feat(app): add widgets

Merge master into canary

README.md

@@ -61,9 +61,9 @@ We'd also like to thank the following partners for their generous support:
 
 <table>
   <tr>
-    <td width="100" align="center">
+    <td>
       <a href="https://dashboard.exa.ai" target="_blank">
-        <img src=".assets/sponsers/exa.png" alt="Exa" width="80" height="80" style="border-radius: .75rem;" />
+        <img src=".assets/sponsers/exa.png" alt="Exa" style="max-width: 8rem; max-height: 8rem; border-radius: .75rem;" />
       </a>
     </td>
     <td>

package.json

@@ -16,13 +16,14 @@
     "@huggingface/transformers": "^3.7.5",
     "@iarna/toml": "^2.2.5",
     "@icons-pack/react-simple-icons": "^12.3.0",
-    "@langchain/anthropic": "^1.0.0",
-    "@langchain/community": "^1.0.0",
-    "@langchain/core": "^1.0.1",
-    "@langchain/google-genai": "^1.0.0",
-    "@langchain/groq": "^1.0.0",
-    "@langchain/ollama": "^1.0.0",
-    "@langchain/openai": "^1.0.0",
+    "@langchain/anthropic": "^1.0.1",
+    "@langchain/community": "^1.0.3",
+    "@langchain/core": "^1.0.5",
+    "@langchain/google-genai": "^1.0.1",
+    "@langchain/groq": "^1.0.1",
+    "@langchain/langgraph": "^1.0.1",
+    "@langchain/ollama": "^1.0.1",
+    "@langchain/openai": "^1.1.1",
     "@langchain/textsplitters": "^1.0.0",
     "@tailwindcss/typography": "^0.5.12",
     "axios": "^1.8.3",
@@ -33,7 +34,7 @@
     "framer-motion": "^12.23.24",
     "html-to-text": "^9.0.5",
     "jspdf": "^3.0.1",
-    "langchain": "^1.0.1",
+    "langchain": "^1.0.4",
     "lucide-react": "^0.363.0",
     "mammoth": "^1.9.1",
     "markdown-to-jsx": "^7.7.2",
@@ -48,7 +49,7 @@
     "tailwind-merge": "^2.2.2",
     "winston": "^3.17.0",
     "yet-another-react-lightbox": "^3.17.2",
-    "zod": "^3.22.4"
+    "zod": "^4.1.12"
   },
   "devDependencies": {
     "@types/better-sqlite3": "^7.6.12",

src/app/api/images/route.ts

@@ -1,4 +1,4 @@
-import handleImageSearch from '@/lib/chains/imageSearchAgent';
+import searchImages from '@/lib/agents/media/image';
 import ModelRegistry from '@/lib/models/registry';
 import { ModelWithProvider } from '@/lib/models/types';
 import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
@@ -13,6 +13,13 @@ export const POST = async (req: Request) => {
   try {
     const body: ImageSearchBody = await req.json();
 
+    const registry = new ModelRegistry();
+
+    const llm = await registry.loadChatModel(
+      body.chatModel.providerId,
+      body.chatModel.key,
+    );
+
     const chatHistory = body.chatHistory
       .map((msg: any) => {
         if (msg.role === 'user') {
@@ -23,16 +30,9 @@ export const POST = async (req: Request) => {
       })
       .filter((msg) => msg !== undefined) as BaseMessage[];
 
-    const registry = new ModelRegistry();
-
-    const llm = await registry.loadChatModel(
-      body.chatModel.providerId,
-      body.chatModel.key,
-    );
-
-    const images = await handleImageSearch(
+    const images = await searchImages(
       {
-        chat_history: chatHistory,
+        chatHistory: chatHistory,
         query: body.query,
       },
       llm,

src/app/api/search/route.ts

@@ -30,12 +30,6 @@ export const POST = async (req: Request) => {
     body.optimizationMode = body.optimizationMode || 'balanced';
     body.stream = body.stream || false;
 
-    const history: BaseMessage[] = body.history.map((msg) => {
-      return msg[0] === 'human'
-        ? new HumanMessage({ content: msg[1] })
-        : new AIMessage({ content: msg[1] });
-    });
-
     const registry = new ModelRegistry();
 
     const [llm, embeddings] = await Promise.all([
@@ -46,6 +40,12 @@ export const POST = async (req: Request) => {
       ),
     ]);
 
+    const history: BaseMessage[] = body.history.map((msg) => {
+      return msg[0] === 'human'
+        ? new HumanMessage({ content: msg[1] })
+        : new AIMessage({ content: msg[1] });
+    });
+
     const searchHandler: MetaSearchAgentType = searchHandlers[body.focusMode];
 
     if (!searchHandler) {
@@ -128,7 +128,7 @@ export const POST = async (req: Request) => {
 
           try {
             controller.close();
-          } catch (error) {}
+          } catch (error) { }
         });
 
         emitter.on('data', (data: string) => {

src/app/api/suggestions/route.ts

@@ -1,7 +1,6 @@
-import generateSuggestions from '@/lib/chains/suggestionGeneratorAgent';
+import generateSuggestions from '@/lib/agents/suggestions';
 import ModelRegistry from '@/lib/models/registry';
 import { ModelWithProvider } from '@/lib/models/types';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
 import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
 
 interface SuggestionsGenerationBody {
@@ -13,6 +12,13 @@ export const POST = async (req: Request) => {
   try {
     const body: SuggestionsGenerationBody = await req.json();
 
+    const registry = new ModelRegistry();
+
+    const llm = await registry.loadChatModel(
+      body.chatModel.providerId,
+      body.chatModel.key,
+    );
+
     const chatHistory = body.chatHistory
       .map((msg: any) => {
         if (msg.role === 'user') {
@@ -23,16 +29,9 @@ export const POST = async (req: Request) => {
       })
       .filter((msg) => msg !== undefined) as BaseMessage[];
 
-    const registry = new ModelRegistry();
-
-    const llm = await registry.loadChatModel(
-      body.chatModel.providerId,
-      body.chatModel.key,
-    );
-
     const suggestions = await generateSuggestions(
       {
-        chat_history: chatHistory,
+        chatHistory,
       },
       llm,
     );

src/app/api/videos/route.ts

@@ -1,4 +1,4 @@
-import handleVideoSearch from '@/lib/chains/videoSearchAgent';
+import handleVideoSearch from '@/lib/agents/media/video';
 import ModelRegistry from '@/lib/models/registry';
 import { ModelWithProvider } from '@/lib/models/types';
 import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
@@ -13,6 +13,13 @@ export const POST = async (req: Request) => {
   try {
     const body: VideoSearchBody = await req.json();
 
+    const registry = new ModelRegistry();
+
+    const llm = await registry.loadChatModel(
+      body.chatModel.providerId,
+      body.chatModel.key,
+    );
+
     const chatHistory = body.chatHistory
       .map((msg: any) => {
         if (msg.role === 'user') {
@@ -23,16 +30,9 @@ export const POST = async (req: Request) => {
       })
       .filter((msg) => msg !== undefined) as BaseMessage[];
 
-    const registry = new ModelRegistry();
-
-    const llm = await registry.loadChatModel(
-      body.chatModel.providerId,
-      body.chatModel.key,
-    );
-
     const videos = await handleVideoSearch(
       {
-        chat_history: chatHistory,
+        chatHistory: chatHistory,
         query: body.query,
       },
       llm,

src/components/ChatWindow.tsx

@@ -8,7 +8,6 @@ import { Settings } from 'lucide-react';
 import Link from 'next/link';
 import NextError from 'next/error';
 import { useChat } from '@/lib/hooks/useChat';
-import Loader from './ui/Loader';
 import SettingsButtonMobile from './Settings/SettingsButtonMobile';
 
 export interface BaseMessage {
@@ -52,7 +51,7 @@ export interface File {
 }
 
 const ChatWindow = () => {
-  const { hasError, isReady, notFound, messages } = useChat();
+  const { hasError, notFound, messages } = useChat();
   if (hasError) {
     return (
       <div className="relative">
@@ -68,24 +67,18 @@ const ChatWindow = () => {
     );
   }
 
-  return isReady ? (
-    notFound ? (
-      <NextError statusCode={404} />
-    ) : (
-      <div>
-        {messages.length > 0 ? (
-          <>
-            <Navbar />
-            <Chat />
-          </>
-        ) : (
-          <EmptyChat />
-        )}
-      </div>
-    )
+  return notFound ? (
+    <NextError statusCode={404} />
   ) : (
-    <div className="flex flex-row items-center justify-center min-h-screen">
-      <Loader />
+    <div>
+      {messages.length > 0 ? (
+        <>
+          <Navbar />
+          <Chat />
+        </>
+      ) : (
+        <EmptyChat />
+      )}
     </div>
   );
 };

src/components/MessageActions/Copy.tsx

@@ -20,9 +20,9 @@ const Copy = ({
         setCopied(true);
         setTimeout(() => setCopied(false), 1000);
       }}
-      className="p-2 text-black/70 dark:text-white/70 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white"
+      className="p-2 text-black/70 dark:text-white/70 rounded-full hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white"
     >
-      {copied ? <Check size={18} /> : <ClipboardList size={18} />}
+      {copied ? <Check size={16} /> : <ClipboardList size={16} />}
     </button>
   );
 };

src/components/MessageActions/Rewrite.tsx

@@ -1,4 +1,4 @@
-import { ArrowLeftRight } from 'lucide-react';
+import { ArrowLeftRight, Repeat } from 'lucide-react';
 
 const Rewrite = ({
   rewrite,
@@ -10,12 +10,11 @@ const Rewrite = ({
   return (
     <button
       onClick={() => rewrite(messageId)}
-      className="py-2 px-3 text-black/70 dark:text-white/70 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white flex flex-row items-center space-x-1"
+      className="p-2 text-black/70 dark:text-white/70 rounded-full hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white flex flex-row items-center space-x-1"
     >
-      <ArrowLeftRight size={18} />
-      <p className="text-xs font-medium">Rewrite</p>
+      <Repeat size={16} />
     </button>
   );
 };
-
+1;
 export default Rewrite;

src/components/MessageBox.tsx

@@ -10,6 +10,7 @@ import {
   StopCircle,
   Layers3,
   Plus,
+  CornerDownRight,
 } from 'lucide-react';
 import Markdown, { MarkdownToJSX } from 'markdown-to-jsx';
 import Copy from './MessageActions/Copy';
@@ -122,14 +123,14 @@ const MessageBox = ({
                 </Markdown>
 
