Better feedback

backend/files/system/openai/create-bot.post.hl

@@ -316,8 +316,14 @@ try
                // Providing feedback to caller.
                sockets.signal:x:@.feedback-channel
                   args
-                     message:Done creating your machine learning model, remember to vectorize it before attempting to use it
-                     type:success
+                     message:Remember to vectorize before attempting to use your model
+                     type:info
+
+            // Informing user that we're done.
+            sockets.signal:x:@.arguments/*/feedback-channel
+               args
+                  message:Done!
+                  type:success
 
    // Returning success to caller.
    unwrap:x:+/*

backend/files/system/openai/import-page.post.hl

@@ -80,7 +80,7 @@ fork
             type:success
       sockets.signal:x:@.arguments/*/feedback-channel
          args
-            message:Done creating OpenAI training data from page
+            message:Done!
             type:success
 
       // Basic logging.

backend/files/system/openai/import-url.post.hl

@@ -90,7 +90,7 @@ signal:magic.ai.crawl-site
       .feedback-channel:x:@.arguments/*/feedback-channel
       sockets.signal:x:@.arguments/*/feedback-channel
          args
-            message:Successfull scraped URL(s), remember to vectorize your model before using it
+            message:Done!
             type:success
 
 

backend/files/system/openai/magic.startup/crawling/magic.ai.crawl-site.hl

@@ -193,21 +193,27 @@ slots.create:magic.ai.crawl-site
                         threshold:x:@.arguments/*/threshold
                         feedback-channel:x:@.arguments/*/feedback-channel
 
-                     // Signaling frontend that we're waiting for n seconds.
-                     strings.concat
-                        .:"Waiting for "
-                        math.divide:x:@.arguments/*/delay
-                           .:int:1000
-                        .:" seconds to avoid exhausting web server"
-                     unwrap:x:+/**
-                     sockets.signal:x:@.arguments/*/feedback-channel
-                        args
-                           message:x:@strings.concat
-                           type:info
-                     sleep:100
+                     // Verifying we've got more snippets before applying Crawl-Delay
+                     if
+                        neq:x:@.dp/#
+                           get-value:x:@signal/@signal/*/urls/0/-
+                        .lambda
 
-                     // Sleeping for [delay] milliseconds to avoid exhausting web server.
-                     sleep:x:@.arguments/*/delay
+                           // Signaling frontend that we're waiting for n seconds.
+                           strings.concat
+                              .:"Waiting for "
+                              math.divide:x:@.arguments/*/delay
+                                 .:int:1000
+                              .:" seconds to avoid exhausting web server"
+                           unwrap:x:+/**
+                           sockets.signal:x:@.arguments/*/feedback-channel
+                              args
+                                 message:x:@strings.concat
+                                 type:info
+                           sleep:100
+
+                           // Sleeping for [delay] milliseconds to avoid exhausting web server.
+                           sleep:x:@.arguments/*/delay
 
                   .catch
 

backend/files/system/openai/magic.startup/magic.ai.vectorise.hl

@@ -63,8 +63,9 @@ select id, prompt, completion
 
             // Signaling frontend.
             strings.concat
-               .:"Vectorising "
+               .:"Vectorizing: \""
                get-value:x:@.dp/#/*/prompt
+               .:"\""
             unwrap:x:+/*/args/*/message
             sockets.signal:x:@.arguments/*/feedback-channel
                args
@@ -241,12 +242,6 @@ insert into vss_ml_training_snippets (rowid, embedding_vss)
          not exists (select rowid from vss_ml_training_snippets vss2 where vss2.rowid = ts.id)"
             type:x:@.arguments/*/type
 
-      // Done with everything, notifying frontend.
-      sockets.signal:x:@.arguments/*/feedback-channel
-         args
-            message:Your machine learning model was successfully vectorised
-            type:info
-
       // Basic logging.
       log.info:Done with creating vectors for model
          type:x:@.arguments/*/type

backend/files/system/openai/vectorise.post.hl

@@ -39,6 +39,11 @@ fork
    add:x:./*/signal
       get-nodes:x:@.arguments/*
    signal:magic.ai.vectorise
+      .onafter
+         sockets.signal:x:@.arguments/*/feedback-channel
+            args
+               message:Done!
+               type:success
 
