Update manager.ts

Merge master into feat/improve-search-architecture

drizzle/0002_daffy_wrecker.sql

@@ -0,0 +1,15 @@
+PRAGMA foreign_keys=OFF;--> statement-breakpoint
+CREATE TABLE `__new_messages` (
+	`id` integer PRIMARY KEY NOT NULL,
+	`messageId` text NOT NULL,
+	`chatId` text NOT NULL,
+	`backendId` text NOT NULL,
+	`query` text NOT NULL,
+	`createdAt` text NOT NULL,
+	`responseBlocks` text DEFAULT '[]',
+	`status` text DEFAULT 'answering'
+);
+--> statement-breakpoint
+DROP TABLE `messages`;--> statement-breakpoint
+ALTER TABLE `__new_messages` RENAME TO `messages`;--> statement-breakpoint
+PRAGMA foreign_keys=ON;

drizzle/meta/0002_snapshot.json

@@ -0,0 +1,132 @@
+{
+  "version": "6",
+  "dialect": "sqlite",
+  "id": "1c5eb804-d6b4-48ec-9a8f-75fb729c8e52",
+  "prevId": "6dedf55f-0e44-478f-82cf-14a21ac686f8",
+  "tables": {
+    "chats": {
+      "name": "chats",
+      "columns": {
+        "id": {
+          "name": "id",
+          "type": "text",
+          "primaryKey": true,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "title": {
+          "name": "title",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "createdAt": {
+          "name": "createdAt",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "focusMode": {
+          "name": "focusMode",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "files": {
+          "name": "files",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": false,
+          "autoincrement": false,
+          "default": "'[]'"
+        }
+      },
+      "indexes": {},
+      "foreignKeys": {},
+      "compositePrimaryKeys": {},
+      "uniqueConstraints": {},
+      "checkConstraints": {}
+    },
+    "messages": {
+      "name": "messages",
+      "columns": {
+        "id": {
+          "name": "id",
+          "type": "integer",
+          "primaryKey": true,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "messageId": {
+          "name": "messageId",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "chatId": {
+          "name": "chatId",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "backendId": {
+          "name": "backendId",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "query": {
+          "name": "query",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "createdAt": {
+          "name": "createdAt",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "responseBlocks": {
+          "name": "responseBlocks",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": false,
+          "autoincrement": false,
+          "default": "'[]'"
+        },
+        "status": {
+          "name": "status",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": false,
+          "autoincrement": false,
+          "default": "'answering'"
+        }
+      },
+      "indexes": {},
+      "foreignKeys": {},
+      "compositePrimaryKeys": {},
+      "uniqueConstraints": {},
+      "checkConstraints": {}
+    }
+  },
+  "views": {},
+  "enums": {},
+  "_meta": {
+    "schemas": {},
+    "tables": {},
+    "columns": {}
+  },
+  "internal": {
+    "indexes": {}
+  }
+}

drizzle/meta/_journal.json

@@ -15,6 +15,13 @@
       "when": 1758863991284,
       "tag": "0001_wise_rockslide",
       "breakpoints": true
+    },
+    {
+      "idx": 2,
+      "version": "6",
+      "when": 1763732708332,
+      "tag": "0002_daffy_wrecker",
+      "breakpoints": true
     }
   ]
 }

next-env.d.ts

@@ -1,5 +1,6 @@
 /// <reference types="next" />
 /// <reference types="next/image-types/global" />
+import "./.next/dev/types/routes.d.ts";
 
 // NOTE: This file should not be edited
 // see https://nextjs.org/docs/app/api-reference/config/typescript for more information.

next.config.mjs

@@ -1,3 +1,5 @@
+import pkg from './package.json' with { type: 'json' };
+
 /** @type {import('next').NextConfig} */
 const nextConfig = {
   output: 'standalone',
@@ -9,6 +11,9 @@ const nextConfig = {
     ],
   },
   serverExternalPackages: ['pdf-parse'],
+  env: {
+    NEXT_PUBLIC_VERSION: pkg.version,
+  },
 };
 
 export default nextConfig;

package.json

@@ -14,50 +14,47 @@
     "@headlessui/react": "^2.2.0",
     "@headlessui/tailwindcss": "^0.2.2",
     "@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/textsplitters": "^1.0.0",
     "@tailwindcss/typography": "^0.5.12",
+    "@types/jspdf": "^2.0.0",
     "axios": "^1.8.3",
     "better-sqlite3": "^11.9.1",
     "clsx": "^2.1.0",
-    "compute-cosine-similarity": "^1.1.0",
     "drizzle-orm": "^0.40.1",
-    "framer-motion": "^12.23.24",
-    "html-to-text": "^9.0.5",
-    "jspdf": "^3.0.1",
-    "langchain": "^1.0.1",
-    "lucide-react": "^0.363.0",
+    "framer-motion": "^12.23.25",
+    "js-tiktoken": "^1.0.21",
+    "jspdf": "^3.0.4",
+    "lightweight-charts": "^5.0.9",
+    "lucide-react": "^0.556.0",
     "mammoth": "^1.9.1",
     "markdown-to-jsx": "^7.7.2",
-    "next": "^15.2.2",
+    "mathjs": "^15.1.0",
+    "next": "^16.0.7",
     "next-themes": "^0.3.0",
-    "pdf-parse": "^1.1.1",
+    "officeparser": "^5.2.2",
+    "ollama": "^0.6.3",
+    "openai": "^6.9.0",
+    "partial-json": "^0.1.7",
+    "pdf-parse": "^2.4.5",
     "react": "^18",
     "react-dom": "^18",
     "react-text-to-speech": "^0.14.5",
     "react-textarea-autosize": "^8.5.3",
+    "rfc6902": "^5.1.2",
     "sonner": "^1.4.41",
     "tailwind-merge": "^2.2.2",
-    "winston": "^3.17.0",
+    "turndown": "^7.2.2",
+    "yahoo-finance2": "^3.10.2",
     "yet-another-react-lightbox": "^3.17.2",
-    "zod": "^3.22.4"
+    "zod": "^4.1.12"
   },
   "devDependencies": {
     "@types/better-sqlite3": "^7.6.12",
-    "@types/html-to-text": "^9.0.4",
-    "@types/jspdf": "^2.0.0",
     "@types/node": "^24.8.1",
     "@types/pdf-parse": "^1.1.4",
     "@types/react": "^18",
     "@types/react-dom": "^18",
+    "@types/turndown": "^5.0.6",
     "autoprefixer": "^10.0.1",
     "drizzle-kit": "^0.30.5",
     "eslint": "^8",

src/app/api/chat/route.ts

@@ -1,14 +1,9 @@
-import crypto from 'crypto';
-import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
-import { EventEmitter } from 'stream';
-import db from '@/lib/db';
-import { chats, messages as messagesSchema } from '@/lib/db/schema';
-import { and, eq, gt } from 'drizzle-orm';
-import { getFileDetails } from '@/lib/utils/files';
-import { searchHandlers } from '@/lib/search';
 import { z } from 'zod';
 import ModelRegistry from '@/lib/models/registry';
 import { ModelWithProvider } from '@/lib/models/types';
+import SearchAgent from '@/lib/agents/search';
+import SessionManager from '@/lib/session';
+import { ChatTurnMessage } from '@/lib/types';
 
 export const runtime = 'nodejs';
 export const dynamic = 'force-dynamic';
@@ -20,47 +15,25 @@ const messageSchema = z.object({
 });
 
 const chatModelSchema: z.ZodType<ModelWithProvider> = z.object({
-  providerId: z.string({
-    errorMap: () => ({
-      message: 'Chat model provider id must be provided',
-    }),
-  }),
-  key: z.string({
-    errorMap: () => ({
-      message: 'Chat model key must be provided',
-    }),
-  }),
+  providerId: z.string({ message: 'Chat model provider id must be provided' }),
+  key: z.string({ message: 'Chat model key must be provided' }),
 });
 
 const embeddingModelSchema: z.ZodType<ModelWithProvider> = z.object({
   providerId: z.string({
-    errorMap: () => ({
-      message: 'Embedding model provider id must be provided',
-    }),
-  }),
-  key: z.string({
-    errorMap: () => ({
-      message: 'Embedding model key must be provided',
-    }),
+    message: 'Embedding model provider id must be provided',
   }),
+  key: z.string({ message: 'Embedding model key must be provided' }),
 });
 
 const bodySchema = z.object({
   message: messageSchema,
   optimizationMode: z.enum(['speed', 'balanced', 'quality'], {
-    errorMap: () => ({
-      message: 'Optimization mode must be one of: speed, balanced, quality',
-    }),
+    message: 'Optimization mode must be one of: speed, balanced, quality',
   }),
   focusMode: z.string().min(1, 'Focus mode is required'),
   history: z
-    .array(
-      z.tuple([z.string(), z.string()], {
-        errorMap: () => ({
-          message: 'History items must be tuples of two strings',
-        }),
-      }),
-    )
+    .array(z.tuple([z.string(), z.string()]))
     .optional()
     .default([]),
   files: z.array(z.string()).optional().default([]),
@@ -78,7 +51,7 @@ const safeValidateBody = (data: unknown) => {
   if (!result.success) {
     return {
       success: false,
-      error: result.error.errors.map((e) => ({
+      error: result.error.issues.map((e: any) => ({
         path: e.path.join('.'),
         message: e.message,
       })),
@@ -91,151 +64,12 @@ const safeValidateBody = (data: unknown) => {
   };
 };
 
-const handleEmitterEvents = async (
-  stream: EventEmitter,
-  writer: WritableStreamDefaultWriter,
-  encoder: TextEncoder,
-  chatId: string,
-) => {
-  let receivedMessage = '';
-  const aiMessageId = crypto.randomBytes(7).toString('hex');
-
-  stream.on('data', (data) => {
-    const parsedData = JSON.parse(data);
-    if (parsedData.type === 'response') {
-      writer.write(
-        encoder.encode(
-          JSON.stringify({
-            type: 'message',
-            data: parsedData.data,
-            messageId: aiMessageId,
-          }) + '\n',
-        ),
-      );
-
-      receivedMessage += parsedData.data;
-    } else if (parsedData.type === 'sources') {
-      writer.write(
-        encoder.encode(
-          JSON.stringify({
-            type: 'sources',
-            data: parsedData.data,
-            messageId: aiMessageId,
-          }) + '\n',
-        ),
-      );
-
-      const sourceMessageId = crypto.randomBytes(7).toString('hex');
-
-      db.insert(messagesSchema)
-        .values({
-          chatId: chatId,
-          messageId: sourceMessageId,
-          role: 'source',
-          sources: parsedData.data,
-          createdAt: new Date().toString(),
-        })
-        .execute();
-    }
-  });
-  stream.on('end', () => {
-    writer.write(
-      encoder.encode(
-        JSON.stringify({
-          type: 'messageEnd',
-        }) + '\n',
-      ),
-    );
-    writer.close();
-
-    db.insert(messagesSchema)
-      .values({
-        content: receivedMessage,
-        chatId: chatId,
-        messageId: aiMessageId,
-        role: 'assistant',
-        createdAt: new Date().toString(),
-      })
-      .execute();
-  });
-  stream.on('error', (data) => {
-    const parsedData = JSON.parse(data);
-    writer.write(
-      encoder.encode(
-        JSON.stringify({
-          type: 'error',
-          data: parsedData.data,
-        }),
-      ),
-    );
-    writer.close();
-  });
-};
-
-const handleHistorySave = async (
-  message: Message,
-  humanMessageId: string,
-  focusMode: string,
-  files: string[],
-) => {
-  const chat = await db.query.chats.findFirst({
-    where: eq(chats.id, message.chatId),
-  });
-
-  const fileData = files.map(getFileDetails);
-
-  if (!chat) {
-    await db
-      .insert(chats)
-      .values({
-        id: message.chatId,
-        title: message.content,
-        createdAt: new Date().toString(),
-        focusMode: focusMode,
-        files: fileData,
-      })
-      .execute();
-  } else if (JSON.stringify(chat.files ?? []) != JSON.stringify(fileData)) {
-    db.update(chats)
-      .set({
-        files: files.map(getFileDetails),
-      })
-      .where(eq(chats.id, message.chatId));
-  }
-
-  const messageExists = await db.query.messages.findFirst({
-    where: eq(messagesSchema.messageId, humanMessageId),
-  });
-
-  if (!messageExists) {
-    await db
-      .insert(messagesSchema)
-      .values({
-        content: message.content,
-        chatId: message.chatId,
-        messageId: humanMessageId,
-        role: 'user',
-        createdAt: new Date().toString(),
-      })
-      .execute();
-  } else {
-    await db
-      .delete(messagesSchema)
-      .where(
-        and(
-          gt(messagesSchema.id, messageExists.id),
-          eq(messagesSchema.chatId, message.chatId),
-        ),
-      )
-      .execute();
-  }
-};
-
 export const POST = async (req: Request) => {
   try {
     const reqBody = (await req.json()) as Body;
 
     const parseBody = safeValidateBody(reqBody);
+
     if (!parseBody.success) {
       return Response.json(
         { message: 'Invalid request body', error: parseBody.error },
@@ -265,48 +99,117 @@ export const POST = async (req: Request) => {
       ),
     ]);
 
-    const humanMessageId =
-      message.messageId ?? crypto.randomBytes(7).toString('hex');
-
-    const history: BaseMessage[] = body.history.map((msg) => {
+    const history: ChatTurnMessage[] = body.history.map((msg) => {
       if (msg[0] === 'human') {
-        return new HumanMessage({
+        return {
+          role: 'user',
           content: msg[1],
-        });
+        };
       } else {
-        return new AIMessage({
+        return {
+          role: 'assistant',
           content: msg[1],
-        });
+        };
       }
     });
 
-    const handler = searchHandlers[body.focusMode];
-
-    if (!handler) {
-      return Response.json(
-        {
-          message: 'Invalid focus mode',
-        },
-        { status: 400 },
-      );
-    }
-
-    const stream = await handler.searchAndAnswer(
-      message.content,
-      history,
-      llm,
-      embedding,
-      body.optimizationMode,
-      body.files,
-      body.systemInstructions as string,
-    );
+    const agent = new SearchAgent();
+    const session = SessionManager.createSession();
 
     const responseStream = new TransformStream();
     const writer = responseStream.writable.getWriter();
     const encoder = new TextEncoder();
 
-    handleEmitterEvents(stream, writer, encoder, message.chatId);
-    handleHistorySave(message, humanMessageId, body.focusMode, body.files);
+    let receivedMessage = '';
+
+    session.addListener('data', (data: any) => {
+      if (data.type === 'response') {
+        writer.write(
+          encoder.encode(
+            JSON.stringify({
+              type: 'message',
+              data: data.data,
+            }) + '\n',
+          ),
+        );
+        receivedMessage += data.data;
+      } else if (data.type === 'sources') {
+        writer.write(
+          encoder.encode(
+            JSON.stringify({
+              type: 'sources',
+              data: data.data,
+            }) + '\n',
+          ),
+        );
+      } else if (data.type === 'block') {
+        writer.write(
+          encoder.encode(
+            JSON.stringify({
+              type: 'block',
+              block: data.block,
+            }) + '\n',
+          ),
+        );
+      } else if (data.type === 'updateBlock') {
+        writer.write(
+          encoder.encode(
+            JSON.stringify({
+              type: 'updateBlock',
+              blockId: data.blockId,
+              patch: data.patch,
+            }) + '\n',
+          ),
+        );
+      } else if (data.type === 'researchComplete') {
+        writer.write(
+          encoder.encode(
+            JSON.stringify({
+              type: 'researchComplete',
+            }) + '\n',
+          ),
+        );
+      }
+    });
+
+    session.addListener('end', () => {
+      writer.write(
+        encoder.encode(
+          JSON.stringify({
+            type: 'messageEnd',
+          }) + '\n',
+        ),
+      );
+      writer.close();
+      session.removeAllListeners();
+    });
+
+    session.addListener('error', (data: any) => {
+      writer.write(
+        encoder.encode(
+          JSON.stringify({
+            type: 'error',
+            data: data.data,
+          }) + '\n',
+        ),
+      );
+      writer.close();
+      session.removeAllListeners();
+    });
+
+    agent.searchAsync(session, {
+      chatHistory: history,
+      followUp: message.content,
+      config: {
+        llm,
+        embedding: embedding,
+        sources: ['web'],
+        mode: body.optimizationMode,
+        fileIds: body.files,
+      },
+    });
+
+    /*  handleHistorySave(message, humanMessageId, body.focusMode, body.files); */
 
     return new Response(responseStream.readable, {
       headers: {

src/app/api/images/route.ts

@@ -1,7 +1,6 @@
-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';
 
 interface ImageSearchBody {
   query: string;
@@ -13,16 +12,6 @@ export const POST = async (req: Request) => {
   try {
     const body: ImageSearchBody = await req.json();
 
-    const chatHistory = body.chatHistory
-      .map((msg: any) => {
-        if (msg.role === 'user') {
-          return new HumanMessage(msg.content);
-        } else if (msg.role === 'assistant') {
-          return new AIMessage(msg.content);
-        }
-      })
-      .filter((msg) => msg !== undefined) as BaseMessage[];
-
     const registry = new ModelRegistry();
 
     const llm = await registry.loadChatModel(
@@ -30,9 +19,9 @@ export const POST = async (req: Request) => {
       body.chatModel.key,
     );
 
-    const images = await handleImageSearch(
+    const images = await searchImages(
       {
-        chat_history: chatHistory,
+        chatHistory: body.chatHistory,
         query: body.query,
       },
       llm,

src/app/api/search/route.ts

@@ -1,8 +1,8 @@
-import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
-import { MetaSearchAgentType } from '@/lib/search/metaSearchAgent';
-import { searchHandlers } from '@/lib/search';
 import ModelRegistry from '@/lib/models/registry';
 import { ModelWithProvider } from '@/lib/models/types';
+import SessionManager from '@/lib/session';
+import SearchAgent from '@/lib/agents/search';
+import { ChatTurnMessage } from '@/lib/types';
 
 interface ChatRequestBody {
   optimizationMode: 'speed' | 'balanced';
@@ -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([
@@ -46,21 +40,27 @@ export const POST = async (req: Request) => {
       ),
     ]);
 
-    const searchHandler: MetaSearchAgentType = searchHandlers[body.focusMode];
+    const history: ChatTurnMessage[] = body.history.map((msg) => {
+      return msg[0] === 'human'
+        ? { role: 'user', content: msg[1] }
+        : { role: 'assistant', content: msg[1] };
+    });
 
-    if (!searchHandler) {
-      return Response.json({ message: 'Invalid focus mode' }, { status: 400 });
-    }
+    const session = SessionManager.createSession();
 
-    const emitter = await searchHandler.searchAndAnswer(
-      body.query,
-      history,
-      llm,
-      embeddings,
-      body.optimizationMode,
-      [],
-      body.systemInstructions || '',
-    );
+    const agent = new SearchAgent();
+
+    agent.searchAsync(session, {
+      chatHistory: history,
+      config: {
+        embedding: embeddings,
+        llm: llm,
+        sources: ['web', 'discussions', 'academic'],
+        mode: 'balanced',
+        fileIds: []
+      },
+      followUp: body.query,
+    });
 
     if (!body.stream) {
       return new Promise(
@@ -71,7 +71,7 @@ export const POST = async (req: Request) => {
           let message = '';
           let sources: any[] = [];
 
-          emitter.on('data', (data: string) => {
+          session.addListener('data', (data: string) => {
             try {
               const parsedData = JSON.parse(data);
               if (parsedData.type === 'response') {
@@ -89,11 +89,11 @@ export const POST = async (req: Request) => {
             }
           });
 
-          emitter.on('end', () => {
+          session.addListener('end', () => {
             resolve(Response.json({ message, sources }, { status: 200 }));
           });
 
-          emitter.on('error', (error: any) => {
+          session.addListener('error', (error: any) => {
             reject(
               Response.json(
                 { message: 'Search error', error },
@@ -124,14 +124,14 @@ export const POST = async (req: Request) => {
         );
 
         signal.addEventListener('abort', () => {
-          emitter.removeAllListeners();
+          session.removeAllListeners();
 
           try {
             controller.close();
           } catch (error) {}
         });
 
-        emitter.on('data', (data: string) => {
+        session.addListener('data', (data: string) => {
           if (signal.aborted) return;
 
           try {
@@ -162,7 +162,7 @@ export const POST = async (req: Request) => {
           }
         });
 
-        emitter.on('end', () => {
+        session.addListener('end', () => {
           if (signal.aborted) return;
 
           controller.enqueue(
@@ -175,7 +175,7 @@ export const POST = async (req: Request) => {
           controller.close();
         });
 
-        emitter.on('error', (error: any) => {
+        session.addListener('error', (error: any) => {
           if (signal.aborted) return;
 
           controller.error(error);

src/app/api/suggestions/route.ts

@@ -1,8 +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 {
   chatHistory: any[];
@@ -13,16 +11,6 @@ export const POST = async (req: Request) => {
   try {
     const body: SuggestionsGenerationBody = await req.json();
 
-    const chatHistory = body.chatHistory
-      .map((msg: any) => {
-        if (msg.role === 'user') {
-          return new HumanMessage(msg.content);
-        } else if (msg.role === 'assistant') {
-          return new AIMessage(msg.content);
-        }
-      })
-      .filter((msg) => msg !== undefined) as BaseMessage[];
-
     const registry = new ModelRegistry();
 
     const llm = await registry.loadChatModel(
@@ -32,7 +20,7 @@ export const POST = async (req: Request) => {
 
     const suggestions = await generateSuggestions(
       {
-        chat_history: chatHistory,
+        chatHistory: body.chatHistory,
       },
       llm,
     );

src/app/api/uploads/route.ts

@@ -1,39 +1,16 @@
 import { NextResponse } from 'next/server';
-import fs from 'fs';
-import path from 'path';
-import crypto from 'crypto';
-import { PDFLoader } from '@langchain/community/document_loaders/fs/pdf';
-import { DocxLoader } from '@langchain/community/document_loaders/fs/docx';
-import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';
-import { Document } from '@langchain/core/documents';
 import ModelRegistry from '@/lib/models/registry';
-
-interface FileRes {
-  fileName: string;
-  fileExtension: string;
-  fileId: string;
-}
-
-const uploadDir = path.join(process.cwd(), 'uploads');
-
-if (!fs.existsSync(uploadDir)) {
-  fs.mkdirSync(uploadDir, { recursive: true });
-}
-
-const splitter = new RecursiveCharacterTextSplitter({
-  chunkSize: 500,
-  chunkOverlap: 100,
-});
+import UploadManager from '@/lib/uploads/manager';
 
 export async function POST(req: Request) {
   try {
     const formData = await req.formData();
 
     const files = formData.getAll('files') as File[];
-    const embedding_model = formData.get('embedding_model_key') as string;
-    const embedding_model_provider = formData.get('embedding_model_provider_id') as string;
+    const embeddingModel = formData.get('embedding_model_key') as string;
+    const embeddingModelProvider = formData.get('embedding_model_provider_id') as string;
 
-    if (!embedding_model || !embedding_model_provider) {
+    if (!embeddingModel || !embeddingModelProvider) {
       return NextResponse.json(
         { message: 'Missing embedding model or provider' },
         { status: 400 },
@@ -42,73 +19,13 @@ export async function POST(req: Request) {
 
     const registry = new ModelRegistry();
 
-    const model = await registry.loadEmbeddingModel(embedding_model_provider, embedding_model);
+    const model = await registry.loadEmbeddingModel(embeddingModelProvider, embeddingModel);
+    
+    const uploadManager = new UploadManager({
+      embeddingModel: model,
+    })
 
-    const processedFiles: FileRes[] = [];
-
-    await Promise.all(
-      files.map(async (file: any) => {
-        const fileExtension = file.name.split('.').pop();
-        if (!['pdf', 'docx', 'txt'].includes(fileExtension!)) {
-          return NextResponse.json(
-            { message: 'File type not supported' },
-            { status: 400 },
-          );
-        }
-
-        const uniqueFileName = `${crypto.randomBytes(16).toString('hex')}.${fileExtension}`;
-        const filePath = path.join(uploadDir, uniqueFileName);
-
-        const buffer = Buffer.from(await file.arrayBuffer());
-        fs.writeFileSync(filePath, new Uint8Array(buffer));
-
-        let docs: any[] = [];
-        if (fileExtension === 'pdf') {
-          const loader = new PDFLoader(filePath);
-          docs = await loader.load();
-        } else if (fileExtension === 'docx') {
-          const loader = new DocxLoader(filePath);
-          docs = await loader.load();
-        } else if (fileExtension === 'txt') {
-          const text = fs.readFileSync(filePath, 'utf-8');
-          docs = [
-            new Document({ pageContent: text, metadata: { title: file.name } }),
-          ];
-        }
-
-        const splitted = await splitter.splitDocuments(docs);
-
-        const extractedDataPath = filePath.replace(/\.\w+$/, '-extracted.json');
-        fs.writeFileSync(
-          extractedDataPath,
-          JSON.stringify({
-            title: file.name,
-            contents: splitted.map((doc) => doc.pageContent),
-          }),
-        );
-
-        const embeddings = await model.embedDocuments(
-          splitted.map((doc) => doc.pageContent),
-        );
-        const embeddingsDataPath = filePath.replace(
-          /\.\w+$/,
-          '-embeddings.json',
-        );
-        fs.writeFileSync(
-          embeddingsDataPath,
-          JSON.stringify({
-            title: file.name,
-            embeddings,
-          }),
-        );
-
-        processedFiles.push({
-          fileName: file.name,
-          fileExtension: fileExtension,
-          fileId: uniqueFileName.replace(/\.\w+$/, ''),
-        });
-      }),
-    );
+    const processedFiles = await uploadManager.processFiles(files);
 
     return NextResponse.json({
       files: processedFiles,

src/app/api/videos/route.ts

@@ -1,7 +1,6 @@
-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';
 
 interface VideoSearchBody {
   query: string;
@@ -13,16 +12,6 @@ export const POST = async (req: Request) => {
   try {
     const body: VideoSearchBody = await req.json();
 
-    const chatHistory = body.chatHistory
-      .map((msg: any) => {
-        if (msg.role === 'user') {
-          return new HumanMessage(msg.content);
-        } else if (msg.role === 'assistant') {
-          return new AIMessage(msg.content);
-        }
-      })
-      .filter((msg) => msg !== undefined) as BaseMessage[];
-
     const registry = new ModelRegistry();
 
     const llm = await registry.loadChatModel(
@@ -32,7 +21,7 @@ export const POST = async (req: Request) => {
 
     const videos = await handleVideoSearch(
       {
-        chat_history: chatHistory,
+        chatHistory: body.chatHistory,
         query: body.query,
       },
       llm,