                 {loading && isLast ? null : (
-                  <div className="flex flex-row items-center justify-between w-full text-black dark:text-white py-4 -mx-2">
-                    <div className="flex flex-row items-center space-x-1">
+                  <div className="flex flex-row items-center justify-between w-full text-black dark:text-white py-4">
+                    <div className="flex flex-row items-center -ml-2">
                       <Rewrite
                         rewrite={rewrite}
                         messageId={section.assistantMessage.messageId}
                       />
                     </div>
-                    <div className="flex flex-row items-center space-x-1">
+                    <div className="flex flex-row items-center -mr-2">
                       <Copy
                         initialMessage={section.assistantMessage.content}
                         section={section}
@@ -142,12 +143,12 @@ const MessageBox = ({
                             start();
                           }
                         }}
-                        className="p-2 text-black/70 dark:text-white/70 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white"
+                        className="p-2 text-black/70 dark:text-white/70 rounded-full hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white"
                       >
                         {speechStatus === 'started' ? (
-                          <StopCircle size={18} />
+                          <StopCircle size={16} />
                         ) : (
-                          <Volume2 size={18} />
+                          <Volume2 size={16} />
                         )}
                       </button>
                     </div>
@@ -159,7 +160,7 @@ const MessageBox = ({
                   section.suggestions.length > 0 &&
                   section.assistantMessage &&
                   !loading && (
-                    <div className="mt-8 pt-6 border-t border-light-200/50 dark:border-dark-200/50">
+                    <div className="mt-6">
                       <div className="flex flex-row items-center space-x-2 mb-4">
                         <Layers3
                           className="text-black dark:text-white"
@@ -173,20 +174,24 @@ const MessageBox = ({
                         {section.suggestions.map(
                           (suggestion: string, i: number) => (
                             <div key={i}>
-                              {i > 0 && (
-                                <div className="h-px bg-light-200/40 dark:bg-dark-200/40 mx-3" />
-                              )}
+                              <div className="h-px bg-light-200/40 dark:bg-dark-200/40" />
                               <button
                                 onClick={() => sendMessage(suggestion)}
-                                className="group w-full px-3 py-4 text-left transition-colors duration-200"
+                                className="group w-full py-4 text-left transition-colors duration-200"
                               >
                                 <div className="flex items-center justify-between gap-3">
-                                  <p className="text-sm text-black/70 dark:text-white/70 group-hover:text-[#24A0ED] transition-colors duration-200 leading-relaxed">
-                                    {suggestion}
-                                  </p>
+                                  <div className="flex flex-row space-x-3 items-center ">
+                                    <CornerDownRight
+                                      size={17}
+                                      className="group-hover:text-sky-400 transition-colors duration-200"
+                                    />
+                                    <p className="text-sm text-black/70 dark:text-white/70 group-hover:text-sky-400 transition-colors duration-200 leading-relaxed">
+                                      {suggestion}
+                                    </p>
+                                  </div>
                                   <Plus
                                     size={16}
-                                    className="text-black/40 dark:text-white/40 group-hover:text-[#24A0ED] transition-colors duration-200 flex-shrink-0"
+                                    className="text-black/40 dark:text-white/40 group-hover:text-sky-400 transition-colors duration-200 flex-shrink-0"
                                   />
                                 </div>
                               </button>
@@ -205,11 +210,11 @@ const MessageBox = ({
           <div className="lg:sticky lg:top-20 flex flex-col items-center space-y-3 w-full lg:w-3/12 z-30 h-full pb-4">
             <SearchImages
               query={section.userMessage.content}
-              chatHistory={chatTurns.slice(0, sectionIndex * 2)}
+              chatHistory={chatTurns}
               messageId={section.assistantMessage.messageId}
             />
             <SearchVideos
-              chatHistory={chatTurns.slice(0, sectionIndex * 2)}
+              chatHistory={chatTurns}
               query={section.userMessage.content}
               messageId={section.assistantMessage.messageId}
             />

src/lib/agents/media/image.ts

@@ -0,0 +1,65 @@
+/* I don't think can be classified as agents but to keep the structure consistent i guess ill keep it here */
+
+import {
+  RunnableSequence,
+  RunnableMap,
+  RunnableLambda,
+} from '@langchain/core/runnables';
+import { ChatPromptTemplate } from '@langchain/core/prompts';
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+import { BaseMessage, HumanMessage, SystemMessage } from '@langchain/core/messages';
+import { StringOutputParser } from '@langchain/core/output_parsers';
+import { searchSearxng } from '@/lib/searxng';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import LineOutputParser from '@/lib/outputParsers/lineOutputParser';
+import { imageSearchFewShots, imageSearchPrompt } from '@/lib/prompts/media/image';
+
+type ImageSearchChainInput = {
+  chatHistory: BaseMessage[];
+  query: string;
+};
+
+type ImageSearchResult = {
+  img_src: string;
+  url: string;
+  title: string;
+}
+
+const outputParser = new LineOutputParser({
+  key: 'query',
+})
+
+const searchImages = async (
+  input: ImageSearchChainInput,
+  llm: BaseChatModel,
+) => {
+  const chatPrompt = await ChatPromptTemplate.fromMessages([
+    new SystemMessage(imageSearchPrompt),
+    ...imageSearchFewShots,
+    new HumanMessage(`<conversation>\n${formatChatHistoryAsString(input.chatHistory)}\n</conversation>\n<follow_up>\n${input.query}\n</follow_up>`)
+  ]).formatMessages({})
+
+  const res = await llm.invoke(chatPrompt)
+
+  const query = await outputParser.invoke(res)
+
+  const searchRes = await searchSearxng(query!, {
+    engines: ['bing images', 'google images'],
+  });
+
+  const images: ImageSearchResult[] = [];
+
+  searchRes.results.forEach((result) => {
+    if (result.img_src && result.url && result.title) {
+      images.push({
+        img_src: result.img_src,
+        url: result.url,
+        title: result.title,
+      });
+    }
+  });
+
+  return images.slice(0, 10);
+};
+
+export default searchImages;

src/lib/agents/media/video.ts

@@ -0,0 +1,65 @@
+import { ChatPromptTemplate } from '@langchain/core/prompts';
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+import { BaseMessage, HumanMessage, SystemMessage } from '@langchain/core/messages';
+import { searchSearxng } from '@/lib/searxng';
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import LineOutputParser from '@/lib/outputParsers/lineOutputParser';
+import { videoSearchFewShots, videoSearchPrompt } from '@/lib/prompts/media/videos';
+
+type VideoSearchChainInput = {
+  chatHistory: BaseMessage[];
+  query: string;
+};
+
+type VideoSearchResult = {
+  img_src: string;
+  url: string;
+  title: string;
+  iframe_src: string;
+}
+
+const outputParser = new LineOutputParser({
+  key: 'query',
+});
+
+const searchVideos = async (
+  input: VideoSearchChainInput,
+  llm: BaseChatModel,
+) => {
+  const chatPrompt = await ChatPromptTemplate.fromMessages([
+    new SystemMessage(videoSearchPrompt),
+    ...videoSearchFewShots,
+    new HumanMessage(`<conversation>${formatChatHistoryAsString(input.chatHistory)}\n</conversation>\n<follow_up>\n${input.query}\n</follow_up>`)
+  ]).formatMessages({})
+
+  const res = await llm.invoke(chatPrompt)
+
+  const query = await outputParser.invoke(res)
+
+  const searchRes = await searchSearxng(query!, {
+    engines: ['youtube'],
+  });
+
+  const videos: VideoSearchResult[] = [];
+
+  searchRes.results.forEach((result) => {
+    if (
+      result.thumbnail &&
+      result.url &&
+      result.title &&
+      result.iframe_src
+    ) {
+      videos.push({
+        img_src: result.thumbnail,
+        url: result.url,
+        title: result.title,
+        iframe_src: result.iframe_src,
+      });
+    }
+  });
+
+  return videos.slice(0, 10);
+
+};
+
+export default searchVideos;

src/lib/agents/search/classifier/index.ts

@@ -0,0 +1,72 @@
+import z from 'zod';
+import { ClassifierInput, ClassifierOutput } from '../types';
+import { WidgetRegistry } from '../widgets';
+import { IntentRegistry } from './intents';
+import { getClassifierPrompt } from '@/lib/prompts/search/classifier';
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+
+class Classifier {
+  async classify(input: ClassifierInput): Promise<ClassifierOutput> {
+    const availableIntents = IntentRegistry.getAvailableIntents({
+      sources: input.enabledSources,
+    });
+    const availableWidgets = WidgetRegistry.getAll();
+
+    const classificationSchema = z.object({
+      skipSearch: z
+        .boolean()
+        .describe(
+          'Set to true to SKIP search. Skip ONLY when: (1) widgets alone fully answer the query (e.g., weather, stocks, calculator), (2) simple greetings or writing tasks (NOT questions). Set to false for ANY question or information request.',
+        ),
+      standaloneFollowUp: z
+        .string()
+        .describe(
+          'A self-contained, context-independent reformulation of the user\'s question. Must include all necessary context from chat history, replace pronouns with specific nouns, and be clear enough to answer without seeing the conversation. Keep the same complexity as the original question.',
+        ),
+      intents: z
+        .array(z.enum(availableIntents.map((i) => i.name)))
+        .describe(
+          'The intent(s) that best describe how to fulfill the user\'s query. Can include multiple intents (e.g., [\'web_search\', \'widget_response\'] for \'weather in NYC and recent news\'). Always include at least one intent when applicable.',
+        ),
+      widgets: z
+        .array(z.union(availableWidgets.map((w) => w.schema)))
+        .describe(
+          'Widgets that can display structured data to answer (fully or partially) the query. Include all applicable widgets regardless of skipSearch value.',
+        ),
+    });
+
+    const classifierPrompt = getClassifierPrompt({
+      intentDesc: IntentRegistry.getDescriptions({
+        sources: input.enabledSources,
+      }),
+      widgetDesc: WidgetRegistry.getDescriptions(),
+    });
+
+    const res = await input.llm.generateObject<
+      z.infer<typeof classificationSchema>
+    >({
+      messages: [
+        {
+          role: 'system',
+          content: classifierPrompt,
+        },
+        {
+          role: 'user',
+          content: `<conversation>${formatChatHistoryAsString(input.chatHistory)}</conversation>\n\n<query>${input.query}</query>`,
+        },
+      ],
+      schema: classificationSchema,
+    });
+
+    res.widgets = res.widgets.map((widgetConfig) => {
+      return {
+        type: widgetConfig.type,
+        params: widgetConfig,
+      };
+    });
+
+    return res as ClassifierOutput;
+  }
+}
+
+export default Classifier;

src/lib/agents/search/classifier/intents/academicSearch.ts

@@ -0,0 +1,11 @@
+import { Intent } from '../../types';
+
+const academicSearchIntent: Intent = {
+  name: 'academic_search',
+  description:
+    'Use this intent to find scholarly articles, research papers, and academic resources when the user is seeking credible and authoritative information on a specific topic.',
+  requiresSearch: true,
+  enabled: (config) => config.sources.includes('academic'),
+};
+
+export default academicSearchIntent;

src/lib/agents/search/classifier/intents/discussionSearch.ts

@@ -0,0 +1,11 @@
+import { Intent } from '../../types';
+
+const discussionSearchIntent: Intent = {
+  name: 'discussion_search',
+  description:
+    'Use this intent to search through discussion forums, community boards, or social media platforms when the user is looking for opinions, experiences, or community-driven information on a specific topic.',
+  requiresSearch: true,
+  enabled: (config) => config.sources.includes('discussions'),
+};
+
+export default discussionSearchIntent;

