TCL / Cognite API v1 / Create entity matcher model
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load ./chilkat.dll
# This example assumes the Chilkat API to have been previously unlocked.
# See Global Unlock Sample for sample code.
set http [new_CkHttp]
# 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
# }
set json [new_CkJsonObject]
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" 0
CkHttp_SetRequestHeader $http "content-type" "application/json"
CkHttp_SetRequestHeader $http "api-key" "{{api-key}}"
# resp is a CkHttpResponse
set resp [CkHttp_PostJson3 $http "https://domain.com/api/v1/projects/{{project}}/context/entitymatching" "application/json" $json]
if {[CkHttp_get_LastMethodSuccess $http] == 0} then {
puts [CkHttp_lastErrorText $http]
delete_CkHttp $http
delete_CkJsonObject $json
exit
}
puts [CkHttpResponse_get_StatusCode $resp]
puts [CkHttpResponse_bodyStr $resp]
delete_CkHttpResponse $resp
delete_CkHttp $http
delete_CkJsonObject $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}"
}
}
}