feat(groq): switch to @langchain/groq for better handling

Fix name of provider in embeddings models error message

package.json

@@ -15,11 +15,14 @@
     "@headlessui/react": "^2.2.0",
     "@iarna/toml": "^2.2.5",
     "@icons-pack/react-simple-icons": "^12.3.0",
-    "@langchain/anthropic": "^0.3.15",
-    "@langchain/community": "^0.3.36",
-    "@langchain/core": "^0.3.42",
-    "@langchain/google-genai": "^0.1.12",
-    "@langchain/openai": "^0.0.25",
+    "@langchain/anthropic": "^0.3.24",
+    "@langchain/community": "^0.3.49",
+    "@langchain/core": "^0.3.66",
+    "@langchain/google-genai": "^0.2.15",
+    "@langchain/groq": "^0.2.3",
+    "@langchain/langgraph": "^0.4.0",
+    "@langchain/ollama": "^0.2.3",
+    "@langchain/openai": "^0.6.2",
     "@langchain/textsplitters": "^0.1.0",
     "@tailwindcss/typography": "^0.5.12",
     "@xenova/transformers": "^2.17.2",
@@ -31,7 +34,7 @@
     "drizzle-orm": "^0.40.1",
     "html-to-text": "^9.0.5",
     "jspdf": "^3.0.1",
-    "langchain": "^0.1.30",
+    "langchain": "^0.3.30",
     "lucide-react": "^0.363.0",
     "mammoth": "^1.9.1",
     "markdown-to-jsx": "^7.7.2",
@@ -40,6 +43,7 @@
     "pdf-parse": "^1.1.1",
     "react": "^18",
     "react-dom": "^18",
+    "react-syntax-highlighter": "^15.6.1",
     "react-text-to-speech": "^0.14.5",
     "react-textarea-autosize": "^8.5.3",
     "sonner": "^1.4.41",
@@ -56,6 +60,7 @@
     "@types/pdf-parse": "^1.1.4",
     "@types/react": "^18",
     "@types/react-dom": "^18",
+    "@types/react-syntax-highlighter": "^15.5.13",
     "autoprefixer": "^10.0.1",
     "drizzle-kit": "^0.30.5",
     "eslint": "^8",
@@ -63,6 +68,6 @@
     "postcss": "^8",
     "prettier": "^3.2.5",
     "tailwindcss": "^3.3.0",
-    "typescript": "^5"
+    "typescript": "5.8.3"
   }
 }

src/app/api/chat/route.ts

@@ -223,7 +223,7 @@ export const POST = async (req: Request) => {
 
     if (body.chatModel?.provider === 'custom_openai') {
       llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
+        apiKey: getCustomOpenaiApiKey(),
         modelName: getCustomOpenaiModelName(),
         temperature: 0.7,
         configuration: {

src/app/api/discover/route.ts

@@ -1,55 +1,77 @@
 import { searchSearxng } from '@/lib/searxng';
 
-const articleWebsites = [
-  'yahoo.com',
-  'www.exchangewire.com',
-  'businessinsider.com',
-  /* 'wired.com',
-  'mashable.com',
-  'theverge.com',
-  'gizmodo.com',
-  'cnet.com',
-  'venturebeat.com', */
-];
+const websitesForTopic = {
+  tech: {
+    query: ['technology news', 'latest tech', 'AI', 'science and innovation'],
+    links: ['techcrunch.com', 'wired.com', 'theverge.com'],
+  },
+  finance: {
+    query: ['finance news', 'economy', 'stock market', 'investing'],
+    links: ['bloomberg.com', 'cnbc.com', 'marketwatch.com'],
+  },
+  art: {
+    query: ['art news', 'culture', 'modern art', 'cultural events'],
+    links: ['artnews.com', 'hyperallergic.com', 'theartnewspaper.com'],
+  },
+  sports: {
+    query: ['sports news', 'latest sports', 'cricket football tennis'],
+    links: ['espn.com', 'bbc.com/sport', 'skysports.com'],
+  },
+  entertainment: {
+    query: ['entertainment news', 'movies', 'TV shows', 'celebrities'],
+    links: ['hollywoodreporter.com', 'variety.com', 'deadline.com'],
+  },
+};
 
-const topics = ['AI', 'tech']; /* TODO: Add UI to customize this */
+type Topic = keyof typeof websitesForTopic;
 
 export const GET = async (req: Request) => {
   try {
     const params = new URL(req.url).searchParams;
+
     const mode: 'normal' | 'preview' =
       (params.get('mode') as 'normal' | 'preview') || 'normal';
+    const topic: Topic = (params.get('topic') as Topic) || 'tech';
+
+    const selectedTopic = websitesForTopic[topic];
 
     let data = [];
 
     if (mode === 'normal') {
+      const seenUrls = new Set();
+
       data = (
-        await Promise.all([
-          ...new Array(articleWebsites.length * topics.length)
-            .fill(0)
-            .map(async (_, i) => {
+        await Promise.all(
+          selectedTopic.links.flatMap((link) =>
+            selectedTopic.query.map(async (query) => {
               return (
-                await searchSearxng(
-                  `site:${articleWebsites[i % articleWebsites.length]} ${
-                    topics[i % topics.length]
-                  }`,
-                  {
-                    engines: ['bing news'],
-                    pageno: 1,
-                  },
-                )
+                await searchSearxng(`site:${link} ${query}`, {
+                  engines: ['bing news'],
+                  pageno: 1,
+                  language: 'en',
+                })
               ).results;
             }),
-        ])
+          ),
+        )
       )
-        .map((result) => result)
         .flat()
+        .filter((item) => {
+          const url = item.url?.toLowerCase().trim();
+          if (seenUrls.has(url)) return false;
+          seenUrls.add(url);
+          return true;
+        })
         .sort(() => Math.random() - 0.5);
     } else {
       data = (
         await searchSearxng(
-          `site:${articleWebsites[Math.floor(Math.random() * articleWebsites.length)]} ${topics[Math.floor(Math.random() * topics.length)]}`,
-          { engines: ['bing news'], pageno: 1 },
+          `site:${selectedTopic.links[Math.floor(Math.random() * selectedTopic.links.length)]} ${selectedTopic.query[Math.floor(Math.random() * selectedTopic.query.length)]}`,
+          {
+            engines: ['bing news'],
+            pageno: 1,
+            language: 'en',
+          },
         )
       ).results;
     }

src/app/api/images/route.ts

@@ -49,7 +49,7 @@ export const POST = async (req: Request) => {
 
     if (body.chatModel?.provider === 'custom_openai') {
       llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
+        apiKey: getCustomOpenaiApiKey(),
         modelName: getCustomOpenaiModelName(),
         temperature: 0.7,
         configuration: {

src/app/api/search/route.ts

@@ -81,8 +81,7 @@ export const POST = async (req: Request) => {
     if (body.chatModel?.provider === 'custom_openai') {
       llm = new ChatOpenAI({
         modelName: body.chatModel?.name || getCustomOpenaiModelName(),
-        openAIApiKey:
-          body.chatModel?.customOpenAIKey || getCustomOpenaiApiKey(),
+        apiKey: body.chatModel?.customOpenAIKey || getCustomOpenaiApiKey(),
         temperature: 0.7,
         configuration: {
           baseURL:

src/app/api/suggestions/route.ts

@@ -48,7 +48,7 @@ export const POST = async (req: Request) => {
 
     if (body.chatModel?.provider === 'custom_openai') {
       llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
+        apiKey: getCustomOpenaiApiKey(),
         modelName: getCustomOpenaiModelName(),
         temperature: 0.7,
         configuration: {

src/app/api/videos/route.ts

@@ -49,7 +49,7 @@ export const POST = async (req: Request) => {
 
     if (body.chatModel?.provider === 'custom_openai') {
       llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
+        apiKey: getCustomOpenaiApiKey(),
         modelName: getCustomOpenaiModelName(),
         temperature: 0.7,
         configuration: {

src/app/api/weather/route.ts

@@ -1,6 +1,10 @@
 export const POST = async (req: Request) => {
   try {
-    const body: { lat: number; lng: number } = await req.json();
+    const body: {
+      lat: number;
+      lng: number;
+      measureUnit: 'Imperial' | 'Metric';
+    } = await req.json();
 
