Uday Kumar Javangula: Agent OK, associate the sequence, maybe creating agent, topic, action and.
Uday Kumar Javangula: Executing the agent, right? Maybe flow also we can we can include. Yeah, let's see how far we are there with the sequence. OK.
Abhishek Lingaraju: Yeah, sure. Okay. So I will start with the global topic concept today.
Abhishek Lingaraju: Oh.
Abhishek Lingaraju: So generally topics are used to set context. The boundaries are used to define the agent behaviours during the conversation. So earlier used to create the topics inside the agent itself.
Abhishek Lingaraju: So here so if you go instead of the open builder screen so we have a new topic tab. So here we used to create and if so in such case if you create any topics so from here so that the topic can be used by this agent itself.
Aarthi Jani: You.
Abhishek Lingaraju: And if any other agents need a similar kind or same kind of topics in such case, it will going to create problem. So we used to create the duplicate version of it in order to make use of it. So in order to avoid that kind of issues, we come up with the concept called the global topic creation. So.
Abhishek Lingaraju: So this is the tab. So we implemented recently the topics.
Abhishek Lingaraju: So here we are creating the topics which we want globally available across all the agents. So if if we create any topics here, so generally we are concentrating the greetings messages, the commonly used FAQ queries.
Abhishek Lingaraju: So most most commonly all the agents required this kind of the topics. If if you create any agent from here by following the process normally what we used to follow during the creation of the topics by specifying the label.
Abhishek Lingaraju: The classical classification description and the scope of this topic and along with the instructions.
Abhishek Lingaraju: Right, so if we create any the topics under this tab, so by default it will be applicable. So you can see here the three topics we have. So here so these all three of these topics will be available under all the agents.
Aarthi Jani: OK.
Abhishek Lingaraju: So I'm selecting one agents. So in the agent builder screen as in global topics tab we have. So here all the the topics that are created outside of this agents rights are available and if you if this agent wants to make use of any of these topics.
Aarthi Jani: OK.
Abhishek Lingaraju: Simply they can select those topics and click on asset so that they can use this the scope of this particular topic to run their actions.
Abhishek Lingaraju: Okay, this is related to the global topic concept. And let me move on to the multilingual support of this agent.
Abhishek Lingaraju: I will select one agent here so so we have enabled our agent to support multiple languages now. So here this is the default language. The ENUS is selected for this agent. So basically if you select.
Aarthi Jani: It.
Aarthi Jani: Yeah.
Aarthi Jani: Good.
Abhishek Lingaraju: We haven't. We have many the list of locales here based on the user or the selections here. For example, if I select Japanese language, this the agent will be responding to the user queries based on this the selected language.
Aarthi Jani: Yeah.
Abhishek Lingaraju: This is the user of the defined default local selection tab and and one more thing so and and now our user is responsible for the regional languages as well. So let me select any other language and I will showcase that to you. So I will select Japanese.
Abhishek Lingaraju: This language and I will go to the chat section so if I say hi.
Abhishek Lingaraju: It will respond back with our the default locals. The Japanese language is selected so it is giving the response with the Japanese language and.
Abhishek Lingaraju: I will take one the regional language and I will just I will try to replicate the same thing.
Abhishek Lingaraju: See if we if user is entering any regional languages with our agents, for example in local language Canada, let's see how our agent will behave.
Abhishek Lingaraju: See it is responding back with the local the the the locales which user user entered the input. So so in such a way that so our the agent is responsible and it can handle the regional languages as well.
Aarthi Jani: OK.
Abhishek Lingaraju: It is supporting multiple languages as well. So it will build a user with a good experience on this product.
Aarthi Jani: Yeah.
Abhishek Lingaraju: End.
Uday Kumar Javangula: So Abhishek, did we try this on the document also not generic response?
Abhishek Lingaraju: Yeah.
Abhishek Lingaraju: Oh, so if you try with the document in such case, it will go with the primary local local itself. So while rendering the data, it will give us the result with the the parent local data itself.
Uday Kumar Javangula: I think we should have that low native local locale available in the document, then it will fetch.
Uday Kumar Javangula: Example, if you were translated, if you translate the document into Canada and make it available, I think your query will match and get that response that is is.
Abhishek Lingaraju: Yes, it it could.
Abhishek Lingaraju: Exactly, exactly. Yeah. So if converted the local details was not there, it will render the data with the parent local the details itself. OK, right now.
Abhishek Lingaraju: And uh, so one more concept is uh, the memory enablement.
Aarthi Jani: Good.
Abhishek Lingaraju: In the setting section itself we have implemented these features. So so here we're explicitly allowing our agent to store user inputs. We here we we have two types of the memory types, long term and chart term.
Aarthi Jani: Bye.
Abhishek Lingaraju: The short term it's specific to the session related and here we are not storing any the history details and if we select long term so we can able to the store the conversational data.
Aarthi Jani: It.
Abhishek Lingaraju: OK, so we have the limitations here up to the 230 KB of the data we can store.
Abhishek Lingaraju: So let me showcase that example to you and the store data will go to display here under the manage memory stack.
Abhishek Lingaraju: OK, so.
Abhishek Lingaraju: Let me clear all the existing data.
Aarthi Jani: Happy.
Aarthi Jani: I.
Abhishek Lingaraju: I cleared it so and I selected the long term at the memory type now. So this if you select long term it is specific to agent. So irrespective of N number of sessions right up to 200 the KB it's gonna store.
Abhishek Lingaraju: The chat history data just I will input into the queries.
Aarthi Jani: Yeah.
Abhishek Lingaraju: Just giving the response. So let me change the locale.
Aarthi Jani: OK.
Abhishek Lingaraju: Rebecca usage.
Abhishek Lingaraju: OK, so it's not updated. Let me use the incalling to that for better you see.
Abhishek Lingaraju: OK, so I will simply select on the Bob catalytic query.
Aarthi Jani: Yeah.
Abhishek Lingaraju: It is taking bit time to respond.
Uday Kumar Javangula: It's from Q&A.
Abhishek Lingaraju: Thank you.
Abhishek Lingaraju: Yeah.
Abhishek Lingaraju: Oh.
Abhishek Lingaraju: So it is giving the proper response, FaceTime to use that input and the one minor issue we observed is the alignment issue after getting the agent response right. It is the user needs to crawl up to get that.
Aarthi Jani: OK.
Abhishek Lingaraju: So so we we asked 2 queries hello as well as the Bobcat delegate query. So let me check whether these two queries were the stored never the manage memory style or not.
Aarthi Jani: Yeah.
Abhishek Lingaraju: So generally it is taking uh a few seconds of time to upload the data.
Abhishek Lingaraju: To reflect over there.
Avinash Khedkar: For the memory setting, whatever the first question we ask on the chat window, only that will display. We are displaying that only.
Uday Kumar Javangula: Session initialization, each session initialization input.
Abhishek Lingaraju: For each session.
Avinash Khedkar: Yes, if we initialize the particular session and we ask the any question or multiple conversation we have done for particular session. In that case only first question we are displaying throughout that session currently.
Uday Kumar Javangula: But the sequential questions also should be there in the memory, right? If it is a long term.
Avinash Khedkar: This is the same experience we have provided. So if we need the sequence of questions, we can add that also. So currently we are just pitching the first question when we are starting the session under the channel.
Aarthi Jani: Bye.
Uday Kumar Javangula: But in Redis, but in Redis it will be there, right? You know you're fetching only first question, but in the Redis you'll be having the sequence.
Avinash Khedkar: Yes, in the red is it will be yes.
Avinash Khedkar: Yes.
Abhishek Lingaraju: OK, so uh, we can able to see uh.
Abhishek Lingaraju: The agent input and the queries here. So this is related to the memory enablement concept.
Abhishek Lingaraju: If you want, you can delete that one in a single chat by selecting the delete all the memories.