src/components/AssistantSteps.tsx

@@ -0,0 +1,267 @@
+'use client';
+
+import {
+  Brain,
+  Search,
+  FileText,
+  ChevronDown,
+  ChevronUp,
+  BookSearch,
+} from 'lucide-react';
+import { motion, AnimatePresence } from 'framer-motion';
+import { useEffect, useState } from 'react';
+import { ResearchBlock, ResearchBlockSubStep } from '@/lib/types';
+import { useChat } from '@/lib/hooks/useChat';
+
+const getStepIcon = (step: ResearchBlockSubStep) => {
+  if (step.type === 'reasoning') {
+    return <Brain className="w-4 h-4" />;
+  } else if (step.type === 'searching' || step.type === 'upload_searching') {
+    return <Search className="w-4 h-4" />;
+  } else if (
+    step.type === 'search_results' ||
+    step.type === 'upload_search_results'
+  ) {
+    return <FileText className="w-4 h-4" />;
+  } else if (step.type === 'reading') {
+    return <BookSearch className="w-4 h-4" />;
+  }
+
+  return null;
+};
+
+const getStepTitle = (
+  step: ResearchBlockSubStep,
+  isStreaming: boolean,
+): string => {
+  if (step.type === 'reasoning') {
+    return isStreaming && !step.reasoning ? 'Thinking...' : 'Thinking';
+  } else if (step.type === 'searching') {
+    return `Searching ${step.searching.length} ${step.searching.length === 1 ? 'query' : 'queries'}`;
+  } else if (step.type === 'search_results') {
+    return `Found ${step.reading.length} ${step.reading.length === 1 ? 'result' : 'results'}`;
+  } else if (step.type === 'reading') {
+    return `Reading ${step.reading.length} ${step.reading.length === 1 ? 'source' : 'sources'}`;
+  } else if (step.type === 'upload_searching') {
+    return 'Scanning your uploaded documents';
+  } else if (step.type === 'upload_search_results') {
+    return `Reading ${step.results.length} ${step.results.length === 1 ? 'document' : 'documents'}`;
+  }
+
+  return 'Processing';
+};
+
+const AssistantSteps = ({
+  block,
+  status,
+}: {
+  block: ResearchBlock;
+  status: 'answering' | 'completed' | 'error';
+}) => {
+  const [isExpanded, setIsExpanded] = useState(true);
+  const { researchEnded, loading } = useChat();
+
+  useEffect(() => {
+    if (researchEnded) {
+      setIsExpanded(false);
+    } else if (status === 'answering') {
+      setIsExpanded(true);
+    }
+  }, [researchEnded, status]);
+
+  if (!block || block.data.subSteps.length === 0) return null;
+
+  return (
+    <div className="rounded-lg bg-light-secondary dark:bg-dark-secondary border border-light-200 dark:border-dark-200 overflow-hidden">
+      <button
+        onClick={() => setIsExpanded(!isExpanded)}
+        className="w-full flex items-center justify-between p-3 hover:bg-light-200 dark:hover:bg-dark-200 transition duration-200"
+      >
+        <div className="flex items-center gap-2">
+          <Brain className="w-4 h-4 text-black dark:text-white" />
+          <span className="text-sm font-medium text-black dark:text-white">
+            Research Progress ({block.data.subSteps.length}{' '}
+            {block.data.subSteps.length === 1 ? 'step' : 'steps'})
+          </span>
+        </div>
+        {isExpanded ? (
+          <ChevronUp className="w-4 h-4 text-black/70 dark:text-white/70" />
+        ) : (
+          <ChevronDown className="w-4 h-4 text-black/70 dark:text-white/70" />
+        )}
+      </button>
+
+      <AnimatePresence>
+        {isExpanded && (
+          <motion.div
+            initial={{ height: 0, opacity: 0 }}
+            animate={{ height: 'auto', opacity: 1 }}
+            exit={{ height: 0, opacity: 0 }}
+            transition={{ duration: 0.2 }}
+            className="border-t border-light-200 dark:border-dark-200"
+          >
+            <div className="p-3 space-y-2">
+              {block.data.subSteps.map((step, index) => {
+                const isLastStep = index === block.data.subSteps.length - 1;
+                const isStreaming = loading && isLastStep && !researchEnded;
+
+                return (
+                  <motion.div
+                    key={step.id}
+                    initial={{ opacity: 0, x: -10 }}
+                    animate={{ opacity: 1, x: 0 }}
+                    transition={{ duration: 0.2, delay: 0 }}
+                    className="flex gap-2"
+                  >
+                    <div className="flex flex-col items-center -mt-0.5">
+                      <div
+                        className={`rounded-full p-1.5 bg-light-100 dark:bg-dark-100 text-black/70 dark:text-white/70 ${isStreaming ? 'animate-pulse' : ''}`}
+                      >
+                        {getStepIcon(step)}
+                      </div>
+                      {index < block.data.subSteps.length - 1 && (
+                        <div className="w-0.5 flex-1 min-h-[20px] bg-light-200 dark:bg-dark-200 mt-1.5" />
+                      )}
+                    </div>
+
+                    <div className="flex-1 pb-1">
+                      <span className="text-sm font-medium text-black dark:text-white">
+                        {getStepTitle(step, isStreaming)}
+                      </span>
+
+                      {step.type === 'reasoning' && (
+                        <>
+                          {step.reasoning && (
+                            <p className="text-xs text-black/70 dark:text-white/70 mt-0.5">
+                              {step.reasoning}
+                            </p>
+                          )}
+                          {isStreaming && !step.reasoning && (
+                            <div className="flex items-center gap-1.5 mt-0.5">
+                              <div
+                                className="w-1.5 h-1.5 bg-black/40 dark:bg-white/40 rounded-full animate-bounce"
+                                style={{ animationDelay: '0ms' }}
+                              />
+                              <div
+                                className="w-1.5 h-1.5 bg-black/40 dark:bg-white/40 rounded-full animate-bounce"
+                                style={{ animationDelay: '150ms' }}
+                              />
+                              <div
+                                className="w-1.5 h-1.5 bg-black/40 dark:bg-white/40 rounded-full animate-bounce"
+                                style={{ animationDelay: '300ms' }}
+                              />
+                            </div>
+                          )}
+                        </>
+                      )}
+
+                      {step.type === 'searching' &&
+                        step.searching.length > 0 && (
+                          <div className="flex flex-wrap gap-1.5 mt-1.5">
+                            {step.searching.map((query, idx) => (
+                              <span
+                                key={idx}
+                                className="inline-flex items-center px-2 py-0.5 rounded-md text-xs font-medium bg-light-100 dark:bg-dark-100 text-black/70 dark:text-white/70 border border-light-200 dark:border-dark-200"
+                              >
+                                {query}
+                              </span>
+                            ))}
+                          </div>
+                        )}
+
+                      {(step.type === 'search_results' ||
+                        step.type === 'reading') &&
+                        step.reading.length > 0 && (
+                          <div className="flex flex-wrap gap-1.5 mt-1.5">
+                            {step.reading.slice(0, 4).map((result, idx) => {
+                              const url = result.metadata.url || '';
+                              const title = result.metadata.title || 'Untitled';
+                              const domain = url ? new URL(url).hostname : '';
+                              const faviconUrl = domain
+                                ? `https://s2.googleusercontent.com/s2/favicons?domain=${domain}&sz=128`
+                                : '';
+
+                              return (
+                                <span
+                                  key={idx}
+                                  className="inline-flex items-center gap-1.5 px-2 py-0.5 rounded-md text-xs font-medium bg-light-100 dark:bg-dark-100 text-black/70 dark:text-white/70 border border-light-200 dark:border-dark-200"
+                                >
+                                  {faviconUrl && (
+                                    <img
+                                      src={faviconUrl}
+                                      alt=""
+                                      className="w-3 h-3 rounded-sm flex-shrink-0"
+                                      onError={(e) => {
+                                        e.currentTarget.style.display = 'none';
+                                      }}
+                                    />
+                                  )}
+                                  <span className="line-clamp-1">{title}</span>
+                                </span>
+                              );
+                            })}
+                          </div>
+                        )}
+
+                      {step.type === 'upload_searching' &&
+                        step.queries.length > 0 && (
+                          <div className="flex flex-wrap gap-1.5 mt-1.5">
+                            {step.queries.map((query, idx) => (
+                              <span
+                                key={idx}
+                                className="inline-flex items-center px-2 py-0.5 rounded-md text-xs font-medium bg-light-100 dark:bg-dark-100 text-black/70 dark:text-white/70 border border-light-200 dark:border-dark-200"
+                              >
+                                {query}
+                              </span>
+                            ))}
+                          </div>
+                        )}
+
+                      {step.type === 'upload_search_results' &&
+                        step.results.length > 0 && (
+                          <div className="mt-1.5 space-y-2">
+                            {step.results.slice(0, 4).map((result, idx) => {
+                              const title =
+                                (result.metadata &&
+                                  (result.metadata.title ||
+                                    result.metadata.fileName)) ||
+                                'Untitled document';
+                              const snippet = (result.content || '')
+                                .replace(/\s+/g, ' ')
+                                .trim()
+                                .slice(0, 220);
+
+                              return (
+                                <div
+                                  key={idx}
+                                  className="flex gap-3 items-start rounded-lg border border-light-200 dark:border-dark-200 bg-light-100 dark:bg-dark-100 p-2"
+                                >
+                                  <div className="mt-0.5 h-10 w-10 rounded-md bg-cyan-100 text-cyan-800 dark:bg-sky-500 dark:text-cyan-50 flex items-center justify-center">
+                                    <FileText className="w-5 h-5" />
+                                  </div>
+                                  <div className="flex-1 min-w-0">
+                                    <div className="text-sm font-semibold text-black dark:text-white line-clamp-1">
+                                      {title}
+                                    </div>
+                                    <div className="text-xs text-black/70 dark:text-white/70 mt-0.5 leading-relaxed line-clamp-3">
+                                      {snippet || 'No preview available.'}
+                                    </div>
+                                  </div>
+                                </div>
+                              );
+                            })}
+                          </div>
+                        )}
+                    </div>
+                  </motion.div>
+                );
+              })}
+            </div>
+          </motion.div>
+        )}
+      </AnimatePresence>
+    </div>
+  );
+};
+
+export default AssistantSteps;

src/components/Chat.tsx

@@ -7,11 +7,12 @@ import MessageBoxLoading from './MessageBoxLoading';
 import { useChat } from '@/lib/hooks/useChat';
 
 const Chat = () => {
-  const { sections, chatTurns, loading, messageAppeared } = useChat();
+  const { sections, loading, messageAppeared, messages } = useChat();
 
   const [dividerWidth, setDividerWidth] = useState(0);
   const dividerRef = useRef<HTMLDivElement | null>(null);
   const messageEnd = useRef<HTMLDivElement | null>(null);
+  const lastScrolledRef = useRef<number>(0);
 
   useEffect(() => {
     const updateDividerWidth = () => {
@@ -22,43 +23,48 @@ const Chat = () => {
 
     updateDividerWidth();
 
+    const resizeObserver = new ResizeObserver(() => {
+      updateDividerWidth();
+    });
+
+    const currentRef = dividerRef.current;
+    if (currentRef) {
+      resizeObserver.observe(currentRef);
+    }
+
     window.addEventListener('resize', updateDividerWidth);
 
     return () => {
+      if (currentRef) {
+        resizeObserver.unobserve(currentRef);
+      }
+      resizeObserver.disconnect();
       window.removeEventListener('resize', updateDividerWidth);
     };
-  }, []);
+  }, [sections.length]);
 
   useEffect(() => {
     const scroll = () => {
       messageEnd.current?.scrollIntoView({ behavior: 'auto' });
     };
 
-    if (chatTurns.length === 1) {
-      document.title = `${chatTurns[0].content.substring(0, 30)} - Perplexica`;
+    if (messages.length === 1) {
+      document.title = `${messages[0].query.substring(0, 30)} - Perplexica`;
     }
 
-    const messageEndBottom =
-      messageEnd.current?.getBoundingClientRect().bottom ?? 0;
-
-    const distanceFromMessageEnd = window.innerHeight - messageEndBottom;
-
-    if (distanceFromMessageEnd >= -100) {
+    if (sections.length > lastScrolledRef.current) {
       scroll();
+      lastScrolledRef.current = sections.length;
     }
-
-    if (chatTurns[chatTurns.length - 1]?.role === 'user') {
-      scroll();
-    }
-  }, [chatTurns]);
+  }, [messages]);
 
   return (
-    <div className="flex flex-col space-y-6 pt-8 pb-44 lg:pb-32 sm:mx-4 md:mx-8">
+    <div className="flex flex-col space-y-6 pt-8 pb-28 sm:mx-4 md:mx-8">
       {sections.map((section, i) => {
         const isLast = i === sections.length - 1;
 
         return (
-          <Fragment key={section.userMessage.messageId}>
+          <Fragment key={section.message.messageId}>
             <MessageBox
               section={section}
               sectionIndex={i}
@@ -74,10 +80,21 @@ const Chat = () => {
       {loading && !messageAppeared && <MessageBoxLoading />}
       <div ref={messageEnd} className="h-0" />
       {dividerWidth > 0 && (
-        <div
-          className="bottom-24 lg:bottom-10 fixed z-40"
-          style={{ width: dividerWidth }}
-        >
+        <div className="bottom-6 fixed z-40" style={{ width: dividerWidth }}>
+          <div
+            className="pointer-events-none absolute -bottom-6 left-0 right-0 h-[calc(100%+24px+24px)] dark:hidden"
+            style={{
+              background:
+                'linear-gradient(to top, #ffffff 0%, #ffffff 35%, rgba(255,255,255,0.95) 45%, rgba(255,255,255,0.85) 55%, rgba(255,255,255,0.7) 65%, rgba(255,255,255,0.5) 75%, rgba(255,255,255,0.3) 85%, rgba(255,255,255,0.1) 92%, transparent 100%)',
+            }}
+          />
+          <div
+            className="pointer-events-none absolute -bottom-6 left-0 right-0 h-[calc(100%+24px+24px)] hidden dark:block"
+            style={{
+              background:
+                'linear-gradient(to top, #0d1117 0%, #0d1117 35%, rgba(13,17,23,0.95) 45%, rgba(13,17,23,0.85) 55%, rgba(13,17,23,0.7) 65%, rgba(13,17,23,0.5) 75%, rgba(13,17,23,0.3) 85%, rgba(13,17,23,0.1) 92%, transparent 100%)',
+            }}
+          />
           <MessageInput />
         </div>
       )}

src/components/ChatWindow.tsx

@@ -1,15 +1,12 @@
 'use client';
 
-import { Document } from '@langchain/core/documents';
 import Navbar from './Navbar';
 import Chat from './Chat';
 import EmptyChat from './EmptyChat';
-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';
+import { Block, Chunk } from '@/lib/types';
 
 export interface BaseMessage {
   chatId: string;
@@ -17,20 +14,27 @@ export interface BaseMessage {
   createdAt: Date;
 }
 
+export interface Message extends BaseMessage {
+  backendId: string;
+  query: string;
+  responseBlocks: Block[];
+  status: 'answering' | 'completed' | 'error';
+}
+
+export interface UserMessage extends BaseMessage {
+  role: 'user';
+  content: string;
+}
+
 export interface AssistantMessage extends BaseMessage {
   role: 'assistant';
   content: string;
   suggestions?: string[];
 }
 
-export interface UserMessage extends BaseMessage {
-  role: 'user';
-  content: string;
-}
-
 export interface SourceMessage extends BaseMessage {
   role: 'source';
-  sources: Document[];
+  sources: Chunk[];
 }
 
 export interface SuggestionMessage extends BaseMessage {
@@ -38,11 +42,12 @@ export interface SuggestionMessage extends BaseMessage {
   suggestions: string[];
 }
 
-export type Message =
+export type LegacyMessage =
   | AssistantMessage
   | UserMessage
   | SourceMessage
   | SuggestionMessage;
+
 export type ChatTurn = UserMessage | AssistantMessage;
 
 export interface File {
@@ -51,8 +56,13 @@ export interface File {
   fileId: string;
 }
 
+export interface Widget {
+  widgetType: string;
+  params: Record<string, any>;
+}
+
 const ChatWindow = () => {
-  const { hasError, isReady, notFound, messages } = useChat();
+  const { hasError, notFound, messages } = useChat();
   if (hasError) {
     return (
       <div className="relative">
@@ -68,24 +78,18 @@ 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

@@ -15,14 +15,21 @@ const Copy = ({
   return (
     <button
       onClick={() => {
-        const contentToCopy = `${initialMessage}${section?.sourceMessage?.sources && section.sourceMessage.sources.length > 0 && `\n\nCitations:\n${section.sourceMessage.sources?.map((source: any, i: any) => `[${i + 1}] ${source.metadata.url}`).join(`\n`)}`}`;
+        const contentToCopy = `${initialMessage}${
+          section?.message.responseBlocks.filter((b) => b.type === 'source')
+            ?.length > 0 &&
+          `\n\nCitations:\n${section.message.responseBlocks
+            .filter((b) => b.type === 'source')
+            ?.map((source: any, i: any) => `[${i + 1}] ${source.metadata.url}`)
+            .join(`\n`)}`
+        }`;
         navigator.clipboard.writeText(contentToCopy);
         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';
@@ -21,6 +22,9 @@ import { useSpeech } from 'react-text-to-speech';
 import ThinkBox from './ThinkBox';
 import { useChat, Section } from '@/lib/hooks/useChat';
 import Citation from './Citation';
+import AssistantSteps from './AssistantSteps';
+import { ResearchBlock } from '@/lib/types';
+import Renderer from './Widgets/Renderer';
 
 const ThinkTagProcessor = ({
   children,
@@ -45,12 +49,21 @@ const MessageBox = ({
   dividerRef?: MutableRefObject<HTMLDivElement | null>;
   isLast: boolean;
 }) => {
-  const { loading, chatTurns, sendMessage, rewrite } = useChat();
+  const { loading, sendMessage, rewrite, messages, researchEnded } = useChat();
 
-  const parsedMessage = section.parsedAssistantMessage || '';
+  const parsedMessage = section.parsedTextBlocks.join('\n\n');
   const speechMessage = section.speechMessage || '';
   const thinkingEnded = section.thinkingEnded;
 
+  const sourceBlocks = section.message.responseBlocks.filter(
+    (block): block is typeof block & { type: 'source' } =>
+      block.type === 'source',
+  );
+
+  const sources = sourceBlocks.flatMap((block) => block.data);
+
+  const hasContent = section.parsedTextBlocks.length > 0;
+
   const { speechStatus, start, stop } = useSpeech({ text: speechMessage });
 
   const markdownOverrides: MarkdownToJSX.Options = {
@@ -71,7 +84,7 @@ const MessageBox = ({
     <div className="space-y-6">
       <div className={'w-full pt-8 break-words'}>
         <h2 className="text-black dark:text-white font-medium text-3xl lg:w-9/12">
-          {section.userMessage.content}
+          {section.message.query}
         </h2>
       </div>
 
@@ -80,21 +93,50 @@ const MessageBox = ({
           ref={dividerRef}
           className="flex flex-col space-y-6 w-full lg:w-9/12"
         >
-          {section.sourceMessage &&
-            section.sourceMessage.sources.length > 0 && (
-              <div className="flex flex-col space-y-2">
-                <div className="flex flex-row items-center space-x-2">
-                  <BookCopy className="text-black dark:text-white" size={20} />
-                  <h3 className="text-black dark:text-white font-medium text-xl">
-                    Sources
-                  </h3>
-                </div>
-                <MessageSources sources={section.sourceMessage.sources} />
+          {sources.length > 0 && (
+            <div className="flex flex-col space-y-2">
+              <div className="flex flex-row items-center space-x-2">
+                <BookCopy className="text-black dark:text-white" size={20} />
+                <h3 className="text-black dark:text-white font-medium text-xl">
+                  Sources
+                </h3>
+              </div>
+              <MessageSources sources={sources} />
+            </div>
+          )}
+
+          {section.message.responseBlocks
+            .filter(
+              (block): block is ResearchBlock =>
+                block.type === 'research' && block.data.subSteps.length > 0,
+            )
+            .map((researchBlock) => (
+              <div key={researchBlock.id} className="flex flex-col space-y-2">
+                <AssistantSteps
+                  block={researchBlock}
+                  status={section.message.status}
+                />
+              </div>
+            ))}
+
+          {section.widgets.length > 0 && <Renderer widgets={section.widgets} />}
+
+          {isLast &&
+            loading &&
+            !researchEnded &&
+            !section.message.responseBlocks.some(
+              (b) => b.type === 'research' && b.data.subSteps.length > 0,
+            ) && (
+              <div className="flex items-center gap-2 p-3 rounded-lg bg-light-secondary dark:bg-dark-secondary border border-light-200 dark:border-dark-200">
+                <Disc3 className="w-4 h-4 text-black dark:text-white animate-spin" />
+                <span className="text-sm text-black/70 dark:text-white/70">
+                  Brainstorming...
+                </span>
               </div>
             )}
 
           <div className="flex flex-col space-y-2">
-            {section.sourceMessage && (
+            {sources.length > 0 && (
               <div className="flex flex-row items-center space-x-2">
                 <Disc3
                   className={cn(
@@ -109,7 +151,7 @@ const MessageBox = ({
               </div>
             )}
 
-            {section.assistantMessage && (
+            {hasContent && (
               <>
                 <Markdown
                   className={cn(
@@ -122,18 +164,15 @@ 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}
+                        messageId={section.message.messageId}
                       />
                     </div>
-                    <div className="flex flex-row items-center space-x-1">
-                      <Copy
-                        initialMessage={section.assistantMessage.content}
-                        section={section}
-                      />
+                    <div className="flex flex-row items-center -mr-2">
+                      <Copy initialMessage={parsedMessage} section={section} />
                       <button
                         onClick={() => {
                           if (speechStatus === 'started') {
@@ -142,12 +181,12 @@ 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>
@@ -157,9 +196,9 @@ const MessageBox = ({
                 {isLast &&
                   section.suggestions &&
                   section.suggestions.length > 0 &&
-                  section.assistantMessage &&
+                  hasContent &&
                   !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 +212,24 @@ 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>
@@ -201,17 +244,17 @@ const MessageBox = ({
           </div>
         </div>
 
-        {section.assistantMessage && (
+        {hasContent && (
           <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)}
-              messageId={section.assistantMessage.messageId}
+              query={section.message.query}
+              chatHistory={messages}
+              messageId={section.message.messageId}
             />
             <SearchVideos
-              chatHistory={chatTurns.slice(0, sectionIndex * 2)}
-              query={section.userMessage.content}
-              messageId={section.assistantMessage.messageId}
+              chatHistory={messages}
+              query={section.message.query}
+              messageId={section.message.messageId}
             />
           </div>
         )}

src/components/MessageInput.tsx

@@ -2,9 +2,7 @@ import { cn } from '@/lib/utils';
 import { ArrowUp } from 'lucide-react';
 import { useEffect, useRef, useState } from 'react';
 import TextareaAutosize from 'react-textarea-autosize';
-import Attach from './MessageInputActions/Attach';
 import CopilotToggle from './MessageInputActions/Copilot';
-import { File } from './ChatWindow';
 import AttachSmall from './MessageInputActions/AttachSmall';
 import { useChat } from '@/lib/hooks/useChat';
 
@@ -64,7 +62,7 @@ const MessageInput = () => {
         }
       }}
       className={cn(
-        'bg-light-secondary dark:bg-dark-secondary p-4 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 shadow-sm shadow-light-200/10 dark:shadow-black/20 transition-all duration-200 focus-within:border-light-300 dark:focus-within:border-dark-300',
+        'relative bg-light-secondary dark:bg-dark-secondary p-4 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 shadow-sm shadow-light-200/10 dark:shadow-black/20 transition-all duration-200 focus-within:border-light-300 dark:focus-within:border-dark-300',
         mode === 'multi' ? 'flex-col rounded-2xl' : 'flex-row rounded-full',
       )}
     >
@@ -103,7 +101,7 @@ const MessageInput = () => {
             />
             <button
               disabled={message.trim().length === 0 || loading}
-              className="bg-[#24A0ED] text-white text-black/50 dark:disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#e0e0dc79] dark:disabled:bg-[#ececec21] rounded-full p-2"
+              className="bg-[#24A0ED] text-white disabled:text-black/50 dark:disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#e0e0dc79] dark:disabled:bg-[#ececec21] rounded-full p-2"
             >
               <ArrowUp className="bg-background" size={17} />
             </button>

src/components/MessageInputActions/Optimization.tsx

@@ -24,7 +24,7 @@ const OptimizationModes = [
   },
   {
     key: 'quality',
-    title: 'Quality (Soon)',
+    title: 'Quality',
     description: 'Get the most thorough and accurate answer',
     icon: (
       <Star
@@ -75,13 +75,11 @@ const Optimization = () => {
                   <PopoverButton
                     onClick={() => setOptimizationMode(mode.key)}
                     key={i}
-                    disabled={mode.key === 'quality'}
                     className={cn(
                       'p-2 rounded-lg flex flex-col items-start justify-start text-start space-y-1 duration-200 cursor-pointer transition focus:outline-none',
                       optimizationMode === mode.key
                         ? 'bg-light-secondary dark:bg-dark-secondary'
                         : 'hover:bg-light-secondary dark:hover:bg-dark-secondary',
-                      mode.key === 'quality' && 'opacity-50 cursor-not-allowed',
                     )}
                   >
                     <div className="flex flex-row items-center space-x-1 text-black dark:text-white">

src/components/MessageSources.tsx

@@ -6,11 +6,11 @@ import {
   Transition,
   TransitionChild,
 } from '@headlessui/react';
-import { Document } from '@langchain/core/documents';
 import { File } from 'lucide-react';
 import { Fragment, useState } from 'react';
+import { Chunk } from '@/lib/types';
 
-const MessageSources = ({ sources }: { sources: Document[] }) => {
+const MessageSources = ({ sources }: { sources: Chunk[] }) => {
   const [isDialogOpen, setIsDialogOpen] = useState(false);
 
   const closeModal = () => {
@@ -37,7 +37,7 @@ const MessageSources = ({ sources }: { sources: Document[] }) => {
           </p>
           <div className="flex flex-row items-center justify-between">
             <div className="flex flex-row items-center space-x-1">
-              {source.metadata.url === 'File' ? (
+              {source.metadata.url.includes('file_id://') ? (
                 <div className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full">
                   <File size={12} className="text-white/70" />
                 </div>
@@ -51,7 +51,9 @@ const MessageSources = ({ sources }: { sources: Document[] }) => {
                 />
               )}
               <p className="text-xs text-black/50 dark:text-white/50 overflow-hidden whitespace-nowrap text-ellipsis">
-                {source.metadata.url.replace(/.+\/\/|www.|\..+/g, '')}
+                {source.metadata.url.includes('file_id://')
+                  ? 'Uploaded File'
+                  : source.metadata.url.replace(/.+\/\/|www.|\..+/g, '')}
               </p>
             </div>
             <div className="flex flex-row items-center space-x-1 text-black/50 dark:text-white/50 text-xs">

src/components/Navbar.tsx

@@ -11,6 +11,7 @@ import {
 } from '@headlessui/react';
 import jsPDF from 'jspdf';
 import { useChat, Section } from '@/lib/hooks/useChat';
+import { SourceBlock } from '@/lib/types';
 
 const downloadFile = (filename: string, content: string, type: string) => {
   const blob = new Blob([content], { type });
@@ -28,35 +29,41 @@ const downloadFile = (filename: string, content: string, type: string) => {
 
 const exportAsMarkdown = (sections: Section[], title: string) => {
   const date = new Date(
-    sections[0]?.userMessage?.createdAt || Date.now(),
+    sections[0].message.createdAt || Date.now(),
   ).toLocaleString();
   let md = `# 💬 Chat Export: ${title}\n\n`;
   md += `*Exported on: ${date}*\n\n---\n`;
 
   sections.forEach((section, idx) => {
-    if (section.userMessage) {
-      md += `\n---\n`;
-      md += `**🧑 User**  
+    md += `\n---\n`;
+    md += `**🧑 User**  
 `;
-      md += `*${new Date(section.userMessage.createdAt).toLocaleString()}*\n\n`;
-      md += `> ${section.userMessage.content.replace(/\n/g, '\n> ')}\n`;
-    }
+    md += `*${new Date(section.message.createdAt).toLocaleString()}*\n\n`;
+    md += `> ${section.message.query.replace(/\n/g, '\n> ')}\n`;
 
-    if (section.assistantMessage) {
+    if (section.message.responseBlocks.length > 0) {
       md += `\n---\n`;
       md += `**🤖 Assistant**  
 `;
-      md += `*${new Date(section.assistantMessage.createdAt).toLocaleString()}*\n\n`;
-      md += `> ${section.assistantMessage.content.replace(/\n/g, '\n> ')}\n`;
+      md += `*${new Date(section.message.createdAt).toLocaleString()}*\n\n`;
+      md += `> ${section.message.responseBlocks
+        .filter((b) => b.type === 'text')
+        .map((block) => block.data)
+        .join('\n')
+        .replace(/\n/g, '\n> ')}\n`;
     }
 
+    const sourceResponseBlock = section.message.responseBlocks.find(
+      (block) => block.type === 'source',
+    ) as SourceBlock | undefined;
+
     if (
-      section.sourceMessage &&
-      section.sourceMessage.sources &&
-      section.sourceMessage.sources.length > 0
+      sourceResponseBlock &&
+      sourceResponseBlock.data &&
+      sourceResponseBlock.data.length > 0
     ) {
       md += `\n**Citations:**\n`;
-      section.sourceMessage.sources.forEach((src: any, i: number) => {
+      sourceResponseBlock.data.forEach((src: any, i: number) => {
         const url = src.metadata?.url || '';
         md += `- [${i + 1}] [${url}](${url})\n`;
       });
@@ -69,7 +76,7 @@ const exportAsMarkdown = (sections: Section[], title: string) => {
 const exportAsPDF = (sections: Section[], title: string) => {
   const doc = new jsPDF();
   const date = new Date(
-    sections[0]?.userMessage?.createdAt || Date.now(),
+    sections[0]?.message?.createdAt || Date.now(),
   ).toLocaleString();
   let y = 15;
   const pageHeight = doc.internal.pageSize.height;
@@ -86,44 +93,38 @@ const exportAsPDF = (sections: Section[], title: string) => {
   doc.setTextColor(30);
 
   sections.forEach((section, idx) => {
-    if (section.userMessage) {
-      if (y > pageHeight - 30) {
-        doc.addPage();
-        y = 15;
-      }
-      doc.setFont('helvetica', 'bold');
-      doc.text('User', 10, y);
-      doc.setFont('helvetica', 'normal');
-      doc.setFontSize(10);
-      doc.setTextColor(120);
-      doc.text(
-        `${new Date(section.userMessage.createdAt).toLocaleString()}`,
-        40,
-        y,
-      );
-      y += 6;
-      doc.setTextColor(30);
-      doc.setFontSize(12);
-      const userLines = doc.splitTextToSize(section.userMessage.content, 180);
-      for (let i = 0; i < userLines.length; i++) {
-        if (y > pageHeight - 20) {
-          doc.addPage();
-          y = 15;
-        }
-        doc.text(userLines[i], 12, y);
-        y += 6;
-      }
-      y += 6;
-      doc.setDrawColor(230);
-      if (y > pageHeight - 10) {
-        doc.addPage();
-        y = 15;
-      }
-      doc.line(10, y, 200, y);
-      y += 4;
+    if (y > pageHeight - 30) {
+      doc.addPage();
+      y = 15;
     }
+    doc.setFont('helvetica', 'bold');
+    doc.text('User', 10, y);
+    doc.setFont('helvetica', 'normal');
+    doc.setFontSize(10);
+    doc.setTextColor(120);
+    doc.text(`${new Date(section.message.createdAt).toLocaleString()}`, 40, y);
+    y += 6;
+    doc.setTextColor(30);
+    doc.setFontSize(12);
+    const userLines = doc.splitTextToSize(section.message.query, 180);
+    for (let i = 0; i < userLines.length; i++) {
+      if (y > pageHeight - 20) {
+        doc.addPage();
+        y = 15;
+      }
+      doc.text(userLines[i], 12, y);
+      y += 6;
+    }
+    y += 6;
+    doc.setDrawColor(230);
+    if (y > pageHeight - 10) {
+      doc.addPage();
+      y = 15;
+    }
+    doc.line(10, y, 200, y);
+    y += 4;
 
-    if (section.assistantMessage) {
+    if (section.message.responseBlocks.length > 0) {
       if (y > pageHeight - 30) {
         doc.addPage();
         y = 15;
@@ -134,7 +135,7 @@ const exportAsPDF = (sections: Section[], title: string) => {
       doc.setFontSize(10);
       doc.setTextColor(120);
       doc.text(
-        `${new Date(section.assistantMessage.createdAt).toLocaleString()}`,
+        `${new Date(section.message.createdAt).toLocaleString()}`,
         40,
         y,
       );
@@ -142,7 +143,7 @@ const exportAsPDF = (sections: Section[], title: string) => {
       doc.setTextColor(30);
       doc.setFontSize(12);
       const assistantLines = doc.splitTextToSize(
-        section.assistantMessage.content,
+        section.parsedTextBlocks.join('\n'),
         180,
       );
       for (let i = 0; i < assistantLines.length; i++) {
@@ -154,10 +155,14 @@ const exportAsPDF = (sections: Section[], title: string) => {
         y += 6;
       }
 
