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Modes","\u002Fconnector-development\u002Fguides\u002Fexecution-modes\u002F","20.connector-development\u002F40.guides\u002F46.execution-modes",{"title":380,"path":893,"stem":894,"type":24,"device":6},"\u002Fconnector-development\u002Fguides\u002Ferror-handling\u002F","20.connector-development\u002F40.guides\u002F50.error-handling",{"title":896,"path":897,"stem":898,"type":24,"device":6},"Serial Troubleshooting","\u002Fconnector-development\u002Fguides\u002Fserial-troubleshooting\u002F","20.connector-development\u002F40.guides\u002F70.serial-troubleshooting",{"title":900,"path":901,"stem":902,"type":24,"device":6},"Testing","\u002Fconnector-development\u002Fguides\u002Ftesting\u002F","20.connector-development\u002F40.guides\u002F80.testing",{"title":904,"path":905,"stem":906,"children":907,"type":6,"device":6},"References","\u002Fconnector-development\u002Freference\u002F","20.connector-development\u002F50.reference\u002Findex",[908],{"title":904,"path":905,"stem":906,"type":200,"device":6},{"id":910,"title":404,"body":911,"description":1456,"device":6,"extension":1457,"links":6,"meta":1458,"navigation":969,"path":1459,"seo":1460,"stem":406,"type":285,"__hash__":1461},"docs\u002F04.concepts\u002F07.automate\u002F50.input.md",{"type":912,"value":913,"toc":1448},"minimark",[914,922,927,935,1059,1074,1078,1081,1137,1141,1162,1228,1231,1247,1264,1268,1271,1358,1362,1420,1424,1444],[915,916,917,921],"p",{},[918,919,920],"strong",{},"Inputs"," are typed parameters you declare on a workflow or phase. They are provided at run time — when a run is started, or when a phase is triggered manually. This lets a single workflow definition handle different samples, concentrations, or experimental conditions without changing the code. This is particularly useful for presenting error recovery options to the human or machine user on the phase level for decision-making.",[923,924,926],"h2",{"id":925},"workflow-inputs","Workflow inputs",[915,928,929,930,934],{},"Declare inputs as typed function parameters on the ",[931,932,933],"code",{},"@workflow"," function:",[936,937,943],"pre",{"className":938,"code":939,"filename":940,"language":941,"meta":942,"style":942},"language-python shiki shiki-themes github-light-high-contrast github-light-high-contrast github-dark-default","from unitelabs.sdk.automate import workflow, phase\n\n@workflow(name=\"ELISA\")\nasync def main_workflow(sample_id: str, concentration: float = 1.0):\n    plate = await sample_preparation(sample_id)\n    await detection(plate, min_concentration=concentration)\n","workflows\u002Felisa.py","python","",[931,944,945,964,971,994,1028,1042],{"__ignoreMap":942},[946,947,950,954,958,961],"span",{"class":948,"line":949},"line",1,[946,951,953],{"class":952},"sxkbv","from",[946,955,957],{"class":956},"sc-9e"," unitelabs.sdk.automate ",[946,959,960],{"class":952},"import",[946,962,963],{"class":956}," workflow, phase\n",[946,965,967],{"class":948,"line":966},2,[946,968,970],{"emptyLinePlaceholder":969},true,"\n",[946,972,974,977,980,984,987,991],{"class":948,"line":973},3,[946,975,933],{"class":976},"s4qFZ",[946,978,979],{"class":956},"(",[946,981,983],{"class":982},"sNqAu","name",[946,985,986],{"class":952},"=",[946,988,990],{"class":989},"sCx38","\"ELISA\"",[946,992,993],{"class":956},")\n",[946,995,997,1000,1003,1006,1009,1013,1016,1019,1022,1025],{"class":948,"line":996},4,[946,998,999],{"class":952},"async",[946,1001,1002],{"class":952}," def",[946,1004,1005],{"class":976}," main_workflow",[946,1007,1008],{"class":956},"(sample_id: ",[946,1010,1012],{"class":1011},"sOiAX","str",[946,1014,1015],{"class":956},", concentration: ",[946,1017,1018],{"class":1011},"float",[946,1020,1021],{"class":952}," =",[946,1023,1024],{"class":1011}," 1.0",[946,1026,1027],{"class":956},"):\n",[946,1029,1031,1034,1036,1039],{"class":948,"line":1030},5,[946,1032,1033],{"class":956},"    plate ",[946,1035,986],{"class":952},[946,1037,1038],{"class":952}," await",[946,1040,1041],{"class":956}," sample_preparation(sample_id)\n",[946,1043,1045,1048,1051,1054,1056],{"class":948,"line":1044},6,[946,1046,1047],{"class":952},"    await",[946,1049,1050],{"class":956}," detection(plate, ",[946,1052,1053],{"class":982},"min_concentration",[946,1055,986],{"class":952},[946,1057,1058],{"class":956},"concentration)\n",[915,1060,1061,1062,1065,1066,1069,1070,1073],{},"When the run is started via the platform UI, the operator provides the ",[931,1063,1064],{},"sample_id",". ",[931,1067,1068],{},"concentration"," is optional and defaults to ",[931,1071,1072],{},"1.0",". If triggered through the API, these values are sent as JSON in the request body.",[923,1075,1077],{"id":1076},"phase-inputs","Phase inputs",[915,1079,1080],{},"Phases also accept typed parameters. They receive values passed by the workflow or from the output of a previous phase:",[936,1082,1085],{"className":938,"code":1083,"filename":1084,"language":941,"meta":942,"style":942},"@phase()\nasync def detection(target: Plate, min_concentration: float):\n    result = await measure(target)\n    assert result.concentration >= min_concentration\n","workflows\u002Fphases.py",[931,1086,1087,1095,1111,1123],{"__ignoreMap":942},[946,1088,1089,1092],{"class":948,"line":949},[946,1090,1091],{"class":976},"@phase",[946,1093,1094],{"class":956},"()\n",[946,1096,1097,1099,1101,1104,1107,1109],{"class":948,"line":966},[946,1098,999],{"class":952},[946,1100,1002],{"class":952},[946,1102,1103],{"class":976}," detection",[946,1105,1106],{"class":956},"(target: Plate, min_concentration: ",[946,1108,1018],{"class":1011},[946,1110,1027],{"class":956},[946,1112,1113,1116,1118,1120],{"class":948,"line":973},[946,1114,1115],{"class":956},"    result ",[946,1117,986],{"class":952},[946,1119,1038],{"class":952},[946,1121,1122],{"class":956}," measure(target)\n",[946,1124,1125,1128,1131,1134],{"class":948,"line":996},[946,1126,1127],{"class":952},"    assert",[946,1129,1130],{"class":956}," result.concentration ",[946,1132,1133],{"class":952},">=",[946,1135,1136],{"class":956}," min_concentration\n",[923,1138,1140],{"id":1139},"reading-inputs-via-the-context-object","Reading inputs via the context object",[915,1142,1143,1144,1147,1148,1151,1152,1154,1155,1157,1158,1161],{},"Only ",[918,1145,1146],{},"workflow"," inputs are published automatically to the run's context. The moment ",[931,1149,1150],{},"main_workflow"," above is called, ",[931,1153,1064],{}," and ",[931,1156,1068],{}," become readable anywhere in the run via ",[931,1159,1160],{},"get_context().workflow_parameters"," — without adding them to any phase's or step's own signature:",[936,1163,1166],{"className":938,"code":1164,"filename":1165,"language":941,"meta":942,"style":942},"from unitelabs.sdk import get_context, step\n\n@step()\nasync def measure(target: Plate):\n    concentration = get_context().workflow_parameters.get(\"concentration\", 1.0)\n    ...