Q: Why would I use Weaviate as my vector search engine engine?
A: Our goal is three-folded. Firstly, we want to make it as easy as possible for others to create their own semantic systems or vector search engines (hence, we are API based). Secondly, we have a strong focus on the semantic element (the “knowledge” in “vector search engine,” if you will). Our ultimate goal is to have Weaviate help you manage, index, and “understand” your data so that you can build newer, better, and faster applications. And thirdly, we want you to be able to run it everywhere. This is the reason why Weaviate comes containerized.
Q: Do you offer Weaviate as a managed service?
A: Yes, it is called the Weaviate Console.
Q: Can I train my own text2vec-contextionary vectorizer module?
A: Not yet (but soon), you can currently use the available contextionaries in a variety of languages and use the transfer learning feature to add custom concepts if needed. Sign up to our newsletter or Slack channel to keep updated about the release of custom contextionary training
Q: Why does Weaviate have a schema and not an ontology?
A: We use a schema because it focusses on the representation of your data (in our case in the GraphQL API) but you can use a Weaviate schema to express an ontology. One of Weaviate’s core features is that it semantically interprets your schema (and with that your ontology) so that you can search for concepts rather than formally defined entities.
Q: What is the difference between a Weaviate data schema, ontologies and taxonomies?
A: Read about how taxonomies, ontologies and schemas are related to Weaviate in this blog post.
Q: Can I use Weaviate to create a traditional knowledge graph.
A: Yes, you can! Weaviate support ontology, RDF-like definitions in its schema, and it runs out of the box. It is scalable, and the GraphQL API will allow you to query through your knowledge graph easily. But now you are here. We like to suggest you really try its semantic features. After all, you are creating a knowledge graph 😉.
Q: Why isn’t there a text2vec-contextionary in my language?
A: Because you are probably one of the first that needs one! Ping us here on Github, and we will make sure in the next iteration it will become available (unless you want it in Silbo Gomero or another language which is whistled).
Q: How to deal with custom terminology?
A: Sometimes, users work with custom terminology, which often comes in the form of abbreviations or jargon. We are currently working on an additional API endpoint, which allows you to add custom synonyms. You can find the state of the implementation here. You can also signup for our newsletter to receive an update when it is ready.
Q: How can you index data near-realtime without losing semantic meaning?
A: Every data object gets its vector representation based on its semantic meaning. In a nutshell, we calculate the vector position of the data object based on the words and concepts used in the data object. The existing model in the contextionary gives already enough context. If you want to get in the nitty-gritty, you can browse the code here, but you can also ask a specific question on Stackoverflow and tag it with Weaviate.
Q: How do you deal with words that have multiple meanings?
A: How can Weaviate interpret that you mean a company, as in business, and not as the division of the army? We do this based on the structure of the schema and the data you add. A schema in Weaviate might contain a company class with the property name and the value Apple. This simple representation (company, name, apple) is already enough to gravitate the vector position of the data object towards businesses or the iPhone. You can read here how we do this, or you can ask a specific question on Stackoverflow and tag it with Weaviate.
Q: Can I connect my own module?
Q: What is the difference between Weaviate and for example Elasticsearch?
A: Other database systems like Elasticsearch rely on inverted indices, which makes search super fast. Weaviate also uses inverted indices to store data and values. But additionally, Weaviate is also a vector-native search database, which means that data is stored as vectors, which enables semantic search. This combination of data storage is unique, and enables fast, filtered and semantic search from end-to-end.
Q: How can slow queries be optimized?
A: Queries containing deeply nested references that need to be filtered or resolved can take some time. Read on optimization strategies here.
Q: Data import takes long / is slow (slower than before v1.0.0), what is causing this and what can I do?
A: The first supported vector index type HNSW is super fast at query time, but slower on vectorization. This means that adding and updating data objects costs relatively more time. When there are other vector index types available, you can try another vector index type.
Q: Why did you use GraphQL instead of SPARQL?
A: Two words, user experience. We want to make it as simple as possible to integrate Weaviate into your stack, and we believe that GraphQL is the answer to this. The community and client libraries around GraphQL are enormous, and you can use almost all of them with Weaviate.
Q: Can I request a feature in Weaviate?
Q: Does Weaviate require NFS volumes on Kubernetes?
A: By default, no NFS volumes are active. In a production setting, we recommend turning etcd disaster recovery on which requires an NFS volume. However, the helm docs contain instructions on how to deploy an nfs-provisioner. For more details, see also this stack overflow answer.
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