+      const sourceResponseBlock = section.message.responseBlocks.find(
+        (block) => block.type === 'source',
+      ) as SourceBlock | undefined;
+
       if (
-        section.sourceMessage &&
-        section.sourceMessage.sources &&
-        section.sourceMessage.sources.length > 0
+        sourceResponseBlock &&
+        sourceResponseBlock.data &&
+        sourceResponseBlock.data.length > 0
       ) {
         doc.setFontSize(11);
         doc.setTextColor(80);
@@ -167,7 +172,7 @@ const exportAsPDF = (sections: Section[], title: string) => {
         }
         doc.text('Citations:', 12, y);
         y += 5;
-        section.sourceMessage.sources.forEach((src: any, i: number) => {
+        sourceResponseBlock.data.forEach((src: any, i: number) => {
           const url = src.metadata?.url || '';
           if (y > pageHeight - 15) {
             doc.addPage();
@@ -198,15 +203,15 @@ const Navbar = () => {
   const { sections, chatId } = useChat();
 
   useEffect(() => {
-    if (sections.length > 0 && sections[0].userMessage) {
+    if (sections.length > 0 && sections[0].message) {
       const newTitle =
-        sections[0].userMessage.content.length > 20
-          ? `${sections[0].userMessage.content.substring(0, 20).trim()}...`
-          : sections[0].userMessage.content;
+        sections[0].message.query.substring(0, 30) + '...' ||
+        'New Conversation';
+
       setTitle(newTitle);
       const newTimeAgo = formatTimeDifference(
         new Date(),
-        sections[0].userMessage.createdAt,
+        sections[0].message.createdAt,
       );
       setTimeAgo(newTimeAgo);
     }
@@ -214,10 +219,10 @@ const Navbar = () => {
 
   useEffect(() => {
     const intervalId = setInterval(() => {
-      if (sections.length > 0 && sections[0].userMessage) {
+      if (sections.length > 0 && sections[0].message) {
         const newTimeAgo = formatTimeDifference(
           new Date(),
-          sections[0].userMessage.createdAt,
+          sections[0].message.createdAt,
         );
         setTimeAgo(newTimeAgo);
       }

src/components/Settings/SettingsDialogue.tsx

@@ -3,6 +3,7 @@ import {
   ArrowLeft,
   BrainCog,
   ChevronLeft,
+  ExternalLink,
   Search,
   Sliders,
   ToggleRight,
@@ -115,35 +116,52 @@ const SettingsDialogue = ({
             </div>
           ) : (
             <div className="flex flex-1 inset-0 h-full overflow-hidden">
-              <div className="hidden lg:flex flex-col w-[240px] border-r border-white-200 dark:border-dark-200 h-full px-3 pt-3 overflow-y-auto">
-                <button
-                  onClick={() => setIsOpen(false)}
-                  className="group flex flex-row items-center hover:bg-light-200 hover:dark:bg-dark-200 p-2 rounded-lg"
-                >
-                  <ChevronLeft
-                    size={18}
-                    className="text-black/50 dark:text-white/50 group-hover:text-black/70 group-hover:dark:text-white/70"
-                  />
-                  <p className="text-black/50 dark:text-white/50 group-hover:text-black/70 group-hover:dark:text-white/70 text-[14px]">
-                    Back
+              <div className="hidden lg:flex flex-col justify-between w-[240px] border-r border-white-200 dark:border-dark-200 h-full px-3 pt-3 overflow-y-auto">
+                <div className="flex flex-col">
+                  <button
+                    onClick={() => setIsOpen(false)}
+                    className="group flex flex-row items-center hover:bg-light-200 hover:dark:bg-dark-200 p-2 rounded-lg"
+                  >
+                    <ChevronLeft
+                      size={18}
+                      className="text-black/50 dark:text-white/50 group-hover:text-black/70 group-hover:dark:text-white/70"
+                    />
+                    <p className="text-black/50 dark:text-white/50 group-hover:text-black/70 group-hover:dark:text-white/70 text-[14px]">
+                      Back
+                    </p>
+                  </button>
+
+                  <div className="flex flex-col items-start space-y-1 mt-8">
+                    {sections.map((section) => (
+                      <button
+                        key={section.dataAdd}
+                        className={cn(
+                          `flex flex-row items-center space-x-2 px-2 py-1.5 rounded-lg w-full text-sm hover:bg-light-200 hover:dark:bg-dark-200 transition duration-200 active:scale-95`,
+                          activeSection === section.key
+                            ? 'bg-light-200 dark:bg-dark-200 text-black/90 dark:text-white/90'
+                            : ' text-black/70 dark:text-white/70',
+                        )}
+                        onClick={() => setActiveSection(section.key)}
+                      >
+                        <section.icon size={17} />
+                        <p>{section.name}</p>
+                      </button>
+                    ))}
+                  </div>
+                </div>
+                <div className="flex flex-col space-y-1 py-[18px] px-2">
+                  <p className="text-xs text-black/70 dark:text-white/70">
+                    Version: {process.env.NEXT_PUBLIC_VERSION}
                   </p>
-                </button>
-                <div className="flex flex-col items-start space-y-1 mt-8">
-                  {sections.map((section) => (
-                    <button
-                      key={section.dataAdd}
-                      className={cn(
-                        `flex flex-row items-center space-x-2 px-2 py-1.5 rounded-lg w-full text-sm hover:bg-light-200 hover:dark:bg-dark-200 transition duration-200 active:scale-95`,
-                        activeSection === section.key
-                          ? 'bg-light-200 dark:bg-dark-200 text-black/90 dark:text-white/90'
-                          : ' text-black/70 dark:text-white/70',
-                      )}
-                      onClick={() => setActiveSection(section.key)}
-                    >
-                      <section.icon size={17} />
-                      <p>{section.name}</p>
-                    </button>
-                  ))}
+                  <a
+                    href="https://github.com/itzcrazykns/perplexica"
+                    target="_blank"
+                    rel="noopener noreferrer"
+                    className="text-xs text-black/70 dark:text-white/70 flex flex-row space-x-1 items-center transition duration-200 hover:text-black/90 hover:dark:text-white/90"
+                  >
+                    <span>GitHub</span>
+                    <ExternalLink size={12} />
+                  </a>
                 </div>
               </div>
               <div className="w-full flex flex-col overflow-hidden">

src/components/WeatherWidget.tsx

@@ -91,7 +91,7 @@ const WeatherWidget = () => {
       setData({
         temperature: data.temperature,
         condition: data.condition,
-        location: location.city,
+        location: 'Mars',
         humidity: data.humidity,
         windSpeed: data.windSpeed,
         icon: data.icon,

src/components/Widgets/Calculation.tsx

@@ -0,0 +1,46 @@
+'use client';
+
+import { Calculator, Equal } from 'lucide-react';
+
+type CalculationWidgetProps = {
+  expression: string;
+  result: number;
+};
+
+const Calculation = ({ expression, result }: CalculationWidgetProps) => {
+  return (
+    <div className="rounded-lg border border-light-200 dark:border-dark-200">
+      <div className="p-4 space-y-4">
+        <div className="space-y-2">
+          <div className="flex items-center gap-2 text-black/60 dark:text-white/70">
+            <Calculator className="w-4 h-4" />
+            <span className="text-xs uppercase font-semibold tracking-wide">
+              Expression
+            </span>
+          </div>
+          <div className="rounded-lg border border-light-200 dark:border-dark-200 bg-light-secondary dark:bg-dark-secondary p-3">
+            <code className="text-sm text-black dark:text-white font-mono break-all">
+              {expression}
+            </code>
+          </div>
+        </div>
+
+        <div className="space-y-2">
+          <div className="flex items-center gap-2 text-black/60 dark:text-white/70">
+            <Equal className="w-4 h-4" />
+            <span className="text-xs uppercase font-semibold tracking-wide">
+              Result
+            </span>
+          </div>
+          <div className="rounded-xl border border-light-200 dark:border-dark-200 bg-light-secondary dark:bg-dark-secondary p-5">
+            <div className="text-4xl font-bold text-black dark:text-white font-mono tabular-nums">
+              {result.toLocaleString()}
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+  );
+};
+
+export default Calculation;

src/components/Widgets/Renderer.tsx

@@ -0,0 +1,76 @@
+import React from 'react';
+import { Widget } from '../ChatWindow';
+import Weather from './Weather';
+import Calculation from './Calculation';
+import Stock from './Stock';
+
+const Renderer = ({ widgets }: { widgets: Widget[] }) => {
+  return widgets.map((widget, index) => {
+    switch (widget.widgetType) {
+      case 'weather':
+        return (
+          <Weather
+            key={index}
+            location={widget.params.location}
+            current={widget.params.current}
+            daily={widget.params.daily}
+            timezone={widget.params.timezone}
+          />
+        );
+      case 'calculation_result':
+        return (
+          <Calculation
+            expression={widget.params.expression}
+            result={widget.params.result}
+            key={index}
+          />
+        );
+      case 'stock':
+        return (
+          <Stock
+            key={index}
+            symbol={widget.params.symbol}
+            shortName={widget.params.shortName}
+            longName={widget.params.longName}
+            exchange={widget.params.exchange}
+            currency={widget.params.currency}
+            marketState={widget.params.marketState}
+            regularMarketPrice={widget.params.regularMarketPrice}
+            regularMarketChange={widget.params.regularMarketChange}
+            regularMarketChangePercent={
+              widget.params.regularMarketChangePercent
+            }
+            regularMarketPreviousClose={
+              widget.params.regularMarketPreviousClose
+            }
+            regularMarketOpen={widget.params.regularMarketOpen}
+            regularMarketDayHigh={widget.params.regularMarketDayHigh}
+            regularMarketDayLow={widget.params.regularMarketDayLow}
+            regularMarketVolume={widget.params.regularMarketVolume}
+            averageDailyVolume3Month={widget.params.averageDailyVolume3Month}
+            marketCap={widget.params.marketCap}
+            fiftyTwoWeekLow={widget.params.fiftyTwoWeekLow}
+            fiftyTwoWeekHigh={widget.params.fiftyTwoWeekHigh}
+            trailingPE={widget.params.trailingPE}
+            forwardPE={widget.params.forwardPE}
+            dividendYield={widget.params.dividendYield}
+            earningsPerShare={widget.params.earningsPerShare}
+            website={widget.params.website}
+            postMarketPrice={widget.params.postMarketPrice}
+            postMarketChange={widget.params.postMarketChange}
+            postMarketChangePercent={widget.params.postMarketChangePercent}
+            preMarketPrice={widget.params.preMarketPrice}
+            preMarketChange={widget.params.preMarketChange}
+            preMarketChangePercent={widget.params.preMarketChangePercent}
+            chartData={widget.params.chartData}
+            comparisonData={widget.params.comparisonData}
+            error={widget.params.error}
+          />
+        );
+      default:
+        return <div key={index}>Unknown widget type: {widget.widgetType}</div>;
+    }
+  });
+};
+
+export default Renderer;

src/components/Widgets/Stock.tsx

@@ -0,0 +1,517 @@
+'use client';
+
+import { Clock, ArrowUpRight, ArrowDownRight, Minus } from 'lucide-react';
+import { useEffect, useRef, useState } from 'react';
+import {
+  createChart,
+  ColorType,
+  LineStyle,
+  BaselineSeries,
+  LineSeries,
+} from 'lightweight-charts';
+
+type StockWidgetProps = {
+  symbol: string;
+  shortName: string;
+  longName?: string;
+  exchange?: string;
+  currency?: string;
+  marketState?: string;
+  regularMarketPrice?: number;
+  regularMarketChange?: number;
+  regularMarketChangePercent?: number;
+  regularMarketPreviousClose?: number;
+  regularMarketOpen?: number;
+  regularMarketDayHigh?: number;
+  regularMarketDayLow?: number;
+  regularMarketVolume?: number;
+  averageDailyVolume3Month?: number;
+  marketCap?: number;
+  fiftyTwoWeekLow?: number;
+  fiftyTwoWeekHigh?: number;
+  trailingPE?: number;
+  forwardPE?: number;
+  dividendYield?: number;
+  earningsPerShare?: number;
+  website?: string;
+  postMarketPrice?: number;
+  postMarketChange?: number;
+  postMarketChangePercent?: number;
+  preMarketPrice?: number;
+  preMarketChange?: number;
+  preMarketChangePercent?: number;
+  chartData?: {
+    '1D'?: { timestamps: number[]; prices: number[] } | null;
+    '5D'?: { timestamps: number[]; prices: number[] } | null;
+    '1M'?: { timestamps: number[]; prices: number[] } | null;
+    '3M'?: { timestamps: number[]; prices: number[] } | null;
+    '6M'?: { timestamps: number[]; prices: number[] } | null;
+    '1Y'?: { timestamps: number[]; prices: number[] } | null;
+    MAX?: { timestamps: number[]; prices: number[] } | null;
+  } | null;
+  comparisonData?: Array<{
+    ticker: string;
+    name: string;
+    chartData: {
+      '1D'?: { timestamps: number[]; prices: number[] } | null;
+      '5D'?: { timestamps: number[]; prices: number[] } | null;
+      '1M'?: { timestamps: number[]; prices: number[] } | null;
+      '3M'?: { timestamps: number[]; prices: number[] } | null;
+      '6M'?: { timestamps: number[]; prices: number[] } | null;
+      '1Y'?: { timestamps: number[]; prices: number[] } | null;
+      MAX?: { timestamps: number[]; prices: number[] } | null;
+    };
+  }> | null;
+  error?: string;
+};
+
+const formatNumber = (num: number | undefined, decimals = 2): string => {
+  if (num === undefined || num === null) return 'N/A';
+  return num.toLocaleString(undefined, {
+    minimumFractionDigits: decimals,
+    maximumFractionDigits: decimals,
+  });
+};
+
+const formatLargeNumber = (num: number | undefined): string => {
+  if (num === undefined || num === null) return 'N/A';
+  if (num >= 1e12) return `$${(num / 1e12).toFixed(2)}T`;
+  if (num >= 1e9) return `$${(num / 1e9).toFixed(2)}B`;
+  if (num >= 1e6) return `$${(num / 1e6).toFixed(2)}M`;
+  if (num >= 1e3) return `$${(num / 1e3).toFixed(2)}K`;
+  return `$${num.toFixed(2)}`;
+};
+
+const Stock = (props: StockWidgetProps) => {
+  const [isDarkMode, setIsDarkMode] = useState(false);
+  const [selectedTimeframe, setSelectedTimeframe] = useState<
+    '1D' | '5D' | '1M' | '3M' | '6M' | '1Y' | 'MAX'
+  >('1M');
+  const chartContainerRef = useRef<HTMLDivElement>(null);
+
+  useEffect(() => {
+    const checkDarkMode = () => {
+      setIsDarkMode(document.documentElement.classList.contains('dark'));
+    };
+
+    checkDarkMode();
+
+    const observer = new MutationObserver(checkDarkMode);
+    observer.observe(document.documentElement, {
+      attributes: true,
+      attributeFilter: ['class'],
+    });
+
+    return () => observer.disconnect();
+  }, []);
+
+  useEffect(() => {
+    const currentChartData = props.chartData?.[selectedTimeframe];
+    if (
+      !chartContainerRef.current ||
+      !currentChartData ||
+      currentChartData.timestamps.length === 0
+    ) {
+      return;
+    }
+
+    const chart = createChart(chartContainerRef.current, {
+      width: chartContainerRef.current.clientWidth,
+      height: 280,
+      layout: {
+        background: { type: ColorType.Solid, color: 'transparent' },
+        textColor: isDarkMode ? '#6b7280' : '#9ca3af',
+        fontSize: 11,
+        attributionLogo: false,
+      },
+      grid: {
+        vertLines: {
+          color: isDarkMode ? '#21262d' : '#e8edf1',
+          style: LineStyle.Solid,
+        },
+        horzLines: {
+          color: isDarkMode ? '#21262d' : '#e8edf1',
+          style: LineStyle.Solid,
+        },
+      },
+      crosshair: {
+        vertLine: {
+          color: isDarkMode ? '#30363d' : '#d0d7de',
+          labelVisible: false,
+        },
+        horzLine: {
+          color: isDarkMode ? '#30363d' : '#d0d7de',
+          labelVisible: true,
+        },
+      },
+      rightPriceScale: {
+        borderVisible: false,
+        visible: false,
+      },
+      leftPriceScale: {
+        borderVisible: false,
+        visible: true,
+      },
+      timeScale: {
+        borderVisible: false,
+        timeVisible: false,
+      },
+      handleScroll: false,
+      handleScale: false,
+    });
+
+    const prices = currentChartData.prices;
+    let baselinePrice: number;
+
+    if (selectedTimeframe === '1D') {
+      baselinePrice = props.regularMarketPreviousClose ?? prices[0];
+    } else {
+      baselinePrice = prices[0];
+    }
+
+    const baselineSeries = chart.addSeries(BaselineSeries);
+
+    baselineSeries.applyOptions({
+      baseValue: { type: 'price', price: baselinePrice },
+      topLineColor: isDarkMode ? '#14b8a6' : '#0d9488',
+      topFillColor1: isDarkMode
+        ? 'rgba(20, 184, 166, 0.28)'
+        : 'rgba(13, 148, 136, 0.24)',
+      topFillColor2: isDarkMode
+        ? 'rgba(20, 184, 166, 0.05)'
+        : 'rgba(13, 148, 136, 0.05)',
+      bottomLineColor: isDarkMode ? '#f87171' : '#dc2626',
+      bottomFillColor1: isDarkMode
+        ? 'rgba(248, 113, 113, 0.05)'
+        : 'rgba(220, 38, 38, 0.05)',
+      bottomFillColor2: isDarkMode
+        ? 'rgba(248, 113, 113, 0.28)'
+        : 'rgba(220, 38, 38, 0.24)',
+      lineWidth: 2,
+      crosshairMarkerVisible: true,
+      crosshairMarkerRadius: 4,
+      crosshairMarkerBorderColor: '',
+      crosshairMarkerBackgroundColor: '',
+    });
+
+    const data = currentChartData.timestamps.map((timestamp, index) => {
+      const price = currentChartData.prices[index];
+      return {
+        time: (timestamp / 1000) as any,
+        value: price,
+      };
+    });
+
+    baselineSeries.setData(data);
+
+    const comparisonColors = ['#8b5cf6', '#f59e0b', '#ec4899'];
+    if (props.comparisonData && props.comparisonData.length > 0) {
+      props.comparisonData.forEach((comp, index) => {
+        const compChartData = comp.chartData[selectedTimeframe];
+        if (compChartData && compChartData.prices.length > 0) {
+          const compData = compChartData.timestamps.map((timestamp, i) => ({
+            time: (timestamp / 1000) as any,
+            value: compChartData.prices[i],
+          }));
+
+          const compSeries = chart.addSeries(LineSeries);
+          compSeries.applyOptions({
+            color: comparisonColors[index] || '#6b7280',
+            lineWidth: 2,
+            crosshairMarkerVisible: true,
+            crosshairMarkerRadius: 4,
+            priceScaleId: 'left',
+          });
+          compSeries.setData(compData);
+        }
+      });
+    }
+
+    chart.timeScale().fitContent();
+
+    const handleResize = () => {
+      if (chartContainerRef.current) {
+        chart.applyOptions({
+          width: chartContainerRef.current.clientWidth,
+        });
+      }
+    };
+
+    window.addEventListener('resize', handleResize);
+
+    return () => {
+      window.removeEventListener('resize', handleResize);
+      chart.remove();
+    };
+  }, [
+    props.chartData,
+    props.comparisonData,
+    selectedTimeframe,
+    isDarkMode,
+    props.regularMarketPreviousClose,
+  ]);
+
+  const isPositive = (props.regularMarketChange ?? 0) >= 0;
+  const isMarketOpen = props.marketState === 'REGULAR';
+  const isPreMarket = props.marketState === 'PRE';
+  const isPostMarket = props.marketState === 'POST';
+
+  const displayPrice = isPostMarket
+    ? props.postMarketPrice ?? props.regularMarketPrice
+    : isPreMarket
+      ? props.preMarketPrice ?? props.regularMarketPrice
+      : props.regularMarketPrice;
+
+  const displayChange = isPostMarket
+    ? props.postMarketChange ?? props.regularMarketChange
+    : isPreMarket
+      ? props.preMarketChange ?? props.regularMarketChange
+      : props.regularMarketChange;
+
+  const displayChangePercent = isPostMarket
+    ? props.postMarketChangePercent ?? props.regularMarketChangePercent
+    : isPreMarket
+      ? props.preMarketChangePercent ?? props.regularMarketChangePercent
+      : props.regularMarketChangePercent;
+
+  const changeColor = isPositive
+    ? 'text-green-600 dark:text-green-400'
+    : 'text-red-600 dark:text-red-400';
+
+  if (props.error) {
+    return (
+      <div className="rounded-lg bg-light-secondary dark:bg-dark-secondary border border-light-200 dark:border-dark-200 p-4">
+        <p className="text-sm text-black dark:text-white">
+          Error: {props.error}
+        </p>
+      </div>
+    );
+  }
+
+  return (
+    <div className="rounded-lg border border-light-200 dark:border-dark-200 overflow-hidden">
+      <div className="p-4 space-y-4">
+        <div className="flex items-start justify-between gap-4 pb-4 border-b border-light-200 dark:border-dark-200">
+          <div className="flex-1">
+            <div className="flex items-center gap-2 mb-1">
+              {props.website && (
+                <img
+                  src={`https://logo.clearbit.com/${new URL(props.website).hostname}`}
+                  alt={`${props.symbol} logo`}
+                  className="w-8 h-8 rounded-lg"
+                  onError={(e) => {
+                    (e.target as HTMLImageElement).style.display = 'none';
+                  }}
+                />
+              )}
+              <h3 className="text-2xl font-bold text-black dark:text-white">
+                {props.symbol}
+              </h3>
+              {props.exchange && (
+                <span className="px-2 py-0.5 text-xs font-medium rounded bg-light-100 dark:bg-dark-100 text-black/60 dark:text-white/60">
+                  {props.exchange}
+                </span>
+              )}
+              {isMarketOpen && (
+                <div className="flex items-center gap-1.5 px-2 py-0.5 rounded-full bg-green-100 dark:bg-green-950/40 border border-green-300 dark:border-green-800">
+                  <div className="w-1.5 h-1.5 rounded-full bg-green-500 animate-pulse" />
+                  <span className="text-xs font-medium text-green-700 dark:text-green-400">
+                    Live
+                  </span>
+                </div>
+              )}
+              {isPreMarket && (
+                <div className="flex items-center gap-1.5 px-2 py-0.5 rounded-full bg-blue-100 dark:bg-blue-950/40 border border-blue-300 dark:border-blue-800">
+                  <Clock className="w-3 h-3 text-blue-600 dark:text-blue-400" />
+                  <span className="text-xs font-medium text-blue-700 dark:text-blue-400">
+                    Pre-Market
+                  </span>
+                </div>
+              )}
+              {isPostMarket && (
+                <div className="flex items-center gap-1.5 px-2 py-0.5 rounded-full bg-orange-100 dark:bg-orange-950/40 border border-orange-300 dark:border-orange-800">
+                  <Clock className="w-3 h-3 text-orange-600 dark:text-orange-400" />
+                  <span className="text-xs font-medium text-orange-700 dark:text-orange-400">
+                    After Hours
+                  </span>
+                </div>
+              )}
+            </div>
+            <p className="text-sm text-black/60 dark:text-white/60">
+              {props.longName || props.shortName}
+            </p>
+          </div>
+
+          <div className="text-right">
+            <div className="flex items-baseline gap-2 mb-1">
+              <span className="text-3xl font-medium text-black dark:text-white">
+                {props.currency === 'USD' ? '$' : ''}
+                {formatNumber(displayPrice)}
+              </span>
+            </div>
+            <div
+              className={`flex items-center justify-end gap-1 ${changeColor}`}
+            >
+              {isPositive ? (
+                <ArrowUpRight className="w-4 h-4" />
+              ) : displayChange === 0 ? (
+                <Minus className="w-4 h-4" />
+              ) : (
+                <ArrowDownRight className="w-4 h-4" />
+              )}
+              <span className="text-lg font-normal">
+                {displayChange !== undefined && displayChange >= 0 ? '+' : ''}
+                {formatNumber(displayChange)}
+              </span>
+              <span className="text-sm font-normal">
+                (
+                {displayChangePercent !== undefined && displayChangePercent >= 0
+                  ? '+'
+                  : ''}
+                {formatNumber(displayChangePercent)}%)
+              </span>
+            </div>
+          </div>
+        </div>
+
+        {props.chartData && (
+          <div className="bg-light-secondary dark:bg-dark-secondary rounded-lg overflow-hidden">
+            <div className="flex items-center justify-between p-3 border-b border-light-200 dark:border-dark-200">
+              <div className="flex items-center gap-1">
+                {(['1D', '5D', '1M', '3M', '6M', '1Y', 'MAX'] as const).map(
+                  (timeframe) => (
+                    <button
+                      key={timeframe}
+                      onClick={() => setSelectedTimeframe(timeframe)}
+                      disabled={!props.chartData?.[timeframe]}
+                      className={`px-3 py-1.5 text-xs font-medium rounded transition-colors ${
+                        selectedTimeframe === timeframe
+                          ? 'bg-black/10 dark:bg-white/10 text-black dark:text-white'
+                          : 'text-black/50 dark:text-white/50 hover:text-black/80 dark:hover:text-white/80'
+                      } disabled:opacity-30 disabled:cursor-not-allowed`}
+                    >
+                      {timeframe}
+                    </button>
+                  ),
+                )}
+              </div>
+
+              {props.comparisonData && props.comparisonData.length > 0 && (
+                <div className="flex items-center gap-3 ml-auto">
+                  <span className="text-xs text-black/50 dark:text-white/50">
+                    {props.symbol}
+                  </span>
+                  {props.comparisonData.map((comp, index) => {
+                    const colors = ['#8b5cf6', '#f59e0b', '#ec4899'];
+                    return (
+                      <div
+                        key={comp.ticker}
+                        className="flex items-center gap-1.5"
+                      >
+                        <div
+                          className="w-2 h-2 rounded-full"
+                          style={{ backgroundColor: colors[index] }}
+                        />
+                        <span className="text-xs text-black/70 dark:text-white/70">
+                          {comp.ticker}
+                        </span>
+                      </div>
+                    );
+                  })}
+                </div>
+              )}
+            </div>
+
+            <div className="p-4">
+              <div ref={chartContainerRef} />
+            </div>
+
+            <div className="grid grid-cols-3 border-t border-light-200 dark:border-dark-200">
+              <div className="flex justify-between p-3 border-r border-light-200 dark:border-dark-200">
+                <span className="text-xs text-black/50 dark:text-white/50">
+                  Prev Close
+                </span>
+                <span className="text-xs text-black dark:text-white font-medium">
+                  ${formatNumber(props.regularMarketPreviousClose)}
+                </span>
+              </div>
+              <div className="flex justify-between p-3 border-r border-light-200 dark:border-dark-200">
+                <span className="text-xs text-black/50 dark:text-white/50">
+                  52W Range
+                </span>
+                <span className="text-xs text-black dark:text-white font-medium">
+                  ${formatNumber(props.fiftyTwoWeekLow, 2)}-$
+                  {formatNumber(props.fiftyTwoWeekHigh, 2)}
+                </span>
+              </div>
+              <div className="flex justify-between p-3">
+                <span className="text-xs text-black/50 dark:text-white/50">
+                  Market Cap
+                </span>
+                <span className="text-xs text-black dark:text-white font-medium">
+                  {formatLargeNumber(props.marketCap)}
+                </span>
+              </div>
+              <div className="flex justify-between p-3 border-t border-r border-light-200 dark:border-dark-200">
+                <span className="text-xs text-black/50 dark:text-white/50">
+                  Open
+                </span>
+                <span className="text-xs text-black dark:text-white font-medium">
+                  ${formatNumber(props.regularMarketOpen)}
+                </span>
+              </div>
+              <div className="flex justify-between p-3 border-t border-r border-light-200 dark:border-dark-200">
+                <span className="text-xs text-black/50 dark:text-white/50">
+                  P/E Ratio
+                </span>
+                <span className="text-xs text-black dark:text-white font-medium">
+                  {props.trailingPE ? formatNumber(props.trailingPE, 2) : 'N/A'}
+                </span>
+              </div>
+              <div className="flex justify-between p-3 border-t border-light-200 dark:border-dark-200">
+                <span className="text-xs text-black/50 dark:text-white/50">
+                  Dividend Yield
+                </span>
+                <span className="text-xs text-black dark:text-white font-medium">
+                  {props.dividendYield
+                    ? `${formatNumber(props.dividendYield * 100, 2)}%`
+                    : 'N/A'}
+                </span>
+              </div>
+              <div className="flex justify-between p-3 border-t border-r border-light-200 dark:border-dark-200">
+                <span className="text-xs text-black/50 dark:text-white/50">
+                  Day Range
+                </span>
+                <span className="text-xs text-black dark:text-white font-medium">
+                  ${formatNumber(props.regularMarketDayLow, 2)}-$
+                  {formatNumber(props.regularMarketDayHigh, 2)}
+                </span>
+              </div>
+              <div className="flex justify-between p-3 border-t border-r border-light-200 dark:border-dark-200">
+                <span className="text-xs text-black/50 dark:text-white/50">
+                  Volume
+                </span>
+                <span className="text-xs text-black dark:text-white font-medium">
+                  {formatLargeNumber(props.regularMarketVolume)}
+                </span>
+              </div>
+              <div className="flex justify-between p-3 border-t border-light-200 dark:border-dark-200">
+                <span className="text-xs text-black/50 dark:text-white/50">
+                  EPS
+                </span>
+                <span className="text-xs text-black dark:text-white font-medium">
+                  $
+                  {props.earningsPerShare
+                    ? formatNumber(props.earningsPerShare, 2)
+                    : 'N/A'}
+                </span>
+              </div>
+            </div>
+          </div>
+        )}
+      </div>
+    </div>
+  );
+};
+
+export default Stock;