\n","workflows\u002Fsteps.py",[931,1167,1168,1180,1184,1191,1203,1223],{"__ignoreMap":942},[946,1169,1170,1172,1175,1177],{"class":948,"line":949},[946,1171,953],{"class":952},[946,1173,1174],{"class":956}," unitelabs.sdk ",[946,1176,960],{"class":952},[946,1178,1179],{"class":956}," get_context, step\n",[946,1181,1182],{"class":948,"line":966},[946,1183,970],{"emptyLinePlaceholder":969},[946,1185,1186,1189],{"class":948,"line":973},[946,1187,1188],{"class":976},"@step",[946,1190,1094],{"class":956},[946,1192,1193,1195,1197,1200],{"class":948,"line":996},[946,1194,999],{"class":952},[946,1196,1002],{"class":952},[946,1198,1199],{"class":976}," measure",[946,1201,1202],{"class":956},"(target: Plate):\n",[946,1204,1205,1208,1210,1213,1216,1219,1221],{"class":948,"line":1030},[946,1206,1207],{"class":956},"    concentration ",[946,1209,986],{"class":952},[946,1211,1212],{"class":956}," get_context().workflow_parameters.get(",[946,1214,1215],{"class":989},"\"concentration\"",[946,1217,1218],{"class":956},", ",[946,1220,1072],{"class":1011},[946,1222,993],{"class":956},[946,1224,1225],{"class":948,"line":1044},[946,1226,1227],{"class":1011},"    ...\n",[915,1229,1230],{},"This is the recommended way to thread run-wide settings — run mode, simulation flags, feature flags — through many phases and steps: declare them once as workflow inputs, read them from context wherever they're needed. Adding a new workflow input later never requires touching an existing phase's or step's signature, since nothing downstream needs to name it as an argument to see it.",[915,1232,1233,1234,1154,1237,1239,1240,1243,1244,1246],{},"Phase inputs, like ",[931,1235,1236],{},"target",[931,1238,1053],{}," above, work differently: they stay ordinary function arguments, passed explicitly by whatever calls the phase, and are not added to ",[931,1241,1242],{},"workflow_parameters",". A phase can still read the parameters its enclosing workflow declared, via the same ",[931,1245,1160],{},".",[1248,1249,1252],"callout",{"color":1250,"icon":1251},"info","i-heroicons-information-circle",[915,1253,1254,1256,1257,1260,1261,1263],{},[931,1255,1242],{}," is a read-only view (",[931,1258,1259],{},"types.MappingProxyType","), populated only from the top-level ",[931,1262,933],{}," call — treat values read from it as immutable. It is only available with SDK >= 0.15.0 or liquid handling SDK >= 0.34.0.",[923,1265,1267],{"id":1266},"input-types","Input types",[915,1269,1270],{},"Inputs support standard Python types and are validated before the run starts:",[1272,1273,1274,1287],"table",{},[1275,1276,1277],"thead",{},[1278,1279,1280,1284],"tr",{},[1281,1282,1283],"th",{},"Type",[1281,1285,1286],{},"Example",[1288,1289,1290,1300,1312,1322,1332,1342,1350],"tbody",{},[1278,1291,1292,1297],{},[1293,1294,1295],"td",{},[931,1296,1012],{},[1293,1298,1299],{},"Sample identifiers, protocol names",[1278,1301,1302,1309],{},[1293,1303,1304,1218,1307],{},[931,1305,1306],{},"int",[931,1308,1018],{},[1293,1310,1311],{},"Volumes, concentrations, durations",[1278,1313,1314,1319],{},[1293,1315,1316],{},[931,1317,1318],{},"bool",[1293,1320,1321],{},"Feature flags, approval gates",[1278,1323,1324,1329],{},[1293,1325,1326],{},[931,1327,1328],{},"list",[1293,1330,1331],{},"Lists of values",[1278,1333,1334,1339],{},[1293,1335,1336],{},[931,1337,1338],{},"dict",[1293,1340,1341],{},"Key-value mappings",[1278,1343,1344,1347],{},[1293,1345,1346],{},"Complex data types",[1293,1348,1349],{},"Nested structures",[1278,1351,1352,1355],{},[1293,1353,1354],{},"Pydantic models",[1293,1356,1357],{},"Structured sample metadata",[923,1359,1361],{"id":1360},"providing-inputs-via-the-api","Providing inputs via the API",[936,1363,1368],{"className":1364,"code":1365,"filename":1366,"language":1367,"meta":942,"style":942},"language-bash