Abhishek Lingaraju: The next concept is the data connectors part.
Abhishek Lingaraju: OK, so here we're allowing our agent to interact with external data sources or applications or we can say services to to to retrieve the data and so this the.
Aarthi Jani: It's.
Abhishek Lingaraju: the crawl data, it will go to index with open search and whenever user needs, right? So you can get that on the data using both Async as well as the KFH application. So we have similar kind of approach in the KFH application as well.
Aarthi Jani: OK.
Abhishek Lingaraju: But there the crawl data is globally available across the applications. OK, such anyone. If anyone is having the proper user group, that means they can able to access it. But here we are restricting that the usage specific to aging.
Aarthi Jani: It.
Abhishek Lingaraju: So here the user wants to create any of. If the user wants to add any of the source means simply they can click on add source. So here we can see right so the 12 the external sources we have.
Abhishek Lingaraju: Slack, JIRA, the OneDrive, so all are similar to our character itself. So if you want to add the data simply we can click over there and we need to fill up all the required details.
Aarthi Jani: Yeah.
Abhishek Lingaraju: Along with the classification and the scope of that data source.
Abhishek Lingaraju: So once we fill all the data and we're done with the account synchronizations, then recently added the source we're gonna display here in this page.
Aarthi Jani: Yeah.
Abhishek Lingaraju: So these are the list of the data sources what we have added and here once we added and the data is indexed, the number of items and the memory size also are going to be displayed here under the kit content section.
Abhishek Lingaraju: And when it got refreshed, the refreshed the last. Lastly the refreshed are the content added times it will want to display here. We can change the status also if we for example consider this.
Abhishek Lingaraju: The OneDrive account and it is currently is having uh 2300 uh plus records and if we if we change the status uh.
Abhishek Lingaraju: To inactive still we can able to access the same the documents from our agent in if any new data attachments are added to this account. In such case if you want to if user wants to call that the recent data he needs to refill.
Abhishek Lingaraju: you need to turn, you need to enable and do the refresh activity. Then the recently added sentence will gonna display here. It will append and it will available for the user from the agent as well as from the KSH application.
Aarthi Jani: ****.
Aarthi Jani: OK.
Abhishek Lingaraju: So let me check a couple of other documents, how our uh the functionality behaves.
Abhishek Lingaraju: So we'll go with, yeah.
Uday Kumar Javangula: OK, one question here, Abhishek. I remember earlier Aarthi raised that point to me. When we sync multiple data sources, right, we can enable all the data sources here, right?
Aarthi Jani: Yes.
Abhishek Lingaraju: Yeah, but we have LLM or the framework limitation to that we can enable, but that is the basic motto right now. If we enable multiple sources, it is creating an issue and it is giving one pop up error like certain description limit it is having. So it is not allowing us to render the proper data.
Aarthi Jani: Yes.
Abhishek Lingaraju: So right now in order to work on that issue, we are enabling the sources which one we wanted right now that the source alone we are enabling and we are verifying that functionality. So team is working on that one. So we already we have that the task in the.
Aarthi Jani: Yeah.
Aarthi Jani: Yeah, it's supposed to be.
Abhishek Lingaraju: OK.
Abhishek Lingaraju: So for example I I I want to access details of the Jira account so just I'm enabling it so.
Abhishek Lingaraju: I'm trying to sync up the latest data.
Abhishek Lingaraju: So the date the project name is data connectors, capture data connectors.
Abhishek Lingaraju: You can see only one task is there, so here the content is showing as one item.
Abhishek Lingaraju: With 50 KB of memory size.
Uday Kumar Javangula: Yeah, if we will create one more task and sync it again, get that.
Abhishek Lingaraju: Yeah, we'll it. It will. It will, it will.
Abhishek Lingaraju: So I will do that. So just I will try to check whether our agent will able to render the data in this applications or not. Yeah, just I copied the content from over there.
Uday Kumar Javangula: So can I can we track that because we need to have the task or something in Sprint?
Aarthi Jani: Yeah.
Kinna Yadav: Sure.
Abhishek Lingaraju: So you can see here, so agent is giving the data exactly similar to
Abhishek Lingaraju: Uh, the task details what we have in the JRAPO, but uh, the content are a bit modified. Our LLM is enhancing the content and it is rephrasing and it is giving with the proper result.
Aarthi Jani: You.
Abhishek Lingaraju: Just adding yeah along with the same content. So if we add one more task for it's secure validation purpose.
Abhishek Lingaraju: You can see the count is appended to two items.
Abhishek Lingaraju: Like this we can achieve the real time synchronization that just by hitting on the three.
Abhishek Lingaraju: And we can do the edit only for the right now we are allowing to, for these two things, go from the classification description. And And we are maintaining the soft delete option here. So if we delete this one, it will change the status to inactive.
Aarthi Jani: 6.
Abhishek Lingaraju: And let me take in one other crawled website example. Yeah, so web crawler.
Aarthi Jani: It should be.
Abhishek Lingaraju: So we have this similar kind of the functionality in our the K admin as well. So we can enter the collection name and details of it and we can select the depth of the crawling.
Abhishek Lingaraju: And local details. Generally we used to follow the web from web itself. We are crawling the websites of the user graph to whom it should visible and the valid URL we need to enter align with description and scope.
Abhishek Lingaraju: So once we uh created this uh.
Abhishek Lingaraju: The web crawl account so it will display here and it will take a bit time to crawl the content from the the websites. Once the crawling is done so we can able to see the crawled items data along with the memory size here.
Abhishek Lingaraju: OK, so these are uh uh the called uh URLs.
Abhishek Lingaraju: For example, if you're dealing with the Bobcat, we can we can fire a related questions of the Bobcat website and we can check that.
Aarthi Jani: Sure.
Abhishek Lingaraju: Don't find I don't if it looks it's talking to, for example, that's where.
Abhishek Lingaraju: Yeah, let's go under attachment. We have Kid Scaler loader options which let let's ask our agent to get details of the Kid loader attachments.
Abhishek Lingaraju: Uh, generally we used to create a.
Abhishek Lingaraju: Yeah.
Uday Kumar Javangula: some question maybe.
Abhishek Lingaraju: Yeah, it's the least of them. So let me refresh. Let me.
Abhishek Lingaraju: So inactivate the subcones and try.
Abhishek Lingaraju: So it's giving uh the result uh based on this content, but the result
Aarthi Jani: 5.
Abhishek Lingaraju: Is not as similar to the website content. It it might be the modified one, so LNM will do the enhancements.
Abhishek Lingaraju: Yeah, so let me just any specifics. If unique data is there, it is going to display the unique data. I have one URL. I think this is already been crawled here.
Aarthi Jani: It.
Abhishek Lingaraju: And index as well.
Abhishek Lingaraju: So.
Abhishek Lingaraju: For example, they're called site.
Aarthi Jani: Yeah.
Abhishek Lingaraju: These are other simple other websites and we have less data.
Uday Kumar Javangula: M.
Abhishek Lingaraju: OK, so 319 items for here.
Abhishek Lingaraju: Yes, I did a refresh, so let me ask.
Abhishek Lingaraju: Question.
Abhishek Lingaraju: I will take some unique content data from the website.
Abhishek Lingaraju: Okay, so these are generally they are using for the documentation creation purpose and for testing purpose they are getting uh similar data here.
Abhishek Lingaraju: OK.
Abhishek Lingaraju: It's so it's not the similar, but so we're rendering the similar kind of data, not exactly the same phrase, yeah.
Uday Kumar Javangula: Yeah, it depends on your instructions, right? The the more instructions you give, then we'll give the elaborated answer.
Abhishek Lingaraju: Yes, yeah.
Abhishek Lingaraju: Yeah.
Abhishek Lingaraju: So.
Abhishek Lingaraju: Let me check with the slack count.
Abhishek Lingaraju: I depressed it.
