API
Metarank's API provides an easy way to integrate Metarank with your applications.
API Endpoint:
/feedbackMethod:
POSTFeedback endpoint receives several types of events: item, user, interaction, ranking.
Integrating these events is crucial for personalization to operate properly and provide relevant results.
A JSON message with the following fields:
accepted: how many events from the submitted batch were processedstatus: "ok" when no errors foundtookMillis: how many milliseconds batch processing tookupdated: how many underlying ranking features were recomputed.
Example:
{"accepted":1,"status":"ok","tookMillis":3,"updated":0}
$> curl http://localhost:8080/feedback -d '{
"event": "ranking",
"id": "id1",
"items": [
{"id":"72998"}, {"id":"589"}, {"id":"134130"}, {"id":"5459"},
{"id":"1917"}, {"id":"2571"}, {"id":"1527"}, {"id":"97752"},
{"id":"1270"}, {"id":"1580"}, {"id":"109487"}, {"id":"79132"}
],
"user": "alice",
"session": "alice1",
"timestamp": 1661431894711
}'
* Trying 127.0.0.1:8080...
* Connected to localhost (127.0.0.1) port 8080 (#0)
> POST /feedback HTTP/1.1
> Host: localhost:8080
> User-Agent: curl/7.86.0
> Accept: */*
> Content-Length: 354
> Content-Type: application/x-www-form-urlencoded
>
< HTTP/1.1 200 OK
< Date: Mon, 28 Nov 2022 13:09:44 GMT
< Content-Length: 55
<
{"accepted":1,"status":"ok","tookMillis":3,"updated":0}
API Endpoint:
/train/<model name>Method:
POSTTrain endpoint runs the training on persisted click-through data. You can run this method at any time to re-train the model. See the Model retraining how-to on how to set up the retraining.
Payload: none
A JSON response with the following fields:
weights: per-field model weightssizeBytes: model size in bytesiterations: test/train error loss while training.
Example:
{
"features": [
{
"name": "vote_avg",
"weight": 629.0
},
{
"name": "profile",
"weight": [
1202.0,
373.0,
627.0,
145.0
]
}
],
"iterations": [
{
"id": 0,
"millis": 274,
"testMetric": 0.5787768851757988,
"trainMetric": 0.593075630098252
},
{
"id": 1,
"millis": 104,
"testMetric": 0.5903952545996365,
"trainMetric": 0.6083208266384491
}
],
"sizeBytes": 843792
}
API Endpoint:
/rank/<model name>Method:
POSTQuerystring Parameters:
explain: boolean: used to provide extra information in the response containing calculated feature values.
Ranking endpoint does the real work of personalizing items that are passed to it. You need to explicitly define which model to invoke.
{
"id": "81f46c34-a4bb-469c-8708-f8127cd67d27",
"timestamp": "1599391467000",
"user": "user1",
"session": "session1",
"fields": [
{"name": "query", "value": "jeans"},
{"name": "source", "value": "search"}
],
"items": [
{"id": "item3", "fields": [{"name": "relevancy", "value": 2.0}]},
{"id": "item1", "fields": [{"name": "relevancy", "value": 1.0}]},
{"id": "item2", "fields": [{"name": "relevancy", "value": 0.1}]}
]
}
id: a request identifier later used to join ranking and interaction events. This will be the same value that you will pass to feedback endpoint for impression and ranking events.user: an optional unique visitor identifier.session: an optional session identifier, a single visitor may have multiple sessions.timestamp: when this event happened. (see timestamp format description on which formats are supported)fields: an optional array of extra fields that you can use in your model, for more information refer to Supported events.items: which particular items were displayed to the visitor.items.id: id of the content item. Should match theitemproperty from item metadata event.items.fields: an optional set of per-item fields, for example BM25 scores coming from ES. See how to use BM25 scores in ranking.
{
"items": [
{"id": "item2", "score": 2.0, "features": [{"name": "popularity", "value": 10 }]},
{"id": "item3", "score": 1.0, "features": [{"name": "popularity", "value": 5 }]},
{"id": "item1", "score": 0.5, "features": [{"name": "popularity", "value": 2 }]}
]
}
items.id: id of the content item. Will matchitemproperty from the item metadata event.items.score: a score calculated by personalization modelitems.features: an array of feature values calculated by pesonaliization model. This field will be returned ifexplainfield is set totruein the request. The structure of this object will vary depending on the feature type.
API Endpoint:
/recommend/<model-name>Method:
POST{
"count": 10,
"user": "alice1",
"items": ["item4"]
}
Where:
count- number of items to recommend.user- optional, current user iditems- context of recommendation. For example, it can be single item for "similar-items" recommendation, and multiple items at once for "cart-style" recommendations.
{
"items": [
{"id": "item2", "score": 2.0},
{"id": "item3", "score": 1.0},
{"id": "item1", "score": 0.5}
]
}
items.id: id of the content item.items.score: a score calculated by recommender model.
API Endpoint:
/metricsMethod:
GETDumps app and JVM metrics in a prometheus format.
> GET /metrics HTTP/1.1
> Host: localhost:8080
> User-Agent: curl/7.86.0
> Accept: */*
>
< HTTP/1.1 200 OK
< Date: Mon, 28 Nov 2022 13:30:41 GMT
< Transfer-Encoding: chunked
<
# HELP metarank_feedback_events_total Number of feedback events received
# TYPE metarank_feedback_events_total counter
metarank_feedback_events_total 58441.0
# HELP metarank_rank_requests_total Number of /rank requests
# TYPE metarank_rank_requests_total counter
metarank_rank_requests_total{model="xgboost",} 5.0
# HELP metarank_rank_latency_seconds rank endpoint latency
# TYPE metarank_rank_latency_seconds summary
metarank_rank_latency_seconds{model="xgboost",quantile="0.5",} 0.011451508
metarank_rank_latency_seconds{model="xgboost",quantile="0.8",} 0.014340056
metarank_rank_latency_seconds{model="xgboost",quantile="0.9",} 0.119447575
metarank_rank_latency_seconds{model="xgboost",quantile="0.95",} 0.119447575
metarank_rank_latency_seconds{model="xgboost",quantile="0.98",} 0.119447575
metarank_rank_latency_seconds{model="xgboost",quantile="0.99",} 0.119447575
Last modified 1mo ago