SAP SMB Executive Assistant Sample Bot powered by SAP Conversational AI

• “How is my sales status for this quarter?”
• “Give me the monthly sales report for 2018”
• “Compare the sales status by quarter for 2017 and 2018”
• “Who contributes the most profits for 2018?”
• “What are the top 5 best-sellers for 2018?”
• …

Here you have the video recordings about the PoC demos of smb-assistant-bot.

In addition, this sample bot can be easily extended to accomodate other analytics question about delivery performance, purchasing, financial etc.Please refer to this document.

The transactional scenario such as placing an order, checking the delivery status etc is also possible through SAP Conversational AI(formerly recast.ai), Service Layer of SAP Business One and OData API of SAP Business ByDesign. However, you may refer to SMB Market Place Assistant Bot for reference, as the transactional scenario is no part of this sample.

The architecture diagram above illustrate the following components

A collaborative end-to-end bot platform for developers.

More Useful resources:

Composed of the following components

• smb-assistant-bot server
  The webhook action implemented with NodeJS for sales-analysis and login-erp intents. Please check the source code.Webhook API to be invoked as action in the skill of SAP Conversational AI:
  • /webhook/analytics: A generic webhook for all kind of analytic intent
    • Call the NLP parser to convert the NLP result(intent/entity) into the data structure(measures, dimensions, filters etc) required by semantic analysis
    • Call the query generator to generate the OData query for the target system.
    • Invoke OData query and format the result for message response
  • Intent to Semantic Mapping models
    • Abstract mapping model: An abstract mapping model between intent/entity of NLP and semantic model/(Measures,Dimensions,Filters) without any system specific information.For instance:
      • The dimension entity in NLP result with value “Client”,”Buyer”,”Customer” etc is mapped an abstract dimension field “Customer”.
      • The measure entity in NLP result with value “Sales”,”Sales Number”,”Revenue” etc is mapped an abstract measure field “Sales Amount”.
    • Fuzzy search: To be more typo-tolerant, fuzzy search is applied to find the abstract fields from entity of NLP during Abstract mapping model. For instance:
      • The misspelled dimension entity in NLP result like “Clent”,”Byer” and “Cutomer” etc are correctly recognised as “Client”, “Buyer”, and “Customer” with fuzzy search, which are eventually mapped an abstract dimension field “Customer”.
      • The misspelled measure entity in NLP result like “Sals”,”Saes Numer”,”Reveue” etc are correctly recognised as “Sales”, “Sales Number”, “Revenue” with fuzzy search, which are mapped an abstract measure field “Sales Amount”.
    • Mapping model for SAP Business One:
      The abstract dimension field “Customer” is mapped into the technical field “BusinessPartnerNameAndCode” in view “SalesQnalysisQuery”.

      The abstract measure field “Sales Amount” is mapped into the technical field “NetSalesAmountLC” in view “SalesQnalysisQuery”

    • Mapping model for SAP Business ByDesign:
      The abstract dimension field “Customer” is mapped into the technical field “CCUSTOMER” in report “BIF – Sales Revenue”.The abstract measure field “Sales Amount” is mapped into the technical field “KCNETSALES” in view “BIF – Sales Revenue Report”
  • NLP parser: Convert the NLP result structure(intent/entity etc) into the data structure(measures, dimensions, filters etc) required by semantic analysis
  • Query Generators
    • B1QueryGenerator:
      Generating OData(version 3.0) query to access the target view in Semantic Layer of SAP Business One through Service Layer.
    • ByDQueryGenerator:
      Generating OData(version 3.0) query to access the target view in Semantic Layer of SAP Business One through Service Layer.

Eventually the smb-assistant-bot server converts the analytics question in human language into an OData query to the semantic layer through Service Layer. Please refer to this blog about how to access semantic layer of SAP Business One through Service Layer.

Eventually the smb-assistant-bot server converts the analytics question in human language into an OData query to the datasource or report. Please refer to this blog about OData APIs for SAP Business ByDesign Analytics.

Take the following example:

User input: “Who are my top 5 customers in 2018?”

You can customise your own entities by adding the label in the training step.The following entities are customised in sales-analysis intent.

NLP result in json format

{
  "nlp": {
    "uuid": "75428ced-7711-4616-91f4-3b49a08d51ab",
    "intents": [
      {
        "slug": "sales-analysis",
        "confidence": 0.99,
        "description": "Sales Analysis by Year, Quarter, Month, Customers and Products"
      }
    ],
    "entities": {
      "sorting_direction": [
        {
          "value": "top",
          "raw": "top",
          "confidence": 0.99
        }
      ],
      "limit_number": [
        {
          "value": "5",
          "raw": "5",
          "confidence": 0.99
        }
      ],
      "dimension": [
        {
          "value": "customers",
          "raw": "customers",
          "confidence": 0.99
        }
      ],
      "datetime": [
        {
          "formatted": "Monday, 01 January 2018 at 12:00:00 AM (+0000)",
          "iso": "2018-01-01T00:00:00+00:00",
          "accuracy": "year",
          "chronology": "past",
          "state": "absolute",
          "raw": "2018",
          "confidence": 0.99
        }
      ]
    },
    "language": "en",
    "sentiment": "positive"
  }
}

Firstly, the parser finds the entity mapping to the abstract model with fuzzy search(for instance, even if the customer is misspelled in the sentence as “cutomer”/”byer”/”clent” etc, still can be recognised as “customer”/”buyer”/”client” and identified its neutral abstract name as “Customer”).

