# How To Combine Different Vectors For Search

**Required Knowledge**: Vectors, Encoding, Vector Search\
**Audience**: Data scientists, Vector enthusiasts, Statisticians, Machine learning engineers\
**Reading time**: 3 minutes

When we are searching, sometimes we may want to combine multiple vectors. Constructing the multivector query can be quite difficult at first so here, so here we show the idea behind these different types of multivector queries.

{% tabs %}
{% tab title="Python SDK" %}

```python
from vectorai import ViClient 
vi = ViClient()
multivector_query = {
    "semantic_search": {"vector": vector_1, "field": {"bert_vector_": 0.3}},
    "bag_of_word_search": {"vector": vector_2, "field": {"bow_vector_": 0.7}}
}
vi.advanced_search(
    collection_name=collection_name,
    multivector_query=multivector_query)
```

{% endtab %}
{% endtabs %}

Breaking down the multivector query

The `semantic_search` and `bag_of_word_search` are aliases of the multivector query. These can be used to improve readability of the different search query constructions.

In the advanced search query, there are 2 required fields. `vector` and `field`. The `vector` field contains the vector and the `field` field contains an object where the key is the vector field and the value has the weight. 

The `advanced_search_query` is designed to provide a flexible way to search collections. This can be helpful to provide a number of ways combine vector spaces if you want to combine the multilingual specialties of one vector space with the slang specialties of another vector space.