src/lib/agents/search/classifier/intents/index.ts

@@ -0,0 +1,14 @@
+import academicSearchIntent from './academicSearch';
+import discussionSearchIntent from './discussionSearch';
+import IntentRegistry from './registry';
+import webSearchIntent from './webSearch';
+import widgetResponseIntent from './widgetResponse';
+import writingTaskIntent from './writingTask';
+
+IntentRegistry.register(webSearchIntent);
+IntentRegistry.register(academicSearchIntent);
+IntentRegistry.register(discussionSearchIntent);
+IntentRegistry.register(widgetResponseIntent);
+IntentRegistry.register(writingTaskIntent);
+
+export { IntentRegistry };

src/lib/agents/search/classifier/intents/registry.ts

@@ -0,0 +1,29 @@
+import { Intent, SearchAgentConfig, SearchSources } from '../../types';
+
+class IntentRegistry {
+  private static intents = new Map<string, Intent>();
+
+  static register(intent: Intent) {
+    this.intents.set(intent.name, intent);
+  }
+
+  static get(name: string): Intent | undefined {
+    return this.intents.get(name);
+  }
+
+  static getAvailableIntents(config: { sources: SearchSources[] }): Intent[] {
+    return Array.from(
+      this.intents.values().filter((intent) => intent.enabled(config)),
+    );
+  }
+
+  static getDescriptions(config: { sources: SearchSources[] }): string {
+    const availableintnets = this.getAvailableIntents(config);
+
+    return availableintnets
+      .map((intent) => `${intent.name}: ${intent.description}`)
+      .join('\n\n');
+  }
+}
+
+export default IntentRegistry;

src/lib/agents/search/classifier/intents/webSearch.ts

@@ -0,0 +1,11 @@
+import { Intent } from '../../types';
+
+const webSearchIntent: Intent = {
+  name: 'web_search',
+  description:
+    'Use this intent to find current information from the web when the user is asking a question or needs up-to-date information that cannot be provided by widgets or other intents.',
+  requiresSearch: true,
+  enabled: (config) => config.sources.includes('web'),
+};
+
+export default webSearchIntent;

src/lib/agents/search/classifier/intents/widgetResponse.ts

@@ -0,0 +1,11 @@
+import { Intent } from '../../types';
+
+const widgetResponseIntent: Intent = {
+  name: 'widget_response',
+  description:
+    'Use this intent to respond to user queries using available widgets when the required information can be obtained from them.',
+  requiresSearch: false,
+  enabled: (config) => true,
+};
+
+export default widgetResponseIntent;

src/lib/agents/search/classifier/intents/writingTask.ts

@@ -0,0 +1,11 @@
+import { Intent } from '../../types';
+
+const writingTaskIntent: Intent = {
+  name: 'writing_task',
+  description:
+    'Use this intent to assist users with writing tasks such as drafting emails, creating documents, or generating content based on their instructions or greetings.',
+  requiresSearch: false,
+  enabled: (config) => true,
+};
+
+export default writingTaskIntent;

src/lib/agents/search/types.ts

@@ -0,0 +1,65 @@
+import { EventEmitter } from 'stream';
+import z from 'zod';
+import BaseLLM from '../../models/base/llm';
+import BaseEmbedding from '@/lib/models/base/embedding';
+
+export type SearchSources = 'web' | 'discussions' | 'academic';
+
+export type SearchAgentConfig = {
+  sources: SearchSources[];
+  llm: BaseLLM<any>;
+  embedding: BaseEmbedding<any>;
+};
+
+export type SearchAgentInput = {
+  chatHistory: Message[];
+  followUp: string;
+  config: SearchAgentConfig;
+};
+
+export interface Intent {
+  name: string;
+  description: string;
+  requiresSearch: boolean;
+  enabled: (config: { sources: SearchSources[] }) => boolean;
+}
+
+export type Widget<TSchema extends z.ZodObject<any> = z.ZodObject<any>> = {
+  name: string;
+  description: string;
+  schema: TSchema;
+  execute: (
+    params: z.infer<TSchema>,
+    additionalConfig: AdditionalConfig,
+  ) => Promise<WidgetOutput>;
+};
+
+export type WidgetConfig = {
+  type: string;
+  params: Record<string, any>;
+};
+
+export type WidgetOutput = {
+  type: string;
+  data: any;
+};
+
+export type ClassifierInput = {
+  llm: BaseLLM<any>;
+  enabledSources: SearchSources[];
+  query: string;
+  chatHistory: Message[];
+};
+
+export type ClassifierOutput = {
+  skipSearch: boolean;
+  standaloneFollowUp: string;
+  intents: string[];
+  widgets: WidgetConfig[];
+};
+
+export type AdditionalConfig = {
+  llm: BaseLLM<any>;
+  embedding: BaseLLM<any>;
+  emitter: EventEmitter;
+};

src/lib/agents/search/widgets/index.ts

@@ -0,0 +1,6 @@
+import WidgetRegistry from './registry';
+import weatherWidget from './weatherWidget';
+
+WidgetRegistry.register(weatherWidget);
+
+export { WidgetRegistry };

src/lib/agents/search/widgets/registry.ts

@@ -0,0 +1,65 @@
+import {
+  AdditionalConfig,
+  SearchAgentConfig,
+  Widget,
+  WidgetConfig,
+  WidgetOutput,
+} from '../types';
+
+class WidgetRegistry {
+  private static widgets = new Map<string, Widget>();
+
+  static register(widget: Widget<any>) {
+    this.widgets.set(widget.name, widget);
+  }
+
+  static get(name: string): Widget | undefined {
+    return this.widgets.get(name);
+  }
+
+  static getAll(): Widget[] {
+    return Array.from(this.widgets.values());
+  }
+
+  static getDescriptions(): string {
+    return Array.from(this.widgets.values())
+      .map((widget) => `${widget.name}: ${widget.description}`)
+      .join('\n\n');
+  }
+
+  static async execute(
+    name: string,
+    params: any,
+    config: AdditionalConfig,
+  ): Promise<WidgetOutput> {
+    const widget = this.get(name);
+
+    if (!widget) {
+      throw new Error(`Widget with name ${name} not found`);
+    }
+
+    return widget.execute(params, config);
+  }
+
+  static async executeAll(
+    widgets: WidgetConfig[],
+    additionalConfig: AdditionalConfig,
+  ): Promise<WidgetOutput[]> {
+    const results: WidgetOutput[] = [];
+
+    await Promise.all(
+      widgets.map(async (widgetConfig) => {
+        const output = await this.execute(
+          widgetConfig.type,
+          widgetConfig.params,
+          additionalConfig,
+        );
+        results.push(output);
+      }),
+    );
+
+    return results;
+  }
+}
+
+export default WidgetRegistry;

src/lib/agents/search/widgets/weatherWidget.ts

@@ -0,0 +1,123 @@
+import z from 'zod';
+import { Widget } from '../types';
+
+const WeatherWidgetSchema = z.object({
+  type: z.literal('weather'),
+  location: z
+    .string()
+    .describe(
+      'Human-readable location name (e.g., "New York, NY, USA", "London, UK"). Use this OR lat/lon coordinates, never both. Leave empty string if providing coordinates.',
+    ),
+  lat: z
+    .number()
+    .describe(
+      'Latitude coordinate in decimal degrees (e.g., 40.7128). Only use when location name is empty.',
+    ),
+  lon: z
+    .number()
+    .describe(
+      'Longitude coordinate in decimal degrees (e.g., -74.0060). Only use when location name is empty.',
+    ),
+});
+
+const weatherWidget = {
+  name: 'weather',
+  description:
+    'Provides current weather information for a specified location. It can return details such as temperature, humidity, wind speed, and weather conditions. It needs either a location name or latitude/longitude coordinates to function.',
+  schema: WeatherWidgetSchema,
+  execute: async (params, _) => {
+    if (
+      params.location === '' &&
+      (params.lat === undefined || params.lon === undefined)
+    ) {
+      throw new Error(
+        'Either location name or both latitude and longitude must be provided.',
+      );
+    }
+
+    if (params.location !== '') {
+      const openStreetMapUrl = `https://nominatim.openstreetmap.org/search?q=${encodeURIComponent(params.location)}&format=json&limit=1`;
+
+      const locationRes = await fetch(openStreetMapUrl, {
+        headers: {
+          'User-Agent': 'Perplexica',
+          'Content-Type': 'application/json',
+        },
+      });
+
+      const data = await locationRes.json();
+
+      const location = data[0];
+
+      if (!location) {
+        throw new Error(
+          `Could not find coordinates for location: ${params.location}`,
+        );
+      }
+
+      const weatherRes = await fetch(
+        `https://api.open-meteo.com/v1/forecast?latitude=${location.lat}&longitude=${location.lon}&current_weather=true`,
+        {
+          headers: {
+            'User-Agent': 'Perplexica',
+            'Content-Type': 'application/json',
+          },
+        },
+      );
+
+      const weatherData = await weatherRes.json();
+
+      /* this is like a very simple implementation just to see the bacckend works, when we're working on the frontend, we'll return more data i guess? */
+      return {
+        type: 'weather',
+        data: {
+          location: params.location,
+          latitude: location.lat,
+          longitude: location.lon,
+          weather: weatherData.current_weather,
+        },
+      };
+    } else if (params.lat !== undefined && params.lon !== undefined) {
+      const [weatherRes, locationRes] = await Promise.all([
+        fetch(
+          `https://api.open-meteo.com/v1/forecast?latitude=${params.lat}&longitude=${params.lon}&current_weather=true`,
+          {
+            headers: {
+              'User-Agent': 'Perplexica',
+              'Content-Type': 'application/json',
+            },
+          },
+        ),
+        fetch(
+          `https://nominatim.openstreetmap.org/reverse?lat=${params.lat}&lon=${params.lon}&format=json`,
+          {
+            headers: {
+              'User-Agent': 'Perplexica',
+              'Content-Type': 'application/json',
+            },
+          },
+        ),
+      ]);
+
+      const weatherData = await weatherRes.json();
+      const locationData = await locationRes.json();
+
+      return {
+        type: 'weather',
+        data: {
+          location: locationData.display_name,
+          latitude: params.lat,
+          longitude: params.lon,
+          weather: weatherData.current_weather,
+        },
+      };
+    }
+
+    return {
+      type: 'weather',
+      data: null,
+    };
+  },
+} satisfies Widget<typeof WeatherWidgetSchema>;
+
+export default weatherWidget;

src/lib/agents/suggestions/index.ts

@@ -0,0 +1,32 @@
+import ListLineOutputParser from '@/lib/outputParsers/listLineOutputParser';
+import { ChatPromptTemplate, PromptTemplate } from '@langchain/core/prompts';
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+import { BaseMessage, HumanMessage, SystemMessage } from '@langchain/core/messages';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { suggestionGeneratorPrompt } from '@/lib/prompts/suggestions';
+
+type SuggestionGeneratorInput = {
+  chatHistory: BaseMessage[];
+};
+
+const outputParser = new ListLineOutputParser({
+  key: 'suggestions',
+});
+
+const generateSuggestions = async (
+  input: SuggestionGeneratorInput,
+  llm: BaseChatModel,
+) => {
+  const chatPrompt = await ChatPromptTemplate.fromMessages([
+    new SystemMessage(suggestionGeneratorPrompt),
+    new HumanMessage(`<conversation>${formatChatHistoryAsString(input.chatHistory)}</conversation>`)
+  ]).formatMessages({})
+
+  const res = await llm.invoke(chatPrompt)
+
+  const suggestions = await outputParser.invoke(res)
+
+  return suggestions
+};
+
+export default generateSuggestions;

