search()

This function performs a search using the given query and returns the results.

// Import the integration from the 'installation' step
import { integration } from './xgenSdkIntegration';
const { search } = integration;

search(query,options);

Params

Parameters passed to the search function:

Options

The search() function accepts a JSON object of options:

{
    "deploymentId": 1234
    "page": 1
    "context": {}
    "skipHistory": true
    "facets": true
}

Response

The return value of the search function is a Promise that resolves to an array of objects containing the products that match the search result, plus facets and metadata.

{
    "page": 0,
    "total_result": 1705,
    "cache_ttl_used": 86400,
    "hrnn_n": 0,
    "search_threshold_achieved": "Not triggered",
    "behavior_id": "bdb7268b-ada6-4310-8353-f7b34713e1b5",
    "response_engine": "ds",
    "is_from_cache": true,
    "is_sorted_by_hrnn": false,
    "is_keyword_config_applied": false,
    "is_ai_sorting_tried": false,
    "is_neural": true,
    "is_cached_filter_applied": true,
    "is_url_redirect": false,
    "products_list": [
        {
			"prod_code": "xyz",
			"prod_name": "Fancy Dress",
			"prod_price": 3800,
			"image": "example.com/image/product_1.jpg"
		},
		{
			"prod_code": "abc",
			"prod_name": "Fun Dress",
			"prod_price": 1800,
			"image": "example.com/image/product_2.jpg"
		},
		{
			"prod_code": "def",
			"prod_name": "Nice Dress",
			"prod_price": 1800,
			"image": "example.com/image/product_3.jpg"
		}
    ],
    "product_code_variant_mapping": {},
    "reason": [],
    "search_term_matches": [],
    "user_data": {},
    "facet": {
        "aggregations": {
            "group": {
                "color": {
                    "buckets": [
                        {
                            "doc_count": 1510,
                            "key": "-"
                        },
                        {
                            "doc_count": 134,
                            "key": "brown"
                        },
                        {
                            "doc_count": 30,
                            "key": "black"
                        },
                        {
                            "doc_count": 20,
                            "key": "tan"
                        }
                    ],
                    "doc_count_error_upper_bound": 0,
                    "sum_other_doc_count": 0
                },
                "is_in_stock": {
                    "buckets": [
                        {
                            "doc_count": 1578,
                            "key": "1"
                        },
                        {
                            "doc_count": 127,
                            "key": "0"
                        }
                    ],
                    "doc_count_error_upper_bound": 0,
                    "sum_other_doc_count": 0
                },
                "type": {
                    "buckets": [
                        {
                            "doc_count": 1591,
                            "key": "dresses"
                        },
                        {
                            "doc_count": 106,
                            "key": "shoes"
                        },
                        {
                            "doc_count": 4,
                            "key": "skirts"
                        }
                    ],
                    "doc_count_error_upper_bound": 0,
                    "sum_other_doc_count": 0
                }
            }
        }
    }
}

The fields in the response are defined as follows:

Understanding the facet object

The facet object returns the facets defined in the Product Filter associated with the deployment. The object can be nested, and has several basic terms that define the object:

There are also two pieces of metadata associated with facets:

Basic search

In this example we are getting products for the query “pants” for the deployment ID “3d8e3aa4-b1e9-4b1b-9ff6-ca4bc64806d0.” Once the promised is resolved, we are looping through and console logging and attribute.

// Get list of products for an element

const { products, total, page, facets } = await search('pants', { deploymentId: '3d8e3aa4-b1e9-4b1b-9ff6-ca4bc64806d0' });

// Console log prod_code of each product recommended

products.forEach(product => console.log(product.prod_code));

Pagination

In the previous example, we got a list of 60 products for the query “pants,” but what if we want the second page? Here we pass in page: 1 to the options (remember counting starts at 0) so that we can get the next 60 products.

// Get list of products for an element

const { products, total, page, facets } = await search('pants', { deploymentId: '3d8e3aa4-b1e9-4b1b-9ff6-ca4bc64806d0', page: 1 });

// Console log prod_code of each product recommended

products.forEach(product => console.log(product.prod_code));

Context

In this example, we are searching for “jeans,” but passing in a context for gender and category. This means we are now able to use those two variables within product filters for this deployment. Since we have gender: "male" and category: "pants" this means we are able to configure the filters to only show male pants that match the search “jeans” and those values are dynamic and can be changed to a different gender or category.

const context = {
  gender: 'male',
  category: 'pants'
}

// Get list of products for an element
const { products, total, page, facets } = await search('jeans', { deploymentId: '3d8e3aa4-b1e9-4b1b-9ff6-ca4bc64806d0', context });