Abhishek Lingaraju: like So if if we add any of the Slack accounts, it will display the name as the channel name.
Abhishek Lingaraju: and account name. But in the KR the 20 is the account name and the channel is data connectors.
Abhishek Lingaraju: Let me log into.
Abhishek Lingaraju: Clack.
Abhishek Lingaraju: So the data connectors 2025.
Abhishek Lingaraju: It's the crawled one here, so.
Abhishek Lingaraju: Let me ask any the questions from here.
Abhishek Lingaraju: Yeah, so it is giving.
Abhishek Lingaraju: The proper result as similar to the the integrated application data and we can. So as soon as we index this data from the service agent, the similar kind of data will be available in the K search application as well.
Aarthi Jani: Yes.
Abhishek Lingaraju: So we'll check that.
Aarthi Jani: You.
Abhishek Lingaraju: Can see. So today I had a data data and via the service agent is similar kind of data is present here as it could indexed and it is.
Aarthi Jani: OK.
Abhishek Lingaraju: I loved it from here as well.
Abhishek Lingaraju: OK, so let me search for uh.
Abhishek Lingaraju: See you.
Abhishek Lingaraju: Yeah, you can see the similar data here.
Abhishek Lingaraju: And.
Abhishek Lingaraju: OK.
Abhishek Lingaraju: OK, so let me search with uh.
Abhishek Lingaraju: The project name mixes. We'll see.
Abhishek Lingaraju: This is the data what we have created recently, just a few minutes ago.
Abhishek Lingaraju: The response is bit low, so not sure. So why it is taking big time to load the content?
Abhishek Lingaraju: You see the QA validation process recently we have added right? So that one got indexed and it is showing the date and time as well. So when it got indexed.
Aarthi Jani: ****.
Aarthi Jani: Yes.
Abhishek Lingaraju: Yeah.
Uday Kumar Javangula: So for multiple data sources, who is checking the that issue?
Abhishek Lingaraju: I think it got it got assigned to Avinash.
Uday Kumar Javangula: Is it Doug Nash or?
Avinash Khedkar: Yesterday.
Abhishek Lingaraju: Yeah, this is the one. OK, so 420.
Abhishek Lingaraju: So yeah, this is the issue what we are getting. So if if we enabled multiple sources right? So during the conversation right with the agent it is giving this error.
Avinash Khedkar: I didn't.
Uday Kumar Javangula: But we did this chunking mechanism, right? Hybrid model.
Avinash Khedkar: Yes, so then for the retrieval purpose we have done, but what is happening? So whatever the data source we are adding, so we are creating the single tool to face the data. So that's why we are adding the all classification description.
Avinash Khedkar: For the all data sources, so open AI framework having the limit for the single or action instructions or tool instructions about 1024 tokens, it will not take, it will throw the error.
Aarthi Jani: Happy.
Avinash Khedkar: So that is why we are getting this issue. So we need to create the multiple tools instead of creating single tool for the all data sources because there are multiple or data sources user can add.
Aarthi Jani: ****.
Avinash Khedkar: And do the conversation.
Uday Kumar Javangula: Or instead of taking all it, maybe whatever is active you can take right? Suppose if I add if I make active Zira and slack so you can combine only those instructions and scope that will satisfy that 1024 tokens.
Aarthi Jani: Yes.
Avinash Khedkar: Yes.
Uday Kumar Javangula: Next.
Avinash Khedkar: Yes, yes. So currently we are doing the same way. So whatever the source we need to.
Avinash Khedkar: Through the conversation, we'll active that only.
Abhishek Lingaraju: Yes.
Abhijit Thakare: Avesh, we discussed about this some chunking strategy also whenever we have a multiple source, I think long back.
Avinash Khedkar: Yes, yes. So while creating the tool for the for the all data sources, so we decided to create the single tool instead of the each data source having the different tool. So and after that we decided to.
Avinash Khedkar: Add the classification descriptions. So on the retrieval part we are able to manage the chunking part from the open search, but for the invoking this tool with the open search framework that time we are facing this limitation from the.
Aarthi Jani: OK.
Avinash Khedkar: Open, open yeah.
Avinash Khedkar: OK.
Avinash Khedkar: So there are two ways to work on this. Either we have to select the particular data source, ohh user want to interact or we we need to create the multiple tools for the each of the data source.
Aarthi Jani: Well.
Avinash Khedkar: Or the combination of the group of the data source.
Aarthi Jani: It.
Aarthi Jani: See.
Abhijit Thakare: I think we discussed that scenario also Avinash that if we add a tool, suppose we consider the case that the data source are now 5 S 5 tools are created. If we get more data sources, that many tools will.
Abhijit Thakare: Occupy the memory and the other things. So instead of that we can combine the data source and assign to 1 tool.
Avinash Khedkar: Please.
Aarthi Jani: OK.
Avinash Khedkar: Yes, yes.
Abhijit Thakare: That is the best strategy. Otherwise what will happen now? You have a full data source, 4 tools created. If the data source goes to 40 then we will have a 42's created.
Avinash Khedkar: Yes, yes.
Abhijit Thakare: And in that case also we can apply a strategy of active data source, inactive data source both both these combinations.
Avinash Khedkar: Yes, because the current situation for the whatever the agent we are creating, so that is specific to particular domain. So user will attach the source to that particular domain only because.
Aarthi Jani: Uh.
Avinash Khedkar: This is the situation where we are adding the multiple data sources which is not relevant to this agent also and we're validating.
Avinash Khedkar: But we need to address this limitation also because there are some scenarios user can add.
Avinash Khedkar: On the single.
Uday Kumar Javangula: At least for a demo, demo standpoint, right? Maybe it's good to have at least two data sources enabled, not entirely, but two data sources by the zero and Slack. I don't know.
Avinash Khedkar: Yes.
Uday Kumar Javangula: The experience will be good from agent experience. It is answering from Jira tickets as well as Slack conversation at one go.
Avinash Khedkar: Yes, yes, now it's.
Uday Kumar Javangula: Let's check that maybe separately. We'll talk along with Abhijit Avinash.
Avinash Khedkar: OK, OK.
Uday Kumar Javangula: What next, Abhishek?
Abhishek Lingaraju: Yeah, and with the web crawler we observe one more issue. So if while crawling the website, right, we have an option for local collection. So if it select any other local other than ENUS.
Abhishek Lingaraju: So it it is giving success pop-up, but the content indexing is not happening. It is using.
Abhishek Lingaraju: So she won't be checking this issue or that right now.
Uday Kumar Javangula: OK.
Abhishek Lingaraju: Yeah.
Abhishek Lingaraju: So the rest of others other sources are the similar itself, just we need to in add the proper data and we are crawling it and we are validating both agent as well as case search.
Abhishek Lingaraju: And so recently we introduced the new features, the prompt template as well as agent flow.
Abhishek Lingaraju: But the prompt template is the generally we created this one in order to minimize human efforts during the topic such agent creation, right? So we used to enter the roles and instructions description details.
Abhishek Lingaraju: Every time. So in order to overcome that issue, so we introduce this the concept. So if you create any the template over here.
Abhishek Lingaraju: By providing all this data so we can utilize the same template during the agent topic and the action type the creation time.
Abhishek Lingaraju: That is the basic moto and you can see her.
Aarthi Jani: Yeah.
Abhishek Lingaraju: We have a three template uh has been created. Add product, let's say the add product form. OK, so the validation points are.
Abhishek Lingaraju: We are creating an agent.
Abhishek Lingaraju: Under role section it has to display the existing prompt template. I think 1 template is in disabled state so that's why it is not showcasing here. So the active template data it will showcase. So if we select that.
Abhishek Lingaraju: uh The description part of that template we're going to display here, so it will save the user timings here instead of entering the recurring details.
Abhishek Lingaraju: And uh, while creating the actions.
