DIP/postman/clustering/DIP-Clustering-Phase-D.post...

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{
"info": {
"name": "DIP Clustering Phase D",
"_postman_id": "0c39a7cf-8fde-43f9-8fdb-5d8890ad7676",
"description": "Spring clustering API examples with generic algorithm parameters and remote Python backend.",
"schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json"
},
"variable": [
{
"key": "baseUrl",
"value": "http://localhost:8080"
},
{
"key": "runId",
"value": ""
}
],
"item": [
{
"name": "Create DBSCAN run for TED notices",
"request": {
"method": "POST",
"header": [
{
"key": "Content-Type",
"value": "application/json"
}
],
"url": {
"raw": "{{baseUrl}}/v1/dip/clustering/runs",
"host": [
"{{baseUrl}}"
],
"path": [
"v1",
"dip",
"clustering",
"runs"
]
},
"body": {
"mode": "raw",
"raw": "{\n \"name\": \"TED notices DBSCAN PCA100\",\n \"algorithm\": \"DBSCAN\",\n \"executionBackend\": \"PYTHON_REMOTE\",\n \"reduction\": {\n \"method\": \"PCA\",\n \"targetDimensions\": 100\n },\n \"selection\": {\n \"documentTypes\": [\n \"TED_NOTICE\"\n ],\n \"representationTypes\": [\n \"SEMANTIC_TEXT\"\n ],\n \"embeddingStatuses\": [\n \"COMPLETED\"\n ],\n \"primaryRepresentationOnly\": true\n },\n \"parameters\": {\n \"eps\": 0.25,\n \"minSamples\": 5,\n \"metric\": \"euclidean\",\n \"normalizeVectors\": true\n }\n}"
}
}
},
{
"name": "Create HDBSCAN run for Leitstand TIME",
"request": {
"method": "POST",
"header": [
{
"key": "Content-Type",
"value": "application/json"
}
],
"url": {
"raw": "{{baseUrl}}/v1/dip/clustering/runs",
"host": [
"{{baseUrl}}"
],
"path": [
"v1",
"dip",
"clustering",
"runs"
]
},
"body": {
"mode": "raw",
"raw": "{\n \"name\": \"Leitstand TIME HDBSCAN PCA50\",\n \"algorithm\": \"HDBSCAN\",\n \"executionBackend\": \"PYTHON_REMOTE\",\n \"reduction\": {\n \"method\": \"PCA\",\n \"targetDimensions\": 50\n },\n \"selection\": {\n \"documentTypes\": [\n \"TIME_ENTRY\"\n ],\n \"builderKeys\": [\n \"time-entry-structured-text\"\n ],\n \"embeddingStatuses\": [\n \"COMPLETED\"\n ],\n \"primaryRepresentationOnly\": true\n },\n \"parameters\": {\n \"minClusterSize\": 15,\n \"minSamples\": 5,\n \"metric\": \"euclidean\",\n \"clusterSelectionMethod\": \"eom\",\n \"normalizeVectors\": true\n }\n}"
}
}
},
{
"name": "Create Agglomerative run",
"request": {
"method": "POST",
"header": [
{
"key": "Content-Type",
"value": "application/json"
}
],
"url": {
"raw": "{{baseUrl}}/v1/dip/clustering/runs",
"host": [
"{{baseUrl}}"
],
"path": [
"v1",
"dip",
"clustering",
"runs"
]
},
"body": {
"mode": "raw",
"raw": "{\n \"name\": \"TED notices Agglomerative\",\n \"algorithm\": \"AGGLOMERATIVE\",\n \"executionBackend\": \"PYTHON_REMOTE\",\n \"reduction\": {\n \"method\": \"PCA\",\n \"targetDimensions\": 100\n },\n \"selection\": {\n \"documentTypes\": [\n \"TED_NOTICE\"\n ],\n \"representationTypes\": [\n \"SEMANTIC_TEXT\"\n ],\n \"embeddingStatuses\": [\n \"COMPLETED\"\n ],\n \"primaryRepresentationOnly\": true\n },\n \"parameters\": {\n \"k\": 25,\n \"linkage\": \"average\",\n \"metric\": \"euclidean\",\n \"normalizeVectors\": true\n }\n}"
}
}
},
{
"name": "Start run",
"request": {
"method": "POST",
"url": {
"raw": "{{baseUrl}}/v1/dip/clustering/runs/{{runId}}/start",
"host": [
"{{baseUrl}}"
],
"path": [
"v1",
"dip",
"clustering",
"runs",
"{{runId}}",
"start"
]
}
}
},
{
"name": "Assignments with text",
"request": {
"method": "GET",
"url": {
"raw": "{{baseUrl}}/v1/dip/clustering/runs/{{runId}}/assignments?includeText=true",
"host": [
"{{baseUrl}}"
],
"path": [
"v1",
"dip",
"clustering",
"runs",
"{{runId}}",
"assignments"
],
"query": [
{
"key": "includeText",
"value": "true"
}
]
}
}
}
]
}