src/components/Widgets/Weather.tsx

@@ -0,0 +1,422 @@
+'use client';
+
+import { getMeasurementUnit } from '@/lib/config/clientRegistry';
+import { Wind, Droplets, Gauge } from 'lucide-react';
+import { useMemo, useEffect, useState } from 'react';
+
+type WeatherWidgetProps = {
+  location: string;
+  current: {
+    time: string;
+    temperature_2m: number;
+    relative_humidity_2m: number;
+    apparent_temperature: number;
+    is_day: number;
+    precipitation: number;
+    weather_code: number;
+    wind_speed_10m: number;
+    wind_direction_10m: number;
+    wind_gusts_10m?: number;
+  };
+  daily: {
+    time: string[];
+    weather_code: number[];
+    temperature_2m_max: number[];
+    temperature_2m_min: number[];
+    precipitation_probability_max: number[];
+  };
+  timezone: string;
+};
+
+const getWeatherInfo = (code: number, isDay: boolean, isDarkMode: boolean) => {
+  const dayNight = isDay ? 'day' : 'night';
+
+  const weatherMap: Record<
+    number,
+    { icon: string; description: string; gradient: string }
+  > = {
+    0: {
+      icon: `clear-${dayNight}.svg`,
+      description: 'Clear',
+      gradient: isDarkMode
+        ? isDay
+          ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #E8F1FA, #7A9DBF 35%, #4A7BA8 60%, #2F5A88)'
+          : 'radial-gradient(ellipse 150% 100% at 50% 100%, #5A6A7E, #3E4E63 40%, #2A3544 65%, #1A2230)'
+        : isDay
+          ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #FFFFFF, #DBEAFE 30%, #93C5FD 60%, #60A5FA)'
+          : 'radial-gradient(ellipse 150% 100% at 50% 100%, #7B8694, #475569 45%, #334155 70%, #1E293B)',
+    },
+    1: {
+      icon: `clear-${dayNight}.svg`,
+      description: 'Mostly Clear',
+      gradient: isDarkMode
+        ? isDay
+          ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #E8F1FA, #7A9DBF 35%, #4A7BA8 60%, #2F5A88)'
+          : 'radial-gradient(ellipse 150% 100% at 50% 100%, #5A6A7E, #3E4E63 40%, #2A3544 65%, #1A2230)'
+        : isDay
+          ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #FFFFFF, #DBEAFE 30%, #93C5FD 60%, #60A5FA)'
+          : 'radial-gradient(ellipse 150% 100% at 50% 100%, #7B8694, #475569 45%, #334155 70%, #1E293B)',
+    },
+    2: {
+      icon: `cloudy-1-${dayNight}.svg`,
+      description: 'Partly Cloudy',
+      gradient: isDarkMode
+        ? isDay
+          ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #D4E1ED, #8BA3B8 35%, #617A93 60%, #426070)'
+          : 'radial-gradient(ellipse 150% 100% at 50% 100%, #6B7583, #4A5563 40%, #3A4450 65%, #2A3340)'
+        : isDay
+          ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #FFFFFF, #E0F2FE 28%, #BFDBFE 58%, #93C5FD)'
+          : 'radial-gradient(ellipse 150% 100% at 50% 100%, #8B99AB, #64748B 45%, #475569 70%, #334155)',
+    },
+    3: {
+      icon: `cloudy-1-${dayNight}.svg`,
+      description: 'Cloudy',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #B8C3CF, #758190 38%, #546270 65%, #3D4A58)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #F5F8FA, #CBD5E1 32%, #94A3B8 65%, #64748B)',
+    },
+    45: {
+      icon: `fog-${dayNight}.svg`,
+      description: 'Foggy',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #C5CDD8, #8892A0 38%, #697380 65%, #4F5A68)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #FFFFFF, #E2E8F0 30%, #CBD5E1 62%, #94A3B8)',
+    },
+    48: {
+      icon: `fog-${dayNight}.svg`,
+      description: 'Rime Fog',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #C5CDD8, #8892A0 38%, #697380 65%, #4F5A68)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #FFFFFF, #E2E8F0 30%, #CBD5E1 62%, #94A3B8)',
+    },
+    51: {
+      icon: `rainy-1-${dayNight}.svg`,
+      description: 'Light Drizzle',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #B8D4E5, #6FA4C5 35%, #4A85AC 60%, #356A8E)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #E5FBFF, #A5F3FC 28%, #67E8F9 60%, #22D3EE)',
+    },
+    53: {
+      icon: `rainy-1-${dayNight}.svg`,
+      description: 'Drizzle',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #B8D4E5, #6FA4C5 35%, #4A85AC 60%, #356A8E)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #E5FBFF, #A5F3FC 28%, #67E8F9 60%, #22D3EE)',
+    },
+    55: {
+      icon: `rainy-2-${dayNight}.svg`,
+      description: 'Heavy Drizzle',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #A5C5D8, #5E92B0 35%, #3F789D 60%, #2A5F82)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #D4F3FF, #7DD3FC 30%, #38BDF8 62%, #0EA5E9)',
+    },
+    61: {
+      icon: `rainy-2-${dayNight}.svg`,
+      description: 'Light Rain',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #A5C5D8, #5E92B0 35%, #3F789D 60%, #2A5F82)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #D4F3FF, #7DD3FC 30%, #38BDF8 62%, #0EA5E9)',
+    },
+    63: {
+      icon: `rainy-2-${dayNight}.svg`,
+      description: 'Rain',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #8DB3C8, #4D819F 38%, #326A87 65%, #215570)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #B8E8FF, #38BDF8 32%, #0EA5E9 65%, #0284C7)',
+    },
+    65: {
+      icon: `rainy-3-${dayNight}.svg`,
+      description: 'Heavy Rain',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #7BA3B8, #3D6F8A 38%, #295973 65%, #1A455D)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #9CD9F5, #0EA5E9 32%, #0284C7 65%, #0369A1)',
+    },
+    71: {
+      icon: `snowy-1-${dayNight}.svg`,
+      description: 'Light Snow',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #E5F0FA, #9BB5CE 32%, #7496B8 58%, #527A9E)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #FFFFFF, #F0F9FF 25%, #E0F2FE 55%, #BAE6FD)',
+    },
+    73: {
+      icon: `snowy-2-${dayNight}.svg`,
+      description: 'Snow',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #D4E5F3, #85A1BD 35%, #6584A8 60%, #496A8E)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #FAFEFF, #E0F2FE 28%, #BAE6FD 60%, #7DD3FC)',
+    },
+    75: {
+      icon: `snowy-3-${dayNight}.svg`,
+      description: 'Heavy Snow',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #BDD8EB, #6F92AE 35%, #4F7593 60%, #365A78)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #F0FAFF, #BAE6FD 30%, #7DD3FC 62%, #38BDF8)',
+    },
+    77: {
+      icon: `snowy-1-${dayNight}.svg`,
+      description: 'Snow Grains',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #E5F0FA, #9BB5CE 32%, #7496B8 58%, #527A9E)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #FFFFFF, #F0F9FF 25%, #E0F2FE 55%, #BAE6FD)',
+    },
+    80: {
+      icon: `rainy-2-${dayNight}.svg`,
+      description: 'Light Showers',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #A5C5D8, #5E92B0 35%, #3F789D 60%, #2A5F82)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #D4F3FF, #7DD3FC 30%, #38BDF8 62%, #0EA5E9)',
+    },
+    81: {
+      icon: `rainy-2-${dayNight}.svg`,
+      description: 'Showers',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #8DB3C8, #4D819F 38%, #326A87 65%, #215570)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #B8E8FF, #38BDF8 32%, #0EA5E9 65%, #0284C7)',
+    },
+    82: {
+      icon: `rainy-3-${dayNight}.svg`,
+      description: 'Heavy Showers',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #7BA3B8, #3D6F8A 38%, #295973 65%, #1A455D)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #9CD9F5, #0EA5E9 32%, #0284C7 65%, #0369A1)',
+    },
+    85: {
+      icon: `snowy-2-${dayNight}.svg`,
+      description: 'Light Snow Showers',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #D4E5F3, #85A1BD 35%, #6584A8 60%, #496A8E)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #FAFEFF, #E0F2FE 28%, #BAE6FD 60%, #7DD3FC)',
+    },
+    86: {
+      icon: `snowy-3-${dayNight}.svg`,
+      description: 'Snow Showers',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #BDD8EB, #6F92AE 35%, #4F7593 60%, #365A78)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #F0FAFF, #BAE6FD 30%, #7DD3FC 62%, #38BDF8)',
+    },
+    95: {
+      icon: `scattered-thunderstorms-${dayNight}.svg`,
+      description: 'Thunderstorm',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #8A95A3, #5F6A7A 38%, #475260 65%, #2F3A48)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #C8D1DD, #94A3B8 32%, #64748B 65%, #475569)',
+    },
+    96: {
+      icon: 'severe-thunderstorm.svg',
+      description: 'Thunderstorm + Hail',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #7A8593, #515C6D 38%, #3A4552 65%, #242D3A)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #B0BBC8, #64748B 32%, #475569 65%, #334155)',
+    },
+    99: {
+      icon: 'severe-thunderstorm.svg',
+      description: 'Severe Thunderstorm',
+      gradient: isDarkMode
+        ? 'radial-gradient(ellipse 150% 100% at 50% 100%, #6A7583, #434E5D 40%, #2F3A47 68%, #1C2530)'
+        : 'radial-gradient(ellipse 150% 100% at 50% 100%, #9BA8B8, #475569 35%, #334155 68%, #1E293B)',
+    },
+  };
+
+  return weatherMap[code] || weatherMap[0];
+};
+
+const Weather = ({
+  location,
+  current,
+  daily,
+  timezone,
+}: WeatherWidgetProps) => {
+  const [isDarkMode, setIsDarkMode] = useState(false);
+  const unit = getMeasurementUnit();
+  const isImperial = unit === 'imperial';
+  const tempUnitLabel = isImperial ? '°F' : '°C';
+  const windUnitLabel = isImperial ? 'mph' : 'km/h';
+
+  const formatTemp = (celsius: number) => {
+    if (!Number.isFinite(celsius)) return 0;
+    return Math.round(isImperial ? (celsius * 9) / 5 + 32 : celsius);
+  };
+
+  const formatWind = (speedKmh: number) => {
+    if (!Number.isFinite(speedKmh)) return 0;
+    return Math.round(isImperial ? speedKmh * 0.621371 : speedKmh);
+  };
+
+  useEffect(() => {
+    const checkDarkMode = () => {
+      setIsDarkMode(document.documentElement.classList.contains('dark'));
+    };
+
+    checkDarkMode();
+
+    const observer = new MutationObserver(checkDarkMode);
+    observer.observe(document.documentElement, {
+      attributes: true,
+      attributeFilter: ['class'],
+    });
+
+    return () => observer.disconnect();
+  }, []);
+
+  const weatherInfo = useMemo(
+    () =>
+      getWeatherInfo(
+        current?.weather_code || 0,
+        current?.is_day === 1,
+        isDarkMode,
+      ),
+    [current?.weather_code, current?.is_day, isDarkMode],
+  );
+
+  const forecast = useMemo(() => {
+    if (!daily?.time || daily.time.length === 0) return [];
+
+    return daily.time.slice(1, 7).map((time, idx) => {
+      const date = new Date(time);
+      const dayName = date.toLocaleDateString('en-US', { weekday: 'short' });
+      const isDay = true;
+      const weatherCode = daily.weather_code[idx + 1];
+      const info = getWeatherInfo(weatherCode, isDay, isDarkMode);
+
+      return {
+        day: dayName,
+        icon: info.icon,
+        high: formatTemp(daily.temperature_2m_max[idx + 1]),
+        low: formatTemp(daily.temperature_2m_min[idx + 1]),
+        precipitation: daily.precipitation_probability_max[idx + 1] || 0,
+      };
+    });
+  }, [daily, isDarkMode, isImperial]);
+
+  if (!current || !daily || !daily.time || daily.time.length === 0) {
+    return (
+      <div className="relative overflow-hidden rounded-lg shadow-md bg-gray-200 dark:bg-gray-800">
+        <div className="p-4 text-black dark:text-white">
+          <p className="text-sm">Weather data unavailable for {location}</p>
+        </div>
+      </div>
+    );
+  }
+
+  return (
+    <div className="relative overflow-hidden rounded-lg shadow-md">
+      <div
+        className="absolute inset-0"
+        style={{
+          background: weatherInfo.gradient,
+        }}
+      />
+
+      <div className="relative p-4 text-gray-800 dark:text-white">
+        <div className="flex items-start justify-between mb-3">
+          <div className="flex items-center gap-3">
+            <img
+              src={`/weather-ico/${weatherInfo.icon}`}
+              alt={weatherInfo.description}
+              className="w-16 h-16 drop-shadow-lg"
+            />
+            <div>
+              <div className="flex items-baseline gap-1">
+                <span className="text-4xl font-bold drop-shadow-md">
+                  {formatTemp(current.temperature_2m)}°
+                </span>
+                <span className="text-lg">{tempUnitLabel}</span>
+              </div>
+              <p className="text-sm font-medium drop-shadow mt-0.5">
+                {weatherInfo.description}
+              </p>
+            </div>
+          </div>
+          <div className="text-right">
+            <p className="text-xs font-medium opacity-90">
+              {formatTemp(daily.temperature_2m_max[0])}°{' '}
+              {formatTemp(daily.temperature_2m_min[0])}°
+            </p>
+          </div>
+        </div>
+
+        <div className="mb-3 pb-3 border-b border-gray-800/20 dark:border-white/20">
+          <h3 className="text-base font-semibold drop-shadow-md">{location}</h3>
+          <p className="text-xs text-gray-700 dark:text-white/80 drop-shadow mt-0.5">
+            {new Date(current.time).toLocaleString('en-US', {
+              weekday: 'short',
+              hour: 'numeric',
+              minute: '2-digit',
+            })}
+          </p>
+        </div>
+
+        <div className="grid grid-cols-6 gap-2 mb-3 pb-3 border-b border-gray-800/20 dark:border-white/20">
+          {forecast.map((day, idx) => (
+            <div
+              key={idx}
+              className="flex flex-col items-center bg-gray-800/10 dark:bg-white/10 backdrop-blur-sm rounded-md p-2"
+            >
+              <p className="text-xs font-medium mb-1">{day.day}</p>
+              <img
+                src={`/weather-ico/${day.icon}`}
+                alt=""
+                className="w-8 h-8 mb-1"
+              />
+              <div className="flex items-center gap-1 text-xs">
+                <span className="font-semibold">{day.high}°</span>
+                <span className="text-gray-600 dark:text-white/60">
+                  {day.low}°
+                </span>
+              </div>
+              {day.precipitation > 0 && (
+                <div className="flex items-center gap-0.5 mt-1">
+                  <Droplets className="w-3 h-3 text-gray-600 dark:text-white/70" />
+                  <span className="text-[10px] text-gray-600 dark:text-white/70">
+                    {day.precipitation}%
+                  </span>
+                </div>
+              )}
+            </div>
+          ))}
+        </div>
+
+        <div className="grid grid-cols-3 gap-2 text-xs">
+          <div className="flex items-center gap-2 bg-gray-800/10 dark:bg-white/10 backdrop-blur-sm rounded-md p-2">
+            <Wind className="w-4 h-4 text-gray-700 dark:text-white/80 flex-shrink-0" />
+            <div>
+              <p className="text-[10px] text-gray-600 dark:text-white/70">
+                Wind
+              </p>
+              <p className="font-semibold">
+                {formatWind(current.wind_speed_10m)} {windUnitLabel}
+              </p>
+            </div>
+          </div>
+
+          <div className="flex items-center gap-2 bg-gray-800/10 dark:bg-white/10 backdrop-blur-sm rounded-md p-2">
+            <Droplets className="w-4 h-4 text-gray-700 dark:text-white/80 flex-shrink-0" />
+            <div>
+              <p className="text-[10px] text-gray-600 dark:text-white/70">
+                Humidity
+              </p>
+              <p className="font-semibold">
+                {Math.round(current.relative_humidity_2m)}%
+              </p>
+            </div>
+          </div>
+
+          <div className="flex items-center gap-2 bg-gray-800/10 dark:bg-white/10 backdrop-blur-sm rounded-md p-2">
+            <Gauge className="w-4 h-4 text-gray-700 dark:text-white/80 flex-shrink-0" />
+            <div>
+              <p className="text-[10px] text-gray-600 dark:text-white/70">
+                Feels Like
+              </p>
+              <p className="font-semibold">
+                {formatTemp(current.apparent_temperature)}
+                {tempUnitLabel}
+              </p>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+  );
+};
+
+export default Weather;

src/lib/actions.ts

@@ -1,6 +1,14 @@
 import { Message } from '@/components/ChatWindow';
 
-export const getSuggestions = async (chatHistory: Message[]) => {
+export const getSuggestions = async (chatHistory: [string, string][]) => {
+  const chatTurns = chatHistory.map(([role, content]) => {
+    if (role === 'human') {
+      return { role: 'user', content };
+    } else {
+      return { role: 'assistant', content };
+    }
+  });
+
   const chatModel = localStorage.getItem('chatModelKey');
   const chatModelProvider = localStorage.getItem('chatModelProviderId');
 
@@ -10,7 +18,7 @@ export const getSuggestions = async (chatHistory: Message[]) => {
       'Content-Type': 'application/json',
     },
     body: JSON.stringify({
-      chatHistory: chatHistory,
+      chatHistory: chatTurns,
       chatModel: {
         providerId: chatModelProvider,
         key: chatModel,

src/lib/agents/media/image.ts

@@ -0,0 +1,66 @@
+/* I don't think can be classified as agents but to keep the structure consistent i guess ill keep it here */
+
+import { searchSearxng } from '@/lib/searxng';
+import {
+  imageSearchFewShots,
+  imageSearchPrompt,
+} from '@/lib/prompts/media/image';
+import BaseLLM from '@/lib/models/base/llm';
+import z from 'zod';
+import { ChatTurnMessage } from '@/lib/types';
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+
+type ImageSearchChainInput = {
+  chatHistory: ChatTurnMessage[];
+  query: string;
+};
+
+type ImageSearchResult = {
+  img_src: string;
+  url: string;
+  title: string;
+};
+
+const searchImages = async (
+  input: ImageSearchChainInput,
+  llm: BaseLLM<any>,
+) => {
+  const schema = z.object({
+    query: z.string().describe('The image search query.'),
+  });
+
+  const res = await llm.generateObject<typeof schema>({
+    messages: [
+      {
+        role: 'system',
+        content: imageSearchPrompt,
+      },
+      ...imageSearchFewShots,
+      {
+        role: 'user',
+        content: `<conversation>\n${formatChatHistoryAsString(input.chatHistory)}\n</conversation>\n<follow_up>\n${input.query}\n</follow_up>`,
+      },
+    ],
+    schema: schema,
+  });
+
+  const searchRes = await searchSearxng(res.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 +1,66 @@
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+import { searchSearxng } from '@/lib/searxng';
+import {
+  videoSearchFewShots,
+  videoSearchPrompt,
+} from '@/lib/prompts/media/videos';
+import { ChatTurnMessage } from '@/lib/types';
+import BaseLLM from '@/lib/models/base/llm';
+import z from 'zod';
+
+type VideoSearchChainInput = {
+  chatHistory: ChatTurnMessage[];
+  query: string;
+};
+
+type VideoSearchResult = {
+  img_src: string;
+  url: string;
+  title: string;
+  iframe_src: string;
+};
+
+const searchVideos = async (
+  input: VideoSearchChainInput,
+  llm: BaseLLM<any>,
+) => {
+  const schema = z.object({
+    query: z.string().describe('The video search query.'),
+  });
+
+  const res = await llm.generateObject<typeof schema>({
+    messages: [
+      {
+        role: 'system',
+        content: videoSearchPrompt,
+      },
+      ...videoSearchFewShots,
+      {
+        role: 'user',
+        content: `<conversation>\n${formatChatHistoryAsString(input.chatHistory)}\n</conversation>\n<follow_up>\n${input.query}\n</follow_up>`,
+      },
+    ],
+    schema: schema,
+  });
+
+  const searchRes = await searchSearxng(res.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.ts

@@ -0,0 +1,53 @@
+import z from 'zod';
+import { ClassifierInput } from './types';
+import { classifierPrompt } from '@/lib/prompts/search/classifier';
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+
+const schema = z.object({
+  classification: z.object({
+    skipSearch: z
+      .boolean()
+      .describe('Indicates whether to skip the search step.'),
+    personalSearch: z
+      .boolean()
+      .describe('Indicates whether to perform a personal search.'),
+    academicSearch: z
+      .boolean()
+      .describe('Indicates whether to perform an academic search.'),
+    discussionSearch: z
+      .boolean()
+      .describe('Indicates whether to perform a discussion search.'),
+    showWeatherWidget: z
+      .boolean()
+      .describe('Indicates whether to show the weather widget.'),
+    showStockWidget: z
+      .boolean()
+      .describe('Indicates whether to show the stock widget.'),
+    showCalculationWidget: z
+      .boolean()
+      .describe('Indicates whether to show the calculation widget.'),
+  }),
+  standaloneFollowUp: z
+    .string()
+    .describe(
+      "A self-contained, context-independent reformulation of the user's question.",
+    ),
+});
+
+export const classify = async (input: ClassifierInput) => {
+  const output = await input.llm.generateObject<typeof schema>({
+    messages: [
+      {
+        role: 'system',
+        content: classifierPrompt,
+      },
+      {
+        role: 'user',
+        content: `<conversation_history>\n${formatChatHistoryAsString(input.chatHistory)}\n</conversation_history>\n<user_query>\n${input.query}\n</user_query>`,
+      },
+    ],
+    schema,
+  });
+
+  return output;
+};

src/lib/agents/search/index.ts

@@ -0,0 +1,103 @@
+import { ResearcherOutput, SearchAgentInput } from './types';
+import SessionManager from '@/lib/session';
+import { classify } from './classifier';
+import Researcher from './researcher';
+import { getWriterPrompt } from '@/lib/prompts/search/writer';
+import { WidgetExecutor } from './widgets';
+
+class SearchAgent {
+  async searchAsync(session: SessionManager, input: SearchAgentInput) {
+    const classification = await classify({
+      chatHistory: input.chatHistory,
+      enabledSources: input.config.sources,
+      query: input.followUp,
+      llm: input.config.llm,
+    });
+
+    const widgetPromise = WidgetExecutor.executeAll({
+      classification,
+      chatHistory: input.chatHistory,
+      followUp: input.followUp,
+      llm: input.config.llm,
+    }).then((widgetOutputs) => {
+      widgetOutputs.forEach((o) => {
+        session.emitBlock({
+          id: crypto.randomUUID(),
+          type: 'widget',
+          data: {
+            widgetType: o.type,
+            params: o.data,
+          },
+        });
+      });
+      return widgetOutputs;
+    });
+
+    let searchPromise: Promise<ResearcherOutput> | null = null;
+
+    if (!classification.classification.skipSearch) {
+      const researcher = new Researcher();
+      searchPromise = researcher.research(session, {
+        chatHistory: input.chatHistory,
+        followUp: input.followUp,
+        classification: classification,
+        config: input.config,
+      });
+    }
+
+    const [widgetOutputs, searchResults] = await Promise.all([
+      widgetPromise,
+      searchPromise,
+    ]);
+
+    session.emit('data', {
+      type: 'researchComplete',
+    });
+
+    const finalContext =
+      searchResults?.searchFindings
+        .map(
+          (f, index) =>
+            `<result index=${index + 1} title=${f.metadata.title}>${f.content}</result>`,
+        )
+        .join('\n') || '';
+
+    const widgetContext = widgetOutputs
+      .map((o) => {
+        return `<result>${o.llmContext}</result>`;
+      })
+      .join('\n-------------\n');
+
+    const finalContextWithWidgets = `<search_results note="These are the search results and assistant can cite these">\n${finalContext}\n</search_results>\n<widgets_result noteForAssistant="Its output is already showed to the user, assistant can use this information to answer the query but do not CITE this as a souce">\n${widgetContext}\n</widgets_result>`;
+
+    const writerPrompt = getWriterPrompt(finalContextWithWidgets);
+    const answerStream = input.config.llm.streamText({
+      messages: [
+        {
+          role: 'system',
+          content: writerPrompt,
+        },
+        ...input.chatHistory,
+        {
+          role: 'user',
+          content: input.followUp,
+        },
+      ],
+    });
+
+    let accumulatedText = '';
+
+    for await (const chunk of answerStream) {
+      accumulatedText += chunk.contentChunk;
+
+      session.emit('data', {
+        type: 'response',
+        data: chunk.contentChunk,
+      });
+    }
+
+    session.emit('end', {});
+  }
+}
+
+export default SearchAgent;

src/lib/agents/search/researcher/actions/done.ts

@@ -0,0 +1,24 @@
+import z from 'zod';
+import { ResearchAction } from '../../types';
+
+const actionDescription = `
+Use this action ONLY when you have completed all necessary research and are ready to provide a final answer to the user. This indicates that you have gathered sufficient information from previous steps and are concluding the research process.
+YOU MUST CALL THIS ACTION TO SIGNAL COMPLETION; DO NOT OUTPUT FINAL ANSWERS DIRECTLY TO THE USER.
+IT WILL BE AUTOMATICALLY TRIGGERED IF MAXIMUM ITERATIONS ARE REACHED SO IF YOU'RE LOW ON ITERATIONS, DON'T CALL IT AND INSTEAD FOCUS ON GATHERING ESSENTIAL INFO FIRST.
+`;
+
+const doneAction: ResearchAction<any> = {
+  name: 'done',
+  schema: z.object({}),
+  getToolDescription: () =>
+    'Only call this after 0_reasoning AND after any other needed tool calls when you truly have enough to answer. Do not call if information is still missing.',
+  getDescription: () => actionDescription,
+  enabled: (_) => true,
+  execute: async (params, additionalConfig) => {
+    return {
+      type: 'done',
+    };
+  },
+};
+
+export default doneAction;

src/lib/agents/search/researcher/actions/index.ts

@@ -0,0 +1,14 @@
+import doneAction from './done';
+import planAction from './plan';
+import ActionRegistry from './registry';
+import scrapeURLAction from './scrapeURL';
+import uploadsSearchAction from './uploadsSearch';
+import webSearchAction from './webSearch';
+
+ActionRegistry.register(webSearchAction);
+ActionRegistry.register(doneAction);
+ActionRegistry.register(planAction);
+ActionRegistry.register(scrapeURLAction);
+ActionRegistry.register(uploadsSearchAction);
+
+export { ActionRegistry };

src/lib/agents/search/researcher/actions/plan.ts

@@ -0,0 +1,40 @@
+import z from 'zod';
+import { ResearchAction } from '../../types';
+
+const schema = z.object({
+  plan: z
+    .string()
+    .describe(
+      'A concise natural-language plan in one short paragraph. Open with a short intent phrase (e.g., "Okay, the user wants to...", "Searching for...", "Looking into...") and lay out the steps you will take.',
+    ),
+});
+
+const actionDescription = `
+Use this tool FIRST on every turn to state your plan in natural language before any other action. Keep it short, action-focused, and tailored to the current query.
+Make sure to not include reference to any tools or actions you might take, just the plan itself. The user isn't aware about tools, but they love to see your thought process.
+
+Here are some examples of good plans:
+<examples>
+- "Okay, the user wants to know the latest advancements in renewable energy. I will start by looking for recent articles and studies on this topic, then summarize the key points." -> "I have gathered enough information to provide a comprehensive answer."
+- "The user is asking about the health benefits of a Mediterranean diet. I will search for scientific studies and expert opinions on this diet, then compile the findings into a clear summary." -> "I have gathered information about the Mediterranean diet and its health benefits, I will now look up for any recent studies to ensure the information is current."
+<examples>
+
+YOU CAN NEVER CALL ANY OTHER TOOL BEFORE CALLING THIS ONE FIRST, IF YOU DO, THAT CALL WOULD BE IGNORED.
+`;
+
+const planAction: ResearchAction<typeof schema> = {
+  name: '0_reasoning',
+  schema: schema,
+  getToolDescription: () =>
+    'Use this FIRST on every turn to state your plan in natural language before any other action. Keep it short, action-focused, and tailored to the current query.',
+  getDescription: () => actionDescription,
+  enabled: (config) => config.mode !== 'speed',
+  execute: async (input, _) => {
+    return {
+      type: 'reasoning',
+      reasoning: input.plan,
+    };
+  },
+};
+
+export default planAction;

src/lib/agents/search/researcher/actions/registry.ts

@@ -0,0 +1,101 @@
+import { Tool, ToolCall } from '@/lib/models/types';
+import {
+  ActionOutput,
+  AdditionalConfig,
+  ClassifierOutput,
+  ResearchAction,
+  SearchAgentConfig,
+} from '../../types';
+
+class ActionRegistry {
+  private static actions: Map<string, ResearchAction> = new Map();
+
+  static register(action: ResearchAction<any>) {
+    this.actions.set(action.name, action);
+  }
+
+  static get(name: string): ResearchAction | undefined {
+    return this.actions.get(name);
+  }
+
+  static getAvailableActions(config: {
+    classification: ClassifierOutput;
+    fileIds: string[];
+    mode: SearchAgentConfig['mode'];
+  }): ResearchAction[] {
+    return Array.from(
+      this.actions.values().filter((action) => action.enabled(config)),
+    );
+  }
+
+  static getAvailableActionTools(config: {
+    classification: ClassifierOutput;
+    fileIds: string[];
+    mode: SearchAgentConfig['mode'];
+  }): Tool[] {
+    const availableActions = this.getAvailableActions(config);
+
+    return availableActions.map((action) => ({
+      name: action.name,
+      description: action.getToolDescription({ mode: config.mode }),
+      schema: action.schema,
+    }));
+  }
+
+  static getAvailableActionsDescriptions(config: {
+    classification: ClassifierOutput;
+    fileIds: string[];
+    mode: SearchAgentConfig['mode'];
+  }): string {
+    const availableActions = this.getAvailableActions(config);
+
+    return availableActions
+      .map(
+        (action) =>
+          `<tool name="${action.name}">\n${action.getDescription({ mode: config.mode })}\n</tool>`,
+      )
+      .join('\n\n');
+  }
+
+  static async execute(
+    name: string,
+    params: any,
+    additionalConfig: AdditionalConfig & {
+      researchBlockId: string;
+      fileIds: string[];
+    },
+  ) {
+    const action = this.actions.get(name);
+
+    if (!action) {
+      throw new Error(`Action with name ${name} not found`);
+    }
+
+    return action.execute(params, additionalConfig);
+  }
+
+  static async executeAll(
+    actions: ToolCall[],
+    additionalConfig: AdditionalConfig & {
+      researchBlockId: string;
+      fileIds: string[];
+    },
+  ): Promise<ActionOutput[]> {
+    const results: ActionOutput[] = [];
+
+    await Promise.all(
+      actions.map(async (actionConfig) => {
+        const output = await this.execute(
+          actionConfig.name,
+          actionConfig.arguments,
+          additionalConfig,
+        );
+        results.push(output);
+      }),
+    );
+
+    return results;
+  }
+}
+
+export default ActionRegistry;

src/lib/agents/search/researcher/actions/scrapeURL.ts

@@ -0,0 +1,139 @@
+import z from 'zod';
+import { ResearchAction } from '../../types';
+import { Chunk, ReadingResearchBlock } from '@/lib/types';
+import TurnDown from 'turndown';
+import path from 'path';
+
+const turndownService = new TurnDown();
+
+const schema = z.object({
+  urls: z.array(z.string()).describe('A list of URLs to scrape content from.'),
+});
+
+const actionDescription = `
+Use this tool to scrape and extract content from the provided URLs. This is useful when you the user has asked you to extract or summarize information from specific web pages. You can provide up to 3 URLs at a time. NEVER CALL THIS TOOL EXPLICITLY YOURSELF UNLESS INSTRUCTED TO DO SO BY THE USER.
+You should only call this tool when the user has specifically requested information from certain web pages, never call this yourself to get extra information without user instruction.
+
+For example, if the user says "Please summarize the content of https://example.com/article", you can call this tool with that URL to get the content and then provide the summary or "What does X mean according to https://example.com/page", you can call this tool with that URL to get the content and provide the explanation.
+`;
+
+const scrapeURLAction: ResearchAction<typeof schema> = {
+  name: 'scrape_url',
+  schema: schema,
+  getToolDescription: () =>
+    'Use this tool to scrape and extract content from the provided URLs. This is useful when you the user has asked you to extract or summarize information from specific web pages. You can provide up to 3 URLs at a time. NEVER CALL THIS TOOL EXPLICITLY YOURSELF UNLESS INSTRUCTED TO DO SO BY THE USER.',
+  getDescription: () => actionDescription,
+  enabled: (_) => true,
+  execute: async (params, additionalConfig) => {
+    params.urls = params.urls.slice(0, 3);
+
+    let readingBlockId = crypto.randomUUID();
+    let readingEmitted = false;
+
+    const researchBlock = additionalConfig.session.getBlock(
+      additionalConfig.researchBlockId,
+    );
+
+    const results: Chunk[] = [];
+
+    await Promise.all(
+      params.urls.map(async (url) => {
+        try {
+          const res = await fetch(url);
+          const text = await res.text();
+
+          const title =
+            text.match(/<title>(.*?)<\/title>/i)?.[1] || `Content from ${url}`;
+
+          if (
+            !readingEmitted &&
+            researchBlock &&
+            researchBlock.type === 'research'
+          ) {
+            readingEmitted = true;
+            researchBlock.data.subSteps.push({
+              id: readingBlockId,
+              type: 'reading',
+              reading: [
+                {
+                  content: '',
+                  metadata: {
+                    url,
+                    title: title,
+                  },
+                },
+              ],
+            });
+
+            additionalConfig.session.updateBlock(
+              additionalConfig.researchBlockId,
+              [
+                {
+                  op: 'replace',
+                  path: '/data/subSteps',
+                  value: researchBlock.data.subSteps,
+                },
+              ],
+            );
+          } else if (
+            readingEmitted &&
+            researchBlock &&
+            researchBlock.type === 'research'
+          ) {
+            const subStepIndex = researchBlock.data.subSteps.findIndex(
+              (step: any) => step.id === readingBlockId,
+            );
+
+            const subStep = researchBlock.data.subSteps[
+              subStepIndex
+            ] as ReadingResearchBlock;
+
+            subStep.reading.push({
+              content: '',
+              metadata: {
+                url,
+                title: title,
+              },
+            });
+
+            additionalConfig.session.updateBlock(
+              additionalConfig.researchBlockId,
+              [
+                {
+                  op: 'replace',
+                  path: '/data/subSteps',
+                  value: researchBlock.data.subSteps,
+                },
+              ],
+            );
+          }
+
+          const markdown = turndownService.turndown(text);
+
+          results.push({
+            content: markdown,
+            metadata: {
+              url,
+              title: title,
+            },
+          });
+        } catch (error) {
+          results.push({
+            content: `Failed to fetch content from ${url}: ${error}`,
+            metadata: {
+              url,
+              title: `Error fetching ${url}`,
+            },
+          });
+        }
+      }),
+    );
+
+    return {
+      type: 'search_results',
+      results,
+    };
+  },
+};
+
+export default scrapeURLAction;