src/lib/chains/imageSearchAgent.ts

@@ -1,105 +0,0 @@
-import {
-  RunnableSequence,
-  RunnableMap,
-  RunnableLambda,
-} from '@langchain/core/runnables';
-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.
-Output only the rephrased query wrapped in an XML <query> element. Do not include any explanation or additional text.
-`;
-
-type ImageSearchChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-interface ImageSearchResult {
-  img_src: string;
-  url: string;
-  title: string;
-}
-
-const strParser = new StringOutputParser();
-
-const createImageSearchChain = (llm: BaseChatModel) => {
-  return RunnableSequence.from([
-    RunnableMap.from({
-      chat_history: (input: ImageSearchChainInput) => {
-        return formatChatHistoryAsString(input.chat_history);
-      },
-      query: (input: ImageSearchChainInput) => {
-        return input.query;
-      },
-    }),
-    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) => {
-      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'],
-      });
-
-      const images: ImageSearchResult[] = [];
-
-      res.results.forEach((result) => {
-        if (result.img_src && result.url && result.title) {
-          images.push({
-            img_src: result.img_src,
-            url: result.url,
-            title: result.title,
-          });
-        }
-      });
-
-      return images.slice(0, 10);
-    }),
-  ]);
-};
-
-const handleImageSearch = (
-  input: ImageSearchChainInput,
-  llm: BaseChatModel,
-) => {
-  const imageSearchChain = createImageSearchChain(llm);
-  return imageSearchChain.invoke(input);
-};
-
-export default handleImageSearch;

src/lib/chains/suggestionGeneratorAgent.ts

@@ -1,55 +0,0 @@
-import { RunnableSequence, RunnableMap } from '@langchain/core/runnables';
-import ListLineOutputParser from '../outputParsers/listLineOutputParser';
-import { PromptTemplate } from '@langchain/core/prompts';
-import formatChatHistoryAsString from '../utils/formatHistory';
-import { BaseMessage } from '@langchain/core/messages';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { ChatOpenAI } from '@langchain/openai';
-
-const suggestionGeneratorPrompt = `
-You are an AI suggestion generator for an AI powered search engine. You will be given a conversation below. You need to generate 4-5 suggestions based on the conversation. The suggestion should be relevant to the conversation that can be used by the user to ask the chat model for more information.
-You need to make sure the suggestions are relevant to the conversation and are helpful to the user. Keep a note that the user might use these suggestions to ask a chat model for more information. 
-Make sure the suggestions are medium in length and are informative and relevant to the conversation.
-
-Provide these suggestions separated by newlines between the XML tags <suggestions> and </suggestions>. For example:
-
-<suggestions>
-Tell me more about SpaceX and their recent projects
-What is the latest news on SpaceX?
-Who is the CEO of SpaceX?
-</suggestions>
-
-Conversation:
-{chat_history}
-`;
-
-type SuggestionGeneratorInput = {
-  chat_history: BaseMessage[];
-};
-
-const outputParser = new ListLineOutputParser({
-  key: 'suggestions',
-});
-
-const createSuggestionGeneratorChain = (llm: BaseChatModel) => {
-  return RunnableSequence.from([
-    RunnableMap.from({
-      chat_history: (input: SuggestionGeneratorInput) =>
-        formatChatHistoryAsString(input.chat_history),
-    }),
-    PromptTemplate.fromTemplate(suggestionGeneratorPrompt),
-    llm,
-    outputParser,
-  ]);
-};
-
-const generateSuggestions = (
-  input: SuggestionGeneratorInput,
-  llm: BaseChatModel,
-) => {
-  (llm as unknown as ChatOpenAI).temperature = 0;
-  const suggestionGeneratorChain = createSuggestionGeneratorChain(llm);
-  return suggestionGeneratorChain.invoke(input);
-};
-
-export default generateSuggestions;

src/lib/chains/videoSearchAgent.ts

@@ -1,110 +0,0 @@
-import {
-  RunnableSequence,
-  RunnableMap,
-  RunnableLambda,
-} from '@langchain/core/runnables';
-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.
-Output only the rephrased query wrapped in an XML <query> element. Do not include any explanation or additional text.
-`;
-
-type VideoSearchChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-interface VideoSearchResult {
-  img_src: string;
-  url: string;
-  title: string;
-  iframe_src: string;
-}
-
-const strParser = new StringOutputParser();
-
-const createVideoSearchChain = (llm: BaseChatModel) => {
-  return RunnableSequence.from([
-    RunnableMap.from({
-      chat_history: (input: VideoSearchChainInput) => {
-        return formatChatHistoryAsString(input.chat_history);
-      },
-      query: (input: VideoSearchChainInput) => {
-        return input.query;
-      },
-    }),
-    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) => {
-      const queryParser = new LineOutputParser({
-        key: 'query',
-      });
-      return await queryParser.parse(input);
-    }),
-    RunnableLambda.from(async (input: string) => {
-      const res = await searchSearxng(input, {
-        engines: ['youtube'],
-      });
-
-      const videos: VideoSearchResult[] = [];
-
-      res.results.forEach((result) => {
-        if (
-          result.thumbnail &&
-          result.url &&
-          result.title &&
-          result.iframe_src
-        ) {
-          videos.push({
-            img_src: result.thumbnail,
-            url: result.url,
-            title: result.title,
-            iframe_src: result.iframe_src,
-          });
-        }
-      });
-
-      return videos.slice(0, 10);
-    }),
-  ]);
-};
-
-const handleVideoSearch = (
-  input: VideoSearchChainInput,
-  llm: BaseChatModel,
-) => {
-  const videoSearchChain = createVideoSearchChain(llm);
-  return videoSearchChain.invoke(input);
-};
-
-export default handleVideoSearch;

src/lib/models/base/embedding.ts

@@ -0,0 +1,7 @@
+abstract class BaseEmbedding<CONFIG> {
+  constructor(protected config: CONFIG) {}
+  abstract embedText(texts: string[]): Promise<number[][]>;
+  abstract embedChunks(chunks: Chunk[]): Promise<number[][]>;
+}
+
+export default BaseEmbedding;

src/lib/models/base/llm.ts

@@ -0,0 +1,22 @@
+import {
+  GenerateObjectInput,
+  GenerateOptions,
+  GenerateTextInput,
+  GenerateTextOutput,
+  StreamTextOutput,
+} from '../types';
+
+abstract class BaseLLM<CONFIG> {
+  constructor(protected config: CONFIG) {}
+  abstract withOptions(options: GenerateOptions): this;
+  abstract generateText(input: GenerateTextInput): Promise<GenerateTextOutput>;
+  abstract streamText(
+    input: GenerateTextInput,
+  ): AsyncGenerator<StreamTextOutput>;
+  abstract generateObject<T>(input: GenerateObjectInput): Promise<T>;
+  abstract streamObject<T>(
+    input: GenerateObjectInput,
+  ): AsyncGenerator<Partial<T>>;
+}
+
+export default BaseLLM;

src/lib/models/base/provider.ts

@@ -1,7 +1,9 @@
 import { Embeddings } from '@langchain/core/embeddings';
 import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
+import { ModelList, ProviderMetadata } from '../types';
 import { UIConfigField } from '@/lib/config/types';
+import BaseLLM from './llm';
+import BaseEmbedding from './embedding';
 
 abstract class BaseModelProvider<CONFIG> {
   constructor(
@@ -11,8 +13,8 @@ abstract class BaseModelProvider<CONFIG> {
   ) {}
   abstract getDefaultModels(): Promise<ModelList>;
   abstract getModelList(): Promise<ModelList>;
-  abstract loadChatModel(modelName: string): Promise<BaseChatModel>;
-  abstract loadEmbeddingModel(modelName: string): Promise<Embeddings>;
+  abstract loadChatModel(modelName: string): Promise<BaseLLM<any>>;
+  abstract loadEmbeddingModel(modelName: string): Promise<BaseEmbedding<any>>;
   static getProviderConfigFields(): UIConfigField[] {
     throw new Error('Method not implemented.');
   }

src/lib/models/providers/index.ts

@@ -1,27 +1,11 @@
 import { ModelProviderUISection } from '@/lib/config/types';
-import { ProviderConstructor } from './baseProvider';
+import { ProviderConstructor } from '../base/provider';
 import OpenAIProvider from './openai';
 import OllamaProvider from './ollama';
-import TransformersProvider from './transformers';
-import AnthropicProvider from './anthropic';
-import GeminiProvider from './gemini';
-import GroqProvider from './groq';
-import DeepSeekProvider from './deepseek';
-import LMStudioProvider from './lmstudio';
-import LemonadeProvider from './lemonade';
-import AimlProvider from '@/lib/models/providers/aiml';
 
 export const providers: Record<string, ProviderConstructor<any>> = {
   openai: OpenAIProvider,
   ollama: OllamaProvider,
-  transformers: TransformersProvider,
-  anthropic: AnthropicProvider,
-  gemini: GeminiProvider,
-  groq: GroqProvider,
-  deepseek: DeepSeekProvider,
-  aiml: AimlProvider,
-  lmstudio: LMStudioProvider,
-  lemonade: LemonadeProvider,
 };
 
 export const getModelProvidersUIConfigSection =

src/lib/models/providers/ollama/index.ts

@@ -1,10 +1,11 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatOllama, OllamaEmbeddings } from '@langchain/ollama';
-import { Embeddings } from '@langchain/core/embeddings';
 import { UIConfigField } from '@/lib/config/types';
 import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
+import BaseModelProvider from '../../base/provider';
+import { Model, ModelList, ProviderMetadata } from '../../types';
+import BaseLLM from '../../base/llm';
+import BaseEmbedding from '../../base/embedding';
+import OllamaLLM from './ollamaLLM';
+import OllamaEmbedding from './ollamaEmbedding';
 
 interface OllamaConfig {
   baseURL: string;
@@ -76,7 +77,7 @@ class OllamaProvider extends BaseModelProvider<OllamaConfig> {
     };
   }
 
-  async loadChatModel(key: string): Promise<BaseChatModel> {
+  async loadChatModel(key: string): Promise<BaseLLM<any>> {
     const modelList = await this.getModelList();
 
     const exists = modelList.chat.find((m) => m.key === key);
@@ -87,14 +88,13 @@ class OllamaProvider extends BaseModelProvider<OllamaConfig> {
       );
     }
 
-    return new ChatOllama({
-      temperature: 0.7,
+    return new OllamaLLM({
+      baseURL: this.config.baseURL,
       model: key,
-      baseUrl: this.config.baseURL,
     });
   }
 
