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Collection schema

Introduction

A collection schema describes how to store and index a set of data objects in Weaviate. This page discuses the collection schema, collection parameters and collection configuration.

"collection" == "class"

We are transitioning from the term "class" to "collection." Expect to see both terms during the transition period.

Collection object

An example of a complete collection object including properties:

{
"class": "Article", // The name of the collection in string format
"description": "An article", // A description for your reference
"vectorIndexType": "hnsw", // Defaults to hnsw
"vectorIndexConfig": {
... // Vector index type specific settings, including distance metric
},
"vectorizer": "text2vec-contextionary", // Vectorizer to use for data objects added to this collection
"moduleConfig": {
"text2vec-contextionary": {
"vectorizeClassName": true // Include the collection name in vector calculation (default true)
}
},
"properties": [ // An array of the properties you are adding, same as a Property Object
{
"name": "title", // The name of the property
"description": "title of the article", // A description for your reference
"dataType": [ // The data type of the object as described above. When
// creating cross-references, a property can have
// multiple data types, hence the array syntax.
"text"
],
"moduleConfig": { // Module-specific settings
"text2vec-contextionary": {
"skip": true, // If true, the whole property will NOT be included in
// vectorization. Default is false, meaning that the
// object will be NOT be skipped.
"vectorizePropertyName": true, // Whether the name of the property is used in the
// calculation for the vector position of data
// objects. Default false.
}
},
"indexFilterable": true, // Optional, default is true. By default each property
// is indexed with a roaring bitmap index where
// available for efficient filtering.
"indexSearchable": true // Optional, default is true. By default each property
// is indexed with a searchable index for
// BM25-suitable Map index for BM25 or hybrid
// searching.
}
],
"invertedIndexConfig": { // Optional, index configuration
"stopwords": {
... // Optional, controls which words should be ignored in the inverted index, see section below
},
"indexTimestamps": false, // Optional, maintains inverted indices for each object by its internal timestamps
"indexNullState": false, // Optional, maintains inverted indices for each property regarding its null state
"indexPropertyLength": false // Optional, maintains inverted indices for each property by its length
},
"shardingConfig": {
... // Optional, controls behavior of the collection in a
// multi-node setting, see section below
},
"multiTenancyConfig": {"enabled": true} // Optional, for enabling multi-tenancy for this
// collection (default: false)
}

Create a collection

Mutability

Some parameters are mutable after creation, other parameters cannot be changed after collection creation. To change immutable parameters, delete the collection and recreate it.

Mutable parameters
Replication factor change in v1.25

In Weaviate v1.25, a replication factor cannot be changed once it is set.

This is due to the schema consensus algorithm change in v1.25. This will be improved in future versions.

  • description
  • invertedIndexConfig
    • bm25
      • b
      • k1
    • cleanupIntervalSeconds
    • stopwords
      • additions
      • preset
      • removals
  • replicationConfig
    • factor (not mutable in v1.25)
  • vectorIndexConfig
    • dynamicEfFactor
    • dynamicEfMin
    • dynamicEfMax
    • flatSearchCutoff
    • skip
    • vectorCacheMaxObjects
    • pq
      • centroids
      • enabled
      • segments
      • trainingLimit
      • encoder
        • type
        • distribution

After you create a collection, you can add new properties. You cannot modify existing properties after you create the collection.

Auto-schema

Added in v1.5

The "Auto-schema" feature generates a collection definition automatically by inferring parameters from data being added. It is enabled by default, and can be disabled (e.g. in docker-compose.yml) by setting AUTOSCHEMA_ENABLED: 'false'.

It will:

  • Create a collection if an object is added to a non-existent collection.
  • Add any missing property from an object being added.
  • Infer array data types, such as int[], text[], number[], boolean[], date[] and object[].
  • Infer nested properties for object and object[] data types (introduced in v1.22.0).
  • Throw an error if an object being added contains a property that conflicts with an existing schema type. (e.g. trying to import text into a field that exists in the schema as int).
Define the collection manually for production use

Generally speaking, we recommend that you disable auto-schema for production use.

