💻How to add exact text search to vector search

Introducing hybrid search and its importance

Assumed Knowledge: Vectors Target Audience: General Audience / Python developers Reading Time: 3 minutes

There are sometimes search cases where pure vector search often does not provide the best solution. For example - we can take the simple case of product SKUs in retail e-commerce. Product SKUs are the name of the products.

E-Commerce Case Study

Weakness Of Vector Search

Below, we use an example of vector search where an individual searches for an SKU. However, the search results encode the letters and fail to realize/return the right SKU. We also attach a code example using the Vector AI client for those interested in trying this out.

search_results = vi_client.search(
collection_name, 
text_encoder.encode('R170NZKAXSA'), 'name_vector_', page_size=3)
vi_client.show_json(search_results, selected_fields=['_id', 'name', 'sku'],
    image_fields=['image_url'], image_width=150)

Result from pure vector search

In these situations, our search should properly return the right value when given the SKU. In turn, hybrid search can return the right result.

search_results = vi_client.hybrid_search(collection_name, 'R170NZKAXSA',
      text_encoder.encode('R170NZKAXSA'),
      fields=['name_vector_'], text_fields=['name'],
      traditional_weight=0.015,
      page_size=3)
vi_client.show_json(search_results, selected_fields=['_id', 'name', 'sku'],
    image_fields=['image_url'], image_width=150)

Hybrid search example

From above, we realize that the value of hybrid search allows us to match items/products when exact values are known by the searcher.

If you are interested in exploring documentation around hybrid search, you can find that here.