refactor: make requests purely client-side

components/ResearchFeedback.vue

@@ -1,7 +1,5 @@
 <script setup lang="ts">
-  import { parsePartialJson } from '@ai-sdk/ui-utils'
+  import { generateFeedback } from '~/lib/feedback'
-  import { useChat } from '@ai-sdk/vue'
-  import { isObject } from '@vueuse/core'
 
   export interface ResearchFeedbackResult {
     assistantQuestion: string
@@ -18,9 +16,8 @@
 
   const feedback = ref<ResearchFeedbackResult[]>([])
 
-  const { messages, input, error, handleSubmit, isLoading } = useChat({
+  const isLoading = ref(false)
-    api: '/api/generate-feedback',
+  const error = ref('')
-  })
 
   const isSubmitButtonDisabled = computed(
     () =>
@@ -34,46 +31,13 @@
 
   async function getFeedback(query: string, numQuestions = 3) {
     clear()
-    // Set input value. (This only makes sure that the library sends the request)
+    isLoading.value = true
-    input.value = query
+    try {
-    handleSubmit(
+      for await (const f of generateFeedback({
-      {},
-      {
-        body: {
         query,
         numQuestions,
-        },
+      })) {
-      },
+        const questions = f.questions!.filter((s) => typeof s === 'string')
-    )
-  }
-  function clear() {
-    messages.value = []
-    input.value = ''
-    error.value = undefined
-    feedback.value = []
-  }
-
-  watch(messages, (m) => {
-    const assistantMessage = m[m.length - 1]
-    if (assistantMessage?.role !== 'assistant') {
-      return {
-        value: undefined,
-        state: 'undefined-input',
-      }
-    }
-
-    const content = removeJsonMarkdown(assistantMessage.content)
-    // Write the questions into modelValue
-    const parseResult = parsePartialJson(content)
-
-    if (parseResult.state === 'repaired-parse' || parseResult.state === 'successful-parse') {
-      if (!isObject(parseResult.value) || Array.isArray(parseResult.value)) {
-        return (feedback.value = [])
-      }
-      const unsafeQuestions = parseResult.value.questions
-      if (!unsafeQuestions || !Array.isArray(unsafeQuestions)) return (feedback.value = [])
-
-      const questions = unsafeQuestions.filter((s) => typeof s === 'string')
         // Incrementally update modelValue
         for (let i = 0; i < questions.length; i += 1) {
           if (feedback.value[i]) {
@@ -85,16 +49,19 @@
             })
           }
         }
-    } else {
-      feedback.value = []
       }
-  })
+    } catch (e: any) {
+      console.error('Error getting feedback:', e)
+      error.value = e.message
+    } finally {
+      isLoading.value = false
+    }
+  }
 
-  watch(error, (e) => {
+  function clear() {
-    if (e) {
+    feedback.value = []
-      console.error(`ResearchFeedback error,`, e)
+    error.value = ''
   }
-  })
 
   defineExpose({
     getFeedback,
@@ -111,6 +78,7 @@
     </template>
 
     <div class="flex flex-col gap-2">
+      <p v-if="error" class="text-red-500">{{ error }}</p>
       <div v-if="!feedback.length && !error">Waiting for model feedback...</div>
       <template v-else>
         <div v-if="error" class="text-red-500">{{ error }}</div>

lib/deep-research.ts

@@ -19,7 +19,7 @@ export interface WriteFinalReportParams {
   prompt: string;
   learnings: string[];
 }
-
+// useRuntimeConfig()
 // Used for streaming response
 export type SearchQuery = z.infer<typeof searchQueriesTypeSchema>['queries'][0];
 export type PartialSearchQuery = DeepPartial<SearchQuery>;

lib/feedback.ts

@@ -5,6 +5,8 @@ import { zodToJsonSchema } from 'zod-to-json-schema'
 import { o3MiniModel } from './ai/providers';
 import { systemPrompt } from './prompt';
 
+type PartialFeedback = DeepPartial<z.infer<typeof feedbackTypeSchema>>
+
 export const feedbackTypeSchema = z.object({
   questions: z.array(z.string())
 })
@@ -28,10 +30,16 @@ export function generateFeedback({
     `Given the following query from the user, ask some follow up questions to clarify the research direction. Return a maximum of ${numQuestions} questions, but feel free to return less if the original query is clear: <query>${query}</query>`,
     `You MUST respond in JSON with the following schema: ${jsonSchema}`,
   ].join('\n\n');
-  return streamText({
+
+  const stream = streamText({
     model: o3MiniModel,
     system: systemPrompt(),
     prompt,
   });
-  // return userFeedback.object.questions.slice(0, numQuestions);
+
+  return parseStreamingJson(
+    stream.textStream,
+    feedbackTypeSchema,
+    (value: PartialFeedback) => !!value.questions && value.questions.length > 0
+  )
 }

server/api/generate-feedback.post.ts

@@ -1,3 +1,4 @@
+// This file is currently unused
 import { generateFeedback } from "~/lib/feedback";
 
 export default defineEventHandler(async event => {