SQL Server / Cognite API v1 / Create entity matcher model
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-- Important: See this note about string length limitations for strings returned by sp_OAMethod calls.
--
CREATE PROCEDURE ChilkatSample
AS
BEGIN
DECLARE @hr int
DECLARE @iTmp0 int
-- Important: Do not use nvarchar(max). See the warning about using nvarchar(max).
DECLARE @sTmp0 nvarchar(4000)
-- This example assumes the Chilkat API to have been previously unlocked.
-- See Global Unlock Sample for sample code.
DECLARE @http int
-- Use "Chilkat_9_5_0.Http" for versions of Chilkat < 10.0.0
EXEC @hr = sp_OACreate 'Chilkat.Http', @http OUT
IF @hr <> 0
BEGIN
PRINT 'Failed to create ActiveX component'
RETURN
END
DECLARE @success int
-- 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
-- }
DECLARE @json int
-- Use "Chilkat_9_5_0.JsonObject" for versions of Chilkat < 10.0.0
EXEC @hr = sp_OACreate 'Chilkat.JsonObject', @json OUT
EXEC sp_OAMethod @json, 'UpdateNewArray', @success OUT, 'trueMatches[0].'
EXEC sp_OAMethod @json, 'UpdateNewArray', @success OUT, 'trueMatches[1].'
EXEC sp_OAMethod @json, 'UpdateString', @success OUT, 'externalId', 'in irure amet'
EXEC sp_OAMethod @json, 'UpdateString', @success OUT, 'name', 'minim'
EXEC sp_OAMethod @json, 'UpdateString', @success OUT, 'description', 'aliqua ipsum culpa eiusmod'
EXEC sp_OAMethod @json, 'UpdateString', @success OUT, 'featureType', 'simple'
EXEC sp_OAMethod @json, 'UpdateString', @success OUT, 'matchFields[0].source', 'name'
EXEC sp_OAMethod @json, 'UpdateString', @success OUT, 'matchFields[0].target', 'name'
EXEC sp_OAMethod @json, 'UpdateString', @success OUT, 'classifier', 'randomforest'
EXEC sp_OAMethod @json, 'UpdateBool', @success OUT, 'ignoreMissingFields', 0
EXEC sp_OAMethod @http, 'SetRequestHeader', NULL, 'content-type', 'application/json'
EXEC sp_OAMethod @http, 'SetRequestHeader', NULL, 'api-key', '{{api-key}}'
DECLARE @resp int
EXEC sp_OAMethod @http, 'PostJson3', @resp OUT, 'https://domain.com/api/v1/projects/{{project}}/context/entitymatching', 'application/json', @json
EXEC sp_OAGetProperty @http, 'LastMethodSuccess', @iTmp0 OUT
IF @iTmp0 = 0
BEGIN
EXEC sp_OAGetProperty @http, 'LastErrorText', @sTmp0 OUT
PRINT @sTmp0
EXEC @hr = sp_OADestroy @http
EXEC @hr = sp_OADestroy @json
RETURN
END
EXEC sp_OAGetProperty @resp, 'StatusCode', @iTmp0 OUT
PRINT @iTmp0
EXEC sp_OAGetProperty @resp, 'BodyStr', @sTmp0 OUT
PRINT @sTmp0
EXEC @hr = sp_OADestroy @resp
EXEC @hr = sp_OADestroy @http
EXEC @hr = sp_OADestroy @json
END
GO
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}"
}
}
}