Chilkat Online Tools

C / Cognite API v1 / Create entity matcher model

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#include <C_CkHttp.h>
#include <C_CkJsonObject.h>
#include <C_CkHttpResponse.h>

void ChilkatSample(void)
    {
    HCkHttp http;
    BOOL success;
    HCkJsonObject json;
    HCkHttpResponse resp;

    // This example assumes the Chilkat API to have been previously unlocked.
    // See Global Unlock Sample for sample code.

    http = CkHttp_Create();

    // Use this online tool to generate code from sample JSON: Generate Code to Create JSON

    // The following JSON is sent in the request body.

    // {
    //   "sources": [
    //     {},
    //     {}
    //   ],
    //   "targets": [
    //     {},
    //     {}
    //   ],
    //   "trueMatches": [
    //     [
    //     ],
    //     [
    //     ]
    //   ],
    //   "externalId": "in irure amet",
    //   "name": "minim",
    //   "description": "aliqua ipsum culpa eiusmod",
    //   "featureType": "simple",
    //   "matchFields": [
    //     {
    //       "source": "name",
    //       "target": "name"
    //     }
    //   ],
    //   "classifier": "randomforest",
    //   "ignoreMissingFields": false
    // }

    json = CkJsonObject_Create();
    CkJsonObject_UpdateNewArray(json,"trueMatches[0].");
    CkJsonObject_UpdateNewArray(json,"trueMatches[1].");
    CkJsonObject_UpdateString(json,"externalId","in irure amet");
    CkJsonObject_UpdateString(json,"name","minim");
    CkJsonObject_UpdateString(json,"description","aliqua ipsum culpa eiusmod");
    CkJsonObject_UpdateString(json,"featureType","simple");
    CkJsonObject_UpdateString(json,"matchFields[0].source","name");
    CkJsonObject_UpdateString(json,"matchFields[0].target","name");
    CkJsonObject_UpdateString(json,"classifier","randomforest");
    CkJsonObject_UpdateBool(json,"ignoreMissingFields",FALSE);

    CkHttp_SetRequestHeader(http,"content-type","application/json");
    CkHttp_SetRequestHeader(http,"api-key","{{api-key}}");

    resp = CkHttp_PostJson3(http,"https://domain.com/api/v1/projects/{{project}}/context/entitymatching","application/json",json);
    if (CkHttp_getLastMethodSuccess(http) == FALSE) {
        printf("%s\n",CkHttp_lastErrorText(http));
        CkHttp_Dispose(http);
        CkJsonObject_Dispose(json);
        return;
    }

    printf("%d\n",CkHttpResponse_getStatusCode(resp));
    printf("%s\n",CkHttpResponse_bodyStr(resp));
    CkHttpResponse_Dispose(resp);


    CkHttp_Dispose(http);
    CkJsonObject_Dispose(json);

    }

Curl Command

curl -X POST
	-H "api-key: {{api-key}}"
	-H "content-type: application/json"
	-d '{
    "sources": [
        {},
        {}
    ],
    "targets": [
        {},
        {}
    ],
    "trueMatches": [
        [],
        []
    ],
    "externalId": "in irure amet",
    "name": "minim",
    "description": "aliqua ipsum culpa eiusmod",
    "featureType": "simple",
    "matchFields": [
        {
            "source": "name",
            "target": "name"
        }
    ],
    "classifier": "randomforest",
    "ignoreMissingFields": false
}'
https://domain.com/api/v1/projects/{{project}}/context/entitymatching

Postman Collection Item JSON

{
  "id": "entityMatchingCreate",
  "name": "Create entity matcher model",
  "request": {
    "url": {
      "host": "{{baseUrl}}",
      "path": [
        "api",
        "v1",
        "projects",
        "{{project}}",
        "context",
        "entitymatching"
      ],
      "query": [
      ],
      "variable": [
      ]
    },
    "method": "POST",
    "header": [
      {
        "key": "api-key",
        "value": "{{api-key}}",
        "description": "An admin can create API keys in the Cognite console."
      },
      {
        "key": "content-type",
        "value": "application/json"
      }
    ],
    "description": "Note: All users on this CDF subscription with assets read-all and entitymatching read-all and write-all capabilities in the project, are able to access the data sent to this endpoint. Train a model that predicts matches between entities (for example, time series names to asset names). This is also known as fuzzy joining. If there are no trueMatches (labeled data), you train a static (unsupervised) model, otherwise a machine learned (supervised) model is trained.",
    "body": {
      "mode": "raw",
      "raw": "{\n    \"sources\": [\n        {},\n        {}\n    ],\n    \"targets\": [\n        {},\n        {}\n    ],\n    \"trueMatches\": [\n        [],\n        []\n    ],\n    \"externalId\": \"in irure amet\",\n    \"name\": \"minim\",\n    \"description\": \"aliqua ipsum culpa eiusmod\",\n    \"featureType\": \"simple\",\n    \"matchFields\": [\n        {\n            \"source\": \"name\",\n            \"target\": \"name\"\n        }\n    ],\n    \"classifier\": \"randomforest\",\n    \"ignoreMissingFields\": false\n}"
    }
  }
}