Abhishek Lingaraju: We have an option add from template data. So here also a similar kind of experience we can select and we can make use of that template data.
Abhishek Lingaraju: Good in topic creation as well.
Abhishek Lingaraju: Select the instructions will go up in here.
Abhishek Lingaraju: OK, this is the concept of the prompt template and.
Abhishek Lingaraju: At the agent flow or yeah, so not to.
Abhishek Lingaraju: Comprised at a complex the functionalities in order to define that one on UI level itself, we introduce this concept. So I validated this functionality with one simple the flow. This is the flow I have created. There were many nodes.
Abhishek Lingaraju: Over that, so let me.
Abhishek Lingaraju: So here uh.
Abhishek Lingaraju: There were the many nodes were there. So by realizing these features we were implementing this the functionality. So just I I I created one simple flow by using actions.
Abhishek Lingaraju: Uh, a human input.
Abhishek Lingaraju: And start and end or the default nodes. So I'm not familiar with all the nodes yet.
Abhishek Lingaraju: I have implemented that flow and the flow is going well from this page as well. Once we're done with the flow implementations, we need to check whether the functionality is working properly or not from this agent flow page as well as.
Aarthi Jani: I.
Aarthi Jani: OK.
Aarthi Jani: Yeah.
Abhishek Lingaraju: From the agent builder page as well. So here this is the flow, the foot safety, the the monitoring systems what we have built here. So if you want to validate that flow, so we need to enter the respective questions data.
Abhishek Lingaraju: So let me feed that data. So here uh.
Abhishek Lingaraju: I'm entering input to access the list of the high severity, the incident data related to food safety.
Aarthi Jani: 3.
Abhishek Lingaraju: It is processing our request and it will give the past the indication here itself and and the flow itself. Here you can see right these the notes got passed and these are the actions.
Aarthi Jani: Yeah.
Aarthi Jani: OK.
Aarthi Jani: It.
Abhishek Lingaraju: Okay, so it is giving the result.
Abhishek Lingaraju: It is giving the data.
Abhishek Lingaraju: It's not displaying from the UI, so let me.
Uday Kumar Javangula: Yeah, this is a regular flow, like adding some components and all, but more detail. I think Manik and Avinash had worked on context parameters, passing the dynamic context parameters between the nodes.
Uday Kumar Javangula: Right. Replacing it, replacing them and using for interaction with the open search or database. So maybe that level also you need to see more Abhishek. OK, this is a very straightforward use case, OK.
Abhishek Lingaraju: Yes, yes, yes, yes. All right. So I'm working on that project. So I'm I'm interacting with Avinash to get more updates on that one right now. So we simply we built one straightforward at the flow and just we check whether the flow is going well.
Aarthi Jani: OK.
Abhishek Lingaraju: With the agent builder as well as the agent flow page or not and the third validation point is from the normal chat window. So here also it should work. So if if we add this flow to any agent site if that agency is in active state.
Aarthi Jani: Huh.
Abhishek Lingaraju: It should work from this chat window also, but that functionality is not yet been implemented. In dev also it is not working there. So Avinash, so you can go ahead and add any inputs and this functionality.
Avinash Khedkar: Yes, I have informed to add this on the chat.
Avinash Khedkar: We have under the flow builder and the agent builder who we are implementing under the chat section.
Aarthi Jani: See.
Avinash Khedkar: Correct.
Abhishek Lingaraju: Yeah, so if you want to cover uh in detail, uh.
Avinash Khedkar: Sure, sure.
Abhishek Lingaraju: Of this notes, you can go ahead and you can explain that one.
Avinash Khedkar: OK.
Avinash Khedkar: Thank you. Thank you.
Abhishek Lingaraju: Thank you.
Avinash Khedkar: I think.
Avinash Khedkar: I'll share my screen.
Uday Kumar Javangula: OK. So far Aarthi, do you have any questions to Abhishek or team?
Aarthi Jani: Actually I had one question Uday. So in the data connectors Abhishek, you were explaining about refreshing the sync, right? So I saw you refreshing the sync multiple times manually. So I was wondering.
Abhishek Lingaraju: Yeah.
Aarthi Jani: If let's say for example if you go open one of the agent and once the agent is published, let's say and it's live. OK, so every time does the refresh happen automatically in the back end?
Aarthi Jani: All it has to be done manually.
Abhishek Lingaraju: You know if that agent account, for example that data source account is in active state, for timing it it will get refreshed by default. If it is in inactive state, we need to refresh that account manually.
Aarthi Jani: Mhm.
Aarthi Jani: OK. And the the part that I had raised earlier, even that is little unclear Uday for me again. So for example, yeah, so here you're saying we cannot and I think the team is working already is what you said that we cannot select multiple sources at present, but it is like going to be.
Abhishek Lingaraju: Yeah.
Aarthi Jani: Possible where you can select multiple sources and let's assume that it is sorted right now. OK, now you can select multiple sources at single time. Will we still have to refresh it? You're saying no only if you turn from inactive to active once you will have to refresh, correct?
Aarthi Jani: Did I understand that right? Okay.
Abhishek Lingaraju: Exactly.
Abhishek Lingaraju: Exactly, exactly. Basic concept is if if I just consider that account is in active state and you you you made some changes with your external application and you are you are waiting for that the synchronization with the service agent application.
Aarthi Jani: OK.
Aarthi Jani: Mhm.
Aarthi Jani: Thank you.
Abhishek Lingaraju: Sometimes because of some other synchronization issue, it will it will not get indexed at that moment. So in order to make it the proper right so you can you need to do the hard refresh or so you can refer the refresh option as well to make it.
Aarthi Jani: It.
Aarthi Jani: OK.
Abhishek Lingaraju: Probably.
Aarthi Jani: OK, OK. So once this issue is sorted, will we be able to activate or toggle on all these sources, all the data sources or will we have any any cap limit that only so many data sources can be enabled or something like that at a time?
Abhishek Lingaraju: So I'm not sure about that. Avinash needs to answer about.
Uday Kumar Javangula: It should be, it should be all that is the capability, right? The agent should have the knowledge of all the data sources that we sync, so it should be all all active.
Aarthi Jani: Yeah, it.
Abhishek Lingaraju: OK.
Aarthi Jani: Correct.
Aarthi Jani: OK, it should be that that should be the ideal approach, but just wanted to clarify. Yeah, yeah.
Uday Kumar Javangula: I guess case.
Uday Kumar Javangula: Yeah, that's right.
Abhishek Lingaraju: Yeah, but still sometimes if we have two sources in active state, sometimes it is giving the proper response, but sometimes we are getting that limitation error. So the team is going to address that soon Aarthi.
Aarthi Jani: Oh, OK. OK.
Abhishek Lingaraju: Yes.
Uday Kumar Javangula: Yeah, which is good.
Avinash Khedkar: No, yes. So I'll explain about the agent workflows. So about this feature. So this feature we have basically implemented for the complex workflows like if we have any use case.
Avinash Khedkar: So currently what we are doing under the chat sections, we are doing the conversation based on the and we will get the response. OK, so there are some cases we need to follow the certain pipeline or case of work. So let's say I want to create the ticket. So in that case I want to do the conversation and.
Avinash Khedkar: To provide the multiple inputs to the chatbot and then in in that case our agent able to create the ticket. So to remove that particular conversation or multiple conversations so we.
Avinash Khedkar: Implement this agent workflow. So we have added the different features or different nodes to where we can create the complex workflows and we'll.
Avinash Khedkar: trigger advice scheduling the particular time or we uh do it manual trigger from the here platform also. So here we have added the different nodes with respect to different purpose. We have each node for the start and end node having the different
Avinash Khedkar: By default node then here we have the action node which is basically we are adding the different types of actions that is like that we need to get the data from the third party APIs, open source or open search database or also SQL database.
Aarthi Jani: It.