     if (!body.lat || !body.lng) {
       return Response.json(
@@ -12,7 +16,9 @@ export const POST = async (req: Request) => {
     }
 
     const res = await fetch(
-      `https://api.open-meteo.com/v1/forecast?latitude=${body.lat}&longitude=${body.lng}&current=weather_code,temperature_2m,is_day,relative_humidity_2m,wind_speed_10m&timezone=auto`,
+      `https://api.open-meteo.com/v1/forecast?latitude=${body.lat}&longitude=${body.lng}&current=weather_code,temperature_2m,is_day,relative_humidity_2m,wind_speed_10m&timezone=auto${
+        body.measureUnit === 'Metric' ? '' : '&temperature_unit=fahrenheit'
+      }${body.measureUnit === 'Metric' ? '' : '&wind_speed_unit=mph'}`,
     );
 
     const data = await res.json();
@@ -33,12 +39,16 @@ export const POST = async (req: Request) => {
       humidity: number;
       windSpeed: number;
       icon: string;
+      temperatureUnit: 'C' | 'F';
+      windSpeedUnit: 'm/s' | 'mph';
     } = {
       temperature: data.current.temperature_2m,
       condition: '',
       humidity: data.current.relative_humidity_2m,
       windSpeed: data.current.wind_speed_10m,
       icon: '',
+      temperatureUnit: body.measureUnit === 'Metric' ? 'C' : 'F',
+      windSpeedUnit: body.measureUnit === 'Metric' ? 'm/s' : 'mph',
     };
 
     const code = data.current.weather_code;

src/app/discover/page.tsx

@@ -4,6 +4,7 @@ import { Search } from 'lucide-react';
 import { useEffect, useState } from 'react';
 import Link from 'next/link';
 import { toast } from 'sonner';
+import { cn } from '@/lib/utils';
 
 interface Discover {
   title: string;
@@ -12,60 +13,66 @@ interface Discover {
   thumbnail: string;
 }
 
+const topics: { key: string; display: string }[] = [
+  {
+    display: 'Tech & Science',
+    key: 'tech',
+  },
+  {
+    display: 'Finance',
+    key: 'finance',
+  },
+  {
+    display: 'Art & Culture',
+    key: 'art',
+  },
+  {
+    display: 'Sports',
+    key: 'sports',
+  },
+  {
+    display: 'Entertainment',
+    key: 'entertainment',
+  },
+];
+
 const Page = () => {
   const [discover, setDiscover] = useState<Discover[] | null>(null);
   const [loading, setLoading] = useState(true);
+  const [activeTopic, setActiveTopic] = useState<string>(topics[0].key);
+
+  const fetchArticles = async (topic: string) => {
+    setLoading(true);
+    try {
+      const res = await fetch(`/api/discover?topic=${topic}`, {
+        method: 'GET',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+      });
+
+      const data = await res.json();
+
+      if (!res.ok) {
+        throw new Error(data.message);
+      }
+
+      data.blogs = data.blogs.filter((blog: Discover) => blog.thumbnail);
+
+      setDiscover(data.blogs);
+    } catch (err: any) {
+      console.error('Error fetching data:', err.message);
+      toast.error('Error fetching data');
+    } finally {
+      setLoading(false);
+    }
+  };
 
   useEffect(() => {
-    const fetchData = async () => {
-      try {
-        const res = await fetch(`/api/discover`, {
-          method: 'GET',
-          headers: {
-            'Content-Type': 'application/json',
-          },
-        });
+    fetchArticles(activeTopic);
+  }, [activeTopic]);
 
-        const data = await res.json();
-
-        if (!res.ok) {
-          throw new Error(data.message);
-        }
-
-        data.blogs = data.blogs.filter((blog: Discover) => blog.thumbnail);
-
-        setDiscover(data.blogs);
-      } catch (err: any) {
-        console.error('Error fetching data:', err.message);
-        toast.error('Error fetching data');
-      } finally {
-        setLoading(false);
-      }
-    };
-
-    fetchData();
-  }, []);
-
-  return loading ? (
-    <div className="flex flex-row items-center justify-center min-h-screen">
-      <svg
-        aria-hidden="true"
-        className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
-        viewBox="0 0 100 101"
-        fill="none"
-        xmlns="http://www.w3.org/2000/svg"
-      >
-        <path
-          d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
-          fill="currentColor"
-        />
-        <path
-          d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
-          fill="currentFill"
-        />
-      </svg>
-    </div>
-  ) : (
+  return (
     <>
       <div>
         <div className="flex flex-col pt-4">
@@ -76,35 +83,73 @@ const Page = () => {
           <hr className="border-t border-[#2B2C2C] my-4 w-full" />
         </div>
 
-        <div className="grid lg:grid-cols-3 sm:grid-cols-2 grid-cols-1 gap-4 pb-28 lg:pb-8 w-full justify-items-center lg:justify-items-start">
-          {discover &&
-            discover?.map((item, i) => (
-              <Link
-                href={`/?q=Summary: ${item.url}`}
-                key={i}
-                className="max-w-sm rounded-lg overflow-hidden bg-light-secondary dark:bg-dark-secondary hover:-translate-y-[1px] transition duration-200"
-                target="_blank"
-              >
-                <img
-                  className="object-cover w-full aspect-video"
-                  src={
-                    new URL(item.thumbnail).origin +
-                    new URL(item.thumbnail).pathname +
-                    `?id=${new URL(item.thumbnail).searchParams.get('id')}`
-                  }
-                  alt={item.title}
-                />
-                <div className="px-6 py-4">
-                  <div className="font-bold text-lg mb-2">
-                    {item.title.slice(0, 100)}...
-                  </div>
-                  <p className="text-black-70 dark:text-white/70 text-sm">
-                    {item.content.slice(0, 100)}...
-                  </p>
-                </div>
-              </Link>
-            ))}
+        <div className="flex flex-row items-center space-x-2 overflow-x-auto">
+          {topics.map((t, i) => (
+            <div
+              key={i}
+              className={cn(
+                'border-[0.1px] rounded-full text-sm px-3 py-1 text-nowrap transition duration-200 cursor-pointer',
+                activeTopic === t.key
+                  ? 'text-cyan-300 bg-cyan-300/30 border-cyan-300/60'
+                  : 'border-white/30 text-white/70 hover:text-white hover:border-white/40 hover:bg-white/5',
+              )}
+              onClick={() => setActiveTopic(t.key)}
+            >
+              <span>{t.display}</span>
+            </div>
+          ))}
         </div>
+
+        {loading ? (
+          <div className="flex flex-row items-center justify-center min-h-screen">
+            <svg
+              aria-hidden="true"
+              className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
+              viewBox="0 0 100 101"
+              fill="none"
+              xmlns="http://www.w3.org/2000/svg"
+            >
+              <path
+                d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
+                fill="currentColor"
+              />
+              <path
+                d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
+                fill="currentFill"
+              />
+            </svg>
+          </div>
+        ) : (
+          <div className="grid lg:grid-cols-3 sm:grid-cols-2 grid-cols-1 gap-4 pb-28 pt-5 lg:pb-8 w-full justify-items-center lg:justify-items-start">
+            {discover &&
+              discover?.map((item, i) => (
+                <Link
+                  href={`/?q=Summary: ${item.url}`}
+                  key={i}
+                  className="max-w-sm rounded-lg overflow-hidden bg-light-secondary dark:bg-dark-secondary hover:-translate-y-[1px] transition duration-200"
+                  target="_blank"
+                >
+                  <img
+                    className="object-cover w-full aspect-video"
+                    src={
+                      new URL(item.thumbnail).origin +
+                      new URL(item.thumbnail).pathname +
+                      `?id=${new URL(item.thumbnail).searchParams.get('id')}`
+                    }
+                    alt={item.title}
+                  />
+                  <div className="px-6 py-4">
+                    <div className="font-bold text-lg mb-2">
+                      {item.title.slice(0, 100)}...
+                    </div>
+                    <p className="text-black-70 dark:text-white/70 text-sm">
+                      {item.content.slice(0, 100)}...
+                    </p>
+                  </div>
+                </Link>
+              ))}
+          </div>
+        )}
       </div>
     </>
   );

src/app/settings/page.tsx

@@ -148,6 +148,9 @@ const Page = () => {
   const [automaticImageSearch, setAutomaticImageSearch] = useState(false);
   const [automaticVideoSearch, setAutomaticVideoSearch] = useState(false);
   const [systemInstructions, setSystemInstructions] = useState<string>('');
+  const [measureUnit, setMeasureUnit] = useState<'Imperial' | 'Metric'>(
+    'Metric',
+  );
   const [savingStates, setSavingStates] = useState<Record<string, boolean>>({});
 
   useEffect(() => {
@@ -210,6 +213,10 @@ const Page = () => {
 
       setSystemInstructions(localStorage.getItem('systemInstructions')!);
 