src/lib/agents/search/researcher/actions/uploadsSearch.ts

@@ -0,0 +1,102 @@
+import z from 'zod';
+import { ResearchAction } from '../../types';
+import UploadStore from '@/lib/uploads/store';
+
+const schema = z.object({
+  queries: z
+    .array(z.string())
+    .describe(
+      'A list of queries to search in user uploaded files. Can be a maximum of 3 queries.',
+    ),
+});
+
+const uploadsSearchAction: ResearchAction<typeof schema> = {
+  name: 'uploads_search',
+  enabled: (config) =>
+    (config.classification.classification.personalSearch &&
+      config.fileIds.length > 0) ||
+    config.fileIds.length > 0,
+  schema,
+  getToolDescription: () =>
+    `Use this tool to perform searches over the user's uploaded files. This is useful when you need to gather information from the user's documents to answer their questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.`,
+  getDescription: () => `
+  Use this tool to perform searches over the user's uploaded files. This is useful when you need to gather information from the user's documents to answer their questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.
+  Always ensure that the queries you use are directly relevant to the user's request and pertain to the content of their uploaded files.
+
+  For example, if the user says "Please find information about X in my uploaded documents", you can call this tool with a query related to X to retrieve the relevant information from their files.
+  Never use this tool to search the web or for information that is not contained within the user's uploaded files.
+  `,
+  execute: async (input, additionalConfig) => {
+    input.queries = input.queries.slice(0, 3);
+
+    const researchBlock = additionalConfig.session.getBlock(
+      additionalConfig.researchBlockId,
+    );
+
+    if (researchBlock && researchBlock.type === 'research') {
+      researchBlock.data.subSteps.push({
+        id: crypto.randomUUID(),
+        type: 'upload_searching',
+        queries: input.queries,
+      });
+
+      additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
+        {
+          op: 'replace',
+          path: '/data/subSteps',
+          value: researchBlock.data.subSteps,
+        },
+      ]);
+    }
+
+    const uploadStore = new UploadStore({
+      embeddingModel: additionalConfig.embedding,
+      fileIds: additionalConfig.fileIds,
+    });
+
+    const results = await uploadStore.query(input.queries, 10);
+
+    const seenIds = new Map<string, number>();
+
+    const filteredSearchResults = results
+      .map((result, index) => {
+        if (result.metadata.url && !seenIds.has(result.metadata.url)) {
+          seenIds.set(result.metadata.url, index);
+          return result;
+        } else if (result.metadata.url && seenIds.has(result.metadata.url)) {
+          const existingIndex = seenIds.get(result.metadata.url)!;
+          const existingResult = results[existingIndex];
+
+          existingResult.content += `\n\n${result.content}`;
+
+          return undefined;
+        }
+
+        return result;
+      })
+      .filter((r) => r !== undefined);
+
+    if (researchBlock && researchBlock.type === 'research') {
+      researchBlock.data.subSteps.push({
+        id: crypto.randomUUID(),
+        type: 'upload_search_results',
+        results: filteredSearchResults,
+      });
+
+      additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
+        {
+          op: 'replace',
+          path: '/data/subSteps',
+          value: researchBlock.data.subSteps,
+        },
+      ]);
+    }
+
+    return {
+      type: 'search_results',
+      results: filteredSearchResults,
+    };
+  },
+};
+
+export default uploadsSearchAction;

src/lib/agents/search/researcher/actions/webSearch.ts

@@ -0,0 +1,181 @@
+import z from 'zod';
+import { ResearchAction } from '../../types';
+import { searchSearxng } from '@/lib/searxng';
+import { Chunk, SearchResultsResearchBlock } from '@/lib/types';
+
+const actionSchema = z.object({
+  type: z.literal('web_search'),
+  queries: z
+    .array(z.string())
+    .describe('An array of search queries to perform web searches for.'),
+});
+
+const speedModePrompt = `
+Use this tool to perform web searches based on the provided queries. This is useful when you need to gather information from the web to answer the user's questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.
+You are currently on speed mode, meaning you would only get to call this tool once. Make sure to prioritize the most important queries that are likely to get you the needed information in one go.
+
+Your queries should be very targeted and specific to the information you need, avoid broad or generic queries.
+Your queries shouldn't be sentences but rather keywords that are SEO friendly and can be used to search the web for information.
+
+For example, if the user is asking about the features of a new technology, you might use queries like "GPT-5.1 features", "GPT-5.1 release date", "GPT-5.1 improvements" rather than a broad query like "Tell me about GPT-5.1".
+
+You can search for 3 queries in one go, make sure to utilize all 3 queries to maximize the information you can gather. If a question is simple, then split your queries to cover different aspects or related topics to get a comprehensive understanding.
+If this tool is present and no other tools are more relevant, you MUST use this tool to get the needed information.
+`;
+
+const balancedModePrompt = `
+Use this tool to perform web searches based on the provided queries. This is useful when you need to gather information from the web to answer the user's questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.
+
+You can call this tool several times if needed to gather enough information.
+Start initially with broader queries to get an overview, then narrow down with more specific queries based on the results you receive.
+
+Your queries shouldn't be sentences but rather keywords that are SEO friendly and can be used to search the web for information.
+
+For example if the user is asking about Tesla, your actions should be like:
+1. 0_reasoning "The user is asking about Tesla. I will start with broader queries to get an overview of Tesla, then narrow down with more specific queries based on the results I receive." then
+2. web_search ["Tesla", "Tesla latest news", "Tesla stock price"] then
+3. 0_reasoning "Based on the previous search results, I will now narrow down my queries to focus on Tesla's recent developments and stock performance." then
+4. web_search ["Tesla Q2 2025 earnings", "Tesla new model 2025", "Tesla stock analysis"] then done.
+5. 0_reasoning "I have gathered enough information to provide a comprehensive answer."
+6. done.
+
+You can search for 3 queries in one go, make sure to utilize all 3 queries to maximize the information you can gather. If a question is simple, then split your queries to cover different aspects or related topics to get a comprehensive understanding.
+If this tool is present and no other tools are more relevant, you MUST use this tool to get the needed information. You can call this tools, multiple times as needed.
+`;
+
+const qualityModePrompt = `
+Use this tool to perform web searches based on the provided queries. This is useful when you need to gather information from the web to answer the user's questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.
+
+You have to call this tool several times to gather enough information unless the question is very simple (like greeting questions or basic facts).
+Start initially with broader queries to get an overview, then narrow down with more specific queries based on the results you receive.
+Never stop before at least 5-6 iterations of searches unless the user question is very simple.
+
+Your queries shouldn't be sentences but rather keywords that are SEO friendly and can be used to search the web for information.
+
+You can search for 3 queries in one go, make sure to utilize all 3 queries to maximize the information you can gather. If a question is simple, then split your queries to cover different aspects or related topics to get a comprehensive understanding.
+If this tool is present and no other tools are more relevant, you MUST use this tool to get the needed information. You can call this tools, multiple times as needed.
+`;
+
+const webSearchAction: ResearchAction<typeof actionSchema> = {
+  name: 'web_search',
+  schema: actionSchema,
+  getToolDescription: () =>
+    "Use this tool to perform web searches based on the provided queries. This is useful when you need to gather information from the web to answer the user's questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.",
+  getDescription: (config) => {
+    let prompt = '';
+
+    switch (config.mode) {
+      case 'speed':
+        prompt = speedModePrompt;
+        break;
+      case 'balanced':
+        prompt = balancedModePrompt;
+        break;
+      case 'quality':
+        prompt = qualityModePrompt;
+        break;
+      default:
+        prompt = speedModePrompt;
+        break;
+    }
+
+    return prompt;
+  },
+  enabled: (config) =>
+    config.classification.classification.skipSearch === false,
+  execute: async (input, additionalConfig) => {
+    input.queries = input.queries.slice(0, 3);
+
+    const researchBlock = additionalConfig.session.getBlock(
+      additionalConfig.researchBlockId,
+    );
+
+    if (researchBlock && researchBlock.type === 'research') {
+      researchBlock.data.subSteps.push({
+        id: crypto.randomUUID(),
+        type: 'searching',
+        searching: input.queries,
+      });
+
+      additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
+        {
+          op: 'replace',
+          path: '/data/subSteps',
+          value: researchBlock.data.subSteps,
+        },
+      ]);
+    }
+
+    const searchResultsBlockId = crypto.randomUUID();
+    let searchResultsEmitted = false;
+
+    let results: Chunk[] = [];
+
+    const search = async (q: string) => {
+      const res = await searchSearxng(q);
+
+      const resultChunks: Chunk[] = res.results.map((r) => ({
+        content: r.content || r.title,
+        metadata: {
+          title: r.title,
+          url: r.url,
+        },
+      }));
+
+      results.push(...resultChunks);
+
+      if (
+        !searchResultsEmitted &&
+        researchBlock &&
+        researchBlock.type === 'research'
+      ) {
+        searchResultsEmitted = true;
+
+        researchBlock.data.subSteps.push({
+          id: searchResultsBlockId,
+          type: 'search_results',
+          reading: resultChunks,
+        });
+
+        additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
+          {
+            op: 'replace',
+            path: '/data/subSteps',
+            value: researchBlock.data.subSteps,
+          },
+        ]);
+      } else if (
+        searchResultsEmitted &&
+        researchBlock &&
+        researchBlock.type === 'research'
+      ) {
+        const subStepIndex = researchBlock.data.subSteps.findIndex(
+          (step) => step.id === searchResultsBlockId,
+        );
+
+        const subStep = researchBlock.data.subSteps[
+          subStepIndex
+        ] as SearchResultsResearchBlock;
+
+        subStep.reading.push(...resultChunks);
+
+        additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
+          {
+            op: 'replace',
+            path: '/data/subSteps',
+            value: researchBlock.data.subSteps,
+          },
+        ]);
+      }
+    };
+
+    await Promise.all(input.queries.map(search));
+
+    return {
+      type: 'search_results',
+      results,
+    };
+  },
+};
+
+export default webSearchAction;

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

@@ -0,0 +1,219 @@
+import { ActionOutput, ResearcherInput, ResearcherOutput } from '../types';
+import { ActionRegistry } from './actions';
+import { getResearcherPrompt } from '@/lib/prompts/search/researcher';
+import SessionManager from '@/lib/session';
+import { Message, ReasoningResearchBlock } from '@/lib/types';
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+import { ToolCall } from '@/lib/models/types';
+
+class Researcher {
+  async research(
+    session: SessionManager,
+    input: ResearcherInput,
+  ): Promise<ResearcherOutput> {
+    let actionOutput: ActionOutput[] = [];
+    let maxIteration =
+      input.config.mode === 'speed'
+        ? 2
+        : input.config.mode === 'balanced'
+          ? 6
+          : 25;
+
+    const availableTools = ActionRegistry.getAvailableActionTools({
+      classification: input.classification,
+      fileIds: input.config.fileIds,
+      mode: input.config.mode,
+    });
+
+    const availableActionsDescription =
+      ActionRegistry.getAvailableActionsDescriptions({
+        classification: input.classification,
+        fileIds: input.config.fileIds,
+        mode: input.config.mode,
+      });
+
+    const researchBlockId = crypto.randomUUID();
+
+    session.emitBlock({
+      id: researchBlockId,
+      type: 'research',
+      data: {
+        subSteps: [],
+      },
+    });
+
+    const agentMessageHistory: Message[] = [
+      {
+        role: 'user',
+        content: `
+          <conversation>
+          ${formatChatHistoryAsString(input.chatHistory.slice(-10))}
+           User: ${input.followUp} (Standalone question: ${input.classification.standaloneFollowUp})
+           </conversation>
+        `,
+      },
+    ];
+
+    for (let i = 0; i < maxIteration; i++) {
+      const researcherPrompt = getResearcherPrompt(
+        availableActionsDescription,
+        input.config.mode,
+        i,
+        maxIteration,
+        input.config.fileIds,
+      );
+
+      const actionStream = input.config.llm.streamText({
+        messages: [
+          {
+            role: 'system',
+            content: researcherPrompt,
+          },
+          ...agentMessageHistory,
+        ],
+        tools: availableTools,
+      });
+
+      const block = session.getBlock(researchBlockId);
+
+      let reasoningEmitted = false;
+      let reasoningId = crypto.randomUUID();
+
+      let finalToolCalls: ToolCall[] = [];
+
+      for await (const partialRes of actionStream) {
+        if (partialRes.toolCallChunk.length > 0) {
+          partialRes.toolCallChunk.forEach((tc) => {
+            if (
+              tc.name === '0_reasoning' &&
+              tc.arguments['plan'] &&
+              !reasoningEmitted &&
+              block &&
+              block.type === 'research'
+            ) {
+              reasoningEmitted = true;
+
+              block.data.subSteps.push({
+                id: reasoningId,
+                type: 'reasoning',
+                reasoning: tc.arguments['plan'],
+              });
+
+              session.updateBlock(researchBlockId, [
+                {
+                  op: 'replace',
+                  path: '/data/subSteps',
+                  value: block.data.subSteps,
+                },
+              ]);
+            } else if (
+              tc.name === '0_reasoning' &&
+              tc.arguments['plan'] &&
+              reasoningEmitted &&
+              block &&
+              block.type === 'research'
+            ) {
+              const subStepIndex = block.data.subSteps.findIndex(
+                (step: any) => step.id === reasoningId,
+              );
+
+              if (subStepIndex !== -1) {
+                const subStep = block.data.subSteps[
+                  subStepIndex
+                ] as ReasoningResearchBlock;
+                subStep.reasoning = tc.arguments['plan'];
+                session.updateBlock(researchBlockId, [
+                  {
+                    op: 'replace',
+                    path: '/data/subSteps',
+                    value: block.data.subSteps,
+                  },
+                ]);
+              }
+            }
+
+            const existingIndex = finalToolCalls.findIndex(
+              (ftc) => ftc.id === tc.id,
+            );
+
+            if (existingIndex !== -1) {
+              finalToolCalls[existingIndex].arguments = tc.arguments;
+            } else {
+              finalToolCalls.push(tc);
+            }
+          });
+        }
+      }
+
+      if (finalToolCalls.length === 0) {
+        break;
+      }
+
+      if (finalToolCalls[finalToolCalls.length - 1].name === 'done') {
+        break;
+      }
+
+      agentMessageHistory.push({
+        role: 'assistant',
+        content: '',
+        tool_calls: finalToolCalls,
+      });
+
+      const actionResults = await ActionRegistry.executeAll(finalToolCalls, {
+        llm: input.config.llm,
+        embedding: input.config.embedding,
+        session: session,
+        researchBlockId: researchBlockId,
+        fileIds: input.config.fileIds,
+      });
+
+      actionOutput.push(...actionResults);
+
+      actionResults.forEach((action, i) => {
+        agentMessageHistory.push({
+          role: 'tool',
+          id: finalToolCalls[i].id,
+          name: finalToolCalls[i].name,
+          content: JSON.stringify(action),
+        });
+      });
+    }
+
+    const searchResults = actionOutput
+      .filter((a) => a.type === 'search_results')
+      .flatMap((a) => a.results);
+
+    const seenUrls = new Map<string, number>();
+
+    const filteredSearchResults = searchResults
+      .map((result, index) => {
+        if (result.metadata.url && !seenUrls.has(result.metadata.url)) {
+          seenUrls.set(result.metadata.url, index);
+          return result;
+        } else if (result.metadata.url && seenUrls.has(result.metadata.url)) {
+          const existingIndex = seenUrls.get(result.metadata.url)!;
+
+          const existingResult = searchResults[existingIndex];
+
+          existingResult.content += `\n\n${result.content}`;
+
+          return undefined;
+        }
+
+        return result;
+      })
+      .filter((r) => r !== undefined);
+
+    session.emit('data', {
+      type: 'sources',
+      data: filteredSearchResults,
+    });
+
+    return {
+      findings: actionOutput,
+      searchFindings: filteredSearchResults,
+    };
+  }
+}
+
+export default Researcher;

src/lib/agents/search/types.ts

@@ -0,0 +1,118 @@
+import z from 'zod';
+import BaseLLM from '../../models/base/llm';
+import BaseEmbedding from '@/lib/models/base/embedding';
+import SessionManager from '@/lib/session';
+import { ChatTurnMessage, Chunk } from '@/lib/types';
+
+export type SearchSources = 'web' | 'discussions' | 'academic';
+
+export type SearchAgentConfig = {
+  sources: SearchSources[];
+  fileIds: string[];
+  llm: BaseLLM<any>;
+  embedding: BaseEmbedding<any>;
+  mode: 'speed' | 'balanced' | 'quality';
+};
+
+export type SearchAgentInput = {
+  chatHistory: ChatTurnMessage[];
+  followUp: string;
+  config: SearchAgentConfig;
+};
+
+export type WidgetInput = {
+  chatHistory: ChatTurnMessage[];
+  followUp: string;
+  classification: ClassifierOutput;
+  llm: BaseLLM<any>;
+};
+
+export type Widget = {
+  type: string;
+  shouldExecute: (classification: ClassifierOutput) => boolean;
+  execute: (input: WidgetInput) => Promise<WidgetOutput | void>;
+};
+
+export type WidgetOutput = {
+  type: string;
+  llmContext: string;
+  data: any;
+};
+
+export type ClassifierInput = {
+  llm: BaseLLM<any>;
+  enabledSources: SearchSources[];
+  query: string;
+  chatHistory: ChatTurnMessage[];
+};
+
+export type ClassifierOutput = {
+  classification: {
+    skipSearch: boolean;
+    personalSearch: boolean;
+    academicSearch: boolean;
+    discussionSearch: boolean;
+    showWeatherWidget: boolean;
+    showStockWidget: boolean;
+    showCalculationWidget: boolean;
+  };
+  standaloneFollowUp: string;
+};
+
+export type AdditionalConfig = {
+  llm: BaseLLM<any>;
+  embedding: BaseEmbedding<any>;
+  session: SessionManager;
+};
+
+export type ResearcherInput = {
+  chatHistory: ChatTurnMessage[];
+  followUp: string;
+  classification: ClassifierOutput;
+  config: SearchAgentConfig;
+};
+
+export type ResearcherOutput = {
+  findings: ActionOutput[];
+  searchFindings: Chunk[];
+};
+
+export type SearchActionOutput = {
+  type: 'search_results';
+  results: Chunk[];
+};
+
+export type DoneActionOutput = {
+  type: 'done';
+};
+
+export type ReasoningResearchAction = {
+  type: 'reasoning';
+  reasoning: string;
+};
+
+export type ActionOutput =
+  | SearchActionOutput
+  | DoneActionOutput
+  | ReasoningResearchAction;
+
+export interface ResearchAction<
+  TSchema extends z.ZodObject<any> = z.ZodObject<any>,
+> {
+  name: string;
+  schema: z.ZodObject<any>;
+  getToolDescription: (config: { mode: SearchAgentConfig['mode'] }) => string;
+  getDescription: (config: { mode: SearchAgentConfig['mode'] }) => string;
+  enabled: (config: {
+    classification: ClassifierOutput;
+    fileIds: string[];
+    mode: SearchAgentConfig['mode'];
+  }) => boolean;
+  execute: (
+    params: z.infer<TSchema>,
+    additionalConfig: AdditionalConfig & {
+      researchBlockId: string;
+      fileIds: string[];
+    },
+  ) => Promise<ActionOutput>;
+}

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

@@ -0,0 +1,67 @@
+import z from 'zod';
+import { Widget } from '../types';
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+import { exp, evaluate as mathEval } from 'mathjs';
+
+const schema = z.object({
+  expression: z
+    .string()
+    .describe('Mathematical expression to calculate or evaluate.'),
+  notPresent: z
+    .boolean()
+    .describe('Whether there is any need for the calculation widget.'),
+});
+
+const system = `
+<role>
+Assistant is a calculation expression extractor. You will recieve a user follow up and a conversation history.
+Your task is to determine if there is a mathematical expression that needs to be calculated or evaluated. If there is, extract the expression and return it. If there is no need for any calculation, set notPresent to true.
+</role>
+
+<instructions>
+Make sure that the extracted expression is valid and can be used to calculate the result with Math JS library (https://mathjs.org/). If the expression is not valid, set notPresent to true.
+If you feel like you cannot extract a valid expression, set notPresent to true.
+</instructions>
+
+<output_format>
+You must respond in the following JSON format without any extra text, explanations or filler sentences:
+{
+  "expression": string,
+  "notPresent": boolean
+}
+</output_format>
+`;
+
+const calculationWidget: Widget = {
+  type: 'calculationWidget',
+  shouldExecute: (classification) =>
+    classification.classification.showCalculationWidget,
+  execute: async (input) => {
+    const output = await input.llm.generateObject<typeof schema>({
+      messages: [
+        {
+          role: 'system',
+          content: system,
+        },
+        {
+          role: 'user',
+          content: `<conversation_history>\n${formatChatHistoryAsString(input.chatHistory)}\n</conversation_history>\n<user_follow_up>\n${input.followUp}\n</user_follow_up>`,
+        },
+      ],
+      schema,
+    });
+
+    const result = mathEval(output.expression);
+
+    return {
+      type: 'calculation_result',
+      llmContext: `The result of the calculation for the expression "${output.expression}" is: ${result}`,
+      data: {
+        expression: output.expression,
+        result,
+      },
+    };
+  },
+};
+
+export default calculationWidget;

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

@@ -0,0 +1,36 @@
+import { Widget, WidgetInput, WidgetOutput } from '../types';
+
+class WidgetExecutor {
+  static widgets = new Map<string, Widget>();
+
+  static register(widget: Widget) {
+    this.widgets.set(widget.type, widget);
+  }
+
+  static getWidget(type: string): Widget | undefined {
+    return this.widgets.get(type);
+  }
+
+  static async executeAll(input: WidgetInput): Promise<WidgetOutput[]> {
+    const results: WidgetOutput[] = [];
+
+    await Promise.all(
+      Array.from(this.widgets.values()).map(async (widget) => {
+        try {
+          if (widget.shouldExecute(input.classification)) {
+            const output = await widget.execute(input);
+            if (output) {
+              results.push(output);
+            }
+          }
+        } catch (e) {
+          console.log(`Error executing widget ${widget.type}:`, e);
+        }
+      }),
+    );
+
+    return results;
+  }
+}
+
+export default WidgetExecutor;

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

@@ -0,0 +1,10 @@
+import calculationWidget from './calculationWidget';
+import WidgetExecutor from './executor';
+import weatherWidget from './weatherWidget';
+import stockWidget from './stockWidget';
+
+WidgetExecutor.register(weatherWidget);
+WidgetExecutor.register(calculationWidget);
+WidgetExecutor.register(stockWidget);
+
+export { WidgetExecutor };

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

@@ -0,0 +1,434 @@
+import z from 'zod';
+import { Widget } from '../types';
+import YahooFinance from 'yahoo-finance2';
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+
+const yf = new YahooFinance({
+  suppressNotices: ['yahooSurvey'],
+});
+
+const schema = z.object({
+  name: z
+    .string()
+    .describe(
+      "The stock name for example Nvidia, Google, Apple, Microsoft etc. You can also return ticker if you're aware of it otherwise just use the name.",
+    ),
+  comparisonNames: z
+    .array(z.string())
+    .max(3)
+    .describe(
+      "Optional array of up to 3 stock names to compare against the base name (e.g., ['Microsoft', 'GOOGL', 'Meta']). Charts will show percentage change comparison.",
+    ),
+  notPresent: z
+    .boolean()
+    .describe('Whether there is no need for the stock widget.'),
+});
+
+const systemPrompt = `
+<role>
+You are a stock ticker/name extractor. You will receive a user follow up and a conversation history.
+Your task is to determine if the user is asking about stock information and extract the stock name(s) they want data for.
+</role>
+
+<instructions>
+- If the user is asking about a stock, extract the primary stock name or ticker.
+- If the user wants to compare stocks, extract up to 3 comparison stock names in comparisonNames.
+- You can use either stock names (e.g., "Nvidia", "Apple") or tickers (e.g., "NVDA", "AAPL").
+- If you cannot determine a valid stock or the query is not stock-related, set notPresent to true.
+- If no comparison is needed, set comparisonNames to an empty array.
+</instructions>
+
+<output_format>
+You must respond in the following JSON format without any extra text, explanations or filler sentences:
+{
+  "name": string,
+  "comparisonNames": string[],
+  "notPresent": boolean
+}
+</output_format>
+`;
+
+const stockWidget: Widget = {
+  type: 'stockWidget',
+  shouldExecute: (classification) =>
+    classification.classification.showStockWidget,
+  execute: async (input) => {
+    const output = await input.llm.generateObject<typeof schema>({
+      messages: [
+        {
+          role: 'system',
+          content: systemPrompt,
+        },
+        {
+          role: 'user',
+          content: `<conversation_history>\n${formatChatHistoryAsString(input.chatHistory)}\n</conversation_history>\n<user_follow_up>\n${input.followUp}\n</user_follow_up>`,
+        },
+      ],
+      schema,
+    });
+
+    if (output.notPresent) {
+      return;
+    }
+
+    const params = output;
+    try {
+      const name = params.name;
+
+      const findings = await yf.search(name);
+
+      if (findings.quotes.length === 0)
+        throw new Error(`Failed to find quote for name/symbol: ${name}`);
+
+      const ticker = findings.quotes[0].symbol as string;
+
+      const quote: any = await yf.quote(ticker);
+
+      const chartPromises = {
+        '1D': yf
+          .chart(ticker, {
+            period1: new Date(Date.now() - 2 * 24 * 60 * 60 * 1000),
+            period2: new Date(),
+            interval: '5m',
+          })
+          .catch(() => null),
+        '5D': yf
+          .chart(ticker, {
+            period1: new Date(Date.now() - 6 * 24 * 60 * 60 * 1000),
+            period2: new Date(),
+            interval: '15m',
+          })
+          .catch(() => null),
+        '1M': yf
+          .chart(ticker, {
+            period1: new Date(Date.now() - 30 * 24 * 60 * 60 * 1000),
+            interval: '1d',
+          })
+          .catch(() => null),
+        '3M': yf
+          .chart(ticker, {
+            period1: new Date(Date.now() - 90 * 24 * 60 * 60 * 1000),
+            interval: '1d',
+          })
+          .catch(() => null),
+        '6M': yf
+          .chart(ticker, {
+            period1: new Date(Date.now() - 180 * 24 * 60 * 60 * 1000),
+            interval: '1d',
+          })
+          .catch(() => null),
+        '1Y': yf
+          .chart(ticker, {
+            period1: new Date(Date.now() - 365 * 24 * 60 * 60 * 1000),
+            interval: '1d',
+          })
+          .catch(() => null),
+        MAX: yf
+          .chart(ticker, {
+            period1: new Date(Date.now() - 10 * 365 * 24 * 60 * 60 * 1000),
+            interval: '1wk',
+          })
+          .catch(() => null),
+      };
+
+      const charts = await Promise.all([
+        chartPromises['1D'],
+        chartPromises['5D'],
+        chartPromises['1M'],
+        chartPromises['3M'],
+        chartPromises['6M'],
+        chartPromises['1Y'],
+        chartPromises['MAX'],
+      ]);
+
+      const [chart1D, chart5D, chart1M, chart3M, chart6M, chart1Y, chartMAX] =
+        charts;
+
+      if (!quote) {
+        throw new Error(`No data found for ticker: ${ticker}`);
+      }
+
+      let comparisonData: any = null;
+      if (params.comparisonNames.length > 0) {
+        const comparisonPromises = params.comparisonNames
+          .slice(0, 3)
+          .map(async (compName) => {
+            try {
+              const compFindings = await yf.search(compName);
+
+              if (compFindings.quotes.length === 0) return null;
+
+              const compTicker = compFindings.quotes[0].symbol as string;
+              const compQuote = await yf.quote(compTicker);
+              const compCharts = await Promise.all([
+                yf
+                  .chart(compTicker, {
+                    period1: new Date(Date.now() - 2 * 24 * 60 * 60 * 1000),
+                    period2: new Date(),
+                    interval: '5m',
+                  })
+                  .catch(() => null),
+                yf
+                  .chart(compTicker, {
+                    period1: new Date(Date.now() - 6 * 24 * 60 * 60 * 1000),
+                    period2: new Date(),
+                    interval: '15m',
+                  })
+                  .catch(() => null),
+                yf
+                  .chart(compTicker, {
+                    period1: new Date(Date.now() - 30 * 24 * 60 * 60 * 1000),
+                    interval: '1d',
+                  })
+                  .catch(() => null),
+                yf
+                  .chart(compTicker, {
+                    period1: new Date(Date.now() - 90 * 24 * 60 * 60 * 1000),
+                    interval: '1d',
+                  })
+                  .catch(() => null),
+                yf
+                  .chart(compTicker, {
+                    period1: new Date(Date.now() - 180 * 24 * 60 * 60 * 1000),
+                    interval: '1d',
+                  })
+                  .catch(() => null),
+                yf
+                  .chart(compTicker, {
+                    period1: new Date(Date.now() - 365 * 24 * 60 * 60 * 1000),
+                    interval: '1d',
+                  })
+                  .catch(() => null),
+                yf
+                  .chart(compTicker, {
+                    period1: new Date(
+                      Date.now() - 10 * 365 * 24 * 60 * 60 * 1000,
+                    ),
+                    interval: '1wk',
+                  })
+                  .catch(() => null),
+              ]);
+              return {
+                ticker: compTicker,
+                name: compQuote.shortName || compTicker,
+                charts: compCharts,
+              };
+            } catch (error) {
+              console.error(
+                `Failed to fetch comparison ticker ${compName}:`,
+                error,
+              );
+              return null;
+            }
+          });
+        const compResults = await Promise.all(comparisonPromises);
+        comparisonData = compResults.filter((r) => r !== null);
+      }
+
+      const stockData = {
+        symbol: quote.symbol,
+        shortName: quote.shortName || quote.longName || ticker,
+        longName: quote.longName,
+        exchange: quote.fullExchangeName || quote.exchange,
+        currency: quote.currency,
+        quoteType: quote.quoteType,
+
+        marketState: quote.marketState,
+        regularMarketTime: quote.regularMarketTime,
+        postMarketTime: quote.postMarketTime,
+        preMarketTime: quote.preMarketTime,
+
+        regularMarketPrice: quote.regularMarketPrice,
+        regularMarketChange: quote.regularMarketChange,
+        regularMarketChangePercent: quote.regularMarketChangePercent,
+        regularMarketPreviousClose: quote.regularMarketPreviousClose,
+        regularMarketOpen: quote.regularMarketOpen,
+        regularMarketDayHigh: quote.regularMarketDayHigh,
+        regularMarketDayLow: quote.regularMarketDayLow,
+
+        postMarketPrice: quote.postMarketPrice,
+        postMarketChange: quote.postMarketChange,
+        postMarketChangePercent: quote.postMarketChangePercent,
+        preMarketPrice: quote.preMarketPrice,
+        preMarketChange: quote.preMarketChange,
+        preMarketChangePercent: quote.preMarketChangePercent,
+
+        regularMarketVolume: quote.regularMarketVolume,
+        averageDailyVolume3Month: quote.averageDailyVolume3Month,
+        averageDailyVolume10Day: quote.averageDailyVolume10Day,
+        bid: quote.bid,
+        bidSize: quote.bidSize,
+        ask: quote.ask,
+        askSize: quote.askSize,
+
+        fiftyTwoWeekLow: quote.fiftyTwoWeekLow,
+        fiftyTwoWeekHigh: quote.fiftyTwoWeekHigh,
+        fiftyTwoWeekChange: quote.fiftyTwoWeekChange,
+        fiftyTwoWeekChangePercent: quote.fiftyTwoWeekChangePercent,
+
+        marketCap: quote.marketCap,
+        trailingPE: quote.trailingPE,
+        forwardPE: quote.forwardPE,
+        priceToBook: quote.priceToBook,
+        bookValue: quote.bookValue,
+        earningsPerShare: quote.epsTrailingTwelveMonths,
+        epsForward: quote.epsForward,
+
+        dividendRate: quote.dividendRate,
+        dividendYield: quote.dividendYield,
+        exDividendDate: quote.exDividendDate,
+        trailingAnnualDividendRate: quote.trailingAnnualDividendRate,
+        trailingAnnualDividendYield: quote.trailingAnnualDividendYield,
+
+        beta: quote.beta,
+
+        fiftyDayAverage: quote.fiftyDayAverage,
+        fiftyDayAverageChange: quote.fiftyDayAverageChange,
+        fiftyDayAverageChangePercent: quote.fiftyDayAverageChangePercent,
+        twoHundredDayAverage: quote.twoHundredDayAverage,
+        twoHundredDayAverageChange: quote.twoHundredDayAverageChange,
+        twoHundredDayAverageChangePercent:
+          quote.twoHundredDayAverageChangePercent,
+
+        sector: quote.sector,
+        industry: quote.industry,
+        website: quote.website,
+
+        chartData: {
+          '1D': chart1D
+            ? {
+                timestamps: chart1D.quotes.map((q: any) => q.date.getTime()),
+                prices: chart1D.quotes.map((q: any) => q.close),
+              }
+            : null,
+          '5D': chart5D
+            ? {
+                timestamps: chart5D.quotes.map((q: any) => q.date.getTime()),
+                prices: chart5D.quotes.map((q: any) => q.close),
+              }
+            : null,
+          '1M': chart1M
+            ? {
+                timestamps: chart1M.quotes.map((q: any) => q.date.getTime()),
+                prices: chart1M.quotes.map((q: any) => q.close),
+              }
+            : null,
+          '3M': chart3M
+            ? {
+                timestamps: chart3M.quotes.map((q: any) => q.date.getTime()),
+                prices: chart3M.quotes.map((q: any) => q.close),
+              }
+            : null,
+          '6M': chart6M
+            ? {
+                timestamps: chart6M.quotes.map((q: any) => q.date.getTime()),
+                prices: chart6M.quotes.map((q: any) => q.close),
+              }
+            : null,
+          '1Y': chart1Y
+            ? {
+                timestamps: chart1Y.quotes.map((q: any) => q.date.getTime()),
+                prices: chart1Y.quotes.map((q: any) => q.close),
+              }
+            : null,
+          MAX: chartMAX
+            ? {
+                timestamps: chartMAX.quotes.map((q: any) => q.date.getTime()),
+                prices: chartMAX.quotes.map((q: any) => q.close),
+              }
+            : null,
+        },
+        comparisonData: comparisonData
+          ? comparisonData.map((comp: any) => ({
+              ticker: comp.ticker,
+              name: comp.name,
+              chartData: {
+                '1D': comp.charts[0]
+                  ? {
+                      timestamps: comp.charts[0].quotes.map((q: any) =>
+                        q.date.getTime(),
+                      ),
+                      prices: comp.charts[0].quotes.map((q: any) => q.close),
+                    }
+                  : null,
+                '5D': comp.charts[1]
+                  ? {
+                      timestamps: comp.charts[1].quotes.map((q: any) =>
+                        q.date.getTime(),
+                      ),
+                      prices: comp.charts[1].quotes.map((q: any) => q.close),
+                    }
+                  : null,
+                '1M': comp.charts[2]
+                  ? {
+                      timestamps: comp.charts[2].quotes.map((q: any) =>
+                        q.date.getTime(),
+                      ),
+                      prices: comp.charts[2].quotes.map((q: any) => q.close),
+                    }
+                  : null,
+                '3M': comp.charts[3]
+                  ? {
+                      timestamps: comp.charts[3].quotes.map((q: any) =>
+                        q.date.getTime(),
+                      ),
+                      prices: comp.charts[3].quotes.map((q: any) => q.close),
+                    }
+                  : null,
+                '6M': comp.charts[4]
+                  ? {
+                      timestamps: comp.charts[4].quotes.map((q: any) =>
+                        q.date.getTime(),
+                      ),
+                      prices: comp.charts[4].quotes.map((q: any) => q.close),
+                    }
+                  : null,
+                '1Y': comp.charts[5]
+                  ? {
+                      timestamps: comp.charts[5].quotes.map((q: any) =>
+                        q.date.getTime(),
+                      ),
+                      prices: comp.charts[5].quotes.map((q: any) => q.close),
+                    }
+                  : null,
+                MAX: comp.charts[6]
+                  ? {
+                      timestamps: comp.charts[6].quotes.map((q: any) =>
+                        q.date.getTime(),
+                      ),
+                      prices: comp.charts[6].quotes.map((q: any) => q.close),
+                    }
+                  : null,
+              },
+            }))
+          : null,
+      };
+
+      return {
+        type: 'stock',
+        llmContext: `Current price of ${stockData.shortName} (${stockData.symbol}) is ${stockData.regularMarketPrice} ${stockData.currency}. Other details: ${JSON.stringify(
+          {
+            marketState: stockData.marketState,
+            regularMarketChange: stockData.regularMarketChange,
+            regularMarketChangePercent: stockData.regularMarketChangePercent,
+            marketCap: stockData.marketCap,
+            peRatio: stockData.trailingPE,
+            dividendYield: stockData.dividendYield,
+          },
+        )}`,
+        data: stockData,
+      };
+    } catch (error: any) {
+      return {
+        type: 'stock',
+        llmContext: 'Failed to fetch stock data.',
+        data: {
+          error: `Error fetching stock data: ${error.message || error}`,
+          ticker: params.name,
+        },
+      };
+    }
+  },
+};
+
+export default stockWidget;

