You can find more examples with Weaviate here.
|Google Colab notebook: Getting started with the Python Client||python (Google Colab)||Google Colab notebook to learn to get started with the Python client. Contains plenty of example code.|
|Demo dataset News Publications with Contextionary||yaml||Docker-compose configuration file of Weaviate with a News Publications demo dataset.|
|Demo dataset News Publications with Transformers, NER, Spellcheck and Q&A||yaml||Docker-compose configuration file of Weaviate with a News Publications demo dataset. The vectorization is done by a text2vec-transformers module, and the spellcheck, Q&A and Named Entity Recognition module are connected.|
|Weaviate simple schema||Python||Easy example of a schema and how to upload it to Weaviate with the Python client|
|Semantic search through wine dataset||Python||Easy example to get started with Weaviate and semantic search with the Transformers module|
|Unmask Superheroes in 5 steps using the Weaviate NLP module and the Python client||Python||Super simple 5 step guide to get started with the Weaviate NLP modules. This is a basic introduction to semantic search with Weaviate and the Python client.|
|Information Retrieval with BERT (Weaviate without vectorizer module)||Python (Jupyter Notebook)||In this example we are going to use Weaviate without vectorization module, and use it as pure vector database to use a BERT transformer to vectorize text documents, then retrieve the closest ones through Weaviate's Search|
If you can't find the answer to your question here, please look at the:
- Frequently Asked Questions. Or,
- Knowledge base of old issues. Or,
- For questions: Stackoverflow. Or,
- For more involved discussion: Weaviate Community Forum. Or,
- We also have a Slack channel.