+      setMeasureUnit(
+        localStorage.getItem('measureUnit')! as 'Imperial' | 'Metric',
+      );
+
       setIsLoading(false);
     };
 
@@ -368,6 +375,8 @@ const Page = () => {
         localStorage.setItem('embeddingModel', value);
       } else if (key === 'systemInstructions') {
         localStorage.setItem('systemInstructions', value);
+      } else if (key === 'measureUnit') {
+        localStorage.setItem('measureUnit', value.toString());
       }
     } catch (err) {
       console.error('Failed to save:', err);
@@ -416,13 +425,35 @@ const Page = () => {
       ) : (
         config && (
           <div className="flex flex-col space-y-6 pb-28 lg:pb-8">
-            <SettingsSection title="Appearance">
+            <SettingsSection title="Preferences">
               <div className="flex flex-col space-y-1">
                 <p className="text-black/70 dark:text-white/70 text-sm">
                   Theme
                 </p>
                 <ThemeSwitcher />
               </div>
+              <div className="flex flex-col space-y-1">
+                <p className="text-black/70 dark:text-white/70 text-sm">
+                  Measurement Units
+                </p>
+                <Select
+                  value={measureUnit ?? undefined}
+                  onChange={(e) => {
+                    setMeasureUnit(e.target.value as 'Imperial' | 'Metric');
+                    saveConfig('measureUnit', e.target.value);
+                  }}
+                  options={[
+                    {
+                      label: 'Metric',
+                      value: 'Metric',
+                    },
+                    {
+                      label: 'Imperial',
+                      value: 'Imperial',
+                    },
+                  ]}
+                />
+              </div>
             </SettingsSection>
 
             <SettingsSection title="Automatic Search">
@@ -516,7 +547,7 @@ const Page = () => {
             <SettingsSection title="System Instructions">
               <div className="flex flex-col space-y-4">
                 <Textarea
-                  value={systemInstructions}
+                  value={systemInstructions ?? undefined}
                   isSaving={savingStates['systemInstructions']}
                   onChange={(e) => {
                     setSystemInstructions(e.target.value);

src/components/ChatWindow.tsx

@@ -354,7 +354,11 @@ const ChatWindow = ({ id }: { id?: string }) => {
     }
   }, [isMessagesLoaded, isConfigReady]);
 
-  const sendMessage = async (message: string, messageId?: string) => {
+  const sendMessage = async (
+    message: string,
+    messageId?: string,
+    rewrite = false,
+  ) => {
     if (loading) return;
     if (!isConfigReady) {
       toast.error('Cannot send message before the configuration is ready');
@@ -403,8 +407,18 @@ const ChatWindow = ({ id }: { id?: string }) => {
             },
           ]);
           added = true;
+          setMessageAppeared(true);
+        } else {
+          setMessages((prev) =>
+            prev.map((message) => {
+              if (message.messageId === data.messageId) {
+                return { ...message, sources: sources };
+              }
+
+              return message;
+            }),
+          );
         }
-        setMessageAppeared(true);
       }
 
       if (data.type === 'message') {
@@ -421,20 +435,20 @@ const ChatWindow = ({ id }: { id?: string }) => {
             },
           ]);
           added = true;
+        } else {
+          setMessages((prev) =>
+            prev.map((message) => {
+              if (message.messageId === data.messageId) {
+                return { ...message, content: message.content + data.data };
+              }
+
+              return message;
+            }),
+          );
+
+          recievedMessage += data.data;
+          setMessageAppeared(true);
         }
-
-        setMessages((prev) =>
-          prev.map((message) => {
-            if (message.messageId === data.messageId) {
-              return { ...message, content: message.content + data.data };
-            }
-
-            return message;
-          }),
-        );
-
-        recievedMessage += data.data;
-        setMessageAppeared(true);
       }
 
       if (data.type === 'messageEnd') {
@@ -482,6 +496,8 @@ const ChatWindow = ({ id }: { id?: string }) => {
       }
     };
 
+    const messageIndex = messages.findIndex((m) => m.messageId === messageId);
+
     const res = await fetch('/api/chat', {
       method: 'POST',
       headers: {
@@ -498,7 +514,9 @@ const ChatWindow = ({ id }: { id?: string }) => {
         files: fileIds,
         focusMode: focusMode,
         optimizationMode: optimizationMode,
-        history: chatHistory,
+        history: rewrite
+          ? chatHistory.slice(0, messageIndex === -1 ? undefined : messageIndex)
+          : chatHistory,
         chatModel: {
           name: chatModelProvider.name,
           provider: chatModelProvider.provider,
@@ -552,7 +570,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
       return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
     });
 
-    sendMessage(message.content, message.messageId);
+    sendMessage(message.content, message.messageId, true);
   };
 
   useEffect(() => {

src/components/MessageBox.tsx

@@ -20,9 +20,19 @@ import SearchImages from './SearchImages';
 import SearchVideos from './SearchVideos';
 import { useSpeech } from 'react-text-to-speech';
 import ThinkBox from './ThinkBox';
+import { Prism as SyntaxHighlighter } from 'react-syntax-highlighter';
+import { SyntaxHighlightedCode } from './SyntaxHighlightedCode';
 
-const ThinkTagProcessor = ({ children }: { children: React.ReactNode }) => {
-  return <ThinkBox content={children as string} />;
+const ThinkTagProcessor = ({
+  children,
+  thinkingEnded,
+}: {
+  children: React.ReactNode;
+  thinkingEnded: boolean;
+}) => {
+  return (
+    <ThinkBox content={children as string} thinkingEnded={thinkingEnded} />
+  );
 };
 
 const MessageBox = ({
@@ -46,6 +56,7 @@ const MessageBox = ({
 }) => {
   const [parsedMessage, setParsedMessage] = useState(message.content);
   const [speechMessage, setSpeechMessage] = useState(message.content);
+  const [thinkingEnded, setThinkingEnded] = useState(false);
 
   useEffect(() => {
     const citationRegex = /\[([^\]]+)\]/g;
@@ -61,6 +72,10 @@ const MessageBox = ({
       }
     }
 
+    if (message.role === 'assistant' && message.content.includes('</think>')) {
+      setThinkingEnded(true);
+    }
+
     if (
       message.role === 'assistant' &&
       message?.sources &&
@@ -88,7 +103,7 @@ const MessageBox = ({
                 if (url) {
                   return `<a href="${url}" target="_blank" className="bg-light-secondary dark:bg-dark-secondary px-1 rounded ml-1 no-underline text-xs text-black/70 dark:text-white/70 relative">${numStr}</a>`;
                 } else {
-                  return `[${numStr}]`;
+                  return ``;
                 }
               })
               .join('');
@@ -99,6 +114,14 @@ const MessageBox = ({
       );
       setSpeechMessage(message.content.replace(regex, ''));
       return;
+    } else if (
+      message.role === 'assistant' &&
+      message?.sources &&
+      message.sources.length === 0
+    ) {
+      setParsedMessage(processedMessage.replace(regex, ''));
+      setSpeechMessage(message.content.replace(regex, ''));
+      return;
     }
 
     setSpeechMessage(message.content.replace(regex, ''));
@@ -111,6 +134,12 @@ const MessageBox = ({
     overrides: {
       think: {
         component: ThinkTagProcessor,
+        props: {
+          thinkingEnded: thinkingEnded,
+        },
+      },
+      code: {
+        component: SyntaxHighlightedCode,
       },
     },
   };