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
+  async loadEmbeddingModel(key: string): Promise<BaseEmbedding<any>> {
     const modelList = await this.getModelList();
     const exists = modelList.embedding.find((m) => m.key === key);
 
@@ -104,9 +104,9 @@ class OllamaProvider extends BaseModelProvider<OllamaConfig> {
       );
     }
 
-    return new OllamaEmbeddings({
+    return new OllamaEmbedding({
       model: key,
-      baseUrl: this.config.baseURL,
+      baseURL: this.config.baseURL,
     });
   }
 

src/lib/models/providers/ollama/ollamaEmbedding.ts

@@ -0,0 +1,39 @@
+import { Ollama } from 'ollama';
+import BaseEmbedding from '../../base/embedding';
+
+type OllamaConfig = {
+  model: string;
+  baseURL?: string;
+};
+
+class OllamaEmbedding extends BaseEmbedding<OllamaConfig> {
+  ollamaClient: Ollama;
+
+  constructor(protected config: OllamaConfig) {
+    super(config);
+
+    this.ollamaClient = new Ollama({
+      host: this.config.baseURL || 'http://localhost:11434',
+    });
+  }
+
+  async embedText(texts: string[]): Promise<number[][]> {
+    const response = await this.ollamaClient.embed({
+      input: texts,
+      model: this.config.model,
+    });
+
+    return response.embeddings;
+  }
+
+  async embedChunks(chunks: Chunk[]): Promise<number[][]> {
+    const response = await this.ollamaClient.embed({
+      input: chunks.map((c) => c.content),
+      model: this.config.model,
+    });
+
+    return response.embeddings;
+  }
+}
+
+export default OllamaEmbedding;

src/lib/models/providers/ollama/ollamaLLM.ts

@@ -0,0 +1,151 @@
+import z from 'zod';
+import BaseLLM from '../../base/llm';
+import {
+  GenerateObjectInput,
+  GenerateOptions,
+  GenerateTextInput,
+  GenerateTextOutput,
+  StreamTextOutput,
+} from '../../types';
+import { Ollama } from 'ollama';
+import { parse } from 'partial-json';
+
+type OllamaConfig = {
+  baseURL: string;
+  model: string;
+  options?: GenerateOptions;
+};
+
+class OllamaLLM extends BaseLLM<OllamaConfig> {
+  ollamaClient: Ollama;
+
+  constructor(protected config: OllamaConfig) {
+    super(config);
+
+    this.ollamaClient = new Ollama({
+      host: this.config.baseURL || 'http://localhost:11434',
+    });
+  }
+
+  withOptions(options: GenerateOptions) {
+    this.config.options = {
+      ...this.config.options,
+      ...options,
+    };
+    return this;
+  }
+
+  async generateText(input: GenerateTextInput): Promise<GenerateTextOutput> {
+    this.withOptions(input.options || {});
+
+    const res = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: input.messages,
+      options: {
+        top_p: this.config.options?.topP,
+        temperature: this.config.options?.temperature,
+        num_predict: this.config.options?.maxTokens,
+        frequency_penalty: this.config.options?.frequencyPenalty,
+        presence_penalty: this.config.options?.presencePenalty,
+        stop: this.config.options?.stopSequences,
+      },
+    });
+
+    return {
+      content: res.message.content,
+      additionalInfo: {
+        reasoning: res.message.thinking,
+      },
+    };
+  }
+
+  async *streamText(
+    input: GenerateTextInput,
+  ): AsyncGenerator<StreamTextOutput> {
+    this.withOptions(input.options || {});
+
+    const stream = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: input.messages,
+      stream: true,
+      options: {
+        top_p: this.config.options?.topP,
+        temperature: this.config.options?.temperature,
+        num_predict: this.config.options?.maxTokens,
+        frequency_penalty: this.config.options?.frequencyPenalty,
+        presence_penalty: this.config.options?.presencePenalty,
+        stop: this.config.options?.stopSequences,
+      },
+    });
+
+    for await (const chunk of stream) {
+      yield {
+        contentChunk: chunk.message.content,
+        done: chunk.done,
+        additionalInfo: {
+          reasoning: chunk.message.thinking,
+        },
+      };
+    }
+  }
+
+  async generateObject<T>(input: GenerateObjectInput): Promise<T> {
+    this.withOptions(input.options || {});
+
+    const response = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: input.messages,
+      format: z.toJSONSchema(input.schema),
+      think: false,
+      options: {
+        top_p: this.config.options?.topP,
+        temperature: 0,
+        num_predict: this.config.options?.maxTokens,
+        frequency_penalty: this.config.options?.frequencyPenalty,
+        presence_penalty: this.config.options?.presencePenalty,
+        stop: this.config.options?.stopSequences,
+      },
+    });
+
+    try {
+      return input.schema.parse(JSON.parse(response.message.content)) as T;
+    } catch (err) {
+      throw new Error(`Error parsing response from Ollama: ${err}`);
+    }
+  }
+
+  async *streamObject<T>(input: GenerateObjectInput): AsyncGenerator<T> {
+    let recievedObj: string = '';
+
+    this.withOptions(input.options || {});
+
+    const stream = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: input.messages,
+      format: z.toJSONSchema(input.schema),
+      stream: true,
+      think: false,
+      options: {
+        top_p: this.config.options?.topP,
+        temperature: this.config.options?.temperature,
+        num_predict: this.config.options?.maxTokens,
+        frequency_penalty: this.config.options?.frequencyPenalty,
+        presence_penalty: this.config.options?.presencePenalty,
+        stop: this.config.options?.stopSequences,
+      },
+    });
+
+    for await (const chunk of stream) {
+      recievedObj += chunk.message.content;
+
+      try {
+        yield parse(recievedObj) as T;
+      } catch (err) {
+        console.log('Error parsing partial object from Ollama:', err);
+        yield {} as T;
+      }
+    }
+  }
+}
+
+export default OllamaLLM;

src/lib/models/providers/openai/index.ts

@@ -1,10 +1,13 @@
 import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
 import { Embeddings } from '@langchain/core/embeddings';
 import { UIConfigField } from '@/lib/config/types';
 import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
+import { Model, ModelList, ProviderMetadata } from '../../types';
+import OpenAIEmbedding from './openaiEmbedding';
+import BaseEmbedding from '../../base/embedding';
+import BaseModelProvider from '../../base/provider';
+import BaseLLM from '../../base/llm';
+import OpenAILLM from './openaiLLM';
 
 interface OpenAIConfig {
   apiKey: string;
@@ -145,7 +148,7 @@ class OpenAIProvider extends BaseModelProvider<OpenAIConfig> {
     };
   }
 
-  async loadChatModel(key: string): Promise<BaseChatModel> {
+  async loadChatModel(key: string): Promise<BaseLLM<any>> {
     const modelList = await this.getModelList();
 
     const exists = modelList.chat.find((m) => m.key === key);
@@ -156,17 +159,14 @@ class OpenAIProvider extends BaseModelProvider<OpenAIConfig> {
       );
     }
 
-    return new ChatOpenAI({
+    return new OpenAILLM({
       apiKey: this.config.apiKey,
-      temperature: 0.7,
       model: key,
-      configuration: {
-        baseURL: this.config.baseURL,
-      },
+      baseURL: this.config.baseURL,
     });
   }
 
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
+  async loadEmbeddingModel(key: string): Promise<BaseEmbedding<any>> {
     const modelList = await this.getModelList();
     const exists = modelList.embedding.find((m) => m.key === key);
 
@@ -176,12 +176,10 @@ class OpenAIProvider extends BaseModelProvider<OpenAIConfig> {
       );
     }
 
-    return new OpenAIEmbeddings({
+    return new OpenAIEmbedding({
       apiKey: this.config.apiKey,
       model: key,
-      configuration: {
-        baseURL: this.config.baseURL,
-      },
+      baseURL: this.config.baseURL,
     });
   }
 

src/lib/models/providers/openai/openaiEmbedding.ts

@@ -0,0 +1,41 @@
+import OpenAI from 'openai';
+import BaseEmbedding from '../../base/embedding';
+
+type OpenAIConfig = {
+  apiKey: string;
+  model: string;
+  baseURL?: string;
+};
+
+class OpenAIEmbedding extends BaseEmbedding<OpenAIConfig> {
+  openAIClient: OpenAI;
+
+  constructor(protected config: OpenAIConfig) {
+    super(config);
+
+    this.openAIClient = new OpenAI({
+      apiKey: config.apiKey,
+      baseURL: config.baseURL,
+    });
+  }
+
+  async embedText(texts: string[]): Promise<number[][]> {
+    const response = await this.openAIClient.embeddings.create({
+      model: this.config.model,
+      input: texts,
+    });
+
+    return response.data.map((embedding) => embedding.embedding);
+  }
+
+  async embedChunks(chunks: Chunk[]): Promise<number[][]> {
+    const response = await this.openAIClient.embeddings.create({
+      model: this.config.model,
+      input: chunks.map((c) => c.content),
+    });
+
+    return response.data.map((embedding) => embedding.embedding);
+  }
+}
+
+export default OpenAIEmbedding;

