Relevance Tuning Guide, Weights and Boosts

Within your Engine, click on Relevance Tuning.

The initial view will show all of your schema fields with their default weight:

Relevance Tuning, Weights - All of your schema field.

Next to our schema fields, there is a query tester.

We want people who are looking for mountains to find a park that is known for the splendor of its mountains.

So, we will use "mountains" as our reference query.

Relevance Tuning, Weights - Within the search bar on the right, we can test queries. A query for "mountains" returns fairly relevant results.

The results that we see include parks that contain mountains.

The term is present within one of the result’s text fields: title or description, most likely.

Our results could be more accurate...

Naturally, National Parks that include the term "mountains" in the title have beautiful mountain ranges.

We should adjust the weight of the title field.

As we do so, the results for our reference query will shift in real time.

Relevance Tuning, Weights - Tuning the weight of the "title" field. The weight is now "3" and we see more relevant results within our sample "mountains" query.

The field we have adjusted, title, is now bolded and marked with the Weights icon.

Two of our parks fell from our top three results, and the overall result set now reflects more relevant parks for those looking for "mountains".

Once we click Save, the change will be live.

Weights are applied at query time.

Within the /search API endpoint, a weight value can be passed within the search_fields object on each search.

The search_field object can define fields.

Only the defined fields will be returned within your result set:

curl -X GET 'https://host-2376rb.api.swiftype.com/api/as/v1/engines/national-parks-demo/search' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer search-soaewu2ye6uc45dr8mcd54v8' \
-d '{
  "search_fields": {
    "title": {
      "weight": 10
    },
    "description": {
      "weight": 1
    },
    "states": {
      "weight": 2
    }
  },
  "query": "mountains"
}'

In our example, we are asking to return only three fields within our results: title, description, and states.

We are weighting each field: 10, 1, and 2, respectively.

Through the API, you can build fluid search, leveraging dynamic weights when you need them.

Read the Weights API Reference.

Click into Relevance Tuning.

Relevance Tuning, Weights - Click Relevance Tuning and see your schema.

Place a reference query within the query tester.

This will give us a baseline that we can use to calibrate the boost.

We will use "old growth".

Relevance Tuning, Weights - The "old growth" query within the query tester.

Now, click or mouse over to the Add Boosts button.

Depending on the type of field - number, text, geolocation, or date - different boosting options will appear.

We want to put a Value Boost on the world_heritage_site field, which is of type text.

Relevance Tuning, Weights - Mouse over or click the Add Boost button, then select the ideal boost.

A Value Boost will look at the value of the field and then apply the boost given what is present.

The world_heritage_site field can be either: true or false.

We want to give sites that are world heritage sites precedence, so we provide true as our value, then calibrate the impact of the boost.

Relevance Tuning, Weights - The value true has been provided and the impact of the boost has been scaled to 4.6.

An icon will appear next to the boosted field that matches the type of boost.

In this case, the world_heritage_site has the Value Boost icon and the field is bolded.

As we drag the impact slider higher or lower, we can see the results shift.

As a result of the boost, Yosemite National Park is now our top rated park for the "old growth" query because it is also a world heritage site.

Once we click Save, the boost will impact all queries.

Like Weights, Boosts are applied at query time.

Using the /search endpoint, a boost object can be passed in along with each unique search.

We can look at a Value Boost:

curl -X GET 'https://host-2376rb.api.swiftype.com/api/as/v1/engines/national-parks-demo/search' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer search-soaewu2ye6uc45dr8mcd54v8' \
-d '{
  "query": "old growth",
  "boosts": {
    "world_heritage_site": [
      {
        "type": "value",
        "value": "true",
        "operation": "multiply",
        "factor": 10
      }
    ]
  }
}'

Within our example Engine, the National Parks demo, we have established a boost on the world_heritage_site field.

The field is a text field and each document has either true or false as their value.

Our query gives a 10x boost to relevance when documents contain world_heritage_site: true.

We could make it a negative boost, which decreases the relevance of parks that are also world heritage sites! But we will stay positive.

A Proximity Boost utilizes deep geolocation capability:

curl -X GET 'https://host-2376rb.api.swiftype.com/api/as/v1/engines/national-parks-demo/search' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer search-soaewu2ye6uc45dr8mcd54v8' \
-d '{
  "query": "old growth",
  "boosts": {
    "location": {
      "type": "proximity",
      "function": "exponential",
      "center": "25.32, -80.93",
      "factor": 3
    }
  }
}'

In this second example, we are looking for "old growth" parks.

But we’re boosting the results based on their location to the Elastic office in San Francisco.

For experimentation, try putting your own coordinates as the center value.

Which "old growth" US National Park is closest to you?

Read the Boosts API Reference.