Then mapping the abstract fields to the technical fields and view in the target system type.

{
  "Intent": "sales-analysis",
  "Language": "en",
  "Message": "Who are my top 5 customers in 2018",
  "MappedSemantics": {
    "SourceType": "HANA View",
    "SystemType": "b1",
    "Accesstype": "Service Layer",
    "DataSource": "SalesAnalysisQuery",
    "Filters": [
      "(PostingYear eq '2018')",
      "DocumentTypeGroup eq 'Order'"
    ],
    "Dimensions": [
      "BusinessPartnerNameAndCode"
    ],
    "Measures": [
      {
        "Measure": "NetSalesAmountLC",
        "Aggregation": "sum"
      },
      {
        "Measure": "GrossProfitLC",
        "Aggregation": "sum"
      }
    ],
    "LimitNumber": "5",
    "Sortings": [
      {
        "SortBy": "NetSalesAmountLC",
        "SortDirection": "desc"
      }
    ]
  }
}

{
  "Intent": "sales-analysis",
  "Language": "en",
  "Message": "Who are my top 5 customers in 2018",
  "MappedSemantics": {
    "SourceType": "Report",
    "SystemType": "byd",
    "Accesstype": "OData",
    "DataSource": "/sap/byd/odata/cc_home_analytics.svc/RPZ1E0943C634B1218DA2EAB7QueryResults",
    "Filters": [
      "(CYEAR eq '2018')"
    ],
    "Dimensions": [
      "TCUSTOMER"
    ],
    "Measures": [
      {
        "Measure": "KCNETSALES",
        "Aggregation": "sum"
      },
      {
        "Measure": "KCGROSSPROFIT",
        "Aggregation": "sum"
      }
    ],
    "LimitNumber": "5",
    "Sortings": [
      {
        "SortBy": "KCNETSALES",
        "SortDirection": "desc"
      }
    ]
  }
}

https://<SeriviceLayerHost>:50000/b1s/v1/sml.svc/SalesAnalysisQuery?$apply=groupby((BusinessPartnerNameAndCode),aggregate(NetSalesAmountLC with sum as NetSalesAmountLC,GrossProfitLC with sum as GrossProfitLC))&$top=5&$filter=(PostingYear eq ‘2018’) and DocumentTypeGroup eq ‘Order’&$orderby=NetSalesAmountLC desc

https://<byd_tenant>.sapbydesign.com/sap/byd/odata/cc_home_analytics.svc/RPZ1E0943C634B1218DA2EAB7QueryResults?$format=json&$select=TCUSTOMER,KCNETSALES,KCGROSSPROFIT&$top=5&$filter=(CYEAR eq ‘2018’)&$orderby=KCNETSALES desc

“`

result in json format

{
  "replies": [
    {
      "type": "list",
      "content": {
        "elements": [
          {
            "title": "Web Customer (C99998)",
            "subtitle": "NetSalesAmountLC: 4103.08\nGrossProfitLC: 1535.08",
            "buttons": [
              {
                "title": "View Details",
                "type": "BUTTON_TYPE",
                "value": "View Details"
              }
            ]
          },
          {
            "title": "Maxi-Teq (C20000)",
            "subtitle": "NetSalesAmountLC: 1200\nGrossProfitLC: -2856.18",
            "buttons": [
              {
                "title": "View Details",
                "type": "BUTTON_TYPE",
                "value": "View Details"
              }
            ]
          }
        ],
        "buttons": [
          {
            "title": "View Chart",
            "type": "BUTTON_TYPE",
            "value": "View Chart"
          }
        ]
      }
    }
  ]
}​

result in json format:

{
  "replies": [
    {
      "type": "list",
      "content": {
        "elements": [
          {
            "title": "Silverstar Wholesale Corp",
            "subtitle": "KCNETSALES: 727951.180000\nKCGROSSPROFIT: 440410.460000",
            "buttons": [
              {
                "title": "View Details",
                "type": "BUTTON_TYPE",
                "value": "View Details"
              }
            ]
          },
          {
            "title": "Unlimited Electrics",
            "subtitle": "KCNETSALES: 629095.640000\nKCGROSSPROFIT: 513449.210000",
            "buttons": [
              {
                "title": "View Details",
                "type": "BUTTON_TYPE",
                "value": "View Details"
              }
            ]
          }
        ],
        "buttons": [
          {
            "title": "View Chart",
            "type": "BUTTON_TYPE",
            "value": "View Chart"
          }
        ]
      }
    }
  ]
}​

Please refer to this document.

This smb-assistant-bot project is subject to MIT LICENSE.Here is to highlight that THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. Therefore no support available. All rights reserved by SAP SE.