src/lib/models/providers/openai/openaiLLM.ts

@@ -0,0 +1,163 @@
+import OpenAI from 'openai';
+import BaseLLM from '../../base/llm';
+import { zodTextFormat, zodResponseFormat } from 'openai/helpers/zod';
+import {
+  GenerateObjectInput,
+  GenerateOptions,
+  GenerateTextInput,
+  GenerateTextOutput,
+  StreamTextOutput,
+} from '../../types';
+import { parse } from 'partial-json';
+
+type OpenAIConfig = {
+  apiKey: string;
+  model: string;
+  baseURL?: string;
+  options?: GenerateOptions;
+};
+
+class OpenAILLM extends BaseLLM<OpenAIConfig> {
+  openAIClient: OpenAI;
+
+  constructor(protected config: OpenAIConfig) {
+    super(config);
+
+    this.openAIClient = new OpenAI({
+      apiKey: this.config.apiKey,
+      baseURL: this.config.baseURL || 'https://api.openai.com/v1',
+    });
+  }
+
+  withOptions(options: GenerateOptions) {
+    this.config.options = {
+      ...this.config.options,
+      ...options,
+    };
+
+    return this;
+  }
+
+  async generateText(input: GenerateTextInput): Promise<GenerateTextOutput> {
+    this.withOptions(input.options || {});
+
+    const response = await this.openAIClient.chat.completions.create({
+      model: this.config.model,
+      messages: input.messages,
+      temperature: this.config.options?.temperature || 1.0,
+      top_p: this.config.options?.topP,
+      max_completion_tokens: this.config.options?.maxTokens,
+      stop: this.config.options?.stopSequences,
+      frequency_penalty: this.config.options?.frequencyPenalty,
+      presence_penalty: this.config.options?.presencePenalty,
+    });
+
+    if (response.choices && response.choices.length > 0) {
+      return {
+        content: response.choices[0].message.content!,
+        additionalInfo: {
+          finishReason: response.choices[0].finish_reason,
+        },
+      };
+    }
+
+    throw new Error('No response from OpenAI');
+  }
+
+  async *streamText(
+    input: GenerateTextInput,
+  ): AsyncGenerator<StreamTextOutput> {
+    this.withOptions(input.options || {});
+
+    const stream = await this.openAIClient.chat.completions.create({
+      model: this.config.model,
+      messages: input.messages,
+      temperature: this.config.options?.temperature || 1.0,
+      top_p: this.config.options?.topP,
+      max_completion_tokens: this.config.options?.maxTokens,
+      stop: this.config.options?.stopSequences,
+      frequency_penalty: this.config.options?.frequencyPenalty,
+      presence_penalty: this.config.options?.presencePenalty,
+      stream: true,
+    });
+
+    for await (const chunk of stream) {
+      if (chunk.choices && chunk.choices.length > 0) {
+        yield {
+          contentChunk: chunk.choices[0].delta.content || '',
+          done: chunk.choices[0].finish_reason !== null,
+          additionalInfo: {
+            finishReason: chunk.choices[0].finish_reason,
+          },
+        };
+      }
+    }
+  }
+
+  async generateObject<T>(input: GenerateObjectInput): Promise<T> {
+    this.withOptions(input.options || {});
+
+    const response = await this.openAIClient.chat.completions.parse({
+      messages: input.messages,
+      model: this.config.model,
+      temperature: this.config.options?.temperature || 1.0,
+      top_p: this.config.options?.topP,
+      max_completion_tokens: this.config.options?.maxTokens,
+      stop: this.config.options?.stopSequences,
+      frequency_penalty: this.config.options?.frequencyPenalty,
+      presence_penalty: this.config.options?.presencePenalty,
+      response_format: zodResponseFormat(input.schema, 'object'),
+    });
+
+    if (response.choices && response.choices.length > 0) {
+      try {
+        return input.schema.parse(response.choices[0].message.parsed) as T;
+      } catch (err) {
+        throw new Error(`Error parsing response from OpenAI: ${err}`);
+      }
+    }
+
+    throw new Error('No response from OpenAI');
+  }
+
+  async *streamObject<T>(input: GenerateObjectInput): AsyncGenerator<T> {
+    let recievedObj: string = '';
+
+    this.withOptions(input.options || {});
+
+    const stream = this.openAIClient.responses.stream({
+      model: this.config.model,
+      input: input.messages,
+      temperature: this.config.options?.temperature || 1.0,
+      top_p: this.config.options?.topP,
+      max_completion_tokens: this.config.options?.maxTokens,
+      stop: this.config.options?.stopSequences,
+      frequency_penalty: this.config.options?.frequencyPenalty,
+      presence_penalty: this.config.options?.presencePenalty,
+      text: {
+        format: zodTextFormat(input.schema, 'object'),
+      },
+    });
+
+    for await (const chunk of stream) {
+      if (chunk.type === 'response.output_text.delta' && chunk.delta) {
+        recievedObj += chunk.delta;
+
+        try {
+          yield parse(recievedObj) as T;
+        } catch (err) {
+          console.log('Error parsing partial object from OpenAI:', err);
+          yield {} as T;
+        }
+      } else if (chunk.type === 'response.output_text.done' && chunk.text) {
+        try {
+          yield parse(chunk.text) as T;
+        } catch (err) {
+          throw new Error(`Error parsing response from OpenAI: ${err}`);
+        }
+      }
+    }
+  }
+}
+
+export default OpenAILLM;

src/lib/models/types.ts

@@ -1,3 +1,5 @@
+import z from 'zod';
+
 type Model = {
   name: string;
   key: string;
@@ -25,10 +27,59 @@ type ModelWithProvider = {
   providerId: string;
 };
 
+type GenerateOptions = {
+  temperature?: number;
+  maxTokens?: number;
+  topP?: number;
+  stopSequences?: string[];
+  frequencyPenalty?: number;
+  presencePenalty?: number;
+};
+
+type GenerateTextInput = {
+  messages: Message[];
+  options?: GenerateOptions;
+};
+
+type GenerateTextOutput = {
+  content: string;
+  additionalInfo?: Record<string, any>;
+};
+
+type StreamTextOutput = {
+  contentChunk: string;
+  additionalInfo?: Record<string, any>;
+  done?: boolean;
+};
+
+type GenerateObjectInput = {
+  schema: z.ZodTypeAny;
+  messages: Message[];
+  options?: GenerateOptions;
+};
+
+type GenerateObjectOutput<T> = {
+  object: T;
+  additionalInfo?: Record<string, any>;
+};
+
+type StreamObjectOutput<T> = {
+  objectChunk: Partial<T>;
+  additionalInfo?: Record<string, any>;
+  done?: boolean;
+};
+
 export type {
   Model,
   ModelList,
   ProviderMetadata,
   MinimalProvider,
   ModelWithProvider,
+  GenerateOptions,
+  GenerateTextInput,
+  GenerateTextOutput,
+  StreamTextOutput,
+  GenerateObjectInput,
+  GenerateObjectOutput,
+  StreamObjectOutput,
 };

src/lib/prompts/media/image.ts

@@ -0,0 +1,26 @@
+import { BaseMessageLike } from "@langchain/core/messages";
+
+export const imageSearchPrompt = `
+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.
+Output only the rephrased query wrapped in an XML <query> element. Do not include any explanation or additional text.
+`;
+
+export const imageSearchFewShots: BaseMessageLike[] = [
+    [
+        '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>']
+]

src/lib/prompts/media/videos.ts

@@ -0,0 +1,25 @@
+import { BaseMessageLike } from "@langchain/core/messages";
+
+export const videoSearchPrompt = `
+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.
+`;
+
+export const videoSearchFewShots: BaseMessageLike[] = [
+    [
+        '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>'],
+]

src/lib/prompts/search/classifier.ts

@@ -0,0 +1,176 @@
+export const getClassifierPrompt = (input: {
+  intentDesc: string;
+  widgetDesc: string;
+}) => {
+  return `
+<role>
+You are an expert query classifier for an intelligent search agent. Your task is to analyze user queries and determine the optimal way to answer them—selecting the right intent(s) and widgets.
+</role>
+
+<task>
+Given a conversation history and follow-up question, you must:
+1. Determine if search should be skipped (skipSearch: boolean)
+2. Generate a standalone, self-contained version of the question (standaloneFollowUp: string)
+3. Identify the intent(s) that describe how to fulfill the query (intent: array)
+4. Select appropriate widgets (widgets: array)
+</task>
+
+<critical_decision_rule>
+**THE MOST IMPORTANT RULE**: skipSearch should be TRUE only in TWO cases:
+1. Widget-only queries (weather, stocks, calculator)
+2. Greetings or simple writing tasks (NOT questions)
+
+**DEFAULT TO skipSearch: false** for everything else, including:
+- Any question ("what is", "how does", "explain", "tell me about")
+- Any request for information or facts
+- Anything you're unsure about
+
+Ask yourself: "Is the user ASKING about something or requesting INFORMATION?"
+- YES → skipSearch: false (use web_search)
+- NO (just greeting or simple writing) → skipSearch: true
+</critical_decision_rule>
+
+<skip_search_decision_tree>
+Follow this decision tree IN ORDER:
+
+1. **Widget-Only Queries** → skipSearch: TRUE, intent: ['widget_response']
+   - Weather queries: "weather in NYC", "temperature in Paris", "is it raining in Seattle"
+   - Stock queries: "AAPL stock price", "how is Tesla doing", "MSFT stock"
+   - Calculator queries: "what is 25% of 80", "calculate 15*23", "sqrt(144)"
+   - These are COMPLETE answers—no search needed
+
+2. **Writing/Greeting Tasks** → skipSearch: TRUE, intent: ['writing_task']
+   - ONLY for greetings and simple writing:
+   - Greetings: "hello", "hi", "how are you", "thanks", "goodbye"
+   - Simple writing needing NO facts: "write a thank you email", "draft a birthday message", "compose a poem"
+   - NEVER for: questions, "what is X", "how does X work", explanations, definitions, facts, code help
+   - If user is ASKING about something (not requesting writing), use web_search
+
+3. **Image Display Queries** → skipSearch: FALSE, intent: ['image_preview']
+   - "Show me images of cats"
+   - "Pictures of the Eiffel Tower"
+   - "Visual examples of modern architecture"
+   - Requests for images to visualize something
+
+4. **Widget + Additional Info** → skipSearch: FALSE, intent: ['web_search', 'widget_response']
+   - "weather in NYC and best things to do there"
+   - "AAPL stock and recent Apple news"
+   - "calculate my mortgage and explain how interest works"
+
+5. **Pure Search Queries** → skipSearch: FALSE
+   - Default to web_search for general questions
+   - Use discussions_search when user explicitly mentions Reddit, forums, opinions, experiences
+   - Use academic_search when user explicitly mentions research, papers, studies, scientific
+   - Can combine multiple search intents when appropriate
+
+6. **Fallback when web_search unavailable** → skipSearch: TRUE, intent: ['writing_task'] or []
+   - If no search intents are available and no widgets apply
+   - Set skipSearch to true and use writing_task or empty intent
+</skip_search_decision_tree>
+
+<examples>
+Example 1: Widget-only query
+Query: "What is the weather in New York?"
+Reasoning: User wants current weather → weather widget provides this completely
+Output: skipSearch: true, intent: ['widget_response'], widgets: [weather widget for New York]
+
+Example 2: Widget-only query
+Query: "AAPL stock price"
+Reasoning: User wants stock price → stock_ticker widget provides this completely
+Output: skipSearch: true, intent: ['widget_response'], widgets: [stock_ticker for AAPL]
+
+Example 3: Widget + search query
+Query: "What's the weather in NYC and what are some good outdoor activities?"
+Reasoning: Weather widget handles weather, but outdoor activities need web search
+Output: skipSearch: false, intent: ['web_search', 'widget_response'], widgets: [weather widget for NYC]
+
+Example 4: Pure search query
+Query: "What are the latest developments in AI?"
+Reasoning: No widget applies, needs current web information
+Output: skipSearch: false, intent: ['web_search'], widgets: []
+
+Example 5: Writing task (greeting/simple writing only)
+Query: "Write me a thank you email for a job interview"
+Reasoning: Simple writing task needing no external facts → writing_task
+Output: skipSearch: true, intent: ['writing_task'], widgets: []
+
+Example 5b: Question about something - ALWAYS needs search
+Query: "What is Kimi K2?"
+Reasoning: User is ASKING about something → needs web search for accurate info
+Output: skipSearch: false, intent: ['web_search'], widgets: []
+
+Example 5c: Another question - needs search
+Query: "Explain how photosynthesis works"
+Reasoning: User is ASKING for explanation → needs web search
+Output: skipSearch: false, intent: ['web_search'], widgets: []
+
+Example 6: Image display
+Query: "Show me images of cats"
+Reasoning: User wants to see images → requires image search
+Output: skipSearch: false, intent: ['image_preview'], widgets: []
+
+Example 7: Multiple search sources
+Query: "What does the research say about meditation benefits?"
+Reasoning: Benefits from both academic papers and web articles
+Output: skipSearch: false, intent: ['academic_search', 'web_search'], widgets: []
+
+Example 8: Discussions search
+Query: "What do people on Reddit think about the new iPhone?"
+Reasoning: User explicitly wants forum/community opinions → discussions_search
+Output: skipSearch: false, intent: ['discussions_search'], widgets: []
+
+Example 9: Academic search only
+Query: "Find scientific papers on climate change effects"
+Reasoning: User explicitly wants academic/research papers
+Output: skipSearch: false, intent: ['academic_search'], widgets: []
+</examples>
+
+<standalone_follow_up_guidelines>
+Transform the follow-up into a self-contained question:
+- Include ALL necessary context from chat history
+- Replace pronouns (it, they, this, that) with specific nouns
+- Replace references ("the previous one", "what you mentioned") with actual content
+- Preserve the original complexity—don't over-elaborate simple questions
+- The question should be answerable without seeing the conversation
+</standalone_follow_up_guidelines>
+
+<intent_selection_rules>
+Available intents:
+${input.intentDesc}
+
+Rules:
+- Include at least one intent when applicable
+- For questions/information requests:
+  - Default to web_search unless user explicitly requests another source
+  - Use discussions_search when user mentions: Reddit, forums, opinions, experiences, "what do people think"
+  - Use academic_search when user mentions: research, papers, studies, scientific, scholarly
+  - Can combine intents (e.g., ['academic_search', 'web_search'])
+- If web_search is NOT in available intents and query needs search:
+  - Check if discussions_search or academic_search applies
+  - If no search intent available and no widgets: use writing_task or empty array []
+- private_search: ONLY when user provides specific URLs/documents
+- widget_response: when widgets fully answer the query
+- writing_task: ONLY for greetings and simple writing (never for questions)
+</intent_selection_rules>
+
+<widget_selection_rules>
+Available widgets:
+${input.widgetDesc}
+
+Rules:
+- Include ALL applicable widgets regardless of skipSearch value
+- Each widget type can only be included once
+- Widgets provide structured, real-time data that enhances any response
+</widget_selection_rules>
+
+<output_format>
+Your classification must be precise and consistent:
+{
+    "skipSearch": <true|false>,
+    "standaloneFollowUp": "<self-contained question>",
+    "intent": [<array of selected intents>],
+    "widgets": [<array of selected widgets>]
+}
+</output_format>
+    `;
+};