src/components/SyntaxHighlightedCode.tsx

@@ -0,0 +1,30 @@
+import React, { HTMLProps } from 'react';
+import { Prism as SyntaxHighlighter } from 'react-syntax-highlighter';
+import {
+  coldarkDark,
+  coldarkCold,
+} from 'react-syntax-highlighter/dist/esm/styles/prism';
+
+export const SyntaxHighlightedCode = (props: HTMLProps<HTMLDivElement>) => {
+  const isDarkTheme = document.documentElement.classList.contains('dark');
+  const language = props.className?.match(/lang-([a-zA-Z0-9_-]+)/)![1];
+
+  return language ? (
+    <div className="not-prose">
+      <SyntaxHighlighter
+        customStyle={{
+          margin: 0,
+          backgroundColor: isDarkTheme ? '#111111' : '#f3f3ee',
+        }}
+        language={language}
+        style={isDarkTheme ? coldarkDark : coldarkCold}
+      >
+        {props.children as string}
+      </SyntaxHighlighter>
+    </div>
+  ) : (
+    <code className="inline bg-light-100 dark:bg-dark-100 px-2 py-1 rounded-lg text-sm not-prose">
+      {props.children}
+    </code>
+  );
+};

src/components/ThinkBox.tsx

@@ -1,15 +1,23 @@
 'use client';
 
-import { useState } from 'react';
-import { cn } from '@/lib/utils';
+import { useEffect, useState } from 'react';
 import { ChevronDown, ChevronUp, BrainCircuit } from 'lucide-react';
 
 interface ThinkBoxProps {
   content: string;
+  thinkingEnded: boolean;
 }
 
-const ThinkBox = ({ content }: ThinkBoxProps) => {
-  const [isExpanded, setIsExpanded] = useState(false);
+const ThinkBox = ({ content, thinkingEnded }: ThinkBoxProps) => {
+  const [isExpanded, setIsExpanded] = useState(true);
+
+  useEffect(() => {
+    if (thinkingEnded) {
+      setIsExpanded(false);
+    } else {
+      setIsExpanded(true);
+    }
+  }, [thinkingEnded]);
 
   return (
     <div className="my-4 bg-light-secondary/50 dark:bg-dark-secondary/50 rounded-xl border border-light-200 dark:border-dark-200 overflow-hidden">

src/components/WeatherWidget.tsx

@@ -9,7 +9,10 @@ const WeatherWidget = () => {
     humidity: 0,
     windSpeed: 0,
     icon: '',
+    temperatureUnit: 'C',
+    windSpeedUnit: 'm/s',
   });
+
   const [loading, setLoading] = useState(true);
 
   useEffect(() => {
@@ -73,6 +76,7 @@ const WeatherWidget = () => {
         body: JSON.stringify({
           lat: location.latitude,
           lng: location.longitude,
+          measureUnit: localStorage.getItem('measureUnit') ?? 'Metric',
         }),
       });
 
@@ -91,6 +95,8 @@ const WeatherWidget = () => {
         humidity: data.humidity,
         windSpeed: data.windSpeed,
         icon: data.icon,
+        temperatureUnit: data.temperatureUnit,
+        windSpeedUnit: data.windSpeedUnit,
       });
       setLoading(false);
     });
@@ -125,7 +131,7 @@ const WeatherWidget = () => {
               className="h-10 w-auto"
             />
             <span className="text-base font-semibold text-black dark:text-white">
-              {data.temperature}°C
+              {data.temperature}°{data.temperatureUnit}
             </span>
           </div>
           <div className="flex flex-col justify-between flex-1 h-full py-1">
@@ -135,7 +141,7 @@ const WeatherWidget = () => {
               </span>
               <span className="flex items-center text-xs text-black/60 dark:text-white/60">
                 <Wind className="w-3 h-3 mr-1" />
-                {data.windSpeed} km/h
+                {data.windSpeed} {data.windSpeedUnit}
               </span>
             </div>
             <span className="text-xs text-black/60 dark:text-white/60 mt-1">

src/lib/chains/imageSearchAgent.ts

@@ -3,32 +3,18 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { PromptTemplate } from '@langchain/core/prompts';
+import { ChatPromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { searchSearxng } from '../searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import LineOutputParser from '../outputParsers/lineOutputParser';
 
 const imageSearchChainPrompt = `
 You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search the web for images.
 You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
-
-Example:
-1. Follow up question: What is a cat?
-Rephrased: A cat
-
-2. Follow up question: What is a car? How does it works?
-Rephrased: Car working
-
-3. Follow up question: How does an AC work?
-Rephrased: AC working
-
-Conversation:
-{chat_history}
-
-Follow up question: {query}
-Rephrased question:
+Output only the rephrased query wrapped in an XML <query> element. Do not include any explanation or additional text.
 `;
 
 type ImageSearchChainInput = {
@@ -54,12 +40,39 @@ const createImageSearchChain = (llm: BaseChatModel) => {
         return input.query;
       },
     }),
-    PromptTemplate.fromTemplate(imageSearchChainPrompt),
+    ChatPromptTemplate.fromMessages([
+      ['system', imageSearchChainPrompt],
+      [
+        'user',
+        '<conversation>\n</conversation>\n<follow_up>\nWhat is a cat?\n</follow_up>',
+      ],
+      ['assistant', '<query>A cat</query>'],
+
+      [
+        'user',
+        '<conversation>\n</conversation>\n<follow_up>\nWhat is a car? How does it work?\n</follow_up>',
+      ],
+      ['assistant', '<query>Car working</query>'],
+      [
+        'user',
+        '<conversation>\n</conversation>\n<follow_up>\nHow does an AC work?\n</follow_up>',
+      ],
+      ['assistant', '<query>AC working</query>'],
+      [
+        'user',
+        '<conversation>{chat_history}</conversation>\n<follow_up>\n{query}\n</follow_up>',
+      ],
+    ]),
     llm,
     strParser,
     RunnableLambda.from(async (input: string) => {
-      input = input.replace(/<think>.*?<\/think>/g, '');
+      const queryParser = new LineOutputParser({
+        key: 'query',
+      });
 
+      return await queryParser.parse(input);
+    }),
+    RunnableLambda.from(async (input: string) => {
       const res = await searchSearxng(input, {
         engines: ['bing images', 'google images'],
       });

src/lib/chains/videoSearchAgent.ts

@@ -3,33 +3,19 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { PromptTemplate } from '@langchain/core/prompts';
+import { ChatPromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import { searchSearxng } from '../searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import LineOutputParser from '../outputParsers/lineOutputParser';
 
-const VideoSearchChainPrompt = `
-  You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search Youtube for videos.
-  You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
-  
-  Example:
-  1. Follow up question: How does a car work?
-  Rephrased: How does a car work?
-  
-  2. Follow up question: What is the theory of relativity?
-  Rephrased: What is theory of relativity
-  
-  3. Follow up question: How does an AC work?
-  Rephrased: How does an AC work
-  
-  Conversation:
-  {chat_history}
-  
-  Follow up question: {query}
-  Rephrased question:
-  `;
+const videoSearchChainPrompt = `
+You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search Youtube for videos.
+You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
+Output only the rephrased query wrapped in an XML <query> element. Do not include any explanation or additional text.
+`;
 
