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      How to Create Use Cases and Populate Questionnaire Evidence

      This guide provides a step-by-step process for creating use cases and posting evidence through the Credo AI API. It assumes the use of environment variables for configuration and relies on Python and the requests library to interact with the API. For more details on the endpoint, see our swagger documentation.


      Note: 

      • Self-hosted customers must replace https://api.credo.ai with your Credo AI URL
      • ${TENANT} is the tenant name used to log in to Credo AI
      • See Authentication for how to get the required ${ACCESS_TOKEN} 

      Step 1: Create a new use case in Credo AI. 

      • Body params
        • name → Use case name must be unique
        • description → Use case description 

      import requests

      url = "https://api.credo.ai/api/v2/${TENANT}/use_cases"

      headers = {

             "content-type": "application/vnd.api+json"

             "Authorization": "Bearer ${ACCESS_TOKEN}"

      }

      payload = {

        "data": {

          "attributes": {

            "ai_type": "string",

            "description": "string",

            "icon": "string",

            "industries": [

              "Fishing, Hunting, & Trapping"

            ],

            "is_vendor": true,

            "monetary_value": 0,

            "name": "string",

            "owner": {

              "owner_id": "user_id",

              "owner_type": "user"

            },

            "questionnaire_ids": [

              "SEQR+1",

              "PRIV+2"

            ],

            "regions": [

              "CA",

              "US"

            ],

            "risk_classification_level": 1

          },

          "type": "resource-type"

        }

      }

      response = requests.post(url, json=payload, headers=headers)

      print(response.text)

       

      Step 2: Retrieve the data for the questionnaire that is linked to the created use case.

      Note: When a questionnaire gets linked to a use case a relational section id is created linking the specific questionnaire with the use case

      • URL params
        • use_case_id → returned from step 1
      • The response contains a section_id and an id. The section_id is the default id for the sections in a questionnaire. The id is the relational id for this version of the questionnaire that has been linked to the use case 
      • The response also contains the question_id which is used for posting evidence in the next step

      import requests

      url = "https://api.credo.ai/api/v2/${TENANT}/use_cases/{use_case_id}/questionnaire_sections"

      headers = {

             "content-type": "application/vnd.api+json"

             "Authorization": "Bearer ${ACCESS_TOKEN}"

      }

      response = requests.get(url, headers=headers)

      print(response.text)

      Step 3: Post evidence data for each question within each section of the use case's questionnaire. 

      • URL params: 
        • use_case_id → returned from step 1
        • use_case_section_id is the relational section id from step 2
      • Body params
        • question_id in the body from step 2

      import requests

      url = "https://api.credo.ai/api/v2/${TENANT}/use_cases/{use_case_id}/questionnaire_sections/{use_case_section_id}/evidences"

      headers = {

            "content-type": "application/vnd.api+json" 
            "Authorization": "Bearer ${ACCESS_TOKEN}"

      }

      payload = 

      {

        "data": {

          "attributes": {

            "data": {

              "value": "This is text evidence"

            },

            "generated_at": "2022-05-03T11:33:25.582138Z",

            "question_id": "string"

          },

          "type": "resource-type"

        }

      }

      }

      response = requests.post(url, json=payload, headers=headers)

      print(response.text)