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

@@ -0,0 +1,203 @@
+import z from 'zod';
+import { Widget } from '../types';
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+
+const schema = z.object({
+  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.',
+    ),
+  notPresent: z
+    .boolean()
+    .describe('Whether there is no need for the weather widget.'),
+});
+
+const systemPrompt = `
+<role>
+You are a location extractor for weather queries. You will receive a user follow up and a conversation history.
+Your task is to determine if the user is asking about weather and extract the location they want weather for.
+</role>
+
+<instructions>
+- If the user is asking about weather, extract the location name OR coordinates (never both).
+- If using location name, set lat and lon to 0.
+- If using coordinates, set location to empty string.
+- If you cannot determine a valid location or the query is not weather-related, set notPresent to true.
+- Location should be specific (city, state/region, country) for best results.
+- You have to give the location so that it can be used to fetch weather data, it cannot be left empty unless notPresent is true.
+- Make sure to infer short forms of location names (e.g., "NYC" -> "New York City", "LA" -> "Los Angeles").
+</instructions>
+
+<output_format>
+You must respond in the following JSON format without any extra text, explanations or filler sentences:
+{
+  "location": string,
+  "lat": number,
+  "lon": number,
+  "notPresent": boolean
+}
+</output_format>
+`;
+
+const weatherWidget: Widget = {
+  type: 'weatherWidget',
+  shouldExecute: (classification) =>
+    classification.classification.showWeatherWidget,
+  execute: async (input) => {
+    const output = await input.llm.generateObject<typeof schema>({
+      messages: [
+        {
+          role: 'system',
+          content: systemPrompt,
+        },
+        {
+          role: 'user',
+          content: `<conversation_history>\n${formatChatHistoryAsString(input.chatHistory)}\n</conversation_history>\n<user_follow_up>\n${input.followUp}\n</user_follow_up>`,
+        },
+      ],
+      schema,
+    });
+
+    if (output.notPresent) {
+      return;
+    }
+
+    const params = output;
+
+    try {
+      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=temperature_2m,relative_humidity_2m,apparent_temperature,is_day,precipitation,rain,showers,snowfall,weather_code,cloud_cover,pressure_msl,surface_pressure,wind_speed_10m,wind_direction_10m,wind_gusts_10m&hourly=temperature_2m,precipitation_probability,precipitation,weather_code&daily=weather_code,temperature_2m_max,temperature_2m_min,precipitation_sum,precipitation_probability_max&timezone=auto&forecast_days=7`,
+          {
+            headers: {
+              'User-Agent': 'Perplexica',
+              'Content-Type': 'application/json',
+            },
+          },
+        );
+
+        const weatherData = await weatherRes.json();
+
+        return {
+          type: 'weather',
+          llmContext: `Weather in ${params.location} is ${JSON.stringify(weatherData.current)}`,
+          data: {
+            location: params.location,
+            latitude: location.lat,
+            longitude: location.lon,
+            current: weatherData.current,
+            hourly: {
+              time: weatherData.hourly.time.slice(0, 24),
+              temperature_2m: weatherData.hourly.temperature_2m.slice(0, 24),
+              precipitation_probability:
+                weatherData.hourly.precipitation_probability.slice(0, 24),
+              precipitation: weatherData.hourly.precipitation.slice(0, 24),
+              weather_code: weatherData.hourly.weather_code.slice(0, 24),
+            },
+            daily: weatherData.daily,
+            timezone: weatherData.timezone,
+          },
+        };
+      } else if (params.lat !== undefined && params.lon !== undefined) {
+        const [weatherRes, locationRes] = await Promise.all([
+          fetch(
+            `https://api.open-meteo.com/v1/forecast?latitude=${params.lat}&longitude=${params.lon}&current=temperature_2m,relative_humidity_2m,apparent_temperature,is_day,precipitation,rain,showers,snowfall,weather_code,cloud_cover,pressure_msl,surface_pressure,wind_speed_10m,wind_direction_10m,wind_gusts_10m&hourly=temperature_2m,precipitation_probability,precipitation,weather_code&daily=weather_code,temperature_2m_max,temperature_2m_min,precipitation_sum,precipitation_probability_max&timezone=auto&forecast_days=7`,
+            {
+              headers: {
+                'User-Agent': 'Perplexica',
+                'Content-Type': 'application/json',
+              },
+            },
+          ),
+          fetch(
+            `https://nominatim.openstreetmap.org/reverse?lat=${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',
+          llmContext: `Weather in ${locationData.display_name} is ${JSON.stringify(weatherData.current)}`,
+          data: {
+            location: locationData.display_name,
+            latitude: params.lat,
+            longitude: params.lon,
+            current: weatherData.current,
+            hourly: {
+              time: weatherData.hourly.time.slice(0, 24),
+              temperature_2m: weatherData.hourly.temperature_2m.slice(0, 24),
+              precipitation_probability:
+                weatherData.hourly.precipitation_probability.slice(0, 24),
+              precipitation: weatherData.hourly.precipitation.slice(0, 24),
+              weather_code: weatherData.hourly.weather_code.slice(0, 24),
+            },
+            daily: weatherData.daily,
+            timezone: weatherData.timezone,
+          },
+        };
+      }
+
+      return {
+        type: 'weather',
+        llmContext: 'No valid location or coordinates provided.',
+        data: null,
+      };
+    } catch (err) {
+      return {
+        type: 'weather',
+        llmContext: 'Failed to fetch weather data.',
+        data: {
+          error: `Error fetching weather data: ${err}`,
+        },
+      };
+    }
+  },
+};
+export default weatherWidget;

src/lib/agents/suggestions/index.ts

@@ -0,0 +1,39 @@
+import formatChatHistoryAsString from '@/lib/utils/formatHistory';
+import { suggestionGeneratorPrompt } from '@/lib/prompts/suggestions';
+import { ChatTurnMessage } from '@/lib/types';
+import z from 'zod';
+import BaseLLM from '@/lib/models/base/llm';
+import { i } from 'mathjs';
+
+type SuggestionGeneratorInput = {
+  chatHistory: ChatTurnMessage[];
+};
+
+const schema = z.object({
+  suggestions: z
+    .array(z.string())
+    .describe('List of suggested questions or prompts'),
+});
+
+const generateSuggestions = async (
+  input: SuggestionGeneratorInput,
+  llm: BaseLLM<any>,
+) => {
+  const res = await llm.generateObject<typeof schema>({
+    messages: [
+      {
+        role: 'system',
+        content: suggestionGeneratorPrompt,
+      },
+      {
+        role: 'user',
+        content: `<chat_history>\n${formatChatHistoryAsString(input.chatHistory)}\n</chat_history>`,
+      },
+    ],
+    schema,
+  });
+
+  return res.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/config/clientRegistry.ts

@@ -17,3 +17,13 @@ export const getShowWeatherWidget = () =>
 
 export const getShowNewsWidget = () =>
   getClientConfig('showNewsWidget', 'true') === 'true';
+
+export const getMeasurementUnit = () => {
+  const value =
+    getClientConfig('measureUnit') ??
+    getClientConfig('measurementUnit', 'metric');
+
+  if (typeof value !== 'string') return 'metric';
+
+  return value.toLowerCase();
+};

src/lib/db/migrate.ts

@@ -18,12 +18,18 @@ db.exec(`
 `);
 
 function sanitizeSql(content: string) {
-  return content
-    .split(/\r?\n/)
-    .filter(
-      (l) => !l.trim().startsWith('-->') && !l.includes('statement-breakpoint'),
+  const statements = content
+    .split(/--> statement-breakpoint/g)
+    .map((stmt) =>
+      stmt
+        .split(/\r?\n/)
+        .filter((l) => !l.trim().startsWith('-->'))
+        .join('\n')
+        .trim(),
     )
-    .join('\n');
+    .filter((stmt) => stmt.length > 0);
+
+  return statements;
 }
 
 fs.readdirSync(migrationsFolder)
@@ -32,7 +38,7 @@ fs.readdirSync(migrationsFolder)
   .forEach((file) => {
     const filePath = path.join(migrationsFolder, file);
     let content = fs.readFileSync(filePath, 'utf-8');
-    content = sanitizeSql(content);
+    const statements = sanitizeSql(content);
 
     const migrationName = file.split('_')[0] || file;
 
@@ -108,7 +114,12 @@ fs.readdirSync(migrationsFolder)
         db.exec('DROP TABLE messages;');
         db.exec('ALTER TABLE messages_with_sources RENAME TO messages;');
       } else {
-        db.exec(content);
+        // Execute each statement separately
+        statements.forEach((stmt) => {
+          if (stmt.trim()) {
+            db.exec(stmt);
+          }
+        });
       }
 
       db.prepare('INSERT OR IGNORE INTO ran_migrations (name) VALUES (?)').run(

src/lib/db/schema.ts

@@ -1,26 +1,23 @@
 import { sql } from 'drizzle-orm';
 import { text, integer, sqliteTable } from 'drizzle-orm/sqlite-core';
-import { Document } from '@langchain/core/documents';
+import { Block } from '../types';
 
 export const messages = sqliteTable('messages', {
   id: integer('id').primaryKey(),
-  role: text('type', { enum: ['assistant', 'user', 'source'] }).notNull(),
-  chatId: text('chatId').notNull(),
-  createdAt: text('createdAt')
-    .notNull()
-    .default(sql`CURRENT_TIMESTAMP`),
   messageId: text('messageId').notNull(),
-
-  content: text('content'),
-
-  sources: text('sources', {
-    mode: 'json',
-  })
-    .$type<Document[]>()
+  chatId: text('chatId').notNull(),
+  backendId: text('backendId').notNull(),
+  query: text('query').notNull(),
+  createdAt: text('createdAt').notNull(),
+  responseBlocks: text('responseBlocks', { mode: 'json' })
+    .$type<Block[]>()
     .default(sql`'[]'`),
+  status: text({ enum: ['answering', 'completed', 'error'] }).default(
+    'answering',
+  ),
 });
 
-interface File {
+interface DBFile {
   name: string;
   fileId: string;
 }
@@ -31,6 +28,6 @@ export const chats = sqliteTable('chats', {
   createdAt: text('createdAt').notNull(),
   focusMode: text('focusMode').notNull(),
   files: text('files', { mode: 'json' })
-    .$type<File[]>()
+    .$type<DBFile[]>()
     .default(sql`'[]'`),
 });

src/lib/hooks/useChat.tsx

@@ -1,13 +1,7 @@
 'use client';
 
-import {
-  AssistantMessage,
-  ChatTurn,
-  Message,
-  SourceMessage,
-  SuggestionMessage,
-  UserMessage,
-} from '@/components/ChatWindow';
+import { Message } from '@/components/ChatWindow';
+import { Block } from '@/lib/types';
 import {
   createContext,
   useContext,
@@ -22,20 +16,20 @@ import { toast } from 'sonner';
 import { getSuggestions } from '../actions';
 import { MinimalProvider } from '../models/types';
 import { getAutoMediaSearch } from '../config/clientRegistry';
+import { applyPatch } from 'rfc6902';
+import { Widget } from '@/components/ChatWindow';
 
 export type Section = {
-  userMessage: UserMessage;
-  assistantMessage: AssistantMessage | undefined;
-  parsedAssistantMessage: string | undefined;
-  speechMessage: string | undefined;
-  sourceMessage: SourceMessage | undefined;
+  message: Message;
+  widgets: Widget[];
+  parsedTextBlocks: string[];
+  speechMessage: string;
   thinkingEnded: boolean;
   suggestions?: string[];
 };
 
 type ChatContext = {
   messages: Message[];
-  chatTurns: ChatTurn[];
   sections: Section[];
   chatHistory: [string, string][];
   files: File[];
@@ -51,6 +45,8 @@ type ChatContext = {
   hasError: boolean;
   chatModelProvider: ChatModelProvider;
   embeddingModelProvider: EmbeddingModelProvider;
+  researchEnded: boolean;
+  setResearchEnded: (ended: boolean) => void;
   setOptimizationMode: (mode: string) => void;
   setFocusMode: (mode: string) => void;
   setFiles: (files: File[]) => void;
@@ -204,18 +200,26 @@ const loadMessages = async (
 
   setMessages(messages);
 
-  const chatTurns = messages.filter(
-    (msg): msg is ChatTurn => msg.role === 'user' || msg.role === 'assistant',
-  );
+  const history: [string, string][] = [];
+  messages.forEach((msg) => {
+    history.push(['human', msg.query]);
 
-  const history = chatTurns.map((msg) => {
-    return [msg.role, msg.content];
-  }) as [string, string][];
+    const textBlocks = msg.responseBlocks
+      .filter(
+        (block): block is Block & { type: 'text' } => block.type === 'text',
+      )
+      .map((block) => block.data)
+      .join('\n');
+
+    if (textBlocks) {
+      history.push(['assistant', textBlocks]);
+    }
+  });
 
   console.debug(new Date(), 'app:messages_loaded');
 
-  if (chatTurns.length > 0) {
-    document.title = chatTurns[0].content;
+  if (messages.length > 0) {
+    document.title = messages[0].query;
   }
 
   const files = data.chat.files.map((file: any) => {
@@ -246,12 +250,12 @@ export const chatContext = createContext<ChatContext>({
   loading: false,
   messageAppeared: false,
   messages: [],
-  chatTurns: [],
   sections: [],
   notFound: false,
   optimizationMode: '',
   chatModelProvider: { key: '', providerId: '' },
   embeddingModelProvider: { key: '', providerId: '' },
+  researchEnded: false,
   rewrite: () => {},
   sendMessage: async () => {},
   setFileIds: () => {},
@@ -260,6 +264,7 @@ export const chatContext = createContext<ChatContext>({
   setOptimizationMode: () => {},
   setChatModelProvider: () => {},
   setEmbeddingModelProvider: () => {},
+  setResearchEnded: () => {},
 });
 
 export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
@@ -273,6 +278,8 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
   const [loading, setLoading] = useState(false);
   const [messageAppeared, setMessageAppeared] = useState(false);
 
+  const [researchEnded, setResearchEnded] = useState(false);
+
   const [chatHistory, setChatHistory] = useState<[string, string][]>([]);
   const [messages, setMessages] = useState<Message[]>([]);
 
@@ -305,66 +312,44 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
 
   const messagesRef = useRef<Message[]>([]);
 
-  const chatTurns = useMemo((): ChatTurn[] => {
-    return messages.filter(
-      (msg): msg is ChatTurn => msg.role === 'user' || msg.role === 'assistant',
-    );
-  }, [messages]);
-
   const sections = useMemo<Section[]>(() => {
-    const sections: Section[] = [];
+    return messages.map((msg) => {
+      const textBlocks: string[] = [];
+      let speechMessage = '';
+      let thinkingEnded = false;
+      let suggestions: string[] = [];
 
-    messages.forEach((msg, i) => {
-      if (msg.role === 'user') {
-        const nextUserMessageIndex = messages.findIndex(
-          (m, j) => j > i && m.role === 'user',
-        );
+      const sourceBlocks = msg.responseBlocks.filter(
+        (block): block is Block & { type: 'source' } => block.type === 'source',
+      );
+      const sources = sourceBlocks.flatMap((block) => block.data);
 
-        const aiMessage = messages.find(
-          (m, j) =>
-            j > i &&
-            m.role === 'assistant' &&
-            (nextUserMessageIndex === -1 || j < nextUserMessageIndex),
-        ) as AssistantMessage | undefined;
+      const widgetBlocks = msg.responseBlocks
+        .filter((b) => b.type === 'widget')
+        .map((b) => b.data) as Widget[];
 
-        const sourceMessage = messages.find(
-          (m, j) =>
-            j > i &&
-            m.role === 'source' &&
-            m.sources &&
-            (nextUserMessageIndex === -1 || j < nextUserMessageIndex),
-        ) as SourceMessage | undefined;
-
-        let thinkingEnded = false;
-        let processedMessage = aiMessage?.content ?? '';
-        let speechMessage = aiMessage?.content ?? '';
-        let suggestions: string[] = [];
-
-        if (aiMessage) {
+      msg.responseBlocks.forEach((block) => {
+        if (block.type === 'text') {
+          let processedText = block.data;
           const citationRegex = /\[([^\]]+)\]/g;
           const regex = /\[(\d+)\]/g;
 
-          if (processedMessage.includes('<think>')) {
-            const openThinkTag =
-              processedMessage.match(/<think>/g)?.length || 0;
+          if (processedText.includes('<think>')) {
+            const openThinkTag = processedText.match(/<think>/g)?.length || 0;
             const closeThinkTag =
-              processedMessage.match(/<\/think>/g)?.length || 0;
+              processedText.match(/<\/think>/g)?.length || 0;
 
             if (openThinkTag && !closeThinkTag) {
-              processedMessage += '</think> <a> </a>';
+              processedText += '</think> <a> </a>';
             }
           }
 
-          if (aiMessage.content.includes('</think>')) {
+          if (block.data.includes('</think>')) {
             thinkingEnded = true;
           }
 
-          if (
-            sourceMessage &&
-            sourceMessage.sources &&
-            sourceMessage.sources.length > 0
-          ) {
-            processedMessage = processedMessage.replace(
+          if (sources.length > 0) {
+            processedText = processedText.replace(
               citationRegex,
               (_, capturedContent: string) => {
                 const numbers = capturedContent
@@ -379,7 +364,7 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
                       return `[${numStr}]`;
                     }
 
-                    const source = sourceMessage.sources?.[number - 1];
+                    const source = sources[number - 1];
                     const url = source?.metadata?.url;
 
                     if (url) {
@@ -393,37 +378,27 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
                 return linksHtml;
               },
             );
-            speechMessage = aiMessage.content.replace(regex, '');
+            speechMessage += block.data.replace(regex, '');
           } else {
-            processedMessage = processedMessage.replace(regex, '');
-            speechMessage = aiMessage.content.replace(regex, '');
+            processedText = processedText.replace(regex, '');
+            speechMessage += block.data.replace(regex, '');
           }
 
-          const suggestionMessage = messages.find(
-            (m, j) =>
-              j > i &&
-              m.role === 'suggestion' &&
-              (nextUserMessageIndex === -1 || j < nextUserMessageIndex),
-          ) as SuggestionMessage | undefined;
-
-          if (suggestionMessage && suggestionMessage.suggestions.length > 0) {
-            suggestions = suggestionMessage.suggestions;
-          }
+          textBlocks.push(processedText);
+        } else if (block.type === 'suggestion') {
+          suggestions = block.data;
         }
+      });
 
-        sections.push({
-          userMessage: msg,
-          assistantMessage: aiMessage,
-          sourceMessage: sourceMessage,
-          parsedAssistantMessage: processedMessage,
-          speechMessage,
-          thinkingEnded,
-          suggestions: suggestions,
-        });
-      }
+      return {
+        message: msg,
+        parsedTextBlocks: textBlocks,
+        speechMessage,
+        thinkingEnded,
+        suggestions,
+        widgets: widgetBlocks,
+      };
     });
-
-    return sections;
   }, [messages]);
 
   useEffect(() => {
@@ -489,24 +464,17 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
 
   const rewrite = (messageId: string) => {
     const index = messages.findIndex((msg) => msg.messageId === messageId);
-    const chatTurnsIndex = chatTurns.findIndex(
-      (msg) => msg.messageId === messageId,
-    );
 
     if (index === -1) return;
 
-    const message = chatTurns[chatTurnsIndex - 1];
+    setMessages((prev) => prev.slice(0, index));
 
-    setMessages((prev) => {
-      return [
-        ...prev.slice(0, messages.length > 2 ? messages.indexOf(message) : 0),
-      ];
-    });
     setChatHistory((prev) => {
-      return [...prev.slice(0, chatTurns.length > 2 ? chatTurnsIndex - 1 : 0)];
+      return prev.slice(0, index * 2);
     });
 
-    sendMessage(message.content, message.messageId, true);
+    const messageToRewrite = messages[index];
+    sendMessage(messageToRewrite.query, messageToRewrite.messageId, true);
   };
 
   useEffect(() => {
@@ -527,88 +495,165 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
   ) => {
     if (loading || !message) return;
     setLoading(true);
+    setResearchEnded(false);
     setMessageAppeared(false);
 
     if (messages.length <= 1) {
       window.history.replaceState(null, '', `/c/${chatId}`);
     }
 
-    let recievedMessage = '';
-    let added = false;
-
     messageId = messageId ?? crypto.randomBytes(7).toString('hex');
+    const backendId = crypto.randomBytes(20).toString('hex');
 
-    setMessages((prevMessages) => [
-      ...prevMessages,
-      {
-        content: message,
-        messageId: messageId,
-        chatId: chatId!,
-        role: 'user',
-        createdAt: new Date(),
-      },
-    ]);
+    const newMessage: Message = {
+      messageId,
+      chatId: chatId!,
+      backendId,
+      query: message,
+      responseBlocks: [],
+      status: 'answering',
+      createdAt: new Date(),
+    };
+
+    setMessages((prevMessages) => [...prevMessages, newMessage]);
+
+    const receivedTextRef = { current: '' };
 
     const messageHandler = async (data: any) => {
       if (data.type === 'error') {
         toast.error(data.data);
         setLoading(false);
+        setMessages((prev) =>
+          prev.map((msg) =>
+            msg.messageId === messageId
+              ? { ...msg, status: 'error' as const }
+              : msg,
+          ),
+        );
         return;
       }
 
+      if (data.type === 'researchComplete') {
+        setResearchEnded(true);
+        if (
+          newMessage.responseBlocks.find(
+            (b) => b.type === 'source' && b.data.length > 0,
+          )
+        ) {
+          setMessageAppeared(true);
+        }
+      }
+
+      if (data.type === 'block') {
+        setMessages((prev) =>
+          prev.map((msg) => {
+            if (msg.messageId === messageId) {
+              return {
+                ...msg,
+                responseBlocks: [...msg.responseBlocks, data.block],
+              };
+            }
+            return msg;
+          }),
+        );
+      }
+
+      if (data.type === 'updateBlock') {
+        setMessages((prev) =>
+          prev.map((msg) => {
+            if (msg.messageId === messageId) {
+              const updatedBlocks = msg.responseBlocks.map((block) => {
+                if (block.id === data.blockId) {
+                  const updatedBlock = { ...block };
+                  applyPatch(updatedBlock, data.patch);
+                  return updatedBlock;
+                }
+                return block;
+              });
+              return { ...msg, responseBlocks: updatedBlocks };
+            }
+            return msg;
+          }),
+        );
+      }
+
       if (data.type === 'sources') {
-        setMessages((prevMessages) => [
-          ...prevMessages,
-          {
-            messageId: data.messageId,
-            chatId: chatId!,
-            role: 'source',
-            sources: data.data,
-            createdAt: new Date(),
-          },
-        ]);
+        const sourceBlock: Block = {
+          id: crypto.randomBytes(7).toString('hex'),
+          type: 'source',
+          data: data.data,
+        };
+
+        setMessages((prev) =>
+          prev.map((msg) => {
+            if (msg.messageId === messageId) {
+              return {
+                ...msg,
+                responseBlocks: [...msg.responseBlocks, sourceBlock],
+              };
+            }
+            return msg;
+          }),
+        );
         if (data.data.length > 0) {
           setMessageAppeared(true);
         }
       }
 
       if (data.type === 'message') {
-        if (!added) {
-          setMessages((prevMessages) => [
-            ...prevMessages,
-            {
-              content: data.data,
-              messageId: data.messageId,
-              chatId: chatId!,
-              role: 'assistant',
-              createdAt: new Date(),
-            },
-          ]);
-          added = true;
-          setMessageAppeared(true);
-        } else {
-          setMessages((prev) =>
-            prev.map((message) => {
-              if (
-                message.messageId === data.messageId &&
-                message.role === 'assistant'
-              ) {
-                return { ...message, content: message.content + data.data };
-              }
+        receivedTextRef.current += data.data;
 
-              return message;
-            }),
-          );
-        }
-        recievedMessage += data.data;
+        setMessages((prev) =>
+          prev.map((msg) => {
+            if (msg.messageId === messageId) {
+              const existingTextBlockIndex = msg.responseBlocks.findIndex(
+                (b) => b.type === 'text',
+              );
+
+              if (existingTextBlockIndex >= 0) {
+                const updatedBlocks = [...msg.responseBlocks];
+                const existingBlock = updatedBlocks[
+                  existingTextBlockIndex
+                ] as Block & { type: 'text' };
+                updatedBlocks[existingTextBlockIndex] = {
+                  ...existingBlock,
+                  data: existingBlock.data + data.data,
+                };
+                return { ...msg, responseBlocks: updatedBlocks };
+              } else {
+                const textBlock: Block = {
+                  id: crypto.randomBytes(7).toString('hex'),
+                  type: 'text',
+                  data: data.data,
+                };
+                return {
+                  ...msg,
+                  responseBlocks: [...msg.responseBlocks, textBlock],
+                };
+              }
+            }
+            return msg;
+          }),
+        );
+        setMessageAppeared(true);
       }
 
       if (data.type === 'messageEnd') {
-        setChatHistory((prevHistory) => [
-          ...prevHistory,
+        const newHistory: [string, string][] = [
+          ...chatHistory,
           ['human', message],
-          ['assistant', recievedMessage],
-        ]);
+          ['assistant', receivedTextRef.current],
+        ];
+
+        setChatHistory(newHistory);
+
+        setMessages((prev) =>
+          prev.map((msg) =>
+            msg.messageId === messageId
+              ? { ...msg, status: 'completed' as const }
+              : msg,
+          ),
+        );
 
         setLoading(false);
 
@@ -626,38 +671,37 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
             ?.click();
         }
 
-        /* Check if there are sources after message id's index and no suggestions */
-
-        const userMessageIndex = messagesRef.current.findIndex(
-          (msg) => msg.messageId === messageId && msg.role === 'user',
+        // Check if there are sources and no suggestions
+        const currentMsg = messagesRef.current.find(
+          (msg) => msg.messageId === messageId,
         );
 
-        const sourceMessage = messagesRef.current.find(
-          (msg, i) => i > userMessageIndex && msg.role === 'source',
-        ) as SourceMessage | undefined;
-
-        const suggestionMessageIndex = messagesRef.current.findIndex(
-          (msg, i) => i > userMessageIndex && msg.role === 'suggestion',
+        const hasSourceBlocks = currentMsg?.responseBlocks.some(
+          (block) => block.type === 'source' && block.data.length > 0,
+        );
+        const hasSuggestions = currentMsg?.responseBlocks.some(
+          (block) => block.type === 'suggestion',
         );
 
-        if (
-          sourceMessage &&
-          sourceMessage.sources.length > 0 &&
-          suggestionMessageIndex == -1
-        ) {
-          const suggestions = await getSuggestions(messagesRef.current);
-          setMessages((prev) => {
-            return [
-              ...prev,
-              {
-                role: 'suggestion',
-                suggestions: suggestions,
-                chatId: chatId!,
-                createdAt: new Date(),
-                messageId: crypto.randomBytes(7).toString('hex'),
-              },
-            ];
-          });
+        if (hasSourceBlocks && !hasSuggestions) {
+          const suggestions = await getSuggestions(newHistory);
+          const suggestionBlock: Block = {
+            id: crypto.randomBytes(7).toString('hex'),
+            type: 'suggestion',
+            data: suggestions,
+          };
+
+          setMessages((prev) =>
+            prev.map((msg) => {
+              if (msg.messageId === messageId) {
+                return {
+                  ...msg,
+                  responseBlocks: [...msg.responseBlocks, suggestionBlock],
+                };
+              }
+              return msg;
+            }),
+          );
         }
       }
     };
@@ -726,7 +770,6 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
     <chatContext.Provider
       value={{
         messages,
-        chatTurns,
         sections,
         chatHistory,
         files,
@@ -750,6 +793,8 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
         chatModelProvider,
         embeddingModelProvider,
         setEmbeddingModelProvider,
+        researchEnded,
+        setResearchEnded,
       }}
     >
       {children}

src/lib/models/base/embedding.ts

@@ -0,0 +1,9 @@
+import { Chunk } from '@/lib/types';
+
+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 z from 'zod';
+import {
+  GenerateObjectInput,
+  GenerateOptions,
+  GenerateTextInput,
+  GenerateTextOutput,
+  StreamTextOutput,
+} from '../types';
+
+abstract class BaseLLM<CONFIG> {
+  constructor(protected config: CONFIG) {}
+  abstract generateText(input: GenerateTextInput): Promise<GenerateTextOutput>;
+  abstract streamText(
+    input: GenerateTextInput,
+  ): AsyncGenerator<StreamTextOutput>;
+  abstract generateObject<T>(input: GenerateObjectInput): Promise<z.infer<T>>;
+  abstract streamObject<T>(
+    input: GenerateObjectInput,
+  ): AsyncGenerator<Partial<z.infer<T>>>;
+}
+
+export default BaseLLM;

src/lib/models/base/provider.ts

@@ -1,7 +1,7 @@
-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 +11,8 @@ 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/aiml.ts