 type VideoSearchChainInput = {
   chat_history: BaseMessage[];
@@ -55,12 +41,37 @@ const createVideoSearchChain = (llm: BaseChatModel) => {
         return input.query;
       },
     }),
-    PromptTemplate.fromTemplate(VideoSearchChainPrompt),
+    ChatPromptTemplate.fromMessages([
+      ['system', videoSearchChainPrompt],
+      [
+        'user',
+        '<conversation>\n</conversation>\n<follow_up>\nHow does a car work?\n</follow_up>',
+      ],
+      ['assistant', '<query>How does a car work?</query>'],
+      [
+        'user',
+        '<conversation>\n</conversation>\n<follow_up>\nWhat is the theory of relativity?\n</follow_up>',
+      ],
+      ['assistant', '<query>Theory of relativity</query>'],
+      [
+        'user',
+        '<conversation>\n</conversation>\n<follow_up>\nHow does an AC work?\n</follow_up>',
+      ],
+      ['assistant', '<query>AC working</query>'],
+      [
+        'user',
+        '<conversation>{chat_history}</conversation>\n<follow_up>\n{query}\n</follow_up>',
+      ],
+    ]),
     llm,
     strParser,
     RunnableLambda.from(async (input: string) => {
-      input = input.replace(/<think>.*?<\/think>/g, '');
-
+      const queryParser = new LineOutputParser({
+        key: 'query',
+      });
+      return await queryParser.parse(input);
+    }),
+    RunnableLambda.from(async (input: string) => {
       const res = await searchSearxng(input, {
         engines: ['youtube'],
       });
@@ -92,8 +103,8 @@ const handleVideoSearch = (
   input: VideoSearchChainInput,
   llm: BaseChatModel,
 ) => {
-  const VideoSearchChain = createVideoSearchChain(llm);
-  return VideoSearchChain.invoke(input);
+  const videoSearchChain = createVideoSearchChain(llm);
+  return videoSearchChain.invoke(input);
 };
 
 export default handleVideoSearch;

src/lib/prompts/webSearch.ts

@@ -65,12 +65,16 @@ export const webSearchResponsePrompt = `
     You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
 
     Your task is to provide answers that are:
-    - **Informative and relevant**: Thoroughly address the user's query using the given context.
+    - **Informative and relevant**: Thoroughly address the user's query using the given  search results.
     - **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
     - **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
-    - **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
+    - **Cited and credible**: Use inline citations with [number] notation to refer to the  search results source(s) for each fact or detail included.
     - **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
 
+    You have access to the following tools which you have to use to answer the user:
+    1. **search_web**: Use this tool to search the web for information to answer the user's question.
+    2. **summarize**: Use this tool to summarize a link.
+    
     ### Formatting Instructions
     - **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
     - **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
@@ -80,13 +84,14 @@ export const webSearchResponsePrompt = `
     - **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
 
     ### Citation Requirements
-    - Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
+    - Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the search results
     - Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
-    - Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
+    - Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided  search results.
     - Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
-    - Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
+    - Always prioritize credibility and accuracy by linking all statements back to their respective  search results sources.
     - Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
-
+    - Never return references to the citation or sources yourself, they're returned to the user internally.
+    
     ### Special Instructions
     - If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
     - If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
@@ -99,12 +104,8 @@ export const webSearchResponsePrompt = `
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.
     - Follow with detailed sections under clear headings, covering all aspects of the query if possible.
-    - Provide explanations or historical context as needed to enhance understanding.
+    - Provide explanations or historical  search results as needed to enhance understanding.
     - End with a conclusion or overall perspective if relevant.
 
-    <context>
-    {context}
-    </context>
-
     Current date & time in ISO format (UTC timezone) is: {date}.
 `;

src/lib/prompts/writingAssistant.ts

@@ -10,8 +10,4 @@ However you do not need to cite it using the same number. You can use different
 ### User instructions
 These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
 {systemInstructions}
-
-<context>
-{context}
-</context>
 `;

src/lib/providers/aimlapi.ts

@@ -38,7 +38,7 @@ export const loadAimlApiChatModels = async () => {
         chatModels[model.id] = {
           displayName: model.name || model.id,
           model: new ChatOpenAI({
-            openAIApiKey: apiKey,
+            apiKey: apiKey,
             modelName: model.id,
             temperature: 0.7,
             configuration: {
@@ -76,7 +76,7 @@ export const loadAimlApiEmbeddingModels = async () => {
         embeddingModels[model.id] = {
           displayName: model.name || model.id,
           model: new OpenAIEmbeddings({
-            openAIApiKey: apiKey,
+            apiKey: apiKey,
             modelName: model.id,
             configuration: {
               baseURL: API_URL,

src/lib/providers/deepseek.ts

@@ -31,7 +31,7 @@ export const loadDeepseekChatModels = async () => {
       chatModels[model.key] = {
         displayName: model.displayName,
         model: new ChatOpenAI({
-          openAIApiKey: deepseekApiKey,
+          apiKey: deepseekApiKey,
           modelName: model.key,
           temperature: 0.7,
           configuration: {

src/lib/providers/gemini.ts

@@ -14,16 +14,12 @@ import { Embeddings } from '@langchain/core/embeddings';
 
 const geminiChatModels: Record<string, string>[] = [
   {
-    displayName: 'Gemini 2.5 Flash Preview 05-20',
-    key: 'gemini-2.5-flash-preview-05-20',
+    displayName: 'Gemini 2.5 Flash',
+    key: 'gemini-2.5-flash',
   },
   {
-    displayName: 'Gemini 2.5 Pro Preview',
-    key: 'gemini-2.5-pro-preview-05-06',
-  },
-  {
-    displayName: 'Gemini 2.5 Pro Experimental',
-    key: 'gemini-2.5-pro-preview-05-06',
+    displayName: 'Gemini 2.5 Pro',
+    key: 'gemini-2.5-pro',
   },
   {
     displayName: 'Gemini 2.0 Flash',
@@ -75,7 +71,7 @@ export const loadGeminiChatModels = async () => {
         displayName: model.displayName,
         model: new ChatGoogleGenerativeAI({
           apiKey: geminiApiKey,
-          modelName: model.key,
+          model: model.key,
           temperature: 0.7,
         }) as unknown as BaseChatModel,
       };
@@ -108,7 +104,7 @@ export const loadGeminiEmbeddingModels = async () => {
 
     return embeddingModels;
   } catch (err) {
-    console.error(`Error loading OpenAI embeddings models: ${err}`);
+    console.error(`Error loading Gemini embeddings models: ${err}`);
     return {};
   }
 };

src/lib/providers/groq.ts

@@ -1,4 +1,4 @@
-import { ChatOpenAI } from '@langchain/openai';
+import { ChatGroq } from '@langchain/groq';
 import { getGroqApiKey } from '../config';
 import { ChatModel } from '.';
 
@@ -28,13 +28,10 @@ export const loadGroqChatModels = async () => {
     groqChatModels.forEach((model: any) => {
       chatModels[model.id] = {
         displayName: model.id,
-        model: new ChatOpenAI({
-          openAIApiKey: groqApiKey,
-          modelName: model.id,
+        model: new ChatGroq({
+          apiKey: groqApiKey,
+          model: model.id,
           temperature: 0.7,
-          configuration: {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
         }) as unknown as BaseChatModel,
       };
     });

src/lib/providers/index.ts

@@ -118,7 +118,7 @@ export const getAvailableChatModelProviders = async () => {
           [customOpenAiModelName]: {
             displayName: customOpenAiModelName,
             model: new ChatOpenAI({
-              openAIApiKey: customOpenAiApiKey,
+              apiKey: customOpenAiApiKey,
               modelName: customOpenAiModelName,
               temperature: 0.7,
               configuration: {

src/lib/providers/lmstudio.ts

@@ -47,7 +47,7 @@ export const loadLMStudioChatModels = async () => {
       chatModels[model.id] = {
         displayName: model.name || model.id,
         model: new ChatOpenAI({
-          openAIApiKey: 'lm-studio',
+          apiKey: 'lm-studio',
           configuration: {
             baseURL: ensureV1Endpoint(endpoint),
           },
@@ -83,7 +83,7 @@ export const loadLMStudioEmbeddingsModels = async () => {
       embeddingsModels[model.id] = {
         displayName: model.name || model.id,
         model: new OpenAIEmbeddings({
-          openAIApiKey: 'lm-studio',
+          apiKey: 'lm-studio',
           configuration: {
             baseURL: ensureV1Endpoint(endpoint),
           },

src/lib/providers/ollama.ts

@@ -6,8 +6,8 @@ export const PROVIDER_INFO = {
   key: 'ollama',
   displayName: 'Ollama',
 };
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
-import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
+import { ChatOllama } from '@langchain/ollama';
+import { OllamaEmbeddings } from '@langchain/ollama';
 
 export const loadOllamaChatModels = async () => {
   const ollamaApiEndpoint = getOllamaApiEndpoint();

