Collection definition
Introduction
A collection definition describes how to store and index a set of data objects in Weaviate. This page discuses the collection definition, collection parameters and collection configuration.
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 indexes for each object by its internal timestamps
"indexNullState": false, // Optional, maintains inverted indexes for each property regarding its null state
"indexPropertyLength": false // Optional, maintains inverted indexes 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, but not all, parameters are mutable after you create your collection. To modify immutable parameters, export your data, create a new collection, and import your data into it.
Mutable parameters
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
moduleConfig
(generative & reranker modules only, from1.26.8
andv1.27.1
)multiTenancyConfig
autoTenantCreation
(introduced inv1.25.0
)autoTenantActivation
(introduced inv1.25.2
)
replicationConfig
asyncEnabled
(introduced inv1.26.0
)factor
(not mutable inv1.25
or higher)
vectorIndexConfig
dynamicEfFactor
dynamicEfMin
dynamicEfMax
filterStrategy
(introduced inv1.27.0
, applicable for HNSW)flatSearchCutoff
pq
centroids
enabled
segments
trainingLimit
encoder
type
distribution
skip
vectorCacheMaxObjects
After you create a collection, you can add new properties. You cannot modify existing properties after you create the collection.
Auto-schema
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[]
andobject[]
. - Infer nested properties for
object
andobject[]
data types (introduced inv1.22.0
). - 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
).
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
- createnumber
columns for any numerical values (as opposed toint
, etc).AUTOSCHEMA_DEFAULT_DATE=date
- createdate
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
orgeoCoordinates
. - 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 (named vectors)
Weaviate collections support multiple named vectors.
Collections can have multiple named vectors.
The vectors in a collection can have their own configurations. Each vector space can set its own index, its own compression algorithm, and its own vectorizer. This means you can use different vectorization models, and apply different distance metrics, to the same object.
To work with named vectors, adjust your queries to specify a target vector for vector search or hybrid search queries.
Adding a property after collection creation
Adding a property after importing objects can lead to limitations in inverted-index related behavior, such as filtering by the new property's length or null status.
This is caused by the inverted index being built at import time. If you add a property after importing objects, the inverted index for metadata such as the length or the null status will not be updated to include the new properties. 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
The class
is the name of the collection.
The collection name starts with an upper case letter. The upper case letter distinguishes collection names from primitive data types when the name is used as a property value.
Consider these examples that use the dataType
property:
dataType: ["text"]
is atext
data type.dataType: ["Text"]
is a cross-reference type to a collection namedText
.
After the first letter, collection names may use any GraphQL-compatible characters.
The collection name validation regex is /^[A-Z][_0-9A-Za-z]*$/
.
Weaviate follows GraphQL naming conventions.
- Start collection names with an upper case letter.
- Start property names with a lower case letter.
If you use an initial upper case letter to define a property name, Weaviate changes it to a lower case letter internally.
description
A description of the collection. This is for your reference only.
invertedIndexConfig
This configures the inverted index for the collection.
bm25
Part of invertedIndexConfig
. 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,
}
]
}
stopwords
(stopword lists)
Part of invertedIndexConfig
. 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:
Parameter | Default value | Acceptable values |
---|---|---|
"preset" | "en" | "en" , "none" |
"additions" | [] | any list of custom words |
"removals" | [] | any list of custom words |
- If
preset
isnone
, then the collection only uses stopwords from theadditions
list. - If the same item is included in both
additions
andremovals
, 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)
indexTimestamps
Part of invertedIndexConfig
. 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
}
indexNullState
Part of invertedIndexConfig
. 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
}
indexPropertyLength
Part of invertedIndexConfig
. 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
}
Using these features requires more resources. The additional inverted indexes must be created and maintained for the lifetime of the collection.
vectorizer
The vectorizer ("vectorizer": "..."
) can be specified per collection in the definition. 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
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 to128
. Weaviate uses virtual shards. This helps in reducing the amount of data moved when resharding."desiredVirtualCount"
: integer, readonly. MatchesdesiredCount * virtualPerPhysical
"actualVirtualCount"
: integer, readonly. LikeactualCount
, 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
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 definition, 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
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
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 Method | Explanation | Indexed Tokens |
---|---|---|
word (default) | Keep only alpha-numeric characters, lowercase them, and split by whitespace. | hello , beautiful , world |
lowercase | Lowercase the entire text and split on whitespace. | hello, , (beautiful) , world |
whitespace | Split the text on whitespace. Searches/filters become case-sensitive. | Hello, , (beautiful) , world |
field | Index the whole field after trimming whitespace characters. | Hello, (beautiful) world |
trigram | Split the property as rolling trigrams. | Hel , ell , llo , lo, , ... |
gse | Use the gse tokenizer to split the property. | See gse docs |
kagome_ja | Use the Kagome tokenizer with a Japanese (IPA) dictionary to split the property. | See kagome docs and the dictionary. |
kagome_kr | Use the Kagome tokenizer with a Korean dictionary to split the property. | See kagome docs and the Korean dictionary. |
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 deprecatedThe string
data type has been deprecated from Weaviate v1.19
onwards. 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
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.