@@ -1,152 +0,0 @@
-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';
-
-interface AimlConfig {
-  apiKey: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your AI/ML API key',
-    required: true,
-    placeholder: 'AI/ML API Key',
-    env: 'AIML_API_KEY',
-    scope: 'server',
-  },
-];
-
-class AimlProvider extends BaseModelProvider<AimlConfig> {
-  constructor(id: string, name: string, config: AimlConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    try {
-      const res = await fetch('https://api.aimlapi.com/models', {
-        method: 'GET',
-        headers: {
-          'Content-Type': 'application/json',
-          Authorization: `Bearer ${this.config.apiKey}`,
-        },
-      });
-
-      const data = await res.json();
-
-      const chatModels: Model[] = data.data
-        .filter((m: any) => m.type === 'chat-completion')
-        .map((m: any) => {
-          return {
-            name: m.id,
-            key: m.id,
-          };
-        });
-
-      const embeddingModels: Model[] = data.data
-        .filter((m: any) => m.type === 'embedding')
-        .map((m: any) => {
-          return {
-            name: m.id,
-            key: m.id,
-          };
-        });
-
-      return {
-        embedding: embeddingModels,
-        chat: chatModels,
-      };
-    } catch (err) {
-      if (err instanceof TypeError) {
-        throw new Error(
-          'Error connecting to AI/ML API. Please ensure your API key is correct and the service is available.',
-        );
-      }
-
-      throw err;
-    }
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [
-        ...defaultModels.embedding,
-        ...configProvider.embeddingModels,
-      ],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading AI/ML API Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatOpenAI({
-      apiKey: this.config.apiKey,
-      temperature: 0.7,
-      model: key,
-      configuration: {
-        baseURL: 'https://api.aimlapi.com',
-      },
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    const modelList = await this.getModelList();
-    const exists = modelList.embedding.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading AI/ML API Embedding Model. Invalid Model Selected.',
-      );
-    }
-
-    return new OpenAIEmbeddings({
-      apiKey: this.config.apiKey,
-      model: key,
-      configuration: {
-        baseURL: 'https://api.aimlapi.com',
-      },
-    });
-  }
-
-  static parseAndValidate(raw: any): AimlConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.apiKey)
-      throw new Error('Invalid config provided. API key must be provided');
-
-    return {
-      apiKey: String(raw.apiKey),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'aiml',
-      name: 'AI/ML API',
-    };
-  }
-}
-
-export default AimlProvider;

src/lib/models/providers/anthropic.ts

@@ -1,115 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatAnthropic } from '@langchain/anthropic';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-
-interface AnthropicConfig {
-  apiKey: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your Anthropic API key',
-    required: true,
-    placeholder: 'Anthropic API Key',
-    env: 'ANTHROPIC_API_KEY',
-    scope: 'server',
-  },
-];
-
-class AnthropicProvider extends BaseModelProvider<AnthropicConfig> {
-  constructor(id: string, name: string, config: AnthropicConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    const res = await fetch('https://api.anthropic.com/v1/models?limit=999', {
-      method: 'GET',
-      headers: {
-        'x-api-key': this.config.apiKey,
-        'anthropic-version': '2023-06-01',
-        'Content-type': 'application/json',
-      },
-    });
-
-    if (!res.ok) {
-      throw new Error(`Failed to fetch Anthropic models: ${res.statusText}`);
-    }
-
-    const data = (await res.json()).data;
-
-    const models: Model[] = data.map((m: any) => {
-      return {
-        key: m.id,
-        name: m.display_name,
-      };
-    });
-
-    return {
-      embedding: [],
-      chat: models,
-    };
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading Anthropic Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatAnthropic({
-      apiKey: this.config.apiKey,
-      temperature: 0.7,
-      model: key,
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    throw new Error('Anthropic provider does not support embedding models.');
-  }
-
-  static parseAndValidate(raw: any): AnthropicConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.apiKey)
-      throw new Error('Invalid config provided. API key must be provided');
-
-    return {
-      apiKey: String(raw.apiKey),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'anthropic',
-      name: 'Anthropic',
-    };
-  }
-}
-
-export default AnthropicProvider;

src/lib/models/providers/deepseek.ts

@@ -1,107 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatOpenAI } from '@langchain/openai';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-
-interface DeepSeekConfig {
-  apiKey: string;
-}
-
-const defaultChatModels: Model[] = [
-  {
-    name: 'Deepseek Chat / DeepSeek V3.2 Exp',
-    key: 'deepseek-chat',
-  },
-  {
-    name: 'Deepseek Reasoner / DeepSeek V3.2 Exp',
-    key: 'deepseek-reasoner',
-  },
-];
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your DeepSeek API key',
-    required: true,
-    placeholder: 'DeepSeek API Key',
-    env: 'DEEPSEEK_API_KEY',
-    scope: 'server',
-  },
-];
-
-class DeepSeekProvider extends BaseModelProvider<DeepSeekConfig> {
-  constructor(id: string, name: string, config: DeepSeekConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    return {
-      embedding: [],
-      chat: defaultChatModels,
-    };
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading DeepSeek Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatOpenAI({
-      apiKey: this.config.apiKey,
-      temperature: 0.7,
-      model: key,
-      configuration: {
-        baseURL: 'https://api.deepseek.com',
-      },
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    throw new Error('DeepSeek provider does not support embedding models.');
-  }
-
-  static parseAndValidate(raw: any): DeepSeekConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.apiKey)
-      throw new Error('Invalid config provided. API key must be provided');
-
-    return {
-      apiKey: String(raw.apiKey),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'deepseek',
-      name: 'Deepseek AI',
-    };
-  }
-}
-
-export default DeepSeekProvider;

src/lib/models/providers/gemini.ts

@@ -1,145 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import {
-  ChatGoogleGenerativeAI,
-  GoogleGenerativeAIEmbeddings,
-} from '@langchain/google-genai';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-
-interface GeminiConfig {
-  apiKey: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your Google Gemini API key',
-    required: true,
-    placeholder: 'Google Gemini API Key',
-    env: 'GEMINI_API_KEY',
-    scope: 'server',
-  },
-];
-
-class GeminiProvider extends BaseModelProvider<GeminiConfig> {
-  constructor(id: string, name: string, config: GeminiConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    const res = await fetch(
-      `https://generativelanguage.googleapis.com/v1beta/models?key=${this.config.apiKey}`,
-      {
-        method: 'GET',
-        headers: {
-          'Content-Type': 'application/json',
-        },
-      },
-    );
-
-    const data = await res.json();
-
-    let defaultEmbeddingModels: Model[] = [];
-    let defaultChatModels: Model[] = [];
-
-    data.models.forEach((m: any) => {
-      if (
-        m.supportedGenerationMethods.some(
-          (genMethod: string) =>
-            genMethod === 'embedText' || genMethod === 'embedContent',
-        )
-      ) {
-        defaultEmbeddingModels.push({
-          key: m.name,
-          name: m.displayName,
-        });
-      } else if (m.supportedGenerationMethods.includes('generateContent')) {
-        defaultChatModels.push({
-          key: m.name,
-          name: m.displayName,
-        });
-      }
-    });
-
-    return {
-      embedding: defaultEmbeddingModels,
-      chat: defaultChatModels,
-    };
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [
-        ...defaultModels.embedding,
-        ...configProvider.embeddingModels,
-      ],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading Gemini Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatGoogleGenerativeAI({
-      apiKey: this.config.apiKey,
-      temperature: 0.7,
-      model: key,
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    const modelList = await this.getModelList();
-    const exists = modelList.embedding.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading Gemini Embedding Model. Invalid Model Selected.',
-      );
-    }
-
-    return new GoogleGenerativeAIEmbeddings({
-      apiKey: this.config.apiKey,
-      model: key,
-    });
-  }
-
-  static parseAndValidate(raw: any): GeminiConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.apiKey)
-      throw new Error('Invalid config provided. API key must be provided');
-
-    return {
-      apiKey: String(raw.apiKey),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'gemini',
-      name: 'Google Gemini',
-    };
-  }
-}
-
-export default GeminiProvider;

src/lib/models/providers/groq.ts

@@ -1,118 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatGroq } from '@langchain/groq';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-
-interface GroqConfig {
-  apiKey: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your Groq API key',
-    required: true,
-    placeholder: 'Groq API Key',
-    env: 'GROQ_API_KEY',
-    scope: 'server',
-  },
-];
-
-class GroqProvider extends BaseModelProvider<GroqConfig> {
-  constructor(id: string, name: string, config: GroqConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    try {
-      const res = await fetch('https://api.groq.com/openai/v1/models', {
-        method: 'GET',
-        headers: {
-          'Content-Type': 'application/json',
-          Authorization: `Bearer ${this.config.apiKey}`,
-        },
-      });
-
-      const data = await res.json();
-
-      const models: Model[] = data.data.map((m: any) => {
-        return {
-          name: m.id,
-          key: m.id,
-        };
-      });
-
-      return {
-        embedding: [],
-        chat: models,
-      };
-    } catch (err) {
-      if (err instanceof TypeError) {
-        throw new Error(
-          'Error connecting to Groq API. Please ensure your API key is correct and the Groq service is available.',
-        );
-      }
-
-      throw err;
-    }
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error('Error Loading Groq Chat Model. Invalid Model Selected');
-    }
-
-    return new ChatGroq({
-      apiKey: this.config.apiKey,
-      temperature: 0.7,
-      model: key,
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    throw new Error('Groq provider does not support embedding models.');
-  }
-
-  static parseAndValidate(raw: any): GroqConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.apiKey)
-      throw new Error('Invalid config provided. API key must be provided');
-
-    return {
-      apiKey: String(raw.apiKey),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'groq',
-      name: 'Groq',
-    };
-  }
-}
-
-export default GroqProvider;

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/lemonade.ts

@@ -1,158 +0,0 @@
-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';
-
-interface LemonadeConfig {
-  baseURL: string;
-  apiKey?: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'string',
-    name: 'Base URL',
-    key: 'baseURL',
-    description: 'The base URL for Lemonade API',
-    required: true,
-    placeholder: 'https://api.lemonade.ai/v1',
-    env: 'LEMONADE_BASE_URL',
-    scope: 'server',
-  },
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your Lemonade API key (optional)',
-    required: false,
-    placeholder: 'Lemonade API Key',
-    env: 'LEMONADE_API_KEY',
-    scope: 'server',
-  },
-];
-
-class LemonadeProvider extends BaseModelProvider<LemonadeConfig> {
-  constructor(id: string, name: string, config: LemonadeConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    try {
-      const headers: Record<string, string> = {
-        'Content-Type': 'application/json',
-      };
-
-      if (this.config.apiKey) {
-        headers['Authorization'] = `Bearer ${this.config.apiKey}`;
-      }
-
-      const res = await fetch(`${this.config.baseURL}/models`, {
-        method: 'GET',
-        headers,
-      });
-
-      const data = await res.json();
-
-      const models: Model[] = data.data.map((m: any) => {
-        return {
-          name: m.id,
-          key: m.id,
-        };
-      });
-
-      return {
-        embedding: models,
-        chat: models,
-      };
-    } catch (err) {
-      if (err instanceof TypeError) {
-        throw new Error(
-          'Error connecting to Lemonade API. Please ensure the base URL is correct and the service is available.',
-        );
-      }
-
-      throw err;
-    }
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [
-        ...defaultModels.embedding,
-        ...configProvider.embeddingModels,
-      ],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading Lemonade Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatOpenAI({
-      apiKey: this.config.apiKey || 'not-needed',
-      temperature: 0.7,
-      model: key,
-      configuration: {
-        baseURL: this.config.baseURL,
-      },
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    const modelList = await this.getModelList();
-    const exists = modelList.embedding.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading Lemonade Embedding Model. Invalid Model Selected.',
-      );
-    }
-
-    return new OpenAIEmbeddings({
-      apiKey: this.config.apiKey || 'not-needed',
-      model: key,
-      configuration: {
-        baseURL: this.config.baseURL,
-      },
-    });
-  }
-
-  static parseAndValidate(raw: any): LemonadeConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.baseURL)
-      throw new Error('Invalid config provided. Base URL must be provided');
-
-    return {
-      baseURL: String(raw.baseURL),
-      apiKey: raw.apiKey ? String(raw.apiKey) : undefined,
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'lemonade',
-      name: 'Lemonade',
-    };
-  }
-}
-
-export default LemonadeProvider;

src/lib/models/providers/lmstudio.ts

@@ -1,148 +0,0 @@
-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';
-
-interface LMStudioConfig {
-  baseURL: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'string',
-    name: 'Base URL',
-    key: 'baseURL',
-    description: 'The base URL for LM Studio server',
-    required: true,
-    placeholder: 'http://localhost:1234',
-    env: 'LM_STUDIO_BASE_URL',
-    scope: 'server',
-  },
-];
-
-class LMStudioProvider extends BaseModelProvider<LMStudioConfig> {
-  constructor(id: string, name: string, config: LMStudioConfig) {
-    super(id, name, config);
-  }
-
-  private normalizeBaseURL(url: string): string {
-    const trimmed = url.trim().replace(/\/+$/, '');
-    return trimmed.endsWith('/v1') ? trimmed : `${trimmed}/v1`;
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    try {
-      const baseURL = this.normalizeBaseURL(this.config.baseURL);
-
-      const res = await fetch(`${baseURL}/models`, {
-        method: 'GET',
-        headers: {
-          'Content-Type': 'application/json',
-        },
-      });
-
-      const data = await res.json();
-
-      const models: Model[] = data.data.map((m: any) => {
-        return {
-          name: m.id,
-          key: m.id,
-        };
-      });
-
-      return {
-        embedding: models,
-        chat: models,
-      };
-    } catch (err) {
-      if (err instanceof TypeError) {
-        throw new Error(
-          'Error connecting to LM Studio. Please ensure the base URL is correct and the LM Studio server is running.',
-        );
-      }
-
-      throw err;
-    }
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [
-        ...defaultModels.embedding,
-        ...configProvider.embeddingModels,
-      ],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading LM Studio Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatOpenAI({
-      apiKey: 'lm-studio',
-      temperature: 0.7,
-      model: key,
-      streaming: true,
-      configuration: {
-        baseURL: this.normalizeBaseURL(this.config.baseURL),
-      },
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    const modelList = await this.getModelList();
-    const exists = modelList.embedding.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading LM Studio Embedding Model. Invalid Model Selected.',
-      );
-    }
-
-    return new OpenAIEmbeddings({
-      apiKey: 'lm-studio',
-      model: key,
-      configuration: {
-        baseURL: this.normalizeBaseURL(this.config.baseURL),
-      },
-    });
-  }
-
-  static parseAndValidate(raw: any): LMStudioConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.baseURL)
-      throw new Error('Invalid config provided. Base URL must be provided');
-
-    return {
-      baseURL: String(raw.baseURL),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'lmstudio',
-      name: 'LM Studio',
-    };
-  }
-}
-
-export default LMStudioProvider;

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 +1,40 @@
+import { Ollama } from 'ollama';
+import BaseEmbedding from '../../base/embedding';
+import { Chunk } from '@/lib/types';
+
+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 +1,253 @@
+import z from 'zod';
+import BaseLLM from '../../base/llm';
+import {
+  GenerateObjectInput,
+  GenerateOptions,
+  GenerateTextInput,
+  GenerateTextOutput,
+  StreamTextOutput,
+} from '../../types';
+import { Ollama, Tool as OllamaTool, Message as OllamaMessage } from 'ollama';
+import { parse } from 'partial-json';
+import crypto from 'crypto';
+import { Message } from '@/lib/types';
+
+type OllamaConfig = {
+  baseURL: string;
+  model: string;
+  options?: GenerateOptions;
+};
+
+const reasoningModels = [
+  'gpt-oss',
+  'deepseek-r1',
+  'qwen3',
+  'deepseek-v3.1',
+  'magistral',
+];
+
+class OllamaLLM extends BaseLLM<OllamaConfig> {
+  ollamaClient: Ollama;
+
+  constructor(protected config: OllamaConfig) {
+    super(config);
+
+    this.ollamaClient = new Ollama({
+      host: this.config.baseURL || 'http://localhost:11434',
+    });
+  }
+
+  convertToOllamaMessages(messages: Message[]): OllamaMessage[] {
+    return messages.map((msg) => {
+      if (msg.role === 'tool') {
+        return {
+          role: 'tool',
+          tool_name: msg.name,
+          content: msg.content,
+        } as OllamaMessage;
+      } else if (msg.role === 'assistant') {
+        return {
+          role: 'assistant',
+          content: msg.content,
+          tool_calls:
+            msg.tool_calls?.map((tc, i) => ({
+              function: {
+                index: i,
+                name: tc.name,
+                arguments: tc.arguments,
+              },
+            })) || [],
+        };
+      }
+
+      return msg;
+    });
+  }
+
+  async generateText(input: GenerateTextInput): Promise<GenerateTextOutput> {
+    const ollamaTools: OllamaTool[] = [];
+
+    input.tools?.forEach((tool) => {
+      ollamaTools.push({
+        type: 'function',
+        function: {
+          name: tool.name,
+          description: tool.description,
+          parameters: z.toJSONSchema(tool.schema).properties,
+        },
+      });
+    });
+
+    const res = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: this.convertToOllamaMessages(input.messages),
+      tools: ollamaTools.length > 0 ? ollamaTools : undefined,
+      ...(reasoningModels.find((m) => this.config.model.includes(m))
+        ? { think: false }
+        : {}),
+      options: {
+        top_p: input.options?.topP ?? this.config.options?.topP,
+        temperature:
+          input.options?.temperature ?? this.config.options?.temperature ?? 0.7,
+        num_predict: input.options?.maxTokens ?? this.config.options?.maxTokens,
+        num_ctx: 32000,
+        frequency_penalty:
+          input.options?.frequencyPenalty ??
+          this.config.options?.frequencyPenalty,
+        presence_penalty:
+          input.options?.presencePenalty ??
+          this.config.options?.presencePenalty,
+        stop:
+          input.options?.stopSequences ?? this.config.options?.stopSequences,
+      },
+    });
+
+    return {
+      content: res.message.content,
+      toolCalls:
+        res.message.tool_calls?.map((tc) => ({
+          id: crypto.randomUUID(),
+          name: tc.function.name,
+          arguments: tc.function.arguments,
+        })) || [],
+      additionalInfo: {
+        reasoning: res.message.thinking,
+      },
+    };
+  }
+
+  async *streamText(
+    input: GenerateTextInput,
+  ): AsyncGenerator<StreamTextOutput> {
+    const ollamaTools: OllamaTool[] = [];
+
+    input.tools?.forEach((tool) => {
+      ollamaTools.push({
+        type: 'function',
+        function: {
+          name: tool.name,
+          description: tool.description,
+          parameters: z.toJSONSchema(tool.schema) as any,
+        },
+      });
+    });
+
+    const stream = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: this.convertToOllamaMessages(input.messages),
+      stream: true,
+      ...(reasoningModels.find((m) => this.config.model.includes(m))
+        ? { think: false }
+        : {}),
+      tools: ollamaTools.length > 0 ? ollamaTools : undefined,
+      options: {
+        top_p: input.options?.topP ?? this.config.options?.topP,
+        temperature:
+          input.options?.temperature ?? this.config.options?.temperature ?? 0.7,
+        num_ctx: 32000,
+        num_predict: input.options?.maxTokens ?? this.config.options?.maxTokens,
+        frequency_penalty:
+          input.options?.frequencyPenalty ??
+          this.config.options?.frequencyPenalty,
+        presence_penalty:
+          input.options?.presencePenalty ??
+          this.config.options?.presencePenalty,
+        stop:
+          input.options?.stopSequences ?? this.config.options?.stopSequences,
+      },
+    });
+
+    for await (const chunk of stream) {
+      yield {
+        contentChunk: chunk.message.content,
+        toolCallChunk:
+          chunk.message.tool_calls?.map((tc, i) => ({
+            id: crypto
+              .createHash('sha256')
+              .update(
+                `${i}-${tc.function.name}`,
+              ) /* Ollama currently doesn't return a tool call ID so we're creating one based on the index and tool call name */
+              .digest('hex'),
+            name: tc.function.name,
+            arguments: tc.function.arguments,
+          })) || [],
+        done: chunk.done,
+        additionalInfo: {
+          reasoning: chunk.message.thinking,
+        },
+      };
+    }
+  }
+
+  async generateObject<T>(input: GenerateObjectInput): Promise<T> {
+    const response = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: this.convertToOllamaMessages(input.messages),
+      format: z.toJSONSchema(input.schema),
+      ...(reasoningModels.find((m) => this.config.model.includes(m))
+        ? { think: false }
+        : {}),
+      options: {
+        top_p: input.options?.topP ?? this.config.options?.topP,
+        temperature:
+          input.options?.temperature ?? this.config.options?.temperature ?? 0.7,
+        num_predict: input.options?.maxTokens ?? this.config.options?.maxTokens,
+        frequency_penalty:
+          input.options?.frequencyPenalty ??
+          this.config.options?.frequencyPenalty,
+        presence_penalty:
+          input.options?.presencePenalty ??
+          this.config.options?.presencePenalty,
+        stop:
+          input.options?.stopSequences ?? 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 = '';
+
+    const stream = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: this.convertToOllamaMessages(input.messages),
+      format: z.toJSONSchema(input.schema),
+      stream: true,
+      ...(reasoningModels.find((m) => this.config.model.includes(m))
+        ? { think: false }
+        : {}),
+      options: {
+        top_p: input.options?.topP ?? this.config.options?.topP,
+        temperature:
+          input.options?.temperature ?? this.config.options?.temperature ?? 0.7,
+        num_predict: input.options?.maxTokens ?? this.config.options?.maxTokens,
+        frequency_penalty:
+          input.options?.frequencyPenalty ??
+          this.config.options?.frequencyPenalty,
+        presence_penalty:
+          input.options?.presencePenalty ??
+          this.config.options?.presencePenalty,
+        stop:
+          input.options?.stopSequences ?? 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 +1,11 @@
-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 +146,7 @@ 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 +157,14 @@ 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 +174,10 @@ 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 +1,42 @@
+import OpenAI from 'openai';
+import BaseEmbedding from '../../base/embedding';
+import { Chunk } from '@/lib/types';
+
+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 +1,268 @@
+import OpenAI from 'openai';
+import BaseLLM from '../../base/llm';
+import { zodTextFormat, zodResponseFormat } from 'openai/helpers/zod';
+import {
+  GenerateObjectInput,
+  GenerateOptions,
+  GenerateTextInput,
+  GenerateTextOutput,
+  StreamTextOutput,
+  ToolCall,
+} from '../../types';
+import { parse } from 'partial-json';
+import z from 'zod';
+import {
+  ChatCompletionAssistantMessageParam,
+  ChatCompletionMessageParam,
+  ChatCompletionTool,
+  ChatCompletionToolMessageParam,
+} from 'openai/resources/index.mjs';
+import { Message } from '@/lib/types';
+
+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',
+    });
+  }
+
+  convertToOpenAIMessages(messages: Message[]): ChatCompletionMessageParam[] {
+    return messages.map((msg) => {
+      if (msg.role === 'tool') {
+        return {
+          role: 'tool',
+          tool_call_id: msg.id,
+          content: msg.content,
+        } as ChatCompletionToolMessageParam;
+      } else if (msg.role === 'assistant') {
+        return {
+          role: 'assistant',
+          content: msg.content,
+          ...(msg.tool_calls &&
+            msg.tool_calls.length > 0 && {
+              tool_calls: msg.tool_calls?.map((tc) => ({
+                id: tc.id,
+                type: 'function',
+                function: {
+                  name: tc.name,
+                  arguments: JSON.stringify(tc.arguments),
+                },
+              })),
+            }),
+        } as ChatCompletionAssistantMessageParam;
+      }
+
+      return msg;
+    });
+  }
+
+  async generateText(input: GenerateTextInput): Promise<GenerateTextOutput> {
+    const openaiTools: ChatCompletionTool[] = [];
+
+    input.tools?.forEach((tool) => {
+      openaiTools.push({
+        type: 'function',
+        function: {
+          name: tool.name,
+          description: tool.description,
+          parameters: z.toJSONSchema(tool.schema),
+        },
+      });
+    });
+
+    const response = await this.openAIClient.chat.completions.create({
+      model: this.config.model,
+      tools: openaiTools.length > 0 ? openaiTools : undefined,
+      messages: this.convertToOpenAIMessages(input.messages),
+      temperature:
+        input.options?.temperature ?? this.config.options?.temperature ?? 1.0,
+      top_p: input.options?.topP ?? this.config.options?.topP,
+      max_completion_tokens:
+        input.options?.maxTokens ?? this.config.options?.maxTokens,
+      stop: input.options?.stopSequences ?? this.config.options?.stopSequences,
+      frequency_penalty:
+        input.options?.frequencyPenalty ??
+        this.config.options?.frequencyPenalty,
+      presence_penalty:
+        input.options?.presencePenalty ?? this.config.options?.presencePenalty,
+    });
+
+    if (response.choices && response.choices.length > 0) {
+      return {
+        content: response.choices[0].message.content!,
+        toolCalls:
+          response.choices[0].message.tool_calls
+            ?.map((tc) => {
+              if (tc.type === 'function') {
+                return {
+                  name: tc.function.name,
+                  id: tc.id,
+                  arguments: JSON.parse(tc.function.arguments),
+                };
+              }
+            })
+            .filter((tc) => tc !== undefined) || [],
+        additionalInfo: {
+          finishReason: response.choices[0].finish_reason,
+        },
+      };
+    }
+
+    throw new Error('No response from OpenAI');
+  }
+
+  async *streamText(
+    input: GenerateTextInput,
+  ): AsyncGenerator<StreamTextOutput> {
+    const openaiTools: ChatCompletionTool[] = [];
+
+    input.tools?.forEach((tool) => {
+      openaiTools.push({
+        type: 'function',
+        function: {
+          name: tool.name,
+          description: tool.description,
+          parameters: z.toJSONSchema(tool.schema),
+        },
+      });
+    });
+
+    const stream = await this.openAIClient.chat.completions.create({
+      model: this.config.model,
+      messages: this.convertToOpenAIMessages(input.messages),
+      tools: openaiTools.length > 0 ? openaiTools : undefined,
+      temperature:
+        input.options?.temperature ?? this.config.options?.temperature ?? 1.0,
+      top_p: input.options?.topP ?? this.config.options?.topP,
+      max_completion_tokens:
+        input.options?.maxTokens ?? this.config.options?.maxTokens,
+      stop: input.options?.stopSequences ?? this.config.options?.stopSequences,
+      frequency_penalty:
+        input.options?.frequencyPenalty ??
+        this.config.options?.frequencyPenalty,
+      presence_penalty:
+        input.options?.presencePenalty ?? this.config.options?.presencePenalty,
+      stream: true,
+    });
+
+    let recievedToolCalls: { name: string; id: string; arguments: string }[] =
+      [];
+
+    for await (const chunk of stream) {
+      if (chunk.choices && chunk.choices.length > 0) {
+        const toolCalls = chunk.choices[0].delta.tool_calls;
+        yield {
+          contentChunk: chunk.choices[0].delta.content || '',
+          toolCallChunk:
+            toolCalls?.map((tc) => {
+              if (tc.type === 'function') {
+                const call = {
+                  name: tc.function?.name!,
+                  id: tc.id!,
+                  arguments: tc.function?.arguments || '',
+                };
+                recievedToolCalls.push(call);
+                return { ...call, arguments: parse(call.arguments || '{}') };
+              } else {
+                const existingCall = recievedToolCalls[tc.index];
+                existingCall.arguments += tc.function?.arguments || '';
+                return {
+                  ...existingCall,
+                  arguments: parse(existingCall.arguments),
+                };
+              }
+            }) || [],
+          done: chunk.choices[0].finish_reason !== null,
+          additionalInfo: {
+            finishReason: chunk.choices[0].finish_reason,
+          },
+        };
+      }
+    }
+  }
+
+  async generateObject<T>(input: GenerateObjectInput): Promise<T> {
+    const response = await this.openAIClient.chat.completions.parse({
+      messages: this.convertToOpenAIMessages(input.messages),
+      model: this.config.model,
+      temperature:
+        input.options?.temperature ?? this.config.options?.temperature ?? 1.0,
+      top_p: input.options?.topP ?? this.config.options?.topP,
+      max_completion_tokens:
+        input.options?.maxTokens ?? this.config.options?.maxTokens,
+      stop: input.options?.stopSequences ?? this.config.options?.stopSequences,
+      frequency_penalty:
+        input.options?.frequencyPenalty ??
+        this.config.options?.frequencyPenalty,
+      presence_penalty:
+        input.options?.presencePenalty ?? 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 = '';
+
+    const stream = this.openAIClient.responses.stream({
+      model: this.config.model,
+      input: input.messages,
+      temperature:
+        input.options?.temperature ?? this.config.options?.temperature ?? 1.0,
+      top_p: input.options?.topP ?? this.config.options?.topP,
+      max_completion_tokens:
+        input.options?.maxTokens ?? this.config.options?.maxTokens,
+      stop: input.options?.stopSequences ?? this.config.options?.stopSequences,
+      frequency_penalty:
+        input.options?.frequencyPenalty ??
+        this.config.options?.frequencyPenalty,
+      presence_penalty:
+        input.options?.presencePenalty ?? 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/providers/transformers.ts

@@ -1,87 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-import { HuggingFaceTransformersEmbeddings } from '@langchain/community/embeddings/huggingface_transformers';
-interface TransformersConfig {}
-
-const defaultEmbeddingModels: Model[] = [
-  {
-    name: 'all-MiniLM-L6-v2',
-    key: 'Xenova/all-MiniLM-L6-v2',
-  },
-  {
-    name: 'mxbai-embed-large-v1',
-    key: 'mixedbread-ai/mxbai-embed-large-v1',
-  },
-  {
-    name: 'nomic-embed-text-v1',
-    key: 'Xenova/nomic-embed-text-v1',
-  },
-];
-
-const providerConfigFields: UIConfigField[] = [];
-
-class TransformersProvider extends BaseModelProvider<TransformersConfig> {
-  constructor(id: string, name: string, config: TransformersConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    return {
-      embedding: [...defaultEmbeddingModels],
-      chat: [],
-    };
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [
-        ...defaultModels.embedding,
-        ...configProvider.embeddingModels,
-      ],
-      chat: [],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    throw new Error('Transformers Provider does not support chat models.');
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    const modelList = await this.getModelList();
-    const exists = modelList.embedding.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading OpenAI Embedding Model. Invalid Model Selected.',
-      );
-    }
-
-    return new HuggingFaceTransformersEmbeddings({
-      model: key,
-    });
-  }
-
-  static parseAndValidate(raw: any): TransformersConfig {
-    return {};
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'transformers',
-      name: 'Transformers',
-    };
-  }
-}
-
-export default TransformersProvider;

src/lib/models/registry.ts

@@ -1,7 +1,5 @@
 import { ConfigModelProvider } from '../config/types';
-import BaseModelProvider, {
-  createProviderInstance,
-} from './providers/baseProvider';
+import BaseModelProvider, { createProviderInstance } from './base/provider';
 import { getConfiguredModelProviders } from '../config/serverRegistry';
 import { providers } from './providers';
 import { MinimalProvider, ModelList } from './types';

src/lib/models/types.ts

@@ -1,3 +1,6 @@
+import z from 'zod';
+import { Message } from '../types';
+
 type Model = {
   name: string;
   key: string;
@@ -25,10 +28,76 @@ type ModelWithProvider = {
   providerId: string;
 };
 
