context.Background())
  if err != nil {
    panic(err)
  }
  fmt.Printf("%v", shards)
}

package io.weaviate;

import io.weaviate.client.Config;
import io.weaviate.client.WeaviateClient;
import io.weaviate.client.base.Result;
import io.weaviate.client.v1.schema.model.Shard;

public class App {
  public static void main(String[] args) {
    Config config = new Config("http", "localhost:8080");
    WeaviateClient client = new WeaviateClient(config);

    Result<Shard[]> result = client.schema()
      .shardsGetter()
      .withClassName("Article")
      .run()
    if (result.hasErrors()) {
      System.out.println(result.getError());
      return;
    }
    System.out.println(result.getResult());
  }
}

curl http://localhost:8080/v1/schema/Article/shards

Update shard status

A shard may have been marked as read-only, for example because the disk was full. You can manually set a shard to READY again using the following API. There is also a convenience function in each client to set the status of all shards of a class.

Method and URL

note

This API was added in v1.12.0

PUT v1/schema/{class_name}/shards/{shard_name}

Parameters

Name	Location	Type	Description
`{class_name}`	URL path	string	The name of the class.
`{shard_name}`	URL path	string	The name/id of the shard.
`status`	body	string	The status to update the shard to. One of `READONLY`, `READY`.

Example requests to update the status of a shard

Python
JavaScript/TypeScript
Go
Java
Curl

import weaviate

client = weaviate.Client("http://localhost:8080")

# Update a shard
client.schema.update_class_shard(
  class_name="Article",
  status="READONLY",
  shard_name="shard-1234",
)

# Convenience method to update all shards in a class
client.schema.update_class_shard(
  class_name="Article",
  status="READONLY",
)

import weaviate from 'weaviate-ts-client';

const client = weaviate.client({
  scheme: 'http',
  host: 'localhost:8080',
});

// Update a shard
await client.schema
  .shardUpdater()
  .withClassName('Article')
  .withShardName('shard-1234')
  .withStatus('READONLY')
  .do();

// Convenience method to update all shards in a class
await client.schema
  .shardsUpdater()
  .withClassName('Article')
  .withStatus('READONLY')
  .do();

package main

import (
  "context"
  "fmt"

  "github.com/weaviate/weaviate-go-client/v4/weaviate"
)

func main() {
  cfg := weaviate.Config{
    Host:   "localhost:8080",
    Scheme: "http",
  }

  client, err := weaviate.NewClient(cfg)
  if err != nil {
    panic(err)
  }

  // Update a shard
  status, err := client.Schema().
    ShardUpdater().
    WithClassName("Article").
    WithShardName("shard-1234").
    WithStatus("READONLY").
    Do(context.Background())
  if err != nil {
    panic(err)
  }
  fmt.Printf("%v", status)

  // Convenience method to update all shards in a class
  shards, err := client.Schema().
    ShardsUpdater().
    WithClassName("Article").
    WithStatus("READY").
    Do(context.Background())
  if err != nil {
    panic(err)
  }
  fmt.Printf("%v", shards)
}

package io.weaviate;

import io.weaviate.client.Config;
import io.weaviate.client.WeaviateClient;
import io.weaviate.client.base.Result;
import io.weaviate.client.v1.schema.model.ShardStatus;
import io.weaviate.client.v1.schema.model.ShardStatuses;

public class App {
  public static void main(String[] args) {
    Config config = new Config("http", "localhost:8080");
    WeaviateClient client = new WeaviateClient(config);

    // Update a shard
    Result<ShardStatus> updateShard = client.schema()
      .shardUpdater()
      .withClassName("Article")
      .withShardName("shard-1234")
      .withStatus(ShardStatuses.READONLY)
      .run();
    if (updateShard.hasErrors()) {
      System.out.println(updateShard.getError());
      return;
    }
    System.out.println(updateShard.getResult());

    // Convenience method to update all shards in a class
    Result<ShardStatus[]> updateShards = client.schema()
      .shardsUpdater()
      .withClassName("Article")
      .withStatus(ShardStatuses.READY)
      .run();
    if (updateShards.hasErrors()) {
      System.out.println(updateShards.getError());
      return;
    }
    System.out.println(updateShards.getResult());
  }
}

curl \
    -X PUT \
    -H "Content-Type: application/json" \
    -d '{
         "status": "READONLY"
        }' \
    http://localhost:8080/v1/schema/Article/shards/shard-1234

Multi-tenancy

Multi-tenancy availability

Multi-tenancy available from version v1.20
(Experimental) Tenant activity status setting available from version v1.21

Tenants are used to separate data between different users or groups of users. They can be specified as follows:

Add tenant(s)

Pass a payload with an array of tenant objects. The available fields are:

Name	Type	Description
`name`	string	(Required) The name of the tenant.
`activityStatus`	string	(Optional, experimental) The activity status of the tenant. Can be `HOT` (default) or `COLD`.

Example payload

[
  {
    "name": "TENANT_A"
  },
  {
    "name": "TENANT_B",
    "activityStatus": "COLD"
  }
]

Allowable tenant names

A tenant name can only contain alphanumeric characters (a-z, A-Z, 0-9), underscore (_), and hyphen (-), with a length of 4 to 64 characters.

POST v1/schema/{class_name}/tenants

List tenants

GET v1/schema/{class_name}/tenants

Remove tenants

Pass a payload with an array of tenant names in the form of ["TENANT_NAME1", "TENANT_NAME2"] to remove from the class.

DELETE v1/schema/{class_name}/tenants

Update tenants

PUT v1/schema/{class_name}/tenants

Pass a payload with an array of tenant objects. For updating tenants, both name and activityStatus are required.

Example payload

[
  {
    "name": "TENANT_A",
    "activityStatus": "COLD"
  },
  {
    "name": "TENANT_B",
    "activityStatus": "HOT"
  }
]

More Resources

For additional information, try these sources.

Get the schema​

Method and URL​

Example request​

Example response​

Create a class​

Method and URL​

Parameters​

Example request for creating a class​

Get a single class from the schema​

Method and URL​

Example request​

Delete a class​

Method and URL​

URL parameters​

Example request for deleting a class​

Update a class​

Method and URL​

Parameters​

Example request for updating a class​

Add a property​

Method and URL​

Parameters​

Example request for adding a property​

Inspect the shards of a class​

Method and URL​

Parameters​

Example request viewing shards of a class​

Update shard status​

Method and URL​

Parameters​

Example requests to update the status of a shard​

Multi-tenancy​

Add tenant(s)​

Example payload​

List tenants​

Remove tenants​

Update tenants​

Example payload​

More Resources​

Get the schema

Method and URL

Example request

Example response

Create a class

Method and URL

Parameters

Example request for creating a class

Get a single class from the schema

Method and URL

Example request

Delete a class

Method and URL

URL parameters

Example request for deleting a class

Update a class

Method and URL

Parameters

Example request for updating a class

Add a property

Method and URL

Parameters

Example request for adding a property

Inspect the shards of a class

Method and URL

Parameters

Example request viewing shards of a class

Update shard status

Method and URL

Parameters

Example requests to update the status of a shard

Multi-tenancy

Add tenant(s)

Example payload

List tenants

Remove tenants

Update tenants

Example payload

More Resources