Chilkat Online Tools

PureBasic / Cognite API v1 / Create entity matcher model

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IncludeFile "CkJsonObject.pb"
IncludeFile "CkHttp.pb"
IncludeFile "CkHttpResponse.pb"

Procedure ChilkatExample()

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

    http.i = CkHttp::ckCreate()
    If http.i = 0
        Debug "Failed to create object."
        ProcedureReturn
    EndIf

    success.i

    ; 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.i = CkJsonObject::ckCreate()
    If json.i = 0
        Debug "Failed to create object."
        ProcedureReturn
    EndIf

    CkJsonObject::ckUpdateNewArray(json,"trueMatches[0].")
    CkJsonObject::ckUpdateNewArray(json,"trueMatches[1].")
    CkJsonObject::ckUpdateString(json,"externalId","in irure amet")
    CkJsonObject::ckUpdateString(json,"name","minim")
    CkJsonObject::ckUpdateString(json,"description","aliqua ipsum culpa eiusmod")
    CkJsonObject::ckUpdateString(json,"featureType","simple")
    CkJsonObject::ckUpdateString(json,"matchFields[0].source","name")
    CkJsonObject::ckUpdateString(json,"matchFields[0].target","name")
    CkJsonObject::ckUpdateString(json,"classifier","randomforest")
    CkJsonObject::ckUpdateBool(json,"ignoreMissingFields",0)

    CkHttp::ckSetRequestHeader(http,"content-type","application/json")
    CkHttp::ckSetRequestHeader(http,"api-key","{{api-key}}")

    resp.i = CkHttp::ckPostJson3(http,"https://domain.com/api/v1/projects/{{project}}/context/entitymatching","application/json",json)
    If CkHttp::ckLastMethodSuccess(http) = 0
        Debug CkHttp::ckLastErrorText(http)
        CkHttp::ckDispose(http)
        CkJsonObject::ckDispose(json)
        ProcedureReturn
    EndIf

    Debug Str(CkHttpResponse::ckStatusCode(resp))
    Debug CkHttpResponse::ckBodyStr(resp)
    CkHttpResponse::ckDispose(resp)



    CkHttp::ckDispose(http)
    CkJsonObject::ckDispose(json)


    ProcedureReturn
EndProcedure

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}"
    }
  }
}