Chilkat Online Tools

Foxpro / Cognite API v1 / Create entity matcher model

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LOCAL loHttp
LOCAL lnSuccess
LOCAL loJson
LOCAL loResp

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

* For versions of Chilkat < 10.0.0, use CreateObject('Chilkat_9_5_0.Http')
loHttp = CreateObject('Chilkat.Http')

* 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
* }

* For versions of Chilkat < 10.0.0, use CreateObject('Chilkat_9_5_0.JsonObject')
loJson = CreateObject('Chilkat.JsonObject')
loJson.UpdateNewArray("trueMatches[0].")
loJson.UpdateNewArray("trueMatches[1].")
loJson.UpdateString("externalId","in irure amet")
loJson.UpdateString("name","minim")
loJson.UpdateString("description","aliqua ipsum culpa eiusmod")
loJson.UpdateString("featureType","simple")
loJson.UpdateString("matchFields[0].source","name")
loJson.UpdateString("matchFields[0].target","name")
loJson.UpdateString("classifier","randomforest")
loJson.UpdateBool("ignoreMissingFields",0)

loHttp.SetRequestHeader("content-type","application/json")
loHttp.SetRequestHeader("api-key","{{api-key}}")

loResp = loHttp.PostJson3("https://domain.com/api/v1/projects/{{project}}/context/entitymatching","application/json",loJson)
IF (loHttp.LastMethodSuccess = 0) THEN
    ? loHttp.LastErrorText
    RELEASE loHttp
    RELEASE loJson
    CANCEL
ENDIF

? STR(loResp.StatusCode)
? loResp.BodyStr
RELEASE loResp

RELEASE loHttp
RELEASE loJson

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}"
    }
  }
}