  • A manual collection definition will provide more precise control.
  • There is a performance penalty associated with inferring the data structure at import time. This may be a costly operation in some cases, such as complex nested properties.

Auto-schema data types

Additional configurations are available to help the auto-schema infer properties to suit your needs.

  • AUTOSCHEMA_DEFAULT_NUMBER=number - create number columns for any numerical values (as opposed to int, etc).
  • AUTOSCHEMA_DEFAULT_DATE=date - create date columns for any date-like values.

The following are not allowed:

  • Any map type is forbidden, unless it clearly matches one of the two supported types phoneNumber or geoCoordinates.
  • Any array type is forbidden, unless it is clearly a reference-type. In this case, Weaviate needs to resolve the beacon and see what collection the resolved beacon is from, since it needs the collection name to be able to alter the schema.

Multiple vectors

Added in v1.24.0

Weaviate collections support multiple, named vectors.

Collections can have multiple, named vectors. Each vector is independent. Each vector space has its own index, its own compression, and its own vectorizer. This means you can create vectors for properties, use different vectorization models, and apply different metrics to the same object.

You do not have to use multiple vectors in your collections, but if you do, you need to adjust your queries to specify a target vector for vector or hybrid queries.

Adding a property after collection creation

Adding a property after importing objects can lead to limitations in inverted-index related behavior.

This is caused by the inverted index being built at import time. If you add a property after importing objects, the inverted index will not be updated. This means that the new property will not be indexed for existing objects. This can lead to unexpected behavior when querying.

To avoid this, you can either:

  • Add the property before importing objects.
  • Delete the collection, re-create it with the new property and then re-import the data.

We are working on a re-indexing API to allow you to re-index the data after adding a property. This will be available in a future release.

Available parameters

class

This is the name of the collection. The name is to start with a capital letter. This helps to distinguish collections from primitive data types when the name is used as a property value. Consider these examples using the dataType property:

  • dataType: ["text"] is text
  • dataType: ["Text"] is a cross-reference type to a collection named Text.

After the first letter, collection names may use any GraphQL-compatible characters. The collection name validation regex is /^[A-Z][_0-9A-Za-z]*$/.

description

A description of the collection. This is for your reference only.

invertedIndexConfig

This configures the inverted index for the collection.

invertedIndexConfig > bm25

The settings for BM25 are the free parameters k1 and b, and they are optional. The defaults (k1 = 1.2 and b = 0.75) work well for most cases.

They can be configured per collection, and can optionally be overridden per property:

{
"class": "Article",
// Configuration of the sparse index
"invertedIndexConfig": {
"bm25": {
"b": 0.75,
"k1": 1.2
}
},
"properties": [
{
"name": "title",
"description": "title of the article",
"dataType": [
"text"
],
// Property-level settings override the collection-level settings
"invertedIndexConfig": {
"bm25": {
"b": 0.75,
"k1": 1.2
}
},
"indexFilterable": true,
"indexSearchable": true,
}
]
}

invertedIndexConfig > stopwords (stopword lists)

note

This feature was introduced in v1.12.0.

text properties may contain words that are very common and don't contribute to search results. Ignoring them speeds up queries that contain stopwords, as they can be automatically removed from queries as well. This speed up is very notable on scored searches, such as BM25.

The stopword configuration uses a preset system. You can select a preset to use the most common stopwords for a particular language (e.g. "en" preset). If you need more fine-grained control, you can add additional stopwords or remove stopwords that you believe should not be part of the list. Alternatively, you can create your custom stopword list by starting with an empty ("none") preset and adding all your desired stopwords as additions.

  "invertedIndexConfig": {
"stopwords": {
"preset": "en",
"additions": ["star", "nebula"],
"removals": ["a", "the"]
}
}

This configuration allows stopwords to be configured by collection. If not set, these values are set to the following defaults:

ParameterDefault valueAcceptable values
"preset""en""en", "none"
"additions"[]any list of custom words
"removals"[]any list of custom words
note
  • If preset is none, then the collection only uses stopwords from the additions list.
  • If the same item is included in both additions and removals, Weaviate returns an error.

