List of examples and tutorials of how to use the Vector Search Engine Weaviate for cool machine-learning related tasks.
- Most examples assume you have a Weaviate running. You can run one locally by following this installation guide in the documentation.
- If you need a specific vectorizer module or another ML module, it will be explained in the tutorial.
- Basic links: Documentation – Github - Slack
| Title | Language | Description |
|---|---|---|
| Semantic search through Wikipedia with the Weaviate vector search engine | GraphQL | Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine |
| PyTorch-BigGraph Wikidata search with the Weaviate vector search engine | GraphQL | Search through Facebook Research's PyTorch BigGraph Wikidata-dataset with the Weaviate vector search engine |
| Multi-Modal Text/Image search using CLIP | Bash, Javascript, React | Use text to search through images using CLIP (multi2vec-clip). Also acts as a demo on how to use Weaviate with React |
| 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 |
| Text search with weaviate using own vectors | Python | A basic and simple example using our own vectors(obtained using SBERT, but any other model can also be used) in weaviate |
| Harry Potter Question Answering with Haystack & Weaviate | Python (Jupyter/Colab notebook) | A demo notebook showing how to use Weaviate as DocumentStore in Haystack. |
| Vegetable classification using image2vec-neural | Python | An image classification example made using image2vec-neural and flask to classify vegetable images |
| Exploring multi2vec-clip with Python and flask | Python | This example explores the multi2vec-clip module to implement an image and text combined search functionality. |
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