Aarthi Jani: Thank you.
Aarthi Jani: YouTube.
Avinash Khedkar: This many kind of actions we can attach under the action node and it will perform the their operations under the action. Let's say I want to perform the FAQ or action here to get the answer. So I'll simply attach this action under the under this node.
Avinash Khedkar: And it will be configured during this flow execution. The the another different types of nodes available as a subflow. Subflow I'll explain later which is combination of these all nodes basically.
Avinash Khedkar: Assignment node is basically we will add the different variables so it will be maintained throughout the context or it will be need to manage the particular input for the next node. We'll configure this type of.
Aarthi Jani: It.
Aarthi Jani: Excuse.
Avinash Khedkar: configuration like variable name, data types of that variable and values. We can mention here and it will be used throughout the flow execution.
Aarthi Jani: It's.
Avinash Khedkar: For the created code.
Avinash Khedkar: And we have another nodes like decision where we can provide the decision to our flow execution. So based on the conditions we can apply the decision. So it will be the true or false or if expressions.
Avinash Khedkar: We can provide the different types of conditions here based on it will go to the next or nodes. So whatever they let's say I have attached the action node and direct message node. So in that case.
Avinash Khedkar: Based on the condition, if it is true or false, it will go to the particular, it will follow the particular path during the flow execution. So in this way we can add the single condition by using the if and multiple conditions like expressions and all this stuff.
Aarthi Jani: Good.
Avinash Khedkar: It.
Avinash Khedkar: And also we have the another or different nodes like wait for conditions. So in the wait for conditions we will add the different types of wait conditions to execute our flow. We will add the time limit. Here we can wait for the that particular time.
Avinash Khedkar: Our flow execution will stop that particular time whatever we are mentioning and then it will execute. Also we can mention the condition here. So it will check for that condition during the flow execution. Also we can mention the date time features.
Aarthi Jani: It's.
Avinash Khedkar: Under the wait for conditions.
Aarthi Jani: 6.
Avinash Khedkar: So we have loop functionality. So whenever we need to look back the particular nodes, or we need to execute the particular inputs multiple times, let's say we have four documents and four documents, we need to execute the particular path.
Avinash Khedkar: OK, in that case we need to use this loop back functionality and this loop back functionality will refer the whatever the previous node which is attached. It's I'm attaching this node here.
Avinash Khedkar: OK, so it will take look back to the whatever the nodes we have added and it will perform the number of iterations we are missing. If we mention the two loops, it will perform the two iteration based on the input. OK and also.
Avinash Khedkar: We have the human input, which is the like we need the human intervention during the flow execution. Let's say we have to perform the some decision and after that there is a critical situation or a risk of the performing the next action. In that case we will add the human input. Here we can.
Aarthi Jani: 6.
Avinash Khedkar: I mentioned the prompt. So what will be the user input we have to take or we'll showcase this prompt to the user and instructions we can provide and accordingly we'll take the one input. Here are the two different conditions we have provided.
Aarthi Jani: OK.
Avinash Khedkar: It's conditional. We can just select the accept and reject for the next node execution or user can type by using this normal functionality under the human inputs. Well, so that prompt template things we can add here.
Aarthi Jani: OK.
Avinash Khedkar: Instead of the adding the instructions manually, we can use the prompt template to add the instructions or prompt.
Avinash Khedkar: And so we have the direct message. With the direct message, we will add simply the direct message, which is nothing but the info or successor warning. Anything we can add and this message will display to the user. The user know about the status of the particular node.
Aarthi Jani: OK.
Avinash Khedkar: And again we have the transform node which is useful for the output, whatever the output we are getting from the LLM. So in which format we need this output? OK, so if we need a CSV HTML.
Avinash Khedkar: So currently we have enabled the HTML and text where we can get the tables and text output based on the whatever the instructions we are mentioning. We have to mention the instructions which field we need in the output and what format we needed.
Aarthi Jani: Thank you.
Avinash Khedkar: Using the transform mode.
Avinash Khedkar: And also we have collection sort and collection filter. So in that we have to just mention the whatever the variable we want to sort, it will be the descending or ascending order. We can sort that particular input variable by using this node.
Aarthi Jani: It's the.
Avinash Khedkar: And the same way you can.
Avinash Khedkar: Or do the collection filter. So whatever the variable will do, apply the filter based on the condition. Let's say I want to apply the filter on the previous data based on the date or anything, then it will filter out the whatever the previous record.
Aarthi Jani: It.
Avinash Khedkar: For the correction filter.
Avinash Khedkar: And also one of the node we are we have implemented currently that is the file upload node. So where we can upload the different types of files and we'll extract the data and we'll use it for the further communication during the flow execution. Let's say we have to summarize the particular.
Aarthi Jani: OK.
Aarthi Jani: Yeah.
Avinash Khedkar: document or we need some information from that particular document. So in that case we will upload this document by the human upload or during the flow execution we can take it from the user or we can enable
Aarthi Jani: OK.
Avinash Khedkar: This option or disable this option and here we can upload during the.
Aarthi Jani: OK, fine.
Avinash Khedkar: creation of the flow. So these all features and these all nodes we have added for the flow creation of the flow. So if we take the any of the use case for this one, we will we need to ohh create the
Aarthi Jani: OK.
Aarthi Jani: It.
Aarthi Jani: Yeah.
Avinash Khedkar: Flow like based on the our use case like I'll show 1 use case or the capture one that we have created.
Aarthi Jani: Oh.
Avinash Khedkar: OK, so this is the complex workflow we have created. So in this basically we have added the combination of the different nodes based on the use case. So where we need the human inputs. In that case we are adding this human input. Let's say I want to perform this.
Avinash Khedkar: Operations of like HL nepress number, this operation it will whatever the user input will provide, it will flow in that direction based on the conditions and whatever the nodes we are adding. So likewise we can create the complex workflows.
Avinash Khedkar: And will or trigger it manually or which schedule the a particular time for the triggering this purpose.
Avinash Khedkar: So this is all about this workflows feature.
Uday Kumar Javangula: So right now I'm just that end to end recall is functioning that flow or any issues is there with functioning.
Avinash Khedkar: Yes, in the day it is for performing today the day we have configured this.
Avinash Khedkar: So for this one, let's say I'm providing any VIN number.
Uday Kumar Javangula: So at the earlier we have one agent enabled for recall right? Now the same thing has been done through a flow end to end flow where it needs human input, where it needs some validation, right?
Aarthi Jani: Yeah.
Aarthi Jani: Um.
Uday Kumar Javangula: So configured through this flow all the components.
Avinash Khedkar: Yes.
Aarthi Jani: Understood. Uh, that.
Avinash Khedkar: Obviously we have that VI number. Can you share that with?
Uday Kumar Javangula: Let me I have a concern.
Avinash Khedkar: It.
Uday Kumar Javangula: Tapping do that in chat.
Avinash Khedkar: So let's say I have this flow present. I have created this flow for the recall use case and then I want to trigger this flow for the execution. So for this we need the user input basically.
Avinash Khedkar: So this is our user input having the VIN number.
Avinash Khedkar: We put this VI number and we'll trigger the flow.
Avinash Khedkar: It will start the execution from this start and decision node where it will check the condition if the particular VI number is in 17 number or not, and it will provide the result. So here we can see the result.
Avinash Khedkar: So the our flow is executed up till the transform node or human input node. So here our flow process through the decision node is check the VI number lane and then it perform this action behind decoding and recall information.
Aarthi Jani: Yes.
Avinash Khedkar: So again you check the uh another decision whether there is a recall present for that number and then it will transform the our output into the particular tabular format. And here we got the result and here we can see
Avinash Khedkar: The process flow also whatever the nodes are executed that we can see here like for the decision we can see whatever the input parameter we have sent and whatever the output we got in that way we can.
Aarthi Jani: I.