src/lib/prompts/suggestions/index.ts

@@ -0,0 +1,15 @@
+export const suggestionGeneratorPrompt = `
+You are an AI suggestion generator for an AI powered search engine. You will be given a conversation below. You need to generate 4-5 suggestions based on the conversation. The suggestion should be relevant to the conversation that can be used by the user to ask the chat model for more information.
+You need to make sure the suggestions are relevant to the conversation and are helpful to the user. Keep a note that the user might use these suggestions to ask a chat model for more information. 
+Make sure the suggestions are medium in length and are informative and relevant to the conversation.
+
+Provide these suggestions separated by newlines between the XML tags <suggestions> and </suggestions>. For example:
+
+<suggestions>
+Tell me more about SpaceX and their recent projects
+What is the latest news on SpaceX?
+Who is the CEO of SpaceX?
+</suggestions>
+
+Today's date is ${new Date().toISOString()}
+`;

src/lib/session.ts

@@ -0,0 +1,45 @@
+import { EventEmitter } from 'stream';
+/* todo implement history saving and better artifact typing and handling */
+class SessionManager {
+  private static sessions = new Map<string, SessionManager>();
+  readonly id: string;
+  private artifacts = new Map<string, Artifact>();
+  private emitter = new EventEmitter();
+
+  constructor() {
+    this.id = crypto.randomUUID();
+  }
+
+  static getSession(id: string): SessionManager | undefined {
+    return this.sessions.get(id);
+  }
+
+  static getAllSessions(): SessionManager[] {
+    return Array.from(this.sessions.values());
+  }
+
+  emit(event: string, data: any) {
+    this.emitter.emit(event, data);
+  }
+
+  emitArtifact(artifact: Artifact) {
+    this.artifacts.set(artifact.id, artifact);
+    this.emitter.emit('addArtifact', artifact);
+  }
+
+  appendToArtifact(artifactId: string, data: any) {
+    const artifact = this.artifacts.get(artifactId);
+    if (artifact) {
+      if (typeof artifact.data === 'string') {
+        artifact.data += data;
+      } else if (Array.isArray(artifact.data)) {
+        artifact.data.push(data);
+      } else if (typeof artifact.data === 'object') {
+        Object.assign(artifact.data, data);
+      }
+      this.emitter.emit('updateArtifact', artifact);
+    }
+  }
+}
+
+export default SessionManager;

src/lib/types.ts

@@ -0,0 +1,15 @@
+type Message = {
+  role: 'user' | 'assistant' | 'system';
+  content: string;
+};
+
+type Chunk = {
+  content: string;
+  metadata: Record<string, any>;
+};
+
+type Artifact = {
+  id: string;
+  type: string;
+  data: any;
+};

yarn.lock

@@ -746,19 +746,19 @@
     "@jridgewell/resolve-uri" "^3.1.0"
     "@jridgewell/sourcemap-codec" "^1.4.14"
 
-"@langchain/anthropic@^1.0.0":
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/@langchain/anthropic/-/anthropic-1.0.0.tgz#48535c5682851bf8fddcf37aa7ca78d4d93da932"
-  integrity sha512-Lud/FrkFmXMYW5R9y0FC+RGdgjBBVQ2JAnG3A8E1I4+sqv5JgJttw3HKRpFkyBUSyacs6LMfSn5dbJ6TT9nMiQ==
+"@langchain/anthropic@^1.0.1":
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/@langchain/anthropic/-/anthropic-1.0.1.tgz#a9f836b11ecbce282fc2afb8d707c52fd37711c4"
+  integrity sha512-yVKePAT+nNHtybyyPlWqiq6lqcoDlIuMgL9B4WMEU5gbmzL170iodiqcgcZNFQLOC1V2wCOzywq6Zr0kB24AFg==
   dependencies:
     "@anthropic-ai/sdk" "^0.65.0"
 
-"@langchain/classic@1.0.0":
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/@langchain/classic/-/classic-1.0.0.tgz#acbc15eebba03499cf93e73d2c93703a3da0a46e"
-  integrity sha512-darZFvO5g5e3TqZ4rvZ938F94D4a34v2ZdWfyipmyu7WB4RXMshmYtWCp98o4ec3bfRD9S4+oHMmaPcnk5cs5A==
+"@langchain/classic@1.0.3":
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/@langchain/classic/-/classic-1.0.3.tgz#92482cb4cb8692407b4ecde0df312f035934472f"
+  integrity sha512-XyoaiJSi4y7SzrZMCb3DdDfC+M3gqIQpVH2cOCh9xQf4244jNrncpLXF/MwOJYWxzTsjfcCAHIbFJ0kSH5nqmg==
   dependencies:
-    "@langchain/openai" "1.0.0-alpha.3"
+    "@langchain/openai" "1.1.1"
     "@langchain/textsplitters" "1.0.0"
     handlebars "^4.7.8"
     js-yaml "^4.1.0"
@@ -771,24 +771,24 @@
   optionalDependencies:
     langsmith "^0.3.64"
 
-"@langchain/community@^1.0.0":
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/@langchain/community/-/community-1.0.0.tgz#8e587605b7c981882e20281aa9e644a166620145"
-  integrity sha512-CM4vUZHaFHq8HpWBMIWPO5bo/rmRPJ1/iaJk7s8CghkkQ0WLaZzDtoG/wJKJZMDJOUVCtZKTw+TytlGu00/9dg==
+"@langchain/community@^1.0.3":
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/@langchain/community/-/community-1.0.3.tgz#278c82eee22ff37b120e182b07b7c23ffc6786ab"
+  integrity sha512-86L7qooSY8Fh5Sf2Tu/X8PvDJqvEXohyZUGusuv0XtnWGivwtecBm0vEbVPkLF07I2ZMtyAGzHJOblbveq6Nmg==
   dependencies:
-    "@langchain/classic" "1.0.0"
-    "@langchain/openai" "1.0.0"
+    "@langchain/classic" "1.0.3"
+    "@langchain/openai" "1.1.1"
     binary-extensions "^2.2.0"
-    expr-eval "^2.0.2"
     flat "^5.0.2"
     js-yaml "^4.1.0"
+    math-expression-evaluator "^2.0.0"
     uuid "^10.0.0"
     zod "^3.25.76 || ^4"
 
-"@langchain/core@^1.0.1":
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/@langchain/core/-/core-1.0.1.tgz#c2bdbdff87649fe17b2c86bf535d749ac73a586c"
-  integrity sha512-hVM3EkojYOk4ISJQKjLuWYSH6kyyOFlZIrLFETDA1L0Z2/Iu0q32aJawZ0FDn6rlXE8QZjBt/9OaOL36rXc05w==
+"@langchain/core@^1.0.5":
+  version "1.0.5"
+  resolved "https://registry.yarnpkg.com/@langchain/core/-/core-1.0.5.tgz#1e20ecce80fa4d0b979ea05b24b879b8357d8092"
+  integrity sha512-9Hy/b9+j+mm0Bhnm8xD9B0KpBYTidroLrDHdbrHoMC2DqXoY2umvi1M3M/9D744qsMSaIMP0ZwFcy5YbqI/dGw==
   dependencies:
     "@cfworker/json-schema" "^4.0.2"
     ansi-styles "^5.0.0"
@@ -802,18 +802,18 @@
     uuid "^10.0.0"
     zod "^3.25.76 || ^4"
 
-"@langchain/google-genai@^1.0.0":
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/@langchain/google-genai/-/google-genai-1.0.0.tgz#2785fa163788cb6214dffc1dc29fcd5bbb751493"
-  integrity sha512-ICUBZl/46nG6+Yhe5v7kp/2TQBGOzqEkpfKPLDeNyJ4x9OOL46xsW3ZZrHJjhGMQuR6/JMmQMTU9kLoYgsd1Tg==
+"@langchain/google-genai@^1.0.1":
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/@langchain/google-genai/-/google-genai-1.0.1.tgz#3601645f652f24e3beb55acc61878070b49c24ed"
+  integrity sha512-a9Bzaswp1P+eA2V8hAWSBypqjxmH+/zhOY1TBdalQuPQBTRH35jBMVgX3CTTAheAzBUGQtlDD4/dR9tyemDbhw==
   dependencies:
     "@google/generative-ai" "^0.24.0"
     uuid "^11.1.0"
 
-"@langchain/groq@^1.0.0":
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/@langchain/groq/-/groq-1.0.0.tgz#413b02158761ff406238467325cd4f9fe0990f3a"
-  integrity sha512-6fG9MEQHNXnxgObFHSPh+BPYyTGcoDnKd+GhI9l96cpHh+QNI+IvypicRCZVSsLdqzRCFHISvBQaH+SP5vgjIw==
+"@langchain/groq@^1.0.1":
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/@langchain/groq/-/groq-1.0.1.tgz#7ec8822cd2f29eef4ae0f9c20f67268d1924ab96"
+  integrity sha512-vDQzv6A3mjG0/W/7vL4Iq+dnmhSbMHln+b7Rna810trjZzfNPZhAP6omqZyzCKIqjsQYUH4ODLnSUCNiarfYsQ==
   dependencies:
     groq-sdk "^0.19.0"
 
