/v1/schema

Before adding data to Weaviate, you need to create a schema. The schema contains the structure of your Weaviate in graph format. You will use the graph format to search through your Weaviate instance using GraphQL. To learn how to create a schema, you can follow this How-To tutorial.

NOTE: From v1.5.0 onwards creating a schema is now optional, learn more about Auto Schema here.

Get the schema

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

Method and URL

GET /v1/schema

Example request

Python

  import weaviate

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

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

Javascript

  const weaviate = require("weaviate-client");

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

client
  .schema
  .getter()
  .do()
  .then(res => {
    console.log(res);
  })
  .catch(err => {
    console.error(err)
  });

  package main

import (
  "context"
  "fmt"

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

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

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

Java

  package technology.semi.weaviate;

import technology.semi.weaviate.client.Config;
import technology.semi.weaviate.client.WeaviateClient;
import technology.semi.weaviate.client.base.Result;
import technology.semi.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

  $ 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": [
            "string"
          ],
          "description": "category name",
          "indexInverted": 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": [
            "string"
          ],
          "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": [
            "string"
          ],
          "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": [
            "string"
          ],
          "description": "title of the article",
          "indexInverted": true,
          "moduleConfig": {
            "text2vec-contextionary": {
              "vectorizePropertyName": false
            }
          },
          "name": "title"
        },
        {
          "dataType": [
            "string"
          ],
          "description": "url of the article",
          "indexInverted": false,
          "moduleConfig": {
            "text2vec-contextionary": {
              "vectorizePropertyName": false
            }
          },
          "name": "url"
        },
        {
          "dataType": [
            "text"
          ],
          "description": "summary of the article",
          "indexInverted": 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 now optional, learn more about Auto Schema here.

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 like `ArticleAuthor`.
`description`	body	string	Description of the classname
`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 in Weaviate’s environment variables
`moduleConfig` > `text2vec-contextionary` > `vectorizeClassName`	body	object	include the class name in vector calculation (default true). Learn more about how to regulate indexing in Weaviate here.
`properties`	body	array	An array of property objects
`properties` > `dataType`	body	array	Click here for a list of 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 name of the property is used in the calculation for the vector position of data objects. default is true. Learn more about how to regulate indexing in Weaviate here.
`properties` > `name`	body	string	The name of the property, multiple words should be concatenated in camelCase like `nameOfAuthor`.
`properties` > `indexInverted`	body	boolean	Should the the data stored in this property be indexed? Learn more about how to regulate indexing in Weaviate here.
`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"`. See more details here.
`invertedIndexConfig` > `stopwords`	body	object	Configure which words should be treated as stopwords and therefore be ignored on inverted indexing and querying. 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 creating a class

Python

  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": [
            "string"
        ],
        "description": "Title of the article",
        "name": "title",
        },
        {
        "dataType": [
            "text"
        ],
        "description": "The content of the article",
        "name": "content"
        }
    ]
}

client.schema.create_class(class_obj)

Javascript

  const weaviate = require("weaviate-client");

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

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

client
  .schema
  .classCreator()
  .withClass(classObj)
  .do()
  .then(res => {
    console.log(res)
  })
  .catch(err => {
    console.error(err)
  });

  package main

import (
    "context"

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

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

    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)
    }
}

Java

  package technology.semi.weaviate;

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

  $ 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

  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": [
                "string"
            ],
            "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
    }
}

client.schema.create_class(class_obj)

Javascript

  const weaviate = require("weaviate-client");

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

var 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": [
                "string"
            ],
            "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
    }
}

client
  .schema
  .classCreator()
  .withClass(classObj)
  .do()
  .then(res => {
    console.log(res)
  })
  .catch(err => {
    console.error(err)
  });

  package main

import (
  "context"

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

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

  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{"string"},
        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,
    },
  }

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

Java

  package technology.semi.weaviate;

import java.util.ArrayList;
import technology.semi.weaviate.client.Config;
import technology.semi.weaviate.client.WeaviateClient;
import technology.semi.weaviate.client.base.Result;
import technology.semi.weaviate.client.v1.misc.model.InvertedIndexConfig;
import technology.semi.weaviate.client.v1.misc.model.ShardingConfig;
import technology.semi.weaviate.client.v1.misc.model.StopwordConfig;
import technology.semi.weaviate.client.v1.misc.model.VectorIndexConfig;
import technology.semi.weaviate.client.v1.schema.model.DataType;
import technology.semi.weaviate.client.v1.schema.model.Property;
import technology.semi.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);

    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();

    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)
      .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

  $ 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": [
                "string"
            ],
            "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
    }
}' \
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

  import weaviate

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

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

Javascript

  const weaviate = require("weaviate-client");

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

var className = 'Article';

client
  .schema
  .classGetter()
  .withClassName(className)
  .do()
  .then(res => {
    console.log(res);
  })
  .catch(err => {
    console.error(err)
  });

  package main

import (
  "context"
  "fmt"

