fixed AI search formatting and added load time logging

Author: sauravpanda

docs/src/components/aiSearch/AIResponse.tsx

@@ -51,13 +51,9 @@ const MemoizedMarkdown = memo(({ content }: { content: string }) => (
       br: ({ node, ...props }) => (
         <br className="mb-2" {...props} />
       ),
-      code({ node, inline, className, children, ...props }) {
+      code({ node, className, children, ...props }) {
         const match = /language-(\w+)/.exec(className || '')
-        return inline ? (
-          <code className={className} {...props}>
-            {children}
-          </code>
-        ) : (
+        return (
           <div className="relative">
             <code className={`language-${match?.[1] || 'text'}`} {...props}>
               {children}

docs/src/lib/aisearch/embeddings.worker.ts

@@ -6,9 +6,21 @@ class EmbeddingPipelineSingleton {
     static instance: Promise<any> | null = null;
 
     static async getInstance(progress_callback: ProgressCallback | null = null) {
-        this.instance ??= pipeline(this.task, this.model, { 
-            progress_callback: progress_callback ?? undefined
-        });
+        if (!this.instance) {
+            console.log('[Embeddings Worker] Initializing embedding pipeline...');
+            const startTime = performance.now();
+            
+            this.instance = pipeline(this.task, this.model, { 
+                progress_callback: (progress) => {
+                    progress_callback?.(progress);
+                }
+            }).then((pipeline) => {
+                const loadTime = (performance.now() - startTime) / 1000;
+                console.log(`[Embeddings Worker] Pipeline ready in ${loadTime.toFixed(2)}s`);
+                return pipeline;
+            });
+        }
+        
         return this.instance;
     }
 }
@@ -18,10 +30,13 @@ self.addEventListener('message', async (event) => {
         self.postMessage({ status: 'progress', progress: x });
     });
 
+    const startTime = performance.now();
     const output = await embedder(event.data.text, {
         pooling: 'mean',
         normalize: true
     });
+    const inferenceTime = (performance.now() - startTime) / 1000;
+    console.log(`[Embeddings Worker] Embedding generated in ${inferenceTime.toFixed(2)}s`);
 
     self.postMessage({
         status: 'complete',

packages/akiradocs/src/components/aiSearch/AIResponse.tsx

@@ -51,13 +51,9 @@ const MemoizedMarkdown = memo(({ content }: { content: string }) => (
       br: ({ node, ...props }) => (
         <br className="mb-2" {...props} />
       ),
-      code({ node, inline, className, children, ...props }) {
+      code({ node, className, children, ...props }) {
         const match = /language-(\w+)/.exec(className || '')
-        return inline ? (
-          <code className={className} {...props}>
-            {children}
-          </code>
-        ) : (
+        return (
           <div className="relative">
             <code className={`language-${match?.[1] || 'text'}`} {...props}>
               {children}

packages/akiradocs/src/lib/aisearch/embeddings.worker.ts

@@ -6,9 +6,21 @@ class EmbeddingPipelineSingleton {
     static instance: Promise<any> | null = null;
 
     static async getInstance(progress_callback: ProgressCallback | null = null) {
-        this.instance ??= pipeline(this.task, this.model, { 
-            progress_callback: progress_callback ?? undefined
-        });
+        if (!this.instance) {
+            console.log('[Embeddings Worker] Initializing embedding pipeline...');
+            const startTime = performance.now();
+            
+            this.instance = pipeline(this.task, this.model, { 
+                progress_callback: (progress) => {
+                    progress_callback?.(progress);
+                }
+            }).then((pipeline) => {
+                const loadTime = (performance.now() - startTime) / 1000;
+                console.log(`[Embeddings Worker] Pipeline ready in ${loadTime.toFixed(2)}s`);
+                return pipeline;
+            });
+        }
+        
         return this.instance;
     }
 }
@@ -18,10 +30,13 @@ self.addEventListener('message', async (event) => {
         self.postMessage({ status: 'progress', progress: x });
     });
 
+    const startTime = performance.now();
     const output = await embedder(event.data.text, {
         pooling: 'mean',
         normalize: true
     });
+    const inferenceTime = (performance.now() - startTime) / 1000;
+    console.log(`[Embeddings Worker] Embedding generated in ${inferenceTime.toFixed(2)}s`);
 
     self.postMessage({
         status: 'complete',