Avinash Khedkar: Check the each node execution input output what we got with respect to results for the recall information. Also we have provided this as a payload and we are we get the this result from the.
Avinash Khedkar: This action for this node and here we have added the human input node. OK, let's say we got this message from the human message like would you like to create the case for your recall issue? OK if the user select yes it will proceed for.
Avinash Khedkar: proceed for the next.
Avinash Khedkar: Execution of the flow.
Avinash Khedkar: OK, so we got the result as your service request has been created successfully and we got the that service request number.
Aarthi Jani: Anu.
Avinash Khedkar: And our flow is executed for the next step like VHL reference number create case action is executed and then again it stop to the human input where we need to provide the again human input with respect to proceed for the next step.
Avinash Khedkar: So likewise this flow will work out end to end. Here also would you like to create the schedule appointment with your preferred dealer? OK if I'm or selecting as is proceed then it will go to the next node.
Aarthi Jani: I
Aarthi Jani: So by mistake, Avinash, if let's say a user instead of just entering the right number or let's say instead of yes, it further goes like a user goes ahead and asks another question, you know how it is, right? So if that happens, then does it go?
Avinash Khedkar: OK.
Aarthi Jani: Go to direct message and then what is the action? How does it respond like?
Avinash Khedkar: Yes. So whenever we are executing this flow, so whenever user providing the user input, there are two conditions. So here we have the conditional state. OK, so in that case user user only see the accept and reject user cannot type any user input.
Aarthi Jani: Yes.
Aarthi Jani: No, I'm talking about the human input. Well, uh, human input. There's option, right? So instead of the expected answer, if let's say user ends up, uh.
Avinash Khedkar: Yes, yes, please.
Avinash Khedkar: Mhm.
Aarthi Jani: You know, entering some other like let's say a wrong VIN number or let's say instead of yes, it answers in a different way. Will it still go ahead?
Avinash Khedkar: Yes.
Avinash Khedkar: Hello.
Avinash Khedkar: Yes, it will go ahead, but it will not execute these actions because whatever the user input or parameters required to perform these actions of that parameter is not provided by user.
Avinash Khedkar: So that is the case.
Aarthi Jani: Yeah, so in in that case it will recognize the error and take it to next flow.
Avinash Khedkar: Yes, yes.
Aarthi Jani: The alternative flow, right? OK.
Avinash Khedkar: Mm.
Avinash Khedkar: So in this way it will work, the flow execution throughout the and it will go to the end.
Aarthi Jani: OK.
Avinash Khedkar: And we'll see the whatever the process flow happened during this conversation.
Aarthi Jani: I'm still OK.
Avinash Khedkar: Yes.
Avinash Khedkar: And the same flow we can attach to the agent currently and we can execute it from the agent also. Let's say we have this agent.
Avinash Khedkar: So we have attached the same recall end to end to end flow here and we can trigger that flow by giving the same user input like if we provided the BI input we'll get that response here also.
Avinash Khedkar: Yeah, we have a time please.
Avinash Khedkar: OK.
Avinash Khedkar: It will take some time because there are we are executing the number of of nodes simultaneously. Here we can see this is our result. We got the same result under the agent also and we'll see the process flow, whatever the nodes are executed, the same experience we can
Avinash Khedkar: it from the agent and the flow builder.
Avinash Khedkar: And we are maintaining the versions of the flow, whatever the whenever the user is, try to update or add anything additional function like if the user created the previous flow and user want to add additional nodes.
Avinash Khedkar: Then user can add and user update the flow. In that case the new version will be created and it will be attached to the agent also. And here we can see the version of that particular flow execution. Here we can add the name of the flow.
Aarthi Jani: I.
Avinash Khedkar: description and these are the instructions to execute this.
Avinash Khedkar: These are the what we have maintained for the.
Avinash Khedkar: Upgraders.
Avinash Khedkar: So these are all about the agent workflows that we build the feature or the addressing the complex workflows.
Avinash Khedkar: Where agent is not able to interact or take the multiple steps in single way.
Uday Kumar Javangula: Yeah, and it's probably later we'll connect and see that instructions dynamically replacing the prompt right prompt template. I think that also we need to showcase later. That's fine. I think we already covered more than one hour. Any further questions Aarthi, you have so far?
Avinash Khedkar: Yes.
Aarthi Jani: I I do have two questions there, but I think I will first try to understand because it might be I'll try to explore and then if it is not clear then I'll reach out.
Uday Kumar Javangula: No problem.
Uday Kumar Javangula: Reach out to Abhishek or Avinash. You can also reach me OK, but go through the system right? So the reason for for pulling you is to have the more updates in action rather than talking about the feature.
Aarthi Jani: Yes, yes.
Avinash Khedkar: Yeah.
Aarthi Jani: Oh.
Aarthi Jani: Yeah, yeah, this is really helpful actually.
Aarthi Jani: This way I'll be updated, yes.
Uday Kumar Javangula: So we'll plan it out for tomorrow. Yeah, tomorrow. What is the plan for tomorrow to execute the demo? We'll think about it in a separate call, OK?
Aarthi Jani: Yeah.
Uday Kumar Javangula: Fine, thanks Tim. I think there are a few action items. Probably Kinna you noted it to take that into our Sprint.
Kinna Yadav: Yes, yeah, I'll, yeah, I will write it down and maybe we can take it up in the next print, right? Or then not the current one or we can do it in next week.
Uday Kumar Javangula: Just add as a task, add as a task. So next week we can take those items not for the demo, but.
Kinna Yadav: OK.
Kinna Yadav: Yeah, I mean, uh, yeah, we can do an add on kind of a thing, right?
Uday Kumar Javangula: Those are mandatory to have example this multiple data connectors. I forgot to check with Abhishek. Aarthi already raised it for me earlier. I mean this reminded that use case OK.
Kinna Yadav: And.
Kinna Yadav: Mhm.
Kinna Yadav: OK.
Uday Kumar Javangula: OK. Thanks, Satya. I think we'll take 10 minutes time, Kenna, just to check the status.
Kinna Yadav: Yeah, I mean, uh, if RT wants to drop off RT, you can drop off.
Uday Kumar Javangula: We'll continue, yeah.
Aarthi Jani: OK. OK. No problem. Thank you. Thank you everyone. Bye.
Kinna Yadav: Yeah. Thank you. Thanks, Aunty.
Uday Kumar Javangula: Thanks.
Kinna Yadav: Alright.
Avinash Khedkar: Thank.
Kinna Yadav: OK. So I'll quickly, OK, I'll start with Abhijit.
Abhijit Thakare: Yeah so for the TIF files I checked I validated the results and posted to then Siva. So we got some good results and some are having discrepancies like file size error and other things. Then I I I talked with Siva and.
Abhijit Thakare: I told him I want miss I require. The complete results including paragraphs and other things, but whenever we convert OCR image to OCR text so that results with the with the reduced TIF. So I got that API. I'm verifying that things.
Abhijit Thakare: So I verified a couple of documents. So from A1 to A7 I verified so we are getting the good results, but from that one or two files as they are exceeding just above 4 MB.
Abhijit Thakare: And our free tier only gives the four MB, not below or not above 4 MB. So we are getting that size limit due to free tier, free tier and I'm very I'm verifying the other things, other documents and accordingly I will modify my algorithm.
Kinna Yadav: Yeah.
Abhijit Thakare: Uh, for that scenarios.
Kinna Yadav: OK.
Abhijit Thakare: Yeah.
Kinna Yadav: So this is this is also like the the POC part, no? I mean we are just making it more rectified, correct? Huh.
Abhijit Thakare: Yeah. Thank you.
Abhijit Thakare: Yeah, we are making it more accurate according to like we get initially got PDF images, then we got TRF images. Now we got reduced TRF images and then we are modifying our code and algorithms accordingly, yeah.