The gse
tokenizer is not loaded by default to save resources. To use it, set the environment variable ENABLE_TOKENIZER_GSE
to true
on the Weaviate instance.
gse
tokenization examples:
"素早い茶色の狐が怠けた犬を飛び越えた"
:["素早", "素早い", "早い", "茶色", "の", "狐", "が", "怠け", "けた", "犬", "を", "飛び", "飛び越え", "越え", "た", "素早い茶色の狐が怠けた犬を飛び越えた"]
"すばやいちゃいろのきつねがなまけたいぬをとびこえた"
:["すばや", "すばやい", "やい", "いち", "ちゃ", "ちゃい", "ちゃいろ", "いろ", "のき", "きつ", "きつね", "つね", "ねが", "がな", "なま", "なまけ", "まけ", "けた", "けたい", "たい", "いぬ", "を", "とび", "とびこえ", "こえ", "た", "すばやいちゃいろのきつねがなまけたいぬをとびこえた"]
kagome_ja
tokenization method
Available starting in v1.28.0
. This is an experimental feature. Use with caution.
For Japanese text, kagome_ja
tokenization method is also available. This uses the Kagome
tokenizer with a Japanese MeCab IPA dictionary to split the property text.
The kagome_ja
tokenizer is not loaded by default to save resources. To use it, set the environment variable ENABLE_TOKENIZER_KAGOME_JA
to true
on the Weaviate instance.
kagome_ja
tokenization examples:
"春の夜の夢はうつつよりもかなしき 夏の夜の夢はうつつに似たり 秋の夜の夢はうつつを超え 冬の夜の夢は心に響く 山のあなたに小さな村が見える 川の音が静かに耳に届く 風が木々を通り抜ける音 星空の下、すべてが平和である"
:- [
"春", "の", "夜", "の", "夢", "は", "うつつ", "より", "も", "かなしき", "\n\t", "夏", "の", "夜", "の", "夢", "は", "うつつ", "に", "似", "たり", "\n\t", "秋", "の", "夜", "の", "夢", "は", "うつつ", "を", "超え", "\n\t", "冬", "の", "夜", "の", "夢", "は", "心", "に", "響く", "\n\n\t", "山", "の", "あなた", "に", "小さな", "村", "が", "見える", "\n\t", "川", "の", "音", "が", "静か", "に", "耳", "に", "届く", "\n\t", "風", "が", "木々", "を", "通り抜ける", "音", "\n\t", "星空", "の", "下", "、", "すべて", "が", "平和", "で", "ある"
]
- [
"素早い茶色の狐が怠けた犬を飛び越えた"
:["素早い", "茶色", "の", "狐", "が", "怠け", "た", "犬", "を", "飛び越え", "た"]
"すばやいちゃいろのきつねがなまけたいぬをとびこえた"
:["すばやい", "ちゃ", "いろ", "の", "きつね", "が", "なまけ", "た", "いぬ", "を", "とびこえ", "た"]
kagome_kr
tokenization method
Available starting in v1.25.7
. This is an experimental feature. Use with caution.
For Korean text, we recommend use of the kagome_kr
tokenization method. This uses the Kagome
tokenizer with a Korean MeCab (mecab-ko-dic) dictionary to split the property text.
The kagome_kr
tokenizer is not loaded by default to save resources. To use it, set the environment variable ENABLE_TOKENIZER_KAGOME_KR
to true
on the Weaviate instance.
kagome_kr
tokenization examples:
"아버지가방에들어가신다"
:["아버지", "가", "방", "에", "들어가", "신다"]
"아버지가 방에 들어가신다"
:["아버지", "가", "방", "에", "들어가", "신다"]
"결정하겠다"
:["결정", "하", "겠", "다"]
Inverted index types
indexInverted
is deprecatedThe indexInverted
parameter has been deprecated from Weaviate v1.19
onwards.
Multiple inverted index types are available in Weaviate. Not all inverted index types are available for all data types. The available inverted index types are:
Inverted index type | Description | Applicable data types | Default | Availability |
---|---|---|---|---|
indexSearchable | A searchable index for BM25-suitable Map index for BM25 or hybrid searching. | text , text[] , | true | v1.19 |
indexFilterable | A Roaring Bitmap index for match-based filtering. | Everything except blob , geoCoordinates , object and phoneNumber data types including arrays thereof | true | v1.19 |
indexRangeFilters | A Roaring Bitmap index for numerical range-based filtering. | int , number and date only | false | v1.26 |
- Enable one or both of
indexFilterable
andindexRangeFilters
to index a property for faster filtering.- If only one is enabled, the respective index is used for filtering.
- If both are enabled,
indexRangeFilters
is used for operations involving comparison operators, andindexFilterable
is used for equality and inequality operations.
Configure semantic indexing
Weaviate can generate vector embeddings for objects using model provider integrations.
For instance, text embedding integrations (e.g. text2vec-cohere
for Cohere, or text2vec-ollama
for Ollama) can generate vectors from text objects. Weaviate follows the collection configuration and a set of predetermined rules to vectorize objects.
Unless specified otherwise in the collection definition, the default behavior is to:
- Only vectorize properties that use the
text
ortext[]
data type (unless skipped) - Sort properties in alphabetical (a-z) order before concatenating values
- If
vectorizePropertyName
istrue
(false
by default) prepend the property name to each property value - Join the (prepended) property values with spaces
- Prepend the class name (unless
vectorizeClassName
isfalse
) - 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)
Related pages
Questions and feedback
If you have any questions or feedback, let us know in the user forum.