Modules - an introduction
In this guide, you will get an introduction to the role that modules play in Weaviate.
As their name suggest, Weaviate modules are options components to enhance Weaviate's functionality, such as to vectorize data or process results (e.g., question answering). The structure of the module name (x2vec) informs you of what the module does. E.g., text2vec generates text embeddings, img2vec image embeddings, etc.
Vectorizers & Rerankers
Vectorizers and rerankers are used for vector search, which goes both for vectorizing the data objects and the queries. For example, if you enable an embedding model integration, semantic searches (nearText) become available. It will automatically vectorize your query and match it against the vectors stored in the index.
You can set up the vectorization per class as follows:
{
"class": "SomeClass",
"vectorizer": "text2vec-openai",
}
Next, you need to tell Weaviate what you want to have vectorized. Only the payload, or do you also want to include the class name and the property name?
{
"class": "SomeClass",
"vectorizer": "text2vec-openai",
"moduleConfig": {
"text2vec-openai": {
"vectorizeClassName": true
}
},
"properties": [
{
"moduleConfig": {
"text2vec-openai": {
"vectorizePropertyName": false
}
}
}
]
}
The reason you can index class names and property names is that they sometimes give semantic context. For example, a class Product could have the property name. If you vectorize everything you get a vector for Product with the name some product. This only goes for text2vec modules.
If you don't want to vectorize a property at all, you can simply skip it.
{
"class": "SomeClass",
"vectorizer": "text2vec-openai",
"moduleConfig": {
"text2vec-openai": {
"vectorizeClassName": true
}
},
"properties": [
{
"moduleConfig": {
"text2vec-openai": {
"vectorizePropertyName": false,
"skip": true
}
}
}
]
}
Example
The following code is a complete example of a schema.
Let's take a look at the definition for the Article class. Look for the "moduleConfig" entries on the class and on the property level.
You will see that the class and property names are not indexed, but the article itself is. So if you now retrieve a single article, you know that the vector comes from the transformers module.
{
"classes": [
{
"class": "Article",
"description": "Normalised types",
"invertedIndexConfig": {
"bm25": {
"b": 0.75,
"k1": 1.2
},
"cleanupIntervalSeconds": 60,
"stopwords": {
"additions": null,
"preset": "en",
"removals": null
}
},
"moduleConfig": {
"text2vec-transformers": {
"poolingStrategy": "masked_mean",
"vectorizeClassName": false
}
},
"properties": [
{
"dataType": [
"text"
],
"description": "title of the article",
"indexFilterable": true,
"indexSearchable": true,
"moduleConfig": {
"text2vec-transformers": {
"skip": false,
"vectorizePropertyName": false
}
},
"name": "title",
"tokenization": "word"
},
{
"dataType": [
"text"
],
"description": "url of the article",
"indexFilterable": true,
"indexSearchable": true,
"moduleConfig": {
"text2vec-transformers": {
"skip": false,
"vectorizePropertyName": false
}
},
"name": "url",
"tokenization": "word"
},
{
"dataType": [
"text"
],
"description": "summary of the article",
"indexFilterable": true,
"indexSearchable": true,
"moduleConfig": {
"text2vec-transformers": {
"skip": false,
"vectorizePropertyName": false
}
},
"name": "summary",
"tokenization": "word"
},
{
"dataType": [
"date"
],
"description": "date of publication of the article",
"moduleConfig": {
"text2vec-transformers": {
"skip": false,
"vectorizePropertyName": false
}
},
"name": "publicationDate"
},
{
"dataType": [
"int"
],
"description": "Words in this article",
"moduleConfig": {
"text2vec-transformers": {
"skip": false,
"vectorizePropertyName": false
}
},
"name": "wordCount"
},
{
"dataType": [
"boolean"
],
"description": "whether the article is currently accessible through the url",
"moduleConfig": {
"text2vec-transformers": {
"skip": false,
"vectorizePropertyName": false
}
},
"name": "isAccessible"
},
{
"dataType": [
"Author",
"Publication"
],
"description": "authors this article has",
"moduleConfig": {
"text2vec-transformers": {
"skip": false,
"vectorizePropertyName": false
}
},
"name": "hasAuthors"
},
{
"dataType": [
"Publication"
],
"description": "publication this article is in",
"moduleConfig": {
"text2vec-transformers": {
"skip": false,
"vectorizePropertyName": false
}
},
"name": "inPublication"
},
{
"dataType": [
"Category"
],
"description": "category this article is of",
"moduleConfig": {
"text2vec-transformers": {
"skip": false,
"vectorizePropertyName": false
}
},
"name": "ofCategory"
}
],
"shardingConfig": {
"virtualPerPhysical": 128,
"desiredCount": 1,
"actualCount": 1,
"desiredVirtualCount": 128,
"actualVirtualCount": 128,
"key": "_id",
"strategy": "hash",
"function": "murmur3"
},
"vectorIndexConfig": {
"skip": false,
"cleanupIntervalSeconds": 300,
"maxConnections": 32,
"efConstruction": 128,
"ef": -1,
"dynamicEfMin": 100,
"dynamicEfMax": 500,
"dynamicEfFactor": 8,
"vectorCacheMaxObjects": 2000000,
"flatSearchCutoff": 40000,
"distance": "cosine"
},
"vectorIndexType": "hnsw",
"vectorizer": "text2vec-transformers"
}
]
}
Readers & Generators
Readers & Generators are used to process data after retrieving the data from the database. Question answering is a good example of this. If you set a limit of 10, the 10 results will be run through the Q&A module.
Some such modules can enables the GraphQL API, for example as shown here.
{
Get {
Article(
# the ask filter is introduced through the QandA module
ask: {
question: "What was the monkey doing during Elon Musk's brain-chip startup release?"
}
limit: 1
) {
_additional {
# the answer properties extend the _additional filters
answer {
result
certainty
}
}
}
}
}
Recap
Modules are add-ons to Weaviate that can perform additional functions. You don't have to use them, but you can.
Questions and feedback
If you have any questions or feedback, let us know in the user forum.