Kinna Yadav: That it.
Kinna Yadav: Hmm.
Kinna Yadav: Got it.
Kinna Yadav: Correct, correct, correct. OK. Shiva, I think you are working on the APIs, right? The ones which you shared, OK.
Abhijit Thakare: Yeah.
Kinna Yadav: OK.
Siva Venkata Rao M: He's currently verifying. I am also trying in the postman. We got the handwritten content as a I think mostly we'll get the proper handwriting content in for the latest TF less content files as movie with Abhijit.
Siva Venkata Rao M: Abhijit verify the all the files. Maybe I will enable the in NLP and OS such OS stored can code. Currently we disable it. Once Abhijit verify it I can enable it and deploy it can.
Kinna Yadav: OK.
Siva Venkata Rao M: OK.
Kinna Yadav: OK. Thanks. Uh, sure. Thanks, Vijit. Uh, Avinash.
Avinash Khedkar: So I have created the some flow execution. I have analyzed how we can improve the architecture point of view for the addition or come combination of the flow and actions under the agent and I have discussed with the agent.
Avinash Khedkar: So currently we'll not do the more modification because whatever the workflows or flows we are adding, that is a separate flow execution throughout the nodes, so it will not like a conversation. So for that we'll add the separate topic instead of the adding for the single.
Avinash Khedkar: topic. So and for the file upload node I have to check. So how we can upload any file in the conversation and we'll ask the follow up question. And I uh validated the flow builder section, so she can deploy that change or the.
Avinash Khedkar: File upload note and I validated it's working fine.
Kinna Yadav: OK, OK. So you're saying that the conversational related thing that you have to validate, right?
Avinash Khedkar: OK.
Avinash Khedkar: Yes, yes, yes. Conversational related to if we need to upload the particular file from the system and we need to ask the question based on the content follow the questions.
Kinna Yadav: OK.
Avinash Khedkar: Yes.
Kinna Yadav: And this you planning to close it today?
Avinash Khedkar: For that I'll prepare the analysis part means what I'll drill down more the task where we have to improve on the code part so that I'll complete today.
Kinna Yadav: OK.
Avinash Khedkar: Yes.
Kinna Yadav: OK.
Kinna Yadav: Thanks so much.
Avinash Khedkar: Thank you.
Manikanta Siva Kumar Bommana: For example, in India we have Bangalore, Chennai, New Delhi, Mumbai. If users are in India, we can provide four options, Bangalore, Chennai, New Delhi, Mumbai. For example, in urgent we have only one central location.
Kinna Yadav: Um.
Kinna Yadav: Manikanta, you saying something?
Avinash Khedkar: Maybe it's having some network issues.
Kinna Yadav: OK.
Uday Kumar Javangula: He'll be rejoining, yeah.
Manikanta Siva Kumar Bommana: Hello, can you hear me?
Abhishek Lingaraju: Yeah.
Kinna Yadav: Yeah, I some cross connection I guess.
Uday Kumar Javangula: I think looks like the Bluetooth device is up. It's correct.
Manikanta Siva Kumar Bommana: Yeah, I had only one in years, so but I just yeah, I can. So what I was saying is so morning, so I got the I got the doubts clarified with respect to the e-mail scheduler and webbook.
Kinna Yadav: Yeah.
Abhishek Lingaraju: Mm.
Kinna Yadav: Mm.
Manikanta Siva Kumar Bommana: So I've started working on those service and also I mean Uday just wanted me to create a real time scenario case for how do we use the prompt templates and open search query that action. So I've created that and.
Kinna Yadav: OK.
Manikanta Siva Kumar Bommana: Creating that I was validating that flow, so I just came across a small issue with respect to a transform mode, so which I deployed and fixed in depth. Yeah, so yeah, and right now I'm just starting to so and the chain is canal so that we were that we're about.
Manikanta Siva Kumar Bommana: To deploy in dev. So there were many changes with respect to Avinash which is supposed to spring 17 branch. So I'm actually validating. I mean I'm merging those changes currently with mine and I'll deploy the changes with respect to the scheduler right? But but in.
Manikanta Siva Kumar Bommana: In the deploying something an update.
Kinna Yadav: So this has been taken care by Sheetal also because Sheetal was also working on the changes, right? She was she was planning to integrate no your start node scheduler one.
Manikanta Siva Kumar Bommana: Oh, yes, she's still working on it, yeah.
Sheetal Amol Dashpute: OK.
Kinna Yadav: OK, OK, OK. And you were saying that which one the prompt template, what exactly to create prompt template for?
Manikanta Siva Kumar Bommana: Yeah.
Manikanta Siva Kumar Bommana: Yeah, so the open search queries right that we have actions for. So so we have the request right like the post to put, delete and everything and get so for them. I mean so I've just validated previously with simple workflows. Now I've created all those actions together and I validated them so.
Kinna Yadav: Mm.
Manikanta Siva Kumar Bommana: Just to replicate some small real time scenario, I've just took an example for when and validated them.
Kinna Yadav: On the prompt templates, you're saying.
Manikanta Siva Kumar Bommana: Yeah, on the prompt template.
Kinna Yadav: OK.
Manikanta Siva Kumar Bommana: Yeah, so currently I'm merging the changes.
Kinna Yadav: So now this whole thing is, yeah, OK, alright, you merging the OK.
Manikanta Siva Kumar Bommana: Yeah, that is that.
Manikanta Siva Kumar Bommana: So I'm merging the changes with respect to the scheduler which I've done yesterday, so I'll deploy those changes in a week today.
Kinna Yadav: It's uh, merging this with print 17 branch.
Manikanta Siva Kumar Bommana: Yeah, two Sprint 17, so the changes from yesterday what Abhinash meant. So I'm taking currently those changes also and I'll deploy them.
Kinna Yadav: And what about this one? Start the e-mail and webhook you said right that you're going to work on that. So I mean which one you taking?
Manikanta Siva Kumar Bommana: Yes.
Manikanta Siva Kumar Bommana: Oh yeah, that I'll take the e-mail one first.
Kinna Yadav: Oh, and then.
Manikanta Siva Kumar Bommana: I mean, the webhook is pretty simple, so I'll just take the e-mail one firstly.
Kinna Yadav: OK.
Kinna Yadav: How much time it takes for you to lose this?
Manikanta Siva Kumar Bommana: Maybe one day can so for the e-mail one.
Kinna Yadav: Tomorrow you will close, is what he's saying.
Manikanta Siva Kumar Bommana: Yeah, I'll close it tomorrow.
Kinna Yadav: OK.
Kinna Yadav: All right. Thanks, honey.
Manikanta Siva Kumar Bommana: Yeah.
Kinna Yadav: Oh, Shivam.
Shivam Singhal: Yeah, hi Kina. Good evening. So Kina, today I in the morning I was gonna uh like uh working with Abhishek for Zendesk inactive issue was there. So like that one I was working and after that I had taken that push notification API.
Shivam Singhal: So that is completed but not yet deployed. If Sheetal and and they can review it once like we can stay back for two to three minutes after this call or if if they are available and like get the feedback on it. After that I'll deploy it and they can start integrations.
Shivam Singhal: I.
Kinna Yadav: OK.
Kinna Yadav: Alright, I'll just complete it and then you know we can spend couple of minutes where this can be reviewed, OK.
Shivam Singhal: Yeah.
Kinna Yadav: Alright, thanks.
Kinna Yadav: Um.
Kinna Yadav: OK, is she done?
Sheetal Amol Dashpute: Yeah, currently I'm working on cron schedule related changes Kina, so those will be completed by today EOD and I have already integrated the file input not related changes in the flow builder and those are deployed in dev.
Kinna Yadav: OK.
Sheetal Amol Dashpute: And one more thing Kina those same same changes I need to integrate in agent chat window. So after these cron changes I will work on that.
Kinna Yadav: Huh.