shiki shiki-themes github-light-high-contrast github-light-high-contrast github-dark-default","curl -X POST https:\u002F\u002Fapi.unitelabs.io\u002Fv1\u002Fworkflows\u002F{workflow_id}\u002Fruns \\\n  -H \"Authorization: Bearer $API_TOKEN\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"inputs\": {\"sample_id\": \"S-042\", \"concentration\": 2.5}}'\n","Terminal","bash",[931,1369,1370,1387,1403,1412],{"__ignoreMap":942},[946,1371,1372,1375,1378,1381,1384],{"class":948,"line":949},[946,1373,1374],{"class":982},"curl",[946,1376,1377],{"class":1011}," -X",[946,1379,1380],{"class":989}," POST",[946,1382,1383],{"class":989}," https:\u002F\u002Fapi.unitelabs.io\u002Fv1\u002Fworkflows\u002F{workflow_id}\u002Fruns",[946,1385,1386],{"class":952}," \\\n",[946,1388,1389,1392,1395,1398,1401],{"class":948,"line":966},[946,1390,1391],{"class":1011},"  -H",[946,1393,1394],{"class":989}," \"Authorization: Bearer ",[946,1396,1397],{"class":956},"$API_TOKEN",[946,1399,1400],{"class":989},"\"",[946,1402,1386],{"class":952},[946,1404,1405,1407,1410],{"class":948,"line":973},[946,1406,1391],{"class":1011},[946,1408,1409],{"class":989}," \"Content-Type: application\u002Fjson\"",[946,1411,1386],{"class":952},[946,1413,1414,1417],{"class":948,"line":996},[946,1415,1416],{"class":1011},"  -d",[946,1418,1419],{"class":989}," '{\"inputs\": {\"sample_id\": \"S-042\", \"concentration\": 2.5}}'\n",[923,1421,1423],{"id":1422},"related-concepts","Related concepts",[1425,1426,1427,1434,1439],"ul",{},[1428,1429,1430,1433],"li",{},[1431,1432,388],"a",{"href":389},": where inputs are declared",[1428,1435,1436,1438],{},[1431,1437,416],{"href":417},": pausing for operator-provided inputs at phase execution time",[1428,1440,1441,1443],{},[1431,1442,400],{"href":401},": inputs are bound to a specific run and stored with the run record",[1445,1446,1447],"style",{},"html pre.shiki code .sxkbv, html code.shiki .sxkbv{--shiki-light:#A0111F;--shiki-default:#A0111F;--shiki-dark:#FF7B72}html pre.shiki code .sc-9e, html code.shiki .sc-9e{--shiki-light:#0E1116;--shiki-default:#0E1116;--shiki-dark:#E6EDF3}html pre.shiki code .s4qFZ, html code.shiki .s4qFZ{--shiki-light:#622CBC;--shiki-default:#622CBC;--shiki-dark:#D2A8FF}html pre.shiki code .sNqAu, html code.shiki .sNqAu{--shiki-light:#702C00;--shiki-default:#702C00;--shiki-dark:#FFA657}html pre.shiki code .sCx38, html code.shiki .sCx38{--shiki-light:#032563;--shiki-default:#032563;--shiki-dark:#A5D6FF}html pre.shiki code .sOiAX, html code.shiki .sOiAX{--shiki-light:#023B95;--shiki-default:#023B95;--shiki-dark:#79C0FF}html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":942,"searchDepth":949,"depth":966,"links":1449},[1450,1451,1452,1453,1454,1455],{"id":925,"depth":966,"text":926},{"id":1076,"depth":966,"text":1077},{"id":1139,"depth":966,"text":1140},{"id":1266,"depth":966,"text":1267},{"id":1360,"depth":966,"text":1361},{"id":1422,"depth":966,"text":1423},"Parameterize a workflow or phase with typed values provided at run time — from the UI, the API, or a human operator.","md",{},"\u002Fconcepts\u002Fautomate\u002Finput",{"title":404,"description":1456},"hX3f90n8rO0TeA-WUcDAKcWdAzGxPEUyQvA4wIYmE5s",[1463,1465],{"title":400,"description":1464,"path":401},"A run is a single execution of a workflow, tracked from start to finish with full state and audit history.",{"title":408,"description":1466,"path":409},"Structured data and files produced by a workflow run — persisted and linked to the run record for traceability and downstream use.",1791294179969]