Glossary
| Term | Description |
|---|---|
| Aggregation | The process of computing summaries or statistics over a set of query results (e.g., counting objects, finding min/max/mean of properties). Typically performed using the GraphQL Aggregate{} function. |
| API key | A secret token used for authenticating requests to a Weaviate instance (especially for Weaviate Cloud) or to integrated third-party services like model providers (e.g., OpenAI, Cohere). |
| Beacon | A beacon is a reference to a particular data object in Weaviate or inside the knowledge network. This data object in turn has a position in the vector space. Often defined as follows: weaviate://{peerName}/{className}/{UUID}. (For Weaviate version < v1.14.0, it is defined as follows: weaviate://{peerName}/{UUID}.) |
| Class | A class is the former name for a collection, a container where data objects sharing the same structure (properties, vectorizer settings, etc.) are stored. |
| Concept | Concepts are related to entities. Often you will use concepts to search in your datasets. If your dataset has data about An Actor with the name Arnold Schwarzenegger and an Actor with the name Al Pacino, the concepts Movie and Terminator will find a closer relation to the first actor rather than the latter. |
| Contextionary | Derived from dictionary with context. Pre-trained vector space which contains vectors for nearly all words used in a specific language. The Contextionary (text2vec-contextionary) gives context to the language used in the dataset, inspired by the Global Vectors for Word Representation concept. Read more about the Contextionary here. |
| Embedding model | A machine learning model that transforms data (text, images, audio, etc.) into numerical vector representations (embeddings). This is the core component within a Vectorizer module. |
| Entity | An entity refers to something -often- in the world around us. E.g., a Company with the name Apple refers to an entity with a relation to a Product with the name iPhone. Weaviate's Contextionary tries to find as many entities in your data as possible. |
| Fuzzy | Opposed to most other data solutions, Weaviate uses fuzzy logic to interpret a query. The upside of this is that it might find answers to queries where a traditional data solution might not. |
| Generative model | An AI model integrated with Weaviate (often via a module like generative-openai) that can generate new content (e.g., summaries, answers) based on the context provided by search results. |
| HNSW | Hierarchical Navigable Small World - a multilayered graph vector index type. |
| Inverted index | An index storing a mapping from data property values, to its locations of data objects in a database (named in contrast to a forward index, which maps from data objects to property data values). |
| Model provider integrations | Weaviate's ability to connect with external services (like OpenAI, Cohere, Hugging Face, Google Vertex AI) that host and serve machine learning models (Embedding models, Generative models, Rerankers) used within Weaviate modules. |
| Multimodal | The capability to process and understand information from multiple types (modalities) of data simultaneously, such as text, images, audio, etc. Multimodal vectorizer modules (e.g., multi2vec-clip) create embeddings that represent combined concepts. |
| Multiple vector embeddings | The capability to store multiple, distinct named vectors for a single data object. This allows representing different aspects or using embeddings from different models for the same object (e.g., one vector for content, one for title). |
| Multi-vectors | Multi-vector embeddings, also known as multi-vectors, represent a single object with multiple vectors, i.e. a 2-dimensional matrix. |
| NearText | A search operator that takes text input, uses the configured Vectorizer module to dynamically generate a query vector, and then performs a vector similarity search based on that vector. |
| Property | All classes have properties. E.g., the class Company might have the property name. In Weaviate, properties can be recognized because they always have a lowercase first character. |
| Quantization | Vector compression techniques (like Product Quantization 'PQ' or Binary Quantization 'BQ') used to reduce the memory footprint of vector embeddings, potentially trading some precision for significant storage and performance gains. |
| Reranker | An AI model integrated with Weaviate (often via a module like reranker-cohere) that takes the initial list of search results and re-orders them based on a secondary relevance calculation, often improving the quality of the top results. |
| Replication | The process of creating copies (replicas) of data shards across different nodes in a Weaviate cluster to ensure data durability and high availability in case of node failures. (Part of Clustering). |
| Schema | In Weaviate, a schema is used to define the types of data you will be adding and querying. You can learn more about it here. |
| Sharding | The process of splitting a Class's data and index horizontally across multiple nodes (shards) in a Weaviate cluster. This allows the dataset size and workload to scale beyond the capacity of a single node. (Part of Clustering). |
| Vector index | A data storage mechanism where data is stored as vectors (long arrays of numbers, also seen as coordinates in a high dimensional space), allowing for context-based search. |
| Vectorizer | A module within Weaviate (e.g., text2vec-openai, multi2vec-clip) responsible for automatically converting specified data properties into vector embeddings using an underlying Embedding model, either during data import or at query time (e.g., for nearText). |
| WCS | Weaviate Cloud Service. The former name for Weaviate's managed cloud offering. Now known as Weaviate Cloud (WCD). |
| Weaviate Cloud (WCD) | WCD is our SaaS for providing cloud instances of Weaviate. |
| Weaviate Cluster | A managed Weaviate cluster. |
Questions and feedback
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