Elevate Your Conversations with SAP BTP SAP Conversational AI! Unleash the power of seamless communication and automation within your business processes. Explore the synergy of SAP Business Technology Platform (BTP) and Conversational AI for a transformative digital experience. Learn more about the future of intelligent conversations!
Make a bot, I have named it as mutualfunds and added casual banter and hello abilities.
Make an expectation @invest – This plan is liable for the main client input, when the client starts a solicitation to contribute. To explain client input, we have made articulations.
I have added below expressions.
3. Make a restricted entity, #Invest. This substance is mindful to find the word put resources into an expectation. In the event that a sentence contains the word contribute, it will direct to contribute aim. The articulations in the goal are then related to the element #invest. SAP BTP SAP Conversational AI.
4. Make another intent @mutualfund. This expectation is liable for the client input about the particular common asset he wishes to contribute. The articulations are the kind of assets which the bot anticipates from the client.
5. Make confined substance #Fundtype. This substance distinguishes the asset name in a given articulation.
It is time to train the bot.
6. Make an expertise called invest.
7. The trigger of this skill would be that intent @Invest should be present.
8. In the necessities tab, we have composed if @invest as contribute, that is on the off chance that contribute is finished, implies in the event that the client has entered that he wishes to contribute. We add a message as type fast answers.
9. We have added quick replies are SBI, ICIC and HDFC.
Press save.
10. In the activity tab – Snap on update discussion and snap on GO TO.
11. Write divert to assign expertise and hang tight for client input.
12. Prior to this step, make one more expertise as allocate. When we present to the client the assets, we would sit tight for the client input and would divert to expertise allocate.
13. The trigger of the distribute expertise is check if plan @mutualfund is available that is assuming the client has entered the asset type.
14. In the requirement tab, we would check if specifically the entity #fundtype is present. Click on new replies for If #funds is complete.
Click on update conversation – edit memory.
Set memory fields and store the user input in the memory. In this, in variable FundName, we have assigned from memory i.e. “{{memory.funds.raw}}” .It will get the value of the entity type.
In the Activities, Compose message as message Asset {{memory.FundName}} is alloted! The memory variable will be tended to as the variable in which memory was put away. Then, at that point, click on update discussion – >edit memory->unset memory fields. This is utilized to unset the client input, very much like clear articulation in abap.
Hope the design is fine. Train the bot and test it.
In the principal explanation ability good tidings is set off. In the subsequent one, ability put is set off in which the client has wanted to contribute and the plan @invest is distinguished by the bot. Consequently as this necessity is satisfied, the bot has asked fast answers from the client. The client can choose one of the buttons.
As client chooses one of the buttons, the expertise dispense is diverted and the bot checks if the purpose @mutualfund and substance #fundtype is found, the memory is caught and the activity text is set off!
All things considered, for certain new presentations as update_conversation, A bot is given an out and out another usefulness. Might we at any point additionally request that the client enter the sum to contribute and call an odata administration to store the asset and the sum! Remain tuned!
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