The search query users typed so far. Please keep the trailing spaces (if any) intact so that we know whether the user has finished typing the last word or is still typing it. For example, the following query means the user has finished typing the word Fight:

{"q": "Fight "}

On the other hand, the following query means the user has not finished typing the last word Clu:

{"q": "Fight Clu"}

A list of additional interaction records. You can use this fields to simulate user interactions without actually writing them to the interaction dataset.

The anonymous visitor who made the query and for whom Miso will personalize the results. Either user_id or anonymous_id needs to be specified for personalization to work.

Defines a query in Elasticsearch query-string syntax (Lucene) that can be used to boost a subset of products to the top of the ranking, or to specific boost positions (See boost_positions parameter below.) For example, the query below will promote all the relevant products whose brand is Nike to the top of recommendation list:

{
    "boost_fq": "brand:\"Nike\""
}

For a slightly more complex example, the query below will promote the Nike products which have also been tagged as ON SALE to the top of the ranking:

{
   "boost_fq": "brand:\"Nike\" AND tags:\"ON SALE\""
}

It is worth mentioning that, Miso will only boost products that are relevant and have high likelihood to convert, and will not boost a low performance product only because it matches the boosting query.

Depending on your boosting rules, in certain cases, you would like to prevent recommendation results from being too monotone due to boosting. With Miso, you have two tools to do so.

First, you can specify boost_positions to place promoted products at specific positions in the ranking. For example, the query below will place boosted products only at the first and fourth places in the ranking (positions are 0-based), and place the remaining products in their original ranking, skipping these two positions.

{
   "boost_fq": "brand:\"Nike\" AND tags:\"ON SALE\"",
   "boost_positions": [0, 3]
}

The second tool is diversification. diversification parameter, on a best-effort basis, will try to maintain a minimum distance between products that have the same attributes. For example, the following query will place products made by the same brand apart from each other.

{
   "boost_fq": "brand:\"Nike\" AND tags:\"ON SALE\"",
   "diversification": {
       "brand": {"minimum_distance": 1}
    }
}

Defines a list of 0-based positions you want to place the boosted products at.

For example, the query below will promote products whose brand is Nike as the top and second recommendations:

{
    "boost_fq": "brand:\"Nike\"",
    "boost_positions": [0, 1]
}

If boost_positions is not specified (which is the default behavior), all the boosted products will be ranked higher than the rest of the products.

Name of the boosting rule. Use this to identify a boosting rule in _boosted_rules in the response

Define a list of boosting rules that will be applied to the search or recommendation results simultaneously. boost_rules parameter is particularly useful when you want to boost more than one sets of products, and promote each of them to different positions. For example, the query below will promote products whose brand is Nike to the top and second results, and products whose brand is Adidas to the third and fourth results:

{
    "boost_rules": [
        {
            "boost_fq": "brand:\"Nike\"",
            "boost_positions": [0, 1]
        },
        {
            "boost_fq": "brand:\"Adidas\"",
            "boost_positions": [2, 3]
        }
    ]
}

Controls the sources of autocompletion candidates. Miso performs autocompletion by matching what the user has typed so far to either the title of products or to other attributes.

By default, we only autocomplete against the value in the title field. The completion_fields parameter lets you specify the attributes you want to perform autocompletion against. For example, the following query will limit the autocompletion candidates to the title and tags of products:

{"completion_fields": ["title", "tags"]}

Autocompletion also works on custom attributes. For example, if you have a custom attribute for the designer_name of the product, the following query limits autocompletion candidates to only the designer names:

{"candidates": ["custom_attributes.designer_name"]}

Whether to dedupe product based on product_group_id. If dedupe_product_group_id=true, Miso will prevent products with the same product_group_id from showing multiple times in the search or recommendation results.

This is particular useful when one product has multiple variants (for example, different sizes, colors, or materials), and you only want to show this product only once in the search or recommendation results. Miso will then return the variant that is most likely to be of the user's interest.

The engine you want to get results from. When you have more than one engine, you can use this parameter to specify the specific engine you want to get results from. If not specified, the default engine will be used.

List of fields to retrieve. For example, the following request retrieves only the title field of each product along with the product_id, which is always returned.

{"fl": ["title"]}

You can also match field names by using * as a wildcard. For example, the query below retrieves the title and any custom attributes under the attributes dictionary.

{"fl": ["title", "attributes.*"]}

The following retrieves all the available fields:

{"fl": ["*"]}

For the lowest latency, use an empty array to retrieve just the product_id field (which is the default).

{"fl": []}

Defines a query in Elasticsearch query-string syntax (Lucene) that can be used to restrict the superset of products to return, without influencing the overall ranking. fq can enable users to drill down to products with specific features based on different product attributes

For example, the query below limits the search results to only show products whose size is either M or S and brand is Nike:

{"fq": "size:(\"M\" OR \"S\") AND brand:\"Nike\""}

You can use fq to apply filters against your custom attributes as well. For example, the query below limits the search results to only products whose designer attribute is Calvin Klein

{"fq": "attributes.designer:\"Calvin Klein\""}

fq can also limit search results by numerical range. For example, the following query limits the results to products that have rating >= 4.

{"fq": "rating:[4 TO *]"}