src/lib/providers/openai.ts

@@ -67,7 +67,7 @@ export const loadOpenAIChatModels = async () => {
       chatModels[model.key] = {
         displayName: model.displayName,
         model: new ChatOpenAI({
-          openAIApiKey: openaiApiKey,
+          apiKey: openaiApiKey,
           modelName: model.key,
           temperature: 0.7,
         }) as unknown as BaseChatModel,
@@ -93,7 +93,7 @@ export const loadOpenAIEmbeddingModels = async () => {
       embeddingModels[model.key] = {
         displayName: model.displayName,
         model: new OpenAIEmbeddings({
-          openAIApiKey: openaiApiKey,
+          apiKey: openaiApiKey,
           modelName: model.key,
         }) as unknown as Embeddings,
       };

src/lib/search/metaSearchAgent.ts

@@ -11,7 +11,12 @@ import {
   RunnableMap,
   RunnableSequence,
 } from '@langchain/core/runnables';
-import { BaseMessage } from '@langchain/core/messages';
+import {
+  AIMessage,
+  BaseMessage,
+  isAIMessage,
+  ToolMessage,
+} from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
 import LineListOutputParser from '../outputParsers/listLineOutputParser';
 import LineOutputParser from '../outputParsers/lineOutputParser';
@@ -24,6 +29,15 @@ import computeSimilarity from '../utils/computeSimilarity';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import { StreamEvent } from '@langchain/core/tracers/log_stream';
+import { DynamicStructuredTool, tool } from '@langchain/core/tools';
+import { MessagesAnnotation, StateGraph } from '@langchain/langgraph';
+import { z } from 'zod';
+import {
+  IterableReadableStream,
+  IterableReadableStreamInterface,
+} from '@langchain/core/utils/stream';
+import EventEmitter from 'node:events';
+import { BaseLanguageModel } from '@langchain/core/language_models/base';
 
 export interface MetaSearchAgentType {
   searchAndAnswer: (
@@ -47,11 +61,6 @@ interface Config {
   activeEngines: string[];
 }
 
-type BasicChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
 class MetaSearchAgent implements MetaSearchAgentType {
   private config: Config;
   private strParser = new StringOutputParser();
@@ -60,239 +69,6 @@ class MetaSearchAgent implements MetaSearchAgentType {
     this.config = config;
   }
 
-  private async createSearchRetrieverChain(llm: BaseChatModel) {
-    (llm as unknown as ChatOpenAI).temperature = 0;
-
-    return RunnableSequence.from([
-      PromptTemplate.fromTemplate(this.config.queryGeneratorPrompt),
-      llm,
-      this.strParser,
-      RunnableLambda.from(async (input: string) => {
-        const linksOutputParser = new LineListOutputParser({
-          key: 'links',
-        });
-
-        const questionOutputParser = new LineOutputParser({
-          key: 'question',
-        });
-
-        const links = await linksOutputParser.parse(input);
-        let question = this.config.summarizer
-          ? await questionOutputParser.parse(input)
-          : input;
-
-        if (question === 'not_needed') {
-          return { query: '', docs: [] };
-        }
-
-        if (links.length > 0) {
-          if (question.length === 0) {
-            question = 'summarize';
-          }
-
-          let docs: Document[] = [];
-
-          const linkDocs = await getDocumentsFromLinks({ links });
-
-          const docGroups: Document[] = [];
-
-          linkDocs.map((doc) => {
-            const URLDocExists = docGroups.find(
-              (d) =>
-                d.metadata.url === doc.metadata.url &&
-                d.metadata.totalDocs < 10,
-            );
-
-            if (!URLDocExists) {
-              docGroups.push({
-                ...doc,
-                metadata: {
-                  ...doc.metadata,
-                  totalDocs: 1,
-                },
-              });
-            }
-
-            const docIndex = docGroups.findIndex(
-              (d) =>
-                d.metadata.url === doc.metadata.url &&
-                d.metadata.totalDocs < 10,
-            );
-
-            if (docIndex !== -1) {
-              docGroups[docIndex].pageContent =
-                docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
-              docGroups[docIndex].metadata.totalDocs += 1;
-            }
-          });
-
-          await Promise.all(
-            docGroups.map(async (doc) => {
-              const res = await llm.invoke(`
-            You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the 
-            text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
-            If the query is \"summarize\", you should provide a detailed summary of the text. If the query is a specific question, you should answer it in the summary.
-            
-            - **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague.
-            - **Thorough and detailed**: Ensure that every key point from the text is captured and that the summary directly answers the query.
-            - **Not too lengthy, but detailed**: The summary should be informative but not excessively long. Focus on providing detailed information in a concise format.
-
-            The text will be shared inside the \`text\` XML tag, and the query inside the \`query\` XML tag.
-
-            <example>
-            1. \`<text>
-            Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers. 
-            It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications 
-            by using containers.
-            </text>
-
-            <query>
-            What is Docker and how does it work?
-            </query>
-
-            Response:
-            Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application 
-            deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in 
-            any environment. Released in 2013, Docker has transformed the way applications are built, deployed, and managed.
-            \`
-            2. \`<text>
-            The theory of relativity, or simply relativity, encompasses two interrelated theories of Albert Einstein: special relativity and general
-            relativity. However, the word "relativity" is sometimes used in reference to Galilean invariance. The term "theory of relativity" was based
-            on the expression "relative theory" used by Max Planck in 1906. The theory of relativity usually encompasses two interrelated theories by
-            Albert Einstein: special relativity and general relativity. Special relativity applies to all physical phenomena in the absence of gravity.
-            General relativity explains the law of gravitation and its relation to other forces of nature. It applies to the cosmological and astrophysical
-            realm, including astronomy.
-            </text>
-
-            <query>
-            summarize
-            </query>
-
-            Response:
-            The theory of relativity, developed by Albert Einstein, encompasses two main theories: special relativity and general relativity. Special
-            relativity applies to all physical phenomena in the absence of gravity, while general relativity explains the law of gravitation and its
-            relation to other forces of nature. The theory of relativity is based on the concept of "relative theory," as introduced by Max Planck in
-            1906. It is a fundamental theory in physics that has revolutionized our understanding of the universe.
-            \`
-            </example>
-
-            Everything below is the actual data you will be working with. Good luck!
-
-            <query>
-            ${question}
-            </query>
-
-            <text>
-            ${doc.pageContent}
-            </text>
-
-            Make sure to answer the query in the summary.
-          `);
-
-              const document = new Document({
-                pageContent: res.content as string,
-                metadata: {
-                  title: doc.metadata.title,
-                  url: doc.metadata.url,
-                },
-              });
-
-              docs.push(document);
-            }),
-          );
-
-          return { query: question, docs: docs };
-        } else {
-          question = question.replace(/<think>.*?<\/think>/g, '');
-
-          const res = await searchSearxng(question, {
-            language: 'en',
-            engines: this.config.activeEngines,
-          });
-
-          const documents = res.results.map(
-            (result) =>
-              new Document({
-                pageContent:
-                  result.content ||
-                  (this.config.activeEngines.includes('youtube')
-                    ? result.title
-                    : '') /* Todo: Implement transcript grabbing using Youtubei (source: https://www.npmjs.com/package/youtubei) */,
-                metadata: {
-                  title: result.title,
-                  url: result.url,
-                  ...(result.img_src && { img_src: result.img_src }),
-                },
-              }),
-          );
-
-          return { query: question, docs: documents };
-        }
-      }),
-    ]);
-  }
-
-  private async createAnsweringChain(
-    llm: BaseChatModel,
-    fileIds: string[],
-    embeddings: Embeddings,
-    optimizationMode: 'speed' | 'balanced' | 'quality',
-    systemInstructions: string,
-  ) {
-    return RunnableSequence.from([
-      RunnableMap.from({
-        systemInstructions: () => systemInstructions,
-        query: (input: BasicChainInput) => input.query,
-        chat_history: (input: BasicChainInput) => input.chat_history,
-        date: () => new Date().toISOString(),
-        context: RunnableLambda.from(async (input: BasicChainInput) => {
-          const processedHistory = formatChatHistoryAsString(
-            input.chat_history,
-          );
-
-          let docs: Document[] | null = null;
-          let query = input.query;
-
-          if (this.config.searchWeb) {
-            const searchRetrieverChain =
-              await this.createSearchRetrieverChain(llm);
-
-            const searchRetrieverResult = await searchRetrieverChain.invoke({
-              chat_history: processedHistory,
-              query,
-            });
-
-            query = searchRetrieverResult.query;
-            docs = searchRetrieverResult.docs;
-          }
-
-          const sortedDocs = await this.rerankDocs(
-            query,
-            docs ?? [],
-            fileIds,
-            embeddings,
-            optimizationMode,
-          );
-
-          return sortedDocs;
-        })
-          .withConfig({
-            runName: 'FinalSourceRetriever',
-          })
-          .pipe(this.processDocs),
-      }),
-      ChatPromptTemplate.fromMessages([
-        ['system', this.config.responsePrompt],
-        new MessagesPlaceholder('chat_history'),
-        ['user', '{query}'],
-      ]),
-      llm,
-      this.strParser,
-    ]).withConfig({
-      runName: 'FinalResponseGenerator',
-    });
-  }
-
   private async rerankDocs(
     query: string,
     docs: Document[],
@@ -432,36 +208,363 @@ class MetaSearchAgent implements MetaSearchAgentType {
   }
 
