# Modules - an introduction :::callout{intent="warning" title="🚧 To be updated 🚧"} This tutorial is currently being updated to reflect the latest features and improvements in Weaviate. We appreciate your patience and invite you to check back soon for the updated content. ::: 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: ```json { "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? ```json { "class": "SomeClass", "vectorizer": "text2vec-openai", "moduleConfig": { "text2vec-openai": { "vectorizeClassName": true } }, "properties": [ { "moduleConfig": { "text2vec-openai": { "vectorizePropertyName": false } } } ] } ``` :::callout{intent="note"} 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. ```json { "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. ```json { "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. ```graphql { 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 Have a question or feedback? Here's how to reach us. ::::card-grid :::card{title="Community Forum" href="https://forum.weaviate.io/c/support" icon="messages-square"} Ask questions and connect with other developers on our **Community forum**. ::: :::card{title="Support" href="/guides/support-overview" icon="life-buoy"} Weaviate Cloud user or customer? Find the right channel on the **Support page**. ::: :::: ## Related pages - [Agents](./agents-index.md) - [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md) - [APIs](./apis-index.md) - [Authorization and authentication](./authorization-and-authentication-index.md) - [Benchmarks](./benchmarks-index.md) - [Best practices](./best-practices-index.md) - [Client libraries](./clients-index.md) - [Client Libraries / SDKs](./client-libraries-index.md) - [Cloud](./cloud-index.md) - [Cloud account management](./cloud-account-management-index.md) # Agent Instructions This portal answers questions programmatically. To receive a synthesized, source-cited answer instead of crawling page by page, append the `?ask=` query parameter to any page URL on this site: /guides/quickstart?ask=how+do+I+authenticate Optional parameters: - `&goal=` steers the answer toward your objective (e.g. `&goal=write+a+python+client`). - `&version=