Weaviate: define a schema and perform a hybrid search

Steps

1. Start a Weaviate instance via Docker Compose or connect to a Weaviate Cloud cluster; instantiate the Python client with weaviate.connect_to_local() or weaviate.connect_to_weaviate_cloud().
2. Create a collection (formerly class) with client.collections.create(name='NAME', properties=[Property(name='text', data_type=DataType.TEXT)], vectorizer_config=Configure.Vectorizer.text2vec_openai()) or another vectorizer.
3. Import objects using collection.data.insert_many([{'text': 'some content', ...}]) to batch-insert documents; Weaviate vectorizes them automatically if a vectorizer is configured.
4. Perform a hybrid search combining keyword (BM25) and vector similarity: collection.query.hybrid(query='search terms', alpha=0.5, limit=10) where alpha controls the balance between keyword and vector results.
5. Access each result object's properties and metadata (including score) from the returned objects list.
6. Add filters with the Filter helper class to restrict results by property values before running the hybrid query.