   private async handleStream(
-    stream: AsyncGenerator<StreamEvent, any, any>,
+    stream: AsyncIterable<[BaseMessage, Record<string, any>]>,
     emitter: eventEmitter,
   ) {
-    for await (const event of stream) {
-      if (
-        event.event === 'on_chain_end' &&
-        event.name === 'FinalSourceRetriever'
-      ) {
-        ``;
+    for await (const [message, _metadata] of stream) {
+      if (isAIMessage(message) && message.tool_calls?.length) {
+      } else if (isAIMessage(message) && message.content) {
         emitter.emit(
           'data',
-          JSON.stringify({ type: 'sources', data: event.data.output }),
+          JSON.stringify({ type: 'response', data: message.content }),
         );
       }
-      if (
-        event.event === 'on_chain_stream' &&
-        event.name === 'FinalResponseGenerator'
-      ) {
-        emitter.emit(
-          'data',
-          JSON.stringify({ type: 'response', data: event.data.chunk }),
-        );
-      }
-      if (
-        event.event === 'on_chain_end' &&
-        event.name === 'FinalResponseGenerator'
-      ) {
-        emitter.emit('end');
-      }
     }
+
+    emitter.emit('end');
+  }
+
+  getTools({
+    llm,
+    emitter,
+  }: {
+    llm: BaseLanguageModel;
+    emitter: EventEmitter;
+  }): DynamicStructuredTool[] {
+    const searchToolInputSchema = z.object({
+      searchMode: z
+        .enum(['normal', 'news'])
+        .describe(
+          'The search mode. If you want latest, live or articles on a topic use the news mode (you also need to use news mode for queries where you think there might be a newswall), otherwise for normal searches use normal mode.',
+        ),
+      query: z.string().describe('The query to search the web for.'),
+      links: z
+        .array(z.string().describe('The link to get data from'))
+        .describe(
+          'A list of links (if shared by user) to generate an answer from.',
+        ),
+    });
+
+    const searchTool = tool(
+      async (input: any) => {
+        if (input.links.length > 0) {
+          let docs: Document[] = [];
+
+          const linkDocs = await getDocumentsFromLinks({ links: input.links });
+
+          const docGroups: Document[] = [];
+
+          linkDocs.map((doc) => {
+            const URLDocExists = docGroups.find(
+              (d) =>
+                d.metadata.url === doc.metadata.url &&
+                d.metadata.totalDocs < 10,
+            );
+
+            if (!URLDocExists) {
+              docGroups.push({
+                ...doc,
+                metadata: {
+                  ...doc.metadata,
+                  totalDocs: 1,
+                },
+              });
+            }
+
+            const docIndex = docGroups.findIndex(
+              (d) =>
+                d.metadata.url === doc.metadata.url &&
+                d.metadata.totalDocs < 10,
+            );
+
+            if (docIndex !== -1) {
+              docGroups[docIndex].pageContent =
+                docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
+              docGroups[docIndex].metadata.totalDocs += 1;
+            }
+          });
+
+          const URLSourcePrompt = `
+              You are a web search question answerer, tasked with finding relevant information from web documents to answer questions. Your job is to extract and summarize the most relevant parts of a document that can help answer the user's query.
+
+              - **Find relevant sections**: Identify parts of the document that directly relate to the question
+              - **Extract key information**: Pull out specific facts, data, or explanations that answer the query
+              - **Summarize concisely**: Provide a focused summary of the relevant information found
+              - **Stay on topic**: Only include information that helps answer the specific question asked
+
+              The document text will be shared inside the \`text\` XML tag, and the query inside the \`query\` XML tag.
+
+              Extract and summarize the relevant information from the document that answers the query.
+              `;
+
+          const URLSourceChatPrompt = ChatPromptTemplate.fromMessages([
+            ['system', URLSourcePrompt],
+            [
+              'human',
+              `
+              <text>
+             Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers. 
+             It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications 
+             by using containers.
+             </text>
+ 
+             <query>
+             What is Docker and how does it work?
+             </query>
+              `,
+            ],
+            [
+              'assistant',
+              `
+                 Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application 
+             deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in 
+             any environment. Released in 2013, Docker has transformed the way applications are built, deployed, and managed.
+                `,
+            ],
+            [
+              'human',
+              `
+     <text>
+     Rising global temperatures are causing significant changes to Earth's climate system. Ice sheets in Greenland and Antarctica are melting at accelerated rates, contributing to sea level rise. Ocean temperatures are increasing, leading to coral bleaching events. Weather patterns are becoming more extreme, with increased frequency of hurricanes, droughts, and flooding. The Arctic is warming twice as fast as the global average, a phenomenon known as Arctic amplification.
+     </text>
+
+     <query>
+     What are the main effects of climate change on the environment?
+     </query>
+   `,
+            ],
+            [
+              'assistant',
+              `
+     Climate change is causing accelerated melting of ice sheets in Greenland and Antarctica, leading to sea level rise. Rising ocean temperatures are causing coral bleaching, while weather patterns are becoming more extreme with increased hurricanes, droughts, and flooding. The Arctic is experiencing particularly rapid warming at twice the global average rate.
+   `,
+            ],
+
+            [
+              'human',
+              `
+     <text>
+     Pasta is a staple food of Italian cuisine made from wheat flour and water. Common shapes include spaghetti, penne, and fusilli. It can be served with various sauces like marinara, alfredo, or pesto. Pasta cooking involves boiling water with salt and cooking until al dente. Different regions of Italy have their own traditional pasta dishes and preparation methods.
+     </text>
+
+     <query>
+     How do solar panels generate electricity?
+     </query>
+   `,
+            ],
+            [
+              'assistant',
+              `
+     The provided document about pasta and Italian cuisine does not contain information about how solar panels generate electricity. No relevant information found to answer the query.
+   `,
+            ],
+
+            [
+              'human',
+              `
+     <text>
+     {content}
+     </text>
+
+     <query>
+     {query}
+     </query>
+   `,
+            ],
+          ]);
+
+          await Promise.all(
+            docGroups.map(async (doc) => {
+              const formattedPrompt = await URLSourceChatPrompt.formatMessages({
+                query: input.query,
+                content: doc.pageContent,
+              });
+
+              const llmResponse = await llm.invoke(formattedPrompt);
+
+              const document = new Document({
+                pageContent: llmResponse.content as string,
+                metadata: {
+                  title: doc.metadata.title,
+                  url: doc.metadata.url,
+                },
+              });
+
+              docs.push(document);
+            }),
+          );
+
+          emitter.emit('data', JSON.stringify({ type: 'sources', data: docs }));
+
+          return this.processDocs(docs);
+        } else {
+          const res = await searchSearxng(input.query, {
+            language: 'en',
+            engines:
+              input.searchMode === 'news'
+                ? ['bing_news']
+                : this.config.activeEngines,
+          });
+
+          const documents = res.results.map(
+            (result) =>
+              new Document({
+                pageContent:
+                  result.content ||
+                  (this.config.activeEngines.includes('youtube')
+                    ? result.title
+                    : '') /* Todo: Implement transcript grabbing using Youtubei (source: https://www.npmjs.com/package/youtubei) */,
+                metadata: {
+                  title: result.title,
+                  url: result.url,
+                  ...(result.img_src && { img_src: result.img_src }),
+                },
+              }),
+          );
+
+          emitter.emit(
+            'data',
+            JSON.stringify({ type: 'sources', data: documents }),
+          );
+
+          return this.processDocs(documents);
+        }
+      },
+      {
+        name: 'search_web',
+        schema: searchToolInputSchema,
+        description: 'This tool allows you to search the web for information.',
+      },
+    );
+
+    const summarizeToolInputSchema = z.object({
+      links: z
+        .array(z.string().describe('The URL to summarize'))
+        .describe('The list of URLs to summarize'),
+    });
+
+    const summarizeTool = tool(
+      async (input: any) => {
+        if (input.links.length > 0) {
+          let docs: Document[] = [];
+
+          const linkDocs = await getDocumentsFromLinks({ links: input.links });
+          const docGroups: Document[] = [];
+
+          linkDocs.map((doc) => {
+            const URLDocExists = docGroups.find(
+              (d) =>
+                d.metadata.url === doc.metadata.url &&
+                d.metadata.totalDocs < 10,
+            );
+
+            if (!URLDocExists) {
+              docGroups.push({
+                ...doc,
+                metadata: {
+                  ...doc.metadata,
+                  totalDocs: 1,
+                },
+              });
+            }
+
+            const docIndex = docGroups.findIndex(
+              (d) =>
+                d.metadata.url === doc.metadata.url &&
+                d.metadata.totalDocs < 10,
+            );
+
+            if (docIndex !== -1) {
+              docGroups[docIndex].pageContent =
+                docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
+              docGroups[docIndex].metadata.totalDocs += 1;
+            }
+          });
+
+          const summarizerPrompt = `
+You are a document summarizer for map-reduce processing of website content. Extract all factual information, data, and key concepts from document chunks into concise paragraph summaries.
+
+**Requirements:**
+- **Extract facts only**: Include concrete information, data, numbers, dates, definitions, and explanations
+- **Ignore fluff**: Skip opinions, filler words, casual language, and non-essential content
+- **Be concise**: Create summaries shorter than the original content
+- **Paragraph format**: Write in plain paragraph form, no markdown, bullet points, or formatting
+- **Preserve specifics**: Keep exact numbers, dates, names, and technical terms
+
+The document text will be provided inside the \`text\` XML tag. Create a factual paragraph summary.
+`;
+
+          const summarizerChatPrompt = ChatPromptTemplate.fromMessages([
+            ['system', summarizerPrompt],
+            [
+              'human',
+              `
+    <text>
+    Docker is a platform-as-a-service product developed by Docker, Inc. and released in 2013 that uses OS-level virtualization to deliver software in containers. Containers are lightweight, portable packages that include application code, runtime, system tools, libraries, and settings.
+    </text>
+    `,
+            ],
+            [
+              'assistant',
+              `Docker is a platform-as-a-service product developed by Docker, Inc. in 2013 that uses OS-level virtualization to deliver software in containers. Containers are lightweight, portable packages containing application code, runtime, system tools, libraries, and settings.`,
+            ],
+            [
+              'human',
+              `
+    <text>
+    Hey there! So like, I was totally thinking about pasta the other day, you know? It's super amazing how there are different shapes. Spaghetti is long and thin, penne has tube shapes, and fusilli is all twisty! Haha, I just love Italian creativity with food.
+    </text>
+    `,
+            ],
+            [
+              'assistant',
+              `Pasta comes in various shapes including spaghetti which is long and thin, penne which has tube shapes, and fusilli which has a twisted form. These are Italian food products.`,
+            ],
+            [
+              'human',
+              `
+    <text>
+    {content}
+    </text>
+    `,
+            ],
+          ]);
+
+          await Promise.all(
+            docGroups.map(async (doc) => {
+              const formattedPrompt = await summarizerChatPrompt.formatMessages(
+                {
+                  content: doc.pageContent,
+                },
+              );
+
+              const llmResponse = await llm.invoke(formattedPrompt);
+
+              const document = new Document({
+                pageContent: llmResponse.content as string,
+                metadata: {
+                  title: doc.metadata.title,
+                  url: doc.metadata.url,
+                },
+              });
+
+              docs.push(document);
+            }),
+          );
+
+          emitter.emit('data', JSON.stringify({ type: 'sources', data: docs }));
+
+          return this.processDocs(docs);
+        }
+      },
+      {
+        name: 'summarize',
+        description: 'This tool can be used to summarize URL(s)',
+        schema: summarizeToolInputSchema,
+      },
+    );
+
+    return [searchTool, summarizeTool];
   }
 