 // Returning success to caller.
 return

frontend/src/app/_general/services/openai.service.ts

@@ -164,10 +164,11 @@ export class OpenAIService {
   /**
    * Vectorises all snippets in the specified type.
    */
-  vectorise(type: string) {
+  vectorise(type: string, channel: string) {
 
     return this.httpService.post<any>('/magic/system/openai/vectorise', {
       type,
+      ['feedback-channel']: channel,
     });
   }
 

frontend/src/app/_protected/pages/create/chatbot-wizard/chatbot-wizard.component.ts

@@ -270,7 +270,7 @@ export class ChatbotWizardComponent implements OnInit, OnDestroy {
            * Too many snippets according to license, still we'll vectorise the model explicitly to make sure
            * user gets at least a somewhat working chatbot to embed on page.
            */
-          this.openAIService.vectorise(this.model).subscribe({
+          this.openAIService.vectorise(this.model, feedbackChannel).subscribe({
             next: () => {
 
               console.log('Vectorising started');

frontend/src/app/_protected/pages/manage/machine-learning/components/machine-learning-import-feedback/machine-learning-import-feedback.component.ts

@@ -75,8 +75,7 @@ export class MachineLearningImportFeedbackComponent implements OnInit, OnDestroy
 
         this.hubConnection.start().then(() => {
 
-          this.generalService.showLoading();
-          if (this.data.site === true) {
+          if (this.data.mode === 'site') {
             this.openAIService.importUrl(
               this.data.url,
               this.data.type,
@@ -96,7 +95,7 @@ export class MachineLearningImportFeedbackComponent implements OnInit, OnDestroy
                 this.generalService.showFeedback('Something went wrong as we tried to start import', 'errorMessage');
               }
             });
-          } else {
+          } else if (this.data.mode === 'single-page') {
             this.openAIService.importPage(
               this.data.url,
               this.data.type,
@@ -112,7 +111,20 @@ export class MachineLearningImportFeedbackComponent implements OnInit, OnDestroy
                 this.generalService.showFeedback('Something went wrong as we tried to spice your model', 'errorMessage');
               }
             });
-        }
+          } else if (this.data.mode === 'vectorize') {
+
+            this.openAIService.vectorise(this.data.type, result.result).subscribe({
+              next: () => {
+
+                this.generalService.hideLoading();
+              },
+              error: () => {
+
+                this.generalService.hideLoading();
+                this.generalService.showFeedback('Something went wrong as we tried to create embeddings for model', 'errorMessage');
+              }
+            });
+          }
         });
       },
       error: () => {

frontend/src/app/_protected/pages/manage/machine-learning/machine-learning-models/machine-learning-models.component.ts

@@ -168,7 +168,7 @@ export class MachineLearningModelsComponent implements OnInit {
                 max: result.max,
                 threshold: result.threshold,
                 summarize: result.summarize,
-                site: true,
+                mode: 'site'
               }
             });
         }
@@ -233,17 +233,14 @@ export class MachineLearningModelsComponent implements OnInit {
         }).afterClosed().subscribe((result: string) => {
 
           if (result === 'confirm') {
-
-            this.openAIService.vectorise(el.type).subscribe({
-              next: () => {
-
-                this.generalService.showFeedback('Started creating embeddings of model', 'successMessage');
-                this.generalService.hideLoading();
-              },
-              error: () => {
-
-                this.generalService.hideLoading();
-                this.generalService.showFeedback('Something went wrong as we tried to create embeddings for model', 'errorMessage');
+            this.dialog
+            .open(MachineLearningImportFeedbackComponent, {
+              width: '80vw',
+              maxWidth: '1280px',
+              data: {
+                url: result,
+                type: el.type,
+                mode: 'vectorize'
               }
             });
           }

frontend/src/app/_protected/pages/manage/machine-learning/machine-learning-training-data/machine-learning-training-data.component.ts

@@ -127,7 +127,7 @@ export class MachineLearningTrainingDataComponent implements OnInit {
                 max: result.max,
                 threshold: result.threshold,
                 summarize: result.summarize,
-                site: false,
+                mode: 'single-page'
               }
             });
         }