如何使用 DashVector 的多向量检索

import dashvectorimport numpy as npfrom dashvector import VectorParam, Doc, WeightedRanker, VectorQuery, RrfRanker
client = dashvector.Client(    api_key='YOUR_API_KEY',    endpoint='YOUR_CLUSTER_ENDPOINT')ret = client.create(    'multi_vector_demo',    vectors={        "title": VectorParam(4),        "content": VectorParam(6, metric="euclidean"),    },    fields_schema={        'author': str,    })assert ret

collection = client.get(name='multi_vector_demo')docs = []for i in range(10):    docs.append(Doc(id=str(i),                    vectors={"title": np.random.random(4),                             "content": np.random.random(6)                            },                    )                )ret = collection.insert(docs)print(ret)

title_vector = [0.1, 0.2, 0.3, 0.4]content_vector = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]vectors = {    "title": VectorQuery(vector=title_vector, num_candidates=10),    "content": VectorQuery(vector=content_vector),}ret = collection.query(    vector=vectors,    include_vector=False,    # 使用RRF融合排序    # rerank=RrfRanker(rank_constant=100)    # 使用加权融合排序    rerank=WeightedRanker(weights={"title": 1.0, "content": 1.0}),    topk=20)assert retprint(ret.output)
# 使用单个向量执行检索ret = collection.query(vector={"title": VectorQuery(vector=title_vector)})assert retprint(ret.output)
# 使用单个向量执行分组向量检索ret = collection.query_group_by(title_vector, group_by_field='author', vector_field='title')assert retprint(ret)