Back to Collection Items
; This example assumes the Chilkat API to have been previously unlocked.
; See Global Unlock Sample for sample code.
$oHttp = ObjCreate("Chilkat.Http")
Local $bSuccess
; Use this online tool to generate code from sample JSON: Generate Code to Create JSON
; The following JSON is sent in the request body.
; {
; "id": 7134912581187470,
; "sources": [
; {},
; {}
; ],
; "targets": [
; {},
; {}
; ],
; "numMatches": 62,
; "scoreThreshold": 0.8299079579527746
; }
$oJson = ObjCreate("Chilkat.JsonObject")
$oJson.UpdateInt("id",123)
$oJson.UpdateInt("numMatches",62)
$oJson.UpdateNumber("scoreThreshold","0.8299079579527746")
$oHttp.SetRequestHeader "content-type","application/json"
$oHttp.SetRequestHeader "api-key","{{api-key}}"
Local $oResp = $oHttp.PostJson3("https://domain.com/api/v1/projects/{{project}}/context/entitymatching/predict","application/json",$oJson)
If ($oHttp.LastMethodSuccess = False) Then
ConsoleWrite($oHttp.LastErrorText & @CRLF)
Exit
EndIf
ConsoleWrite($oResp.StatusCode & @CRLF)
ConsoleWrite($oResp.BodyStr & @CRLF)
Curl Command
curl -X POST
-H "api-key: {{api-key}}"
-H "content-type: application/json"
-d '{
"id": 7134912581187470,
"sources": [
{},
{}
],
"targets": [
{},
{}
],
"numMatches": 62,
"scoreThreshold": 0.8299079579527746
}'
https://domain.com/api/v1/projects/{{project}}/context/entitymatching/predict
Postman Collection Item JSON
{
"id": "entityMatchingPredict",
"name": "Predict matches",
"request": {
"url": {
"host": "{{baseUrl}}",
"path": [
"api",
"v1",
"projects",
"{{project}}",
"context",
"entitymatching",
"predict"
],
"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. Predicts entity matches using a trained model.",
"body": {
"mode": "raw",
"raw": "{\n \"id\": 7134912581187470,\n \"sources\": [\n {},\n {}\n ],\n \"targets\": [\n {},\n {}\n ],\n \"numMatches\": 62,\n \"scoreThreshold\": 0.8299079579527746\n}"
}
}
}