@@ -842,30 +842,30 @@
     "@langchain/langgraph-sdk" "~1.0.0"
     uuid "^10.0.0"
 
-"@langchain/ollama@^1.0.0":
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/@langchain/ollama/-/ollama-1.0.0.tgz#803c353e9dfb1a9e7b20f1460a6a201fec29bb77"
-  integrity sha512-zqn6i7haMjvZW4FQWo0GrF4wYL5mLurdL0qoe+moYWYSCGaay4K7e/4dqM5C/MR16/HPFDzFbBRMkni2PDRBgA==
+"@langchain/langgraph@^1.0.1":
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/@langchain/langgraph/-/langgraph-1.0.1.tgz#d0be714653e8a27665f86ea795c5c34189455406"
+  integrity sha512-7y8OTDLrHrpJ55Y5x7c7zU2BbqNllXwxM106Xrd+NaQB5CpEb4hbUfIwe4XmhhscKPwvhXAq3tjeUxw9MCiurQ==
+  dependencies:
+    "@langchain/langgraph-checkpoint" "^1.0.0"
+    "@langchain/langgraph-sdk" "~1.0.0"
+    uuid "^10.0.0"
+
+"@langchain/ollama@^1.0.1":
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/@langchain/ollama/-/ollama-1.0.1.tgz#c63ac6db65110beef4020a5e2b167ad0bc678d33"
+  integrity sha512-Pe32hhTpMvnRlNFJxkdu6r1QzsONGz5uvoLiMU1TpgAUu7EyKr2osymlgjBLqDe2vMKUmqHb+yWRH0IppDBUOg==
   dependencies:
     ollama "^0.5.12"
     uuid "^10.0.0"
 
-"@langchain/openai@1.0.0", "@langchain/openai@^1.0.0":
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/@langchain/openai/-/openai-1.0.0.tgz#03b15312286b30ce0149f6052620c6c95b4387bc"
-  integrity sha512-olKEUIjb3HBOiD/NR056iGJz4wiN6HhQ/u65YmGWYadWWoKOcGwheBw/FE0x6SH4zDlI3QmP+vMhuQoaww19BQ==
+"@langchain/openai@1.1.1", "@langchain/openai@^1.1.1":
+  version "1.1.1"
+  resolved "https://registry.yarnpkg.com/@langchain/openai/-/openai-1.1.1.tgz#67ddcf54ee7ac402f6b75b4b9e25447e78c56a93"
+  integrity sha512-0kUaXejo/sn6QAohWHDaAUapC4CJRkJIajGaWfJC+llSqpDBnmBE1oHg1M2fi1OCeP+ns9SxB6BTsq4Qbiqmig==
   dependencies:
     js-tiktoken "^1.0.12"
-    openai "^6.3.0"
-    zod "^3.25.76 || ^4"
-
-"@langchain/openai@1.0.0-alpha.3":
-  version "1.0.0-alpha.3"
-  resolved "https://registry.yarnpkg.com/@langchain/openai/-/openai-1.0.0-alpha.3.tgz#35c4e770e3421b75a226087af54fbeff147e201a"
-  integrity sha512-re2NXLYeLatPzoB6YRoFgB1fW6i5ygcLGa7PlNOhi3f93uU1vSlWMgjkO9dcN9ALmr/bhoruqJEn7U0Eva+6/w==
-  dependencies:
-    js-tiktoken "^1.0.12"
-    openai "^6.3.0"
+    openai "^6.9.0"
     zod "^3.25.76 || ^4"
 
 "@langchain/textsplitters@1.0.0", "@langchain/textsplitters@^1.0.0":
@@ -2607,11 +2607,6 @@ expand-template@^2.0.3:
   resolved "https://registry.yarnpkg.com/expand-template/-/expand-template-2.0.3.tgz#6e14b3fcee0f3a6340ecb57d2e8918692052a47c"
   integrity sha512-XYfuKMvj4O35f/pOXLObndIRvyQ+/+6AhODh+OKWj9S9498pHHn/IMszH+gt0fBCRWMNfk1ZSp5x3AifmnI2vg==
 
-expr-eval@^2.0.2:
-  version "2.0.2"
-  resolved "https://registry.yarnpkg.com/expr-eval/-/expr-eval-2.0.2.tgz#fa6f044a7b0c93fde830954eb9c5b0f7fbc7e201"
-  integrity sha512-4EMSHGOPSwAfBiibw3ndnP0AvjDWLsMvGOvWEZ2F96IGk0bIVdjQisOHxReSkE13mHcfbuCiXw+G4y0zv6N8Eg==
-
 fast-deep-equal@^3.1.1, fast-deep-equal@^3.1.3:
   version "3.1.3"
   resolved "https://registry.yarnpkg.com/fast-deep-equal/-/fast-deep-equal-3.1.3.tgz#3a7d56b559d6cbc3eb512325244e619a65c6c525"
@@ -3514,17 +3509,16 @@ kuler@^2.0.0:
   resolved "https://registry.yarnpkg.com/kuler/-/kuler-2.0.0.tgz#e2c570a3800388fb44407e851531c1d670b061b3"
   integrity sha512-Xq9nH7KlWZmXAtodXDDRE7vs6DU1gTU8zYDHDiWLSip45Egwq3plLHzPn27NgvzL2r1LMPC1vdqh98sQxtqj4A==
 
-langchain@^1.0.1:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/langchain/-/langchain-1.0.1.tgz#fb181176f4aa443ef02e9e5b563bcb4e170dfeb6"
-  integrity sha512-IT4JBVbKBh2AjaUFT9OsmOfeK3UbKy3SgdzZOuvet25sAaMpAR8IaM9XVddRs+OXQqVg6sOS01KUUVCJksVhHg==
+langchain@^1.0.4:
+  version "1.0.4"
+  resolved "https://registry.yarnpkg.com/langchain/-/langchain-1.0.4.tgz#c4fa22d927f41d56c356ecfccea5c08ae7b682ef"
+  integrity sha512-g7z2kKvnXOecybbVGHfI2ZmdmP309mxC1FYlq6WC/7RsKgX5MwY9gBjwK16mpKOaozOD9QCo1Ia7o2UcUBRb9Q==
   dependencies:
     "@langchain/langgraph" "^1.0.0"
     "@langchain/langgraph-checkpoint" "^1.0.0"
+    langsmith "~0.3.74"
     uuid "^10.0.0"
     zod "^3.25.76 || ^4"
-  optionalDependencies:
-    langsmith "^0.3.64"
 
 langsmith@^0.3.64:
   version "0.3.74"
@@ -3539,6 +3533,19 @@ langsmith@^0.3.64:
     semver "^7.6.3"
     uuid "^10.0.0"
 
+langsmith@~0.3.74:
+  version "0.3.79"
+  resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.3.79.tgz#6c845644da26e7fdd8e9b80706091669fc43bda4"
+  integrity sha512-j5uiAsyy90zxlxaMuGjb7EdcL51Yx61SpKfDOI1nMPBbemGju+lf47he4e59Hp5K63CY8XWgFP42WeZ+zuIU4Q==
+  dependencies:
+    "@types/uuid" "^10.0.0"
+    chalk "^4.1.2"
+    console-table-printer "^2.12.1"
+    p-queue "^6.6.2"
+    p-retry "4"
+    semver "^7.6.3"
+    uuid "^10.0.0"
+
 language-subtag-registry@^0.3.20:
   version "0.3.22"
   resolved "https://registry.yarnpkg.com/language-subtag-registry/-/language-subtag-registry-0.3.22.tgz#2e1500861b2e457eba7e7ae86877cbd08fa1fd1d"
@@ -3686,6 +3693,11 @@ matcher@^3.0.0:
   dependencies:
     escape-string-regexp "^4.0.0"
 
+math-expression-evaluator@^2.0.0:
+  version "2.0.7"
+  resolved "https://registry.yarnpkg.com/math-expression-evaluator/-/math-expression-evaluator-2.0.7.tgz#dc99a80ce2bf7f9b7df878126feb5c506c1fdf5f"
+  integrity sha512-uwliJZ6BPHRq4eiqNWxZBDzKUiS5RIynFFcgchqhBOloVLVBpZpNG8jRYkedLcBvhph8TnRyWEuxPqiQcwIdog==
+
 math-intrinsics@^1.1.0:
   version "1.1.0"
   resolved "https://registry.yarnpkg.com/math-intrinsics/-/math-intrinsics-1.1.0.tgz#a0dd74be81e2aa5c2f27e65ce283605ee4e2b7f9"
@@ -4025,10 +4037,10 @@ onnxruntime-web@1.22.0-dev.20250409-89f8206ba4:
     platform "^1.3.6"
     protobufjs "^7.2.4"
 
-openai@^6.3.0:
-  version "6.5.0"
-  resolved "https://registry.yarnpkg.com/openai/-/openai-6.5.0.tgz#7dd9c4c0ca6e394c1d1e738b2000e084024685b2"
-  integrity sha512-bNqJ15Ijbs41KuJ2iYz/mGAruFHzQQt7zXo4EvjNLoB64aJdgn1jlMeDTsXjEg+idVYafg57QB/5Rd16oqvZ6A==
+openai@^6.9.0:
+  version "6.9.0"
+  resolved "https://registry.yarnpkg.com/openai/-/openai-6.9.0.tgz#acd15b2233c42b165981f3de8f4cfce27f844fce"
+  integrity sha512-n2sJRYmM+xfJ0l3OfH8eNnIyv3nQY7L08gZQu3dw6wSdfPtKAk92L83M2NIP5SS8Cl/bsBBG3yKzEOjkx0O+7A==
 
 openapi-types@^12.1.3:
   version "12.1.3"
@@ -5491,12 +5503,7 @@ yocto-queue@^0.1.0:
   resolved "https://registry.yarnpkg.com/yocto-queue/-/yocto-queue-0.1.0.tgz#0294eb3dee05028d31ee1a5fa2c556a6aaf10a1b"
   integrity sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==
 
-zod@^3.22.4:
-  version "3.22.4"
-  resolved "https://registry.yarnpkg.com/zod/-/zod-3.22.4.tgz#f31c3a9386f61b1f228af56faa9255e845cf3fff"
-  integrity sha512-iC+8Io04lddc+mVqQ9AZ7OQ2MrUKGN+oIQyq1vemgt46jwCwLfhq7/pwnBnNXXXZb8VTVLKwp9EDkx+ryxIWmg==
-
-"zod@^3.25.76 || ^4":
+"zod@^3.25.76 || ^4", zod@^4.1.12:
   version "4.1.12"
   resolved "https://registry.yarnpkg.com/zod/-/zod-4.1.12.tgz#64f1ea53d00eab91853195653b5af9eee68970f0"
   integrity sha512-JInaHOamG8pt5+Ey8kGmdcAcg3OL9reK8ltczgHTAwNhMys/6ThXHityHxVV2p3fkw/c+MAvBHFVYHFZDmjMCQ==