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đź’ˇ You are looking at older or release candidate documentation. The current Weaviate version is v1.15.2


Weaviate is completely modularized. The Core of Weaviate, without any modules attached, is a pure vector-native database and search engine. Data is stored as vectors, and these vectors are searchable by the provide vector index (ANN) algorithm. The functionality of the vector-native database can be enriched by modules. One or more modules can be attached, for example to vectorize data or other functionalities like question answering.

Vectorization modules

Vectorization modules, like the text2vec-contextionary, text2vec-transformers and img2vec-neural, transform data into vectors. Depending on the type of data you want to store and search (text, images, etc), and depending on the use case domain (science, healthcare, common daily language, etc), you can choose and attach a module that best fits your use case.

Note: at the moment, text vectorization modules cannot be combined in a single setup. This means that you can either enable the text2vec-contextionary, the text2vec-transformers or no text vectorization module. An image vectorization module can be combined with at max one text vectorization module.

Modules with additional functionalities

Modules can also add a certain functionality to Weaviate. For example, the qna-transformers module adds a question answering feature, which can be used to query data using GraphQL.

Custom modules

Check here how you can create and use your own modules.


Modules can be dependent on other modules to be present. For example, to use the qna-transformers module, exactly one text vectorization module is required.

Module ecosystem

This graphic shows the available modules of the latest Weaviate version (v1.15.2).

Weaviate module ecosystem


Modules can be “vectorizers” (defines how the numbers in the vectors are chosen from the data) or other modules providing additional functions like question answering, custom classification, etc. Modules have the following characteristics:

  • Naming convention:
    • Vectorizer: <media>2vec-<name>-<optional>, for example text2vec-contextionary, image2vec-RESNET or text2vec-transformers.
    • Other modules: <functionality>-<name>-<optional>, for example qna-transformers.
    • A module name must be url-safe, meaning it must not contain any characters which would require url-encoding.
    • A module name is not case-sensitive. text2vec-bert would be the same module as text2vec-BERT.
  • Module information is accessible through the v1/modules/<module-name>/<module-specific-endpoint> RESTful endpoint.
  • General module information (which modules are attached, version, etc.) is accessible through Weaviate’s v1/meta endpoint.
  • Modules can add additional properties in the RESTful API and _additional properties in the GraphQL API.
  • A module can add filters in GraphQL queries.
  • Which vectorizer and other modules are applied to which data classes is configured in the schema.

Weaviate without modules

Weaviate can also be used without any modules, as pure vector native database and search engine. If you choose not to include any modules, you will need to enter a vector for each data entry. You can then search through the objects by a vector as well.

More Resources

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

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