Kinna Yadav: That that you're talking about that file input node pop up thing, OK.
Sheetal Amol Dashpute: Yes, yes.
Kinna Yadav: OK.
Kinna Yadav: And this this will be done like today. Um, Sheetal, I guess.
Sheetal Amol Dashpute: Yeah, will be completed by today Kina and in the agent chat window I will add those file input related changes tomorrow. It will not take much time. By 11 it will be completed tomorrow.
Kinna Yadav: OK. OK, fine. OK. Thank you.
Sheetal Amol Dashpute: Yeah.
Kinna Yadav: Sumish.
Someshwar Shyamala: In chart related alignment issues result and I have added no results images in agent builder agent page and I to add some functionality.
Kinna Yadav: OK.
Someshwar Shyamala: It is.
Kinna Yadav: And that was related to which one?
Someshwar Shyamala: In agent builder no results image but functionality not inform to.
Kinna Yadav: OK, OK.
Kinna Yadav: Got it. Thanks, Sumish.
Someshwar Shyamala: Thanks.
Kinna Yadav: So Sheetal this this you will take uh tomorrow this.
Sheetal Amol Dashpute: Uh, yes, yes.
Kinna Yadav: OK, OK, you going to close this start node first, right?
Sheetal Amol Dashpute: Uh, yes.
Kinna Yadav: All right. Thanks so much. Abhishek.
Kinna Yadav: OK.
Abhishek Lingaraju: So the last one is just 421 and 422. Yes, yes, so those are UI related, it's assigned to sheet.
Kinna Yadav: OK.
Kinna Yadav: New issues that you have.
Kinna Yadav: OK.
Kinna Yadav: Alright, no blockers, right? Uh, Abhishek.
Abhishek Lingaraju: No, no blockers are working.
Kinna Yadav: OK, fine. Got it. Thanks, Abhishek. Um.
Kinna Yadav: So OK, I I know that you know, but can we make sure that the issues that you know are coming up on the UI side, if we can just cross check and we can try to avoid it, what what is needed if we can figure it out right so that.
Kinna Yadav: Going forward, we can avoid such issues.
Kinna Yadav: Oh, OK, yeah, nothing else. Uh, is there anything you want to add on?
Uday Kumar Javangula: A few icons maybe so much you need to check that in in the data sources. I see some emails are not loading.
Uday Kumar Javangula: Some icons are not loading.
Someshwar Shyamala: OK. Yeah.
Uday Kumar Javangula: We saw the during the demo.
Someshwar Shyamala: Yes, yes, yeah. And I think I so I will look into it.
Uday Kumar Javangula: The data connectors. Some of the icons are not loading. Just check that.
Someshwar Shyamala: OK.
Uday Kumar Javangula: From your side, OK.
Someshwar Shyamala: OK.
Uday Kumar Javangula: OK, fine. Yeah, we'll go through it. Maybe I'll reach out to your team while preparing this each node level, especially this template one. Anikanta and Avinash, OK.
Avinash Khedkar: OK.
Manikanta Siva Kumar Bommana: No.
Uday Kumar Javangula: OK.
Uday Kumar Javangula: That's it, Kina. Thanks, Dean. Shiva may want to continue on this call.
Kinna Yadav: Yeah, thank you.
Shivam Singhal: Uh, yeah, they just had to take. I'll not take much time. We'll take just two to three minutes. Um, maybe they can drop off uh money content. She tell like uh you we can continue.
Uday Kumar Javangula: Yeah.
Kinna Yadav: Sure.
Abhijit Thakare: Thank you too.
Abhishek Lingaraju: OK. Thank you too.
Kinna Yadav: OK. Thank you too.
Siva Venkata Rao M: Thank you. Thank you.
Uday Kumar Javangula: Thanks to.
Shivam Singhal: Yeah, so let me share my screen. So is my screen is visible at the?
Uday Kumar Javangula: Yeah.
Shivam Singhal: So like I created this simple one API for pushing the notifications. As of now the formatting part on the UI side is not yet completed. So just that like giving the proper response like I guess I'm I can add one more additional field like execution executed at.
Shivam Singhal: Like when the flow is executed, so storing currently session ID, workflow ID and the execution status and what is the message that we will need to show and saving this particular per data into our database and corresponding whenever any notification comes in this API can be like hit by.
Shivam Singhal: I triggered by Manikanta after the flow execution and whenever this API is being triggered we can see one notification pops up onto the UI site similarly. So if do we need any click functionality over it so like that it's just one point there.
Shivam Singhal: And any other feedbacks that Sheetal and Manikanta can give him? Yeah, that's all.
Uday Kumar Javangula: So you get the OK instead of flow ID, right? Give the flow name, flow name, OK.
Shivam Singhal: Flow name. Yes, yes, it's just for for a test case I have added this.
Uday Kumar Javangula: Yeah, so let's include flow names so that it's easy to identify, but in the background you can track that based on ID.
Shivam Singhal: Uh, yes, yes.
Shivam Singhal: Right, yes.
Uday Kumar Javangula: So on click of it, right? What I told earlier, we should navigate to that flow, OK?
Shivam Singhal: Yeah.
Shivam Singhal: Yeah, to the flow audit audit window.
Uday Kumar Javangula: No, no. We can navigate to flow and open that. Yeah, audit also find, I believe, how it is executed and all.
Sheetal Amol Dashpute: Flow details page right over there.
Uday Kumar Javangula: flow details page we can go to it yeah
Sheetal Amol Dashpute: Not this one. Uh, click on the flow name. Flow name. Click on flow name.
Shivam Singhal: No name.
Sheetal Amol Dashpute: Yeah.
Uday Kumar Javangula: So here we don't have any audit, right? It's only it's only view.
Shivam Singhal: Yes, yes.
Sheetal Amol Dashpute: Yes.
Uday Kumar Javangula: We can include the audit in the read on even audit. Also it's read only, right? We're not changing anything in the reads.
Shivam Singhal: Yes, yes. After the execution we are storing the audit logs. So I've already prepared the API for that one as well. We need to integrate that one onto the Python side, so that we discussed, yes.
Uday Kumar Javangula: Yeah.
Uday Kumar Javangula: Correct. Yeah, that is true.
Uday Kumar Javangula: It is due. Maybe we can navigate to that audit, current audit, recent audit that has been done by process flow. Okay.
Shivam Singhal: OK, so OK again, yes.
Uday Kumar Javangula: Because user can see, user can see what is executed, what is the format and all everything.
Shivam Singhal: Is.
Shivam Singhal: Hey.
Shivam Singhal: OK, Uday, so just this thing I wanted to focus.
Uday Kumar Javangula: Yeah, this looks good. You can continue that. Just remove that low ID.
Sheetal Amol Dashpute: Hello.
Shivam Singhal: OK.
Shivam Singhal: Yeah, I know. Yes.
Shivam Singhal: Sure.
Uday Kumar Javangula: Yeah, man, but they have something to say.
Manikanta Siva Kumar Bommana: No other. So yeah that is what. So this one like after the background task is completed so we I would push this AP in order to trigger the notification and what we and Sharon thought of this will also trigger.
Manikanta Siva Kumar Bommana: The audit log API, so he has already shared with me where then just input the date, add the audit details that instead of like editing it to the flow window.
Uday Kumar Javangula: OK, sure.
Manikanta Siva Kumar Bommana: Yes.
Uday Kumar Javangula: OK.
Shivam Singhal: Yeah.
Manikanta Siva Kumar Bommana: Yeah.
Uday Kumar Javangula: Finding, yeah, thanks.
Sheetal Amol Dashpute: Thank you.
Shivam Singhal: Thank you.
Manikanta Siva Kumar Bommana: Yeah. Thank you.
Sheetal Amol Dashpute: Thank you.
Uday Kumar Javangula: Thank you. Thank you. Bye.