Live Monitoring

You can use the REST API to start an EVAL activity that generates model output for the missed time range. The activity status can be tracked within Activity view on the Falkonry UI.

Note

This is a recommended option, if you do not want to disrupt or restart live monitoring for an entity in order to set the retroactive time option to generate model output on the historical time range.

The API requires live model ID, common model ID (i.e. M[0]), entity name and time range for which model output needs to be generated. Use the Live Context API</apis/fetch_output> to get the common model ID and use the Falkonry UI to get the live model ID of the relevant assessment. Then use the below Flows API to start generating model output for the historical time range.

Patterns (AI) Model

Request Payload

{
  "flowType": "EVAL",
  "datastream": "zzzzzzzzzzzzzzz",
  "assessment": "yyyyyyyyyyyyyyy",
  "model": "mmmmmmmmmmmmmm",
  "name": "Model output run-1",
  "spec": {
    "commonModelId": "ccccccccccccccc",
    "segments": [{
      "entities": ["<entity_name_1>", "<entity_name_2>"],
      "timeRange": {
        "startTime": "0000000000000000", // in nanoseconds
        "endTime": "0000000000000000" // in nanoseconds
      }
    }],
    "forLive": true,
    "withEpisodes": true
  }
}

cURL request

$ curl --location --request POST 'https://app3.falkonry.ai/api/1.2/accounts/xxxxxxxxxxxxxxx/flows' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data-raw '{
      "flowType": "EVAL", 
      "datastream": "zzzzzzzzzzzzzzz",
      "assessment": "yyyyyyyyyyyyyyy", 
      "model": "mmmmmmmmmmmmmm", 
      "name": "Model output run-1", 
      "spec": {
          "commonModelId": "ccccccccccccccc", 
          "segments": [{
              "entities": ["<entity_name>"], 
              "timeRange": {
                  "startTime": "0000000000000000", // in nanoseconds
                  "endTime": "0000000000000000" // in nanoseconds 
                      }}],
      "forLive": true, 
      "withEpisodes": true } }'

Python

import requests 

URL = 'https://app3.falkonry.ai/api/1.2/accounts/xxxxxxxxxxxxxxx/flows'
TOKEN = '<token>' 
HEADERS = {'Authorization': f'Bearer {TOKEN}'} 
PAYLOAD = { "flowType": "EVAL", 
              "datastream":"zzzzzzzzzzzzzzz", 
              "assessment": "yyyyyyyyyyyyyyy", 
              "model": "mmmmmmmmmmmmmm", 
              "name": "Model output run-1", 
              "spec": {
                  "commonModelId": "ccccccccccccccc", 
                  "segments": [ {
                          "entities": ["<entity_name>"], 
                          "timeRange": { "startTime": "0000000000000000", # in nanoseconds 
                          "endTime": "00000000000000000" # in nanoseconds 
                              }}], 
              "forLive": true,
              "withEpisodes": true}}

response = requests.post(URL, headers=HEADERS, data=PAYLOAD)
print(response.json())

Example response

{
  "id": "1124191510837645312",
  "type": "entities.flow",
  "flowType": "EVAL",
  "status": "CREATED",
  "name": "Model output run-1",
  "datastream": "zzzzzzzzzzzzzzz",
  "assessment": "yyyyyyyyyyyyyyy",
  "model": "mmmmmmmmmmmmmm",
  "spec": {
    "isBatch": false,
    "datastreamStats": {},
    "model": "mmmmmmmmmmmmmm",
    "commonModelId": "ccccccccccccccc",
    "segments": [
      {
        "entities": [
          "machine-1"
        ],
        "timeRange": {
          "startTime": "1684843200000000000",
          "endTime": "1687521600000000000"
        }
      }
    ],
    "assessmentRate": 2000000000,
    "config": {},
    "inputList": [],
    "withEpisodes": true,
    "forEpisode": false,
    "tolerateNoData": false,
    "forLive": true
  },
  "createTime": 1688098542651,
  "updateTime": 1688098542651,
  "createdBy": "1013056801100267520",
  "updatedBy": "1013056801100267520",
  "archived": false,
  "links": []
}

Insights (AI), Rules, and Calculations Model

To start or stop a model for continuous evaluation of incoming data, use the STARTLIVEMODEL and STOPLIVEMODEL flows, respectively. These flows require the model ID of the model you wish to activate or deactivate. - model: The ID of the model that must be made live.

Model ID can be retrieved from the application via a GET request to obtain the Model ID of a common model. In this example, the ID of the base signal will be used as the assessment ID in the subsequent GET request.

https://app.falkonry.ai/api/1.2/accounts/<accountID>/models?assessment=<baseSignalID>&isCommonModel=true&limit=1

• For Insights (Anomaly) model, baseSignalID = ID of the Signal
• For Rules model, baseSignalID = ID of the Rule
• For Calculations Transform model, baseSignalID = ID of the Calculations

Start live monitoring

To start an live model live, use the STARTLIVEMODEL flow.

Request Payload

{
    "flowType": "STARTLIVEMODEL",
    "name": "Start monitoring signal current",
    "spec": {
        "model": "1402745199344193536"
    }
}

cURL request

$ curl --location --request POST 'https://app3.falkonry.ai/api/1.2/accounts/xxxxxxxxxxxxxxx/flows' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data-raw '{
      "flowType": "STARTLIVEMODEL", 
      "name": "Start monitoring signal current",
      "spec": {
          "model": "1402745199344193536"
      }'

Python

import requests 

URL = 'https://app3.falkonry.ai/api/1.2/accounts/xxxxxxxxxxxxxxx/flows'
TOKEN = '<token>' 
HEADERS = {'Authorization': f'Bearer {TOKEN}'} 
PAYLOAD = <payload>

response = requests.post(URL, headers=HEADERS, data=PAYLOAD)
print(response.json())

Stop live monitoring

To stop an live model live, use the STOPLIVEMODEL flow.

Request Payload

{
    "flowType": "STOPLIVEMODEL",
    "name": "Start monitoring signal current",
    "spec": {
              "model": "1402745199344193536"
            }
}

cURL request

$ curl --location --request POST 'https://app3.falkonry.ai/api/1.2/accounts/xxxxxxxxxxxxxxx/flows' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data-raw '{
      "flowType": "STOPLIVEMODEL", 
      "name": "Start monitoring signal current",
      "spec": {
          "model": "1402745199344193536"
      }'

Python

import requests 

URL = 'https://app3.falkonry.ai/api/1.2/accounts/xxxxxxxxxxxxxxx/flows'
TOKEN = '<token>' 
HEADERS = {'Authorization': f'Bearer {TOKEN}'} 
PAYLOAD = <payload>

response = requests.post(URL, headers=HEADERS, data=PAYLOAD)
print(response.json())