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

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

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

Java

  package technology.semi.weaviate;

import technology.semi.weaviate.client.Config;
import technology.semi.weaviate.client.WeaviateClient;
import technology.semi.weaviate.client.base.Result;
import technology.semi.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

  $ 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}

Parameters

name	location	type	description
`{class_name}`	URL	string	The name of the class

Example request for deleting a class

Python

  import weaviate

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

client.schema.delete_class('Article') # deletes the class 'Article' along with all data points of class 'Article'
# OR
client.schema.delete_all() # deletes all classes along with the whole data

Javascript

  const weaviate = require("weaviate-client");

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

var className = 'Article';

client.schema
  .classDeleter()
  .withClassName(className)
  .do()
  .then(res => {
    console.log(res);
  })
  .catch(err => {
    console.error(err)
  });

  package main

import (
  "context"

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

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

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

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

Java

  package technology.semi.weaviate;

import technology.semi.weaviate.client.Config;
import technology.semi.weaviate.client.WeaviateClient;
import technology.semi.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

  $ 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

Parameter in the PUT request:

name	location	type	description
`{class_name}`	URL	string	The name of the class

Parameter in the PUT body:

name	location	type	description
`class`	body	string	The name of the class, multiple words should be concatenated in CamelCase like `ArticleAuthor`.
`description`	body	string	Description of the classname
`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 in Weaviate’s environment variables
`moduleConfig` > `text2vec-contextionary` > `vectorizeClassName`	body	object	include the class name in vector calculation (default true). Learn more about how to regulate indexing in Weaviate here.
`properties`	body	array	An array of property objects
`properties` > `dataType`	body	array	Click here for a list of 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 name of the property is used in the calculation for the vector position of data objects. default is true. Learn more about how to regulate indexing in Weaviate here.
`properties` > `name`	body	string	The name of the property, multiple words should be concatenated in camelCase like `nameOfAuthor`.
`properties` > `indexInverted`	body	boolean	Should the the data stored in this property be indexed? Learn more about how to regulate indexing in Weaviate here.
`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"`. See more details here.
`invertedIndexConfig` > `stopwords`	body	object	Configure which words should be treated as stopwords and therefore be ignored on inverted indexing and querying. 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

  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": [
            "string"
        ],
        "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

  $ 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": [
                "string"
            ],
            "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	Click here for a list of available data types.
`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 name of the property is used in the calculation for the vector position of data objects. default is true. Learn more about how to regulate indexing in Weaviate here.
`name`	body	string	The name of the property, multiple words should be concatenated in camelCase like `nameOfAuthor`.
`indexInverted`	body	boolean	Should the the data stored in this property be indexed? Learn more about how to regulate indexing in Weaviate here.

Example request for adding a property

Python

  import weaviate

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

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

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

Javascript

  const weaviate = require("weaviate-client");

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

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

client.schema
      .propertyCreator()
      .withClassName(className)
      .withProperty(prop)
      .do()
      .then(res => {
    console.log(res);
  })
  .catch(err => {
    console.error(err)
  });

  package main

import (
  "context"

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

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

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

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

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

Java

  package technology.semi.weaviate;

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

  $ 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	string	The name of the class

Example request viewing shards of a class

Python

  import weaviate

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

client.schema.get_class_shards('Article')

Javascript

  const weaviate = require("weaviate-client");

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

client.schema
  .shardsGetter()
  .withClassName("Article")
  .do()

  package main

import (
  "context"
  "fmt"

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

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

Java

  package technology.semi.weaviate;

import technology.semi.weaviate.client.Config;
import technology.semi.weaviate.client.WeaviateClient;
import technology.semi.weaviate.client.base.Result;
import technology.semi.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

  $ 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	string	The name of the class
`{shard_name}`	URL	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

  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',
)

Javascript

  const weaviate = require("weaviate-client");

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

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

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

  package main

import (
  "context"
  "fmt"

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

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

  client := weaviate.New(cfg)

  // 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)
}

Java

  package technology.semi.weaviate;

import technology.semi.weaviate.client.Config;
import technology.semi.weaviate.client.WeaviateClient;
import technology.semi.weaviate.client.base.Result;
import technology.semi.weaviate.client.v1.schema.model.ShardStatus;
import technology.semi.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

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

More Resources

If you can’t find the answer to your question here, please look at the:

Frequently Asked Questions. Or,
Knowledge base of old issues. Or,
For questions: Stackoverflow. Or,
For issues: Github. Or,
Ask your question in the Slack channel: Slack.

Edit on Github

Get the schema
Create a class
Get a single class from the schema
Delete a class
Update a class
Add a property
Inspect the shards of a class
Update shard status
More Resources

More resources?

Meetups

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Blog

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Github

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weaviate

Retrievers & Vectorizers

Readers & Generators

Other Modules

/v1/schema

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

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

More Resources

Table of Contents