   async searchAndAnswer(
@@ -475,21 +578,94 @@ class MetaSearchAgent implements MetaSearchAgentType {
   ) {
     const emitter = new eventEmitter();
 
-    const answeringChain = await this.createAnsweringChain(
+    const tools = this.getTools({
+      emitter,
       llm,
-      fileIds,
-      embeddings,
-      optimizationMode,
-      systemInstructions,
-    );
+    });
 
-    const stream = answeringChain.streamEvents(
+    const shouldContinue = (state: typeof MessagesAnnotation.State) => {
+      const lastMessage = state.messages[
+        state.messages.length - 1
+      ] as AIMessage;
+
+      if (lastMessage.tool_calls && lastMessage.tool_calls.length) {
+        return 'tools';
+      }
+
+      return '__end__';
+    };
+
+    const callTools = async (
+      state: typeof MessagesAnnotation.State,
+    ): Promise<Partial<typeof MessagesAnnotation.State>> => {
+      const lastMessage = state.messages[
+        state.messages.length - 1
+      ] as AIMessage;
+
+      const toolResults: BaseMessage[] = [];
+
+      if (lastMessage.tool_calls && lastMessage.tool_calls.length) {
+        await Promise.all(
+          lastMessage.tool_calls.map(async (t) => {
+            const toolToCall = tools.find((i) => i.name === t.name);
+
+            const result = await toolToCall?.invoke(t.args)!;
+
+            toolResults.push(
+              new ToolMessage({
+                content: result,
+                tool_call_id: t.id!,
+              }),
+            );
+          }),
+        );
+      }
+
+      return {
+        messages: [...toolResults],
+      };
+    };
+
+    const boundModel = llm.bindTools?.(tools)!;
+
+    const callModel = async (
+      state: typeof MessagesAnnotation.State,
+    ): Promise<Partial<typeof MessagesAnnotation.State>> => {
+      const { messages } = state;
+      const res = await boundModel?.invoke(messages);
+
+      return {
+        messages: [res!],
+      };
+    };
+
+    const workflow = new StateGraph(MessagesAnnotation)
+      .addNode('agent', callModel)
+      .addNode(
+        'tools',
+        RunnableLambda.from(callTools).withConfig({
+          tags: ['nostream'],
+        }),
+      )
+      .addEdge('__start__', 'agent')
+      .addEdge('tools', 'agent')
+      .addConditionalEdges('agent', shouldContinue);
+
+    const app = workflow.compile();
+
+    const filledPrompt = await PromptTemplate.fromTemplate(
+      this.config.responsePrompt,
+    ).format({
+      systemInstructions: systemInstructions,
+      date: Date.now(),
+    });
+
+    const stream = await app.stream(
       {
-        chat_history: history,
-        query: message,
+        messages: [['system', filledPrompt], ...history, ['human', message]],
       },
       {
-        version: 'v1',
+        streamMode: 'messages',
       },
     );
 

src/lib/utils/formatHistory.ts

@@ -1,8 +1,11 @@
-import { BaseMessage } from '@langchain/core/messages';
+import { BaseMessage, isAIMessage } from '@langchain/core/messages';
 
 const formatChatHistoryAsString = (history: BaseMessage[]) => {
   return history
-    .map((message) => `${message._getType()}: ${message.content}`)
+    .map(
+      (message) =>
+        `${isAIMessage(message) ? 'AI' : 'User'}: ${message.content}`,
+    )
     .join('\n');
 };