hamilton_rag_weaviate_gql.py

response = (
    weaviate_client.query.get(
        "Chunk",  # Class
        [  # Properties to retrieve
            "chunk_index",
            "content",
            "summary",
            "fromDocument {... on Document {_additional{id}}}",  # Property of linked object
        ],
    )
    .with_hybrid(  # hybrid search parameters
        query=rag_query,  # user text query
        properties=["content"],  # properties of `Chunk` to search on (specified above)
        vector=query_embedding,  # user query embedding/vector
        alpha=hybrid_search_alpha,  # hybrid search parameter
    )
    .with_additional(["score"])  # compute relevance score
    .with_limit(retrieve_top_k)  # return top k objects
    .do()
)