As of v1.18, stopwords are indexed. Thus stopwords are included in the inverted index, but not in the tokenized query. As a result, when the BM25 algorithm is applied, stopwords are ignored in the input for relevance ranking but will affect the score.

Stopwords can now be configured at runtime. You can use the RESTful API to update the list of stopwords after your data has been indexed.

Note that stopwords are only removed when tokenization is set to word.

Below is an example request on how to update the list of stopwords:

import weaviate

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

collection_obj = {
"invertedIndexConfig": {
"stopwords": {
"preset": "en",
"additions": ["where", "is", "the"]
}
}
}

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

invertedIndexConfig > indexTimestamps

note

This feature was introduced in v1.13.0.

To perform queries that are filtered by timestamps, configure the target collection to maintain an inverted index based on the objects' internal timestamps. Currently the timestamps include creationTimeUnix and lastUpdateTimeUnix.

To configure timestamp based indexing, set indexTimestamps to true in the invertedIndexConfig object.

  "invertedIndexConfig": {
"indexTimestamps": true
}

invertedIndexConfig > indexNullState

note

This feature was introduced in v1.16.0.

To perform queries that filter on null, configure the target collection to maintain an inverted index that tracks null values for each property in a collection .

To configure null based indexing, setting indexNullState to true in the invertedIndexConfig object.

  "invertedIndexConfig": {
"indexNullState": true
}

invertedIndexConfig > indexPropertyLength

note

This feature was introduced in v1.16.0.

To perform queries that filter by the length of a property, configure the target collection to maintain an inverted index based on the length of the properties.

To configure indexing based on property length, set indexPropertyLength to true in the invertedIndexConfig object.

  "invertedIndexConfig": {
"indexPropertyLength": true
}
note

Using these features requires more resources. The additional inverted indices must be created and maintained for the lifetime of the collection.

vectorizer

The vectorizer ("vectorizer": "...") can be specified per collection in the schema object. Check the modules page for available vectorizer modules.

You can use Weaviate without a vectorizer by setting "vectorizer": "none". This is useful if you want to upload your own vectors from a custom model (see how here), or if you want to create a collection without any vectors.

vectorIndexType

The vectorIndexType parameter controls the type of vector index that is used for this collection. The options are hnsw (default) and flat.

vectorIndexConfig

The vectorIndexConfig parameter controls the configuration of the vector index. The available parameters depend on the vectorIndexType that is used.

See the vector index configuration page for more details.

shardingConfig

note

Introduced in v1.8.0.

The "shardingConfig" controls how a collection is sharded and distributed across multiple nodes. All values are optional and default to the following settings:

  "shardingConfig": {
"virtualPerPhysical": 128,
"desiredCount": 1, // defaults to the amount of Weaviate nodes in the cluster
"actualCount": 1,
"desiredVirtualCount": 128,
"actualVirtualCount": 128,
"key": "_id",
"strategy": "hash",
"function": "murmur3"
}

These parameters are explained below:

  • "desiredCount": integer, immutable, optional, defaults to the number of nodes in the cluster. This value controls how many shards should be created for this collection index. The typical setting is that a collection should be distributed across all the nodes in the cluster, but you can explicitly set this value to a lower value. If the "desiredCount" is larger than the amount of physical nodes in the cluster, then some nodes will contain multiple shards.

  • "actualCount": integer, read-only. Typically matches desired count, unless there was a problem initiating the shards at creation time.

  • "virtualPerPhysical": integer, immutable, optional, defaults to 128. Weaviate uses virtual shards. This helps in reducing the amount of data moved when resharding.

  • "desiredVirtualCount": integer, readonly. Matches desiredCount * virtualPerPhysical

  • "actualVirtualCount": integer, readonly. Like actualCount, but for virtual shards, instead of physical.

  • "strategy": string, optional, immutable. Only supports "hash". This value controls how Weaviate should decide which (virtual - and therefore physical) shard a new object belongs to. The hash is performed on the field specified in "key".

  • "key": string, optional, immutable. Only supports "_id". This value controls the partitioning key that is used for the hashing function to determine the target shard. As of now, only the internal id-field (containing the object's UUID) can be used to determine the target shard. Custom keys may be supported at a later point.

  • "function": string, optional, immutable. Only "murmur3" is supported as a hashing function. It describes the hashing function used on the "key" property to determine the hash which in turn determines the target (virtual - and therefore physical) shard. "murmur3" creates a 64bit hash making hash collisions very unlikely.

replicationConfig

Replication factor change in v1.25

In Weaviate v1.25, a replication factor cannot be changed once it is set.

This is due to the schema consensus algorithm change in v1.25. This will be improved in future versions.

Replication configurations can be set using the schema, through the replicationConfig parameter.

The factor parameter sets the number of copies of to be stored for objects in this collection.

{
"class": "Article",
"vectorizer": "text2vec-openai",
"replicationConfig": {
"factor": 3,
},
}

multiTenancyConfig

Added in v1.20

The multiTenancyConfig value determines if multi-tenancy is enabled for this collection. If enabled, objects of this collection will be isolated for each tenant. It is disabled by default.

To enable it, set the enabled key to true, as shown below:

{
"class": "MultiTenancyClass",
"multiTenancyConfig": {"enabled": true}
}

properties

Properties define the data structure of objects to be stored and indexed. For each property in the collection, you must specify at least the name and its dataType.

name

Property names can contain the following characters: /[_A-Za-z][_0-9A-Za-z]*/.

Property object example

An example of a complete property object:

{
"name": "title", // The name of the property
"description": "title of the article", // A description for your reference
"dataType": [ // The data type of the object as described above. When creating cross-references, a property can have multiple dataTypes.
"text"
],
"tokenization": "word", // Split field contents into word-tokens when indexing into the inverted index. See "Property Tokenization" below for more detail.
"moduleConfig": { // Module-specific settings
"text2vec-contextionary": {
"skip": true, // If true, the whole property is NOT included in vectorization. Default is false, meaning that the object will be NOT be skipped.
"vectorizePropertyName": true // Whether the name of the property is used in the calculation for the vector position of data objects. Default false.
}
},
"indexFilterable": true, // Optional, default is true. By default each property is indexed with a roaring bitmap index where available for efficient filtering.
"indexSearchable": true // Optional, default is true. By default each property is indexed with a searchable index for BM25-suitable Map index for BM25 or hybrid searching.
}

Reserved words

The following words are reserved and cannot be used as property names:

  • _additional
  • id
  • _id

Additionally, we strongly recommend that you do not use the following words as property names, due to potential conflicts with future reserved words:

  • vector
  • _vector

tokenization

note

This feature was introduced in v1.12.0.

You can customize how text data is tokenized and indexed in the inverted index. Tokenization influences the results returned by the bm25 and hybrid operators, and where filters.

Tokenization is a property-level configuration for text properties. See how to set the tokenization option using a client library

Example property configuration
{
"classes": [
{
"class": "Question",
"properties": [
{
"dataType": ["text"],
"name": "question",
"tokenization": "word"
},
],
...
"vectorizer": "text2vec-openai"
}
]
}

Each token will be indexed separately in the inverted index. For example, if you have a text property with the value Hello, (beautiful) world, the following table shows how the tokens would be indexed for each tokenization method:

Tokenization MethodExplanationIndexed Tokens
word (default)Keep only alpha-numeric characters, lowercase them, and split by whitespace.hello, beautiful, world
lowercaseLowercase the entire text and split on whitespace.hello,, (beautiful), world
whitespaceSplit the text on whitespace. Searches/filters become case-sensitive.Hello,, (beautiful), world
fieldIndex the whole field after trimming whitespace characters.Hello, (beautiful) world
trigramSplit the property as rolling trigrams.Hel, ell, llo, lo,, ...
gseUse the gse tokenizer to split the property.See gse docs

Tokenization and search / filtering

Tokenization impacts how filters or keywords searches behave. The filter or keyword search query is also tokenized before being matched against the inverted index.

The following table shows an example scenario showing whether a filter or keyword search would identify a text property with value Hello, (beautiful) world as a hit.

  • Row: Various tokenization methods.
  • Column: Various search strings.
Beautiful(Beautiful)(beautiful)Hello, (beautiful) world
word (default)
lowercase
whitespace
field
string is deprecated

The string data type has been deprecated from Weaviate v1.19 onwards. Please use text instead.

Pre v1.19 tokenization behavior

Tokenization with text

text properties are always tokenized, and by all non-alphanumerical characters. Tokens are then lowercased before being indexed. For example, a text property value Hello, (beautiful) world, would be indexed by tokens hello, beautiful, and world.

Each of these tokens will be indexed separately in the inverted index. This means that a search for any of the three tokens with the Equal operator under valueText would return this object regardless of the case.

Tokenization with string

string properties allow the user to set whether it should be tokenized, by setting the tokenization collection property.

If tokenization for a string property is set to word, the field will be tokenized. The tokenization behavior for string is different from text, however, as string values are only tokenized by white spaces, and casing is not altered.

So, a string property value Hello, (beautiful) world with tokenization set as word would be split into the tokens Hello,, (beautiful), and world. In this case, the Equal operator would need the exact match including non-alphanumerics and case (e.g. Hello,, not hello) to retrieve this object.

string properties can also be indexed as the entire value, by setting tokenization as field. In such a case the Equal operator would require the value Hello, (beautiful) world before returning the object as a match.

Default behavior

text and string properties default to word level tokenization for backward-compatibility.

gse and trigram tokenization methods

Added in 1.24

For Japanese and Chinese text, we recommend use of gse or trigram tokenization methods. These methods work better with these languages than the other methods as these languages are not easily able to be tokenized using whitespaces.

indexFilterable and indexSearchable

indexInverted is deprecated

The indexInverted parameter has been deprecated from Weaviate v1.19 onwards in lieu of indexFilterable and indexSearchable.

The indexFilterable and indexSearchable parameters control whether a property is going to be indexed for filtering and searching, respectively.

  • indexFilterable enables/disables a Roaring Bitmap index for fast filtering (default: true).
  • indexSearchable enables/disables a searchable index for BM25-suitable Map index for BM25 or hybrid searching (default: true).

Configure semantic indexing

Weaviate generates vector embeddings at the object level (rather than for individual properties). For instance text2vec-* modules can generate vectors from text objects. To produce the string to be vectorized from each object, Weaviate follows the schema configuration for the relevant class.

Unless specified otherwise in the schema, the default behavior is to:

  • Only vectorize properties that use the text data type (unless skipped)
  • Sort properties in alphabetical (a-z) order before concatenating values
  • If vectorizePropertyName is true (false by default) prepend the property name to each property value
  • Join the (prepended) property values with spaces
  • Prepend the class name (unless vectorizeClassName is false)
  • Convert the produced string to lowercase

For example, this data object,

Article = {
summary: "Cows lose their jobs as milk prices drop",
text: "As his 100 diary cows lumbered over for their Monday..."
}

will be vectorized as:

article cows lose their jobs as milk prices drop as his 100 diary cows lumbered over for their monday...

By default, the calculation includes the collection name and all property values, but the property names are not indexed.

To configure vectorization behavior on a per-collection basis, use vectorizeClassName.

To configure vectorization on a per-property basis, use skip and vectorizePropertyName.

Default distance metric

Weaviate allows you to configure the DEFAULT_VECTOR_DISTANCE_METRIC which will be applied to every collection unless overridden individually. You can choose from: cosine (default), dot, l2-squared, manhattan, hamming.

collection_obj = {
"class": "Article",
"vectorIndexConfig": {
"distance": "dot",
},
}

client.schema.create_class(collection_obj)

Questions and feedback

If you have any questions or feedback, let us know in the user forum.