+type GenerateOptions = {
+  temperature?: number;
+  maxTokens?: number;
+  topP?: number;
+  stopSequences?: string[];
+  frequencyPenalty?: number;
+  presencePenalty?: number;
+};
+
+type Tool = {
+  name: string;
+  description: string;
+  schema: z.ZodObject<any>;
+};
+
+type ToolCall = {
+  id: string;
+  name: string;
+  arguments: Record<string, any>;
+};
+
+type GenerateTextInput = {
+  messages: Message[];
+  tools?: Tool[];
+  options?: GenerateOptions;
+};
+
+type GenerateTextOutput = {
+  content: string;
+  toolCalls: ToolCall[];
+  additionalInfo?: Record<string, any>;
+};
+
+type StreamTextOutput = {
+  contentChunk: string;
+  toolCallChunk: ToolCall[];
+  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,
+  Tool,
+  ToolCall,
 };

src/lib/outputParsers/lineOutputParser.ts

@@ -1,48 +0,0 @@
-import { BaseOutputParser } from '@langchain/core/output_parsers';
-
-interface LineOutputParserArgs {
-  key?: string;
-}
-
-class LineOutputParser extends BaseOutputParser<string | undefined> {
-  private key = 'questions';
-
-  constructor(args?: LineOutputParserArgs) {
-    super();
-    this.key = args?.key ?? this.key;
-  }
-
-  static lc_name() {
-    return 'LineOutputParser';
-  }
-
-  lc_namespace = ['langchain', 'output_parsers', 'line_output_parser'];
-
-  async parse(text: string): Promise<string | undefined> {
-    text = text.trim() || '';
-
-    const regex = /^(\s*(-|\*|\d+\.\s|\d+\)\s|\u2022)\s*)+/;
-    const startKeyIndex = text.indexOf(`<${this.key}>`);
-    const endKeyIndex = text.indexOf(`</${this.key}>`);
-
-    if (startKeyIndex === -1 || endKeyIndex === -1) {
-      return undefined;
-    }
-
-    const questionsStartIndex =
-      startKeyIndex === -1 ? 0 : startKeyIndex + `<${this.key}>`.length;
-    const questionsEndIndex = endKeyIndex === -1 ? text.length : endKeyIndex;
-    const line = text
-      .slice(questionsStartIndex, questionsEndIndex)
-      .trim()
-      .replace(regex, '');
-
-    return line;
-  }
-
-  getFormatInstructions(): string {
-    throw new Error('Not implemented.');
-  }
-}
-
-export default LineOutputParser;

src/lib/outputParsers/listLineOutputParser.ts

@@ -1,50 +0,0 @@
-import { BaseOutputParser } from '@langchain/core/output_parsers';
-
-interface LineListOutputParserArgs {
-  key?: string;
-}
-
-class LineListOutputParser extends BaseOutputParser<string[]> {
-  private key = 'questions';
-
-  constructor(args?: LineListOutputParserArgs) {
-    super();
-    this.key = args?.key ?? this.key;
-  }
-
-  static lc_name() {
-    return 'LineListOutputParser';
-  }
-
-  lc_namespace = ['langchain', 'output_parsers', 'line_list_output_parser'];
-
-  async parse(text: string): Promise<string[]> {
-    text = text.trim() || '';
-
-    const regex = /^(\s*(-|\*|\d+\.\s|\d+\)\s|\u2022)\s*)+/;
-    const startKeyIndex = text.indexOf(`<${this.key}>`);
-    const endKeyIndex = text.indexOf(`</${this.key}>`);
-
-    if (startKeyIndex === -1 || endKeyIndex === -1) {
-      return [];
-    }
-
-    const questionsStartIndex =
-      startKeyIndex === -1 ? 0 : startKeyIndex + `<${this.key}>`.length;
-    const questionsEndIndex = endKeyIndex === -1 ? text.length : endKeyIndex;
-    const lines = text
-      .slice(questionsStartIndex, questionsEndIndex)
-      .trim()
-      .split('\n')
-      .filter((line) => line.trim() !== '')
-      .map((line) => line.replace(regex, ''));
-
-    return lines;
-  }
-
-  getFormatInstructions(): string {
-    throw new Error('Not implemented.');
-  }
-}
-
-export default LineListOutputParser;

src/lib/prompts/index.ts

@@ -1,13 +0,0 @@
-import {
-  webSearchResponsePrompt,
-  webSearchRetrieverFewShots,
-  webSearchRetrieverPrompt,
-} from './webSearch';
-import { writingAssistantPrompt } from './writingAssistant';
-
-export default {
-  webSearchResponsePrompt,
-  webSearchRetrieverPrompt,
-  webSearchRetrieverFewShots,
-  writingAssistantPrompt,
-};

src/lib/prompts/media/image.ts

@@ -0,0 +1,29 @@
+import { ChatTurnMessage } from '@/lib/types';
+
+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 in query key JSON format. Do not include any explanation or additional text.
+`;
+
+export const imageSearchFewShots: ChatTurnMessage[] = [
+  {
+    role: 'user',
+    content:
+      '<conversation>\n</conversation>\n<follow_up>\nWhat is a cat?\n</follow_up>',
+  },
+  { role: 'assistant', content: '{"query":"A cat"}' },
+
+  {
+    role: 'user',
+    content:
+      '<conversation>\n</conversation>\n<follow_up>\nWhat is a car? How does it work?\n</follow_up>',
+  },
+  { role: 'assistant', content: '{"query":"Car working"}' },
+  {
+    role: 'user',
+    content:
+      '<conversation>\n</conversation>\n<follow_up>\nHow does an AC work?\n</follow_up>',
+  },
+  { role: 'assistant', content: '{"query":"AC working"}' },
+];

src/lib/prompts/media/videos.ts

@@ -0,0 +1,28 @@
+import { ChatTurnMessage } from '@/lib/types';
+
+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 in query key JSON format. Do not include any explanation or additional text.
+`;
+
+export const videoSearchFewShots: ChatTurnMessage[] = [
+  {
+    role: 'user',
+    content:
+      '<conversation>\n</conversation>\n<follow_up>\nHow does a car work?\n</follow_up>',
+  },
+  { role: 'assistant', content: '{"query":"How does a car work?"}' },
+  {
+    role: 'user',
+    content:
+      '<conversation>\n</conversation>\n<follow_up>\nWhat is the theory of relativity?\n</follow_up>',
+  },
+  { role: 'assistant', content: '{"query":"Theory of relativity"}' },
+  {
+    role: 'user',
+    content:
+      '<conversation>\n</conversation>\n<follow_up>\nHow does an AC work?\n</follow_up>',
+  },
+  { role: 'assistant', content: '{"query":"AC working"}' },
+];

src/lib/prompts/search/classifier.ts

@@ -0,0 +1,63 @@
+export const classifierPrompt = `
+<role>
+Assistant is an advanced AI system designed to analyze the user query and the conversation history to determine the most appropriate classification for the search operation.
+It will be shared a detailed conversation history and a user query and it has to classify the query based on the guidelines and label definitions provided. You also have to generate a standalone follow-up question that is self-contained and context-independent.
+</role>
+
+<labels>
+NOTE: BY GENERAL KNOWLEDGE WE MEAN INFORMATION THAT IS OBVIOUS, WIDELY KNOWN, OR CAN BE INFERRED WITHOUT EXTERNAL SOURCES FOR EXAMPLE MATHEMATICAL FACTS, BASIC SCIENTIFIC KNOWLEDGE, COMMON HISTORICAL EVENTS, ETC.
+1. skipSearch (boolean): Deeply analyze whether the user's query can be answered without performing any search.
+   - Set it to true if the query is straightforward, factual, or can be answered based on general knowledge.
+   - Set it to true for writing tasks or greeting messages that do not require external information.
+   - Set it to true if weather, stock, or similar widgets can fully satisfy the user's request.
+   - Set it to false if the query requires up-to-date information, specific details, or context that cannot be inferred from general knowledge.
+   - ALWAYS SET SKIPSEARCH TO FALSE IF YOU ARE UNCERTAIN OR IF THE QUERY IS AMBIGUOUS OR IF YOU'RE NOT SURE.
+2. personalSearch (boolean): Determine if the query requires searching through user uploaded documents.
+   - Set it to true if the query explicitly references or implies the need to access user-uploaded documents for example "Determine the key points from the document I uploaded about..." or "Who is the author?", "Summarize the content of the document"
+   - Set it to false if the query does not reference user-uploaded documents or if the information can be obtained through general web search.
+   - ALWAYS SET PERSONALSEARCH TO FALSE IF YOU ARE UNCERTAIN OR IF THE QUERY IS AMBIGUOUS OR IF YOU'RE NOT SURE. AND SET SKIPSEARCH TO FALSE AS WELL.
+3. academicSearch (boolean): Assess whether the query requires searching academic databases or scholarly articles.
+   - Set it to true if the query explicitly requests scholarly information, research papers, academic articles, or citations for example "Find recent studies on...", "What does the latest research say about...", or "Provide citations for..."
+   - Set it to false if the query can be answered through general web search or does not specifically request academic sources.
+4. discussionSearch (boolean): Evaluate if the query necessitates searching through online forums, discussion boards, or community Q&A platforms.
+   - Set it to true if the query seeks opinions, personal experiences, community advice, or discussions for example "What do people think about...", "Are there any discussions on...", or "What are the common issues faced by..."
+   - Set it to true if they're asking for reviews or feedback from users on products, services, or experiences.
+   - Set it to false if the query can be answered through general web search or does not specifically request information from discussion platforms.
+5. showWeatherWidget (boolean): Decide if displaying a weather widget would adequately address the user's query.
+   - Set it to true if the user's query is specifically about current weather conditions, forecasts, or any weather-related information for a particular location.
+   - Set it to true for queries like "What's the weather like in [Location]?" or "Will it rain tomorrow in [Location]?" or "Show me the weather" (Here they mean weather of their current location).
+   - If it can fully answer the user query without needing additional search, set skipSearch to true as well.
+6. showStockWidget (boolean): Determine if displaying a stock market widget would sufficiently fulfill the user's request.
+   - Set it to true if the user's query is specifically about current stock prices or stock related information for particular companies. Never use it for a market analysis or news about stock market.
+   - Set it to true for queries like "What's the stock price of [Company]?" or "How is the [Stock] performing today?" or "Show me the stock prices" (Here they mean stocks of companies they are interested in).
+   - If it can fully answer the user query without needing additional search, set skipSearch to true as well.
+7. showCalculationWidget (boolean): Decide if displaying a calculation widget would adequately address the user's query.
+   - Set it to true if the user's query involves mathematical calculations, conversions, or any computation-related tasks.
+   - Set it to true for queries like "What is 25% of 80?" or "Convert 100 USD to EUR" or "Calculate the square root of 256" or "What is 2 * 3 + 5?" or other mathematical expressions.
+   - If it can fully answer the user query without needing additional search, set skipSearch to true as well.
+</labels>
+
+<standalone_followup>
+For the standalone follow up, you have to generate a self contained, context independant reformulation of the user's query.
+You basically have to rephrase the user's query in a way that it can be understood without any prior context from the conversation history.
+Say for example the converastion is about cars and the user says "How do they work" then the standalone follow up should be "How do cars work?"
+
+Do not contain excess information or everything that has been discussed before, just reformulate the user's last query in a self contained manner.
+The standalone follow-up should be concise and to the point.
+</standalone_followup>
+
+<output_format>
+You must respond in the following JSON format without any extra text, explanations or filler sentences:
+{
+  "classification": {
+    "skipSearch": boolean,
+    "personalSearch": boolean,
+    "academicSearch": boolean,
+    "discussionSearch": boolean,
+    "showWeatherWidget": boolean,
+    "showStockWidget": boolean
+  },
+  "standaloneFollowUp": string
+}
+</output_format>
+`;

src/lib/prompts/search/researcher.ts

@@ -0,0 +1,354 @@
+import BaseEmbedding from '@/lib/models/base/embedding';
+import UploadStore from '@/lib/uploads/store';
+
+const getSpeedPrompt = (
+  actionDesc: string,
+  i: number,
+  maxIteration: number,
+  fileDesc: string,
+) => {
+  const today = new Date().toLocaleDateString('en-US', {
+    year: 'numeric',
+    month: 'long',
+    day: 'numeric',
+  });
+
+  return `
+  Assistant is an action orchestrator. Your job is to fulfill user requests by selecting and executing the available tools—no free-form replies.
+  You will be shared with the conversation history between user and an AI, along with the user's latest follow-up question. Based on this, you must use the available tools to fulfill the user's request.
+
+  Today's date: ${today}
+
+  You are currently on iteration ${i + 1} of your research process and have ${maxIteration} total iterations so act efficiently.
+  When you are finished, you must call the \`done\` tool. Never output text directly.
+
+  <goal>
+  Fulfill the user's request as quickly as possible using the available tools.
+  Call tools to gather information or perform tasks as needed.
+  </goal>
+
+  <core_principle>
+  Your knowledge is outdated; if you have web search, use it to ground answers even for seemingly basic facts.
+  </core_principle>
+
+  <examples>
+
+  ## Example 1: Unknown Subject
+  User: "What is Kimi K2?"
+  Action: web_search ["Kimi K2", "Kimi K2 AI"] then done.
+
+  ## Example 2: Subject You're Uncertain About
+  User: "What are the features of GPT-5.1?"
+  Action: web_search ["GPT-5.1", "GPT-5.1 features", "GPT-5.1 release"] then done.
+
+  ## Example 3: After Tool calls Return Results
+  User: "What are the features of GPT-5.1?"
+  [Previous tool calls returned the needed info]
+  Action: done.
+
+  </examples>
+
+  <available_tools>
+  ${actionDesc}
+  </available_tools>
+
+  <mistakes_to_avoid>
+
+1. **Over-assuming**: Don't assume things exist or don't exist - just look them up
+
+2. **Verification obsession**: Don't waste tool calls "verifying existence" - just search for the thing directly
+
+3. **Endless loops**: If 2-3 tool calls don't find something, it probably doesn't exist - report that and move on
+
+4. **Ignoring task context**: If user wants a calendar event, don't just search - create the event
+
+5. **Overthinking**: Keep reasoning simple and tool calls focused
+
+</mistakes_to_avoid>
+
+  <response_protocol>
+- NEVER output normal text to the user. ONLY call tools.
+- Choose the appropriate tools based on the action descriptions provided above.
+- Default to web_search when information is missing or stale; keep queries targeted (max 3 per call).
+- Call done when you have gathered enough to answer or performed the required actions.
+- Do not invent tools. Do not return JSON.
+  </response_protocol>
+
+  ${
+    fileDesc.length > 0
+      ? `<user_uploaded_files>
+  The user has uploaded the following files which may be relevant to their request:
+  ${fileDesc}
+  You can use the uploaded files search tool to look for information within these documents if needed.
+  </user_uploaded_files>`
+      : ''
+  }
+  `;
+};
+
+const getBalancedPrompt = (
+  actionDesc: string,
+  i: number,
+  maxIteration: number,
+  fileDesc: string,
+) => {
+  const today = new Date().toLocaleDateString('en-US', {
+    year: 'numeric',
+    month: 'long',
+    day: 'numeric',
+  });
+
+  return `
+  Assistant is an action orchestrator. Your job is to fulfill user requests by reasoning briefly and executing the available tools—no free-form replies.
+  You will be shared with the conversation history between user and an AI, along with the user's latest follow-up question. Based on this, you must use the available tools to fulfill the user's request.
+
+  Today's date: ${today}
+
+  You are currently on iteration ${i + 1} of your research process and have ${maxIteration} total iterations so act efficiently.
+  When you are finished, you must call the \`done\` tool. Never output text directly.
+
+  <goal>
+  Fulfill the user's request with concise reasoning plus focused actions.
+  You must call the 0_reasoning tool before every tool call in this assistant turn. Alternate: 0_reasoning → tool → 0_reasoning → tool ... and finish with 0_reasoning → done. Open each 0_reasoning with a brief intent phrase (e.g., "Okay, the user wants to...", "Searching for...", "Looking into...") and lay out your reasoning for the next step. Keep it natural language, no tool names.
+  </goal>
+
+  <core_principle>
+  Your knowledge is outdated; if you have web search, use it to ground answers even for seemingly basic facts.
+  You can call at most 6 tools total per turn: up to 2 reasoning (0_reasoning counts as reasoning), 2-3 information-gathering calls, and 1 done. If you hit the cap, stop after done.
+  Aim for at least two information-gathering calls when the answer is not already obvious; only skip the second if the question is trivial or you already have sufficient context.
+  Do not spam searches—pick the most targeted queries.
+  </core_principle>
+
+  <done_usage>
+  Call done only after the reasoning plus the necessary tool calls are completed and you have enough to answer. If you call done early, stop. If you reach the tool cap, call done to conclude.
+  </done_usage>
+
+  <examples>
+
+  ## Example 1: Unknown Subject
+  User: "What is Kimi K2?"
+  Reason: "Okay, the user wants to know about Kimi K2. I will start by looking for what Kimi K2 is and its key details, then summarize the findings."
+  Action: web_search ["Kimi K2", "Kimi K2 AI"] then reasoning then done.
+
+  ## Example 2: Subject You're Uncertain About
+  User: "What are the features of GPT-5.1?"
+  Reason: "The user is asking about GPT-5.1 features. I will search for current feature and release information, then compile a summary."
+  Action: web_search ["GPT-5.1", "GPT-5.1 features", "GPT-5.1 release"] then reasoning then done.
+
+  ## Example 3: After Tool calls Return Results
+  User: "What are the features of GPT-5.1?"
+  [Previous tool calls returned the needed info]
+  Reason: "I have gathered enough information about GPT-5.1 features; I will now wrap up."
+  Action: done.
+
+  </examples>
+
+  <available_tools>
+  YOU MUST CALL 0_reasoning BEFORE EVERY TOOL CALL IN THIS ASSISTANT TURN. IF YOU DO NOT CALL IT, THE TOOL CALL WILL BE IGNORED.
+  ${actionDesc}
+  </available_tools>
+
+  <mistakes_to_avoid>
+
+1. **Over-assuming**: Don't assume things exist or don't exist - just look them up
+
+2. **Verification obsession**: Don't waste tool calls "verifying existence" - just search for the thing directly
+
+3. **Endless loops**: If 2-3 tool calls don't find something, it probably doesn't exist - report that and move on
+
+4. **Ignoring task context**: If user wants a calendar event, don't just search - create the event
+
+5. **Overthinking**: Keep reasoning simple and tool calls focused
+
+6. **Skipping the reasoning step**: Always call 0_reasoning first to outline your approach before other actions
+
+</mistakes_to_avoid>
+
+  <response_protocol>
+- NEVER output normal text to the user. ONLY call tools.
+- Start with 0_reasoning and call 0_reasoning before every tool call (including done): open with intent phrase ("Okay, the user wants to...", "Looking into...", etc.) and lay out your reasoning for the next step. No tool names.
+- Choose tools based on the action descriptions provided above.
+- Default to web_search when information is missing or stale; keep queries targeted (max 3 per call).
+- Use at most 6 tool calls total (0_reasoning + 2-3 info calls + 0_reasoning + done). If done is called early, stop.
+- Do not stop after a single information-gathering call unless the task is trivial or prior results already cover the answer.
+- Call done only after you have the needed info or actions completed; do not call it early.
+- Do not invent tools. Do not return JSON.
+  </response_protocol>
+
+  ${
+    fileDesc.length > 0
+      ? `<user_uploaded_files>
+  The user has uploaded the following files which may be relevant to their request:
+  ${fileDesc}
+  You can use the uploaded files search tool to look for information within these documents if needed.
+  </user_uploaded_files>`
+      : ''
+  }
+  `;
+};
+
+const getQualityPrompt = (
+  actionDesc: string,
+  i: number,
+  maxIteration: number,
+  fileDesc: string,
+) => {
+  const today = new Date().toLocaleDateString('en-US', {
+    year: 'numeric',
+    month: 'long',
+    day: 'numeric',
+  });
+
+  return `
+  Assistant is a deep-research orchestrator. Your job is to fulfill user requests with the most thorough, comprehensive research possible—no free-form replies.
+  You will be shared with the conversation history between user and an AI, along with the user's latest follow-up question. Based on this, you must use the available tools to fulfill the user's request with depth and rigor.
+
+  Today's date: ${today}
+
+  You are currently on iteration ${i + 1} of your research process and have ${maxIteration} total iterations. Use every iteration wisely to gather comprehensive information.
+  When you are finished, you must call the \`done\` tool. Never output text directly.
+
+  <goal>
+  Conduct the deepest, most thorough research possible. Leave no stone unturned.
+  Follow an iterative reason-act loop: call 0_reasoning before every tool call to outline the next step, then call the tool, then 0_reasoning again to reflect and decide the next step. Repeat until you have exhaustive coverage.
+  Open each 0_reasoning with a brief intent phrase (e.g., "Okay, the user wants to know about...", "From the results, it looks like...", "Now I need to dig into...") and describe what you'll do next. Keep it natural language, no tool names.
+  Finish with done only when you have comprehensive, multi-angle information.
+  </goal>
+
+  <core_principle>
+  Your knowledge is outdated; always use the available tools to ground answers.
+  This is DEEP RESEARCH mode—be exhaustive. Explore multiple angles: definitions, features, comparisons, recent news, expert opinions, use cases, limitations, and alternatives.
+  You can call up to 10 tools total per turn. Use an iterative loop: 0_reasoning → tool call(s) → 0_reasoning → tool call(s) → ... → 0_reasoning → done.
+  Never settle for surface-level answers. If results hint at more depth, reason about your next step and follow up. Cross-reference information from multiple queries.
+  </core_principle>
+
+  <done_usage>
+  Call done only after you have gathered comprehensive, multi-angle information. Do not call done early—exhaust your research budget first. If you reach the tool cap, call done to conclude.
+  </done_usage>
+
+  <examples>
+
+  ## Example 1: Unknown Subject - Deep Dive
+  User: "What is Kimi K2?"
+  Reason: "Okay, the user wants to know about Kimi K2. I'll start by finding out what it is and its key capabilities."
+  [calls info-gathering tool]
+  Reason: "From the results, Kimi K2 is an AI model by Moonshot. Now I need to dig into how it compares to competitors and any recent news."
+  [calls info-gathering tool]
+  Reason: "Got comparison info. Let me also check for limitations or critiques to give a balanced view."
+  [calls info-gathering tool]
+  Reason: "I now have comprehensive coverage—definition, capabilities, comparisons, and critiques. Wrapping up."
+  Action: done.
+
+  ## Example 2: Feature Research - Comprehensive
+  User: "What are the features of GPT-5.1?"
+  Reason: "The user wants comprehensive GPT-5.1 feature information. I'll start with core features and specs."
+  [calls info-gathering tool]
+  Reason: "Got the basics. Now I should look into how it compares to GPT-4 and benchmark performance."
+  [calls info-gathering tool]
+  Reason: "Good comparison data. Let me also gather use cases and expert opinions for depth."
+  [calls info-gathering tool]
+  Reason: "I have exhaustive coverage across features, comparisons, benchmarks, and reviews. Done."
+  Action: done.
+
+  ## Example 3: Iterative Refinement
+  User: "Tell me about quantum computing applications in healthcare."
+  Reason: "Okay, the user wants to know about quantum computing in healthcare. I'll start with an overview of current applications."
+  [calls info-gathering tool]
+  Reason: "Results mention drug discovery and diagnostics. Let me dive deeper into drug discovery use cases."
+  [calls info-gathering tool]
+  Reason: "Now I'll explore the diagnostics angle and any recent breakthroughs."
+  [calls info-gathering tool]
+  Reason: "Comprehensive coverage achieved. Wrapping up."
+  Action: done.
+
+  </examples>
+
+  <available_tools>
+  YOU MUST CALL 0_reasoning BEFORE EVERY TOOL CALL IN THIS ASSISTANT TURN. IF YOU DO NOT CALL IT, THE TOOL CALL WILL BE IGNORED.
+  ${actionDesc}
+  </available_tools>
+
+  <research_strategy>
+  For any topic, consider searching:
+  1. **Core definition/overview** - What is it?
+  2. **Features/capabilities** - What can it do?
+  3. **Comparisons** - How does it compare to alternatives?
+  4. **Recent news/updates** - What's the latest?
+  5. **Reviews/opinions** - What do experts say?
+  6. **Use cases** - How is it being used?
+  7. **Limitations/critiques** - What are the downsides?
+  </research_strategy>
+
+  <mistakes_to_avoid>
+
+1. **Shallow research**: Don't stop after one or two searches—dig deeper from multiple angles
+
+2. **Over-assuming**: Don't assume things exist or don't exist - just look them up
+
+3. **Missing perspectives**: Search for both positive and critical viewpoints
+
+4. **Ignoring follow-ups**: If results hint at interesting sub-topics, explore them
+
+5. **Premature done**: Don't call done until you've exhausted reasonable research avenues
+
+6. **Skipping the reasoning step**: Always call 0_reasoning first to outline your research strategy
+
+</mistakes_to_avoid>
+
+  <response_protocol>
+- NEVER output normal text to the user. ONLY call tools.
+- Follow an iterative loop: 0_reasoning → tool call → 0_reasoning → tool call → ... → 0_reasoning → done.
+- Each 0_reasoning should reflect on previous results (if any) and state the next research step. No tool names in the reasoning.
+- Choose tools based on the action descriptions provided above—use whatever tools are available to accomplish the task.
+- Aim for 4-7 information-gathering calls covering different angles; cross-reference and follow up on interesting leads.
+- Call done only after comprehensive, multi-angle research is complete.
+- Do not invent tools. Do not return JSON.
+  </response_protocol>
+
+  ${
+    fileDesc.length > 0
+      ? `<user_uploaded_files>
+  The user has uploaded the following files which may be relevant to their request:
+  ${fileDesc}
+  You can use the uploaded files search tool to look for information within these documents if needed.
+  </user_uploaded_files>`
+      : ''
+  }
+  `;
+};
+
+export const getResearcherPrompt = (
+  actionDesc: string,
+  mode: 'speed' | 'balanced' | 'quality',
+  i: number,
+  maxIteration: number,
+  fileIds: string[],
+) => {
+  let prompt = '';
+
+  const filesData = UploadStore.getFileData(fileIds);
+
+  const fileDesc = filesData
+    .map(
+      (f) =>
+        `<file><name>${f.fileName}</name><initial_content>${f.initialContent}</initial_content></file>`,
+    )
+    .join('\n');
+
+  switch (mode) {
+    case 'speed':
+      prompt = getSpeedPrompt(actionDesc, i, maxIteration, fileDesc);
+      break;
+    case 'balanced':
+      prompt = getBalancedPrompt(actionDesc, i, maxIteration, fileDesc);
+      break;
+    case 'quality':
+      prompt = getQualityPrompt(actionDesc, i, maxIteration, fileDesc);
+      break;
+    default:
+      prompt = getSpeedPrompt(actionDesc, i, maxIteration, fileDesc);
+      break;
+  }
+
+  return prompt;
+};

src/lib/prompts/search/writer.ts

@@ -1,95 +1,6 @@
-import { BaseMessageLike } from '@langchain/core/messages';
-
-export const webSearchRetrieverPrompt = `
-You are an AI question rephraser. You will be given a conversation and a follow-up question,  you will have to rephrase the follow up question so it is a standalone question and can be used by another LLM to search the web for information to answer it.
-If it is a simple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
-If the user asks some question from some URL or wants you to summarize a PDF or a webpage (via URL) you need to return the links inside the \`links\` XML block and the question inside the \`question\` XML block. If the user wants to you to summarize the webpage or the PDF you need to return \`summarize\` inside the \`question\` XML block in place of a question and the link to summarize in the \`links\` XML block.
-You must always return the rephrased question inside the \`question\` XML block, if there are no links in the follow-up question then don't insert a \`links\` XML block in your response.
-
-**Note**: All user messages are individual entities and should be treated as such do not mix conversations.
-`;
-
-export const webSearchRetrieverFewShots: BaseMessageLike[] = [
-  [
-    'user',
-    `<conversation>
-</conversation>
-<query>
-What is the capital of France
-</query>`,
-  ],
-  [
-    'assistant',
-    `<question>
-Capital of france
-</question>`,
-  ],
-  [
-    'user',
-    `<conversation>
-</conversation>
-<query>
-Hi, how are you?
-</query>`,
-  ],
-  [
-    'assistant',
-    `<question>
-not_needed
-</question>`,
-  ],
-  [
-    'user',
-    `<conversation>
-</conversation>
-<query>
-What is Docker?
-</query>`,
-  ],
-  [
-    'assistant',
-    `<question>
-What is Docker
-</question>`,
-  ],
-  [
-    'user',
-    `<conversation>
-</conversation>
-<query>
-Can you tell me what is X from https://example.com
-</query>`,
-  ],
-  [
-    'assistant',
-    `<question>
-What is X?
-</question>
-<links>
-https://example.com
-</links>`,
-  ],
-  [
-    'user',
-    `<conversation>
-</conversation>
-<query>
-Summarize the content from https://example.com
-</query>`,
-  ],
-  [
-    'assistant',
-    `<question>
-summarize
-</question>
-<links>
-https://example.com
-</links>`,
-  ],
-];
-
-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.
+export const getWriterPrompt = (context: string) => {
+  return `
+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.
@@ -119,9 +30,6 @@ export const webSearchResponsePrompt = `
     - If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
     - If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
 
-    ### 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}
 
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.
@@ -130,8 +38,9 @@ export const webSearchResponsePrompt = `
     - End with a conclusion or overall perspective if relevant.
 
     <context>
-    {context}
+    ${context}
     </context>
 
-    Current date & time in ISO format (UTC timezone) is: {date}.
+    Current date & time in ISO format (UTC timezone) is: ${new Date().toISOString()}.
 `;
+};

src/lib/prompts/suggestions/index.ts

@@ -0,0 +1,17 @@
+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.
+
+Sample suggestions for a conversation about Elon Musk:
+{
+    "suggestions": [
+        "What are Elon Musk's plans for SpaceX in the next decade?",
+        "How has Tesla's stock performance been influenced by Elon Musk's leadership?",
+        "What are the key innovations introduced by Elon Musk in the electric vehicle industry?",
+        "How does Elon Musk's vision for renewable energy impact global sustainability efforts?"
+    ]
+}
+
+Today's date is ${new Date().toISOString()}
+`;

src/lib/prompts/writingAssistant.ts

@@ -1,17 +0,0 @@
-export const writingAssistantPrompt = `
-You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query. 
-Since you are a writing assistant, you would not perform web searches. If you think you lack information to answer the query, you can ask the user for more information or suggest them to switch to a different focus mode.
-You will be shared a context that can contain information from files user has uploaded to get answers from. You will have to generate answers upon that.
-
-You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
-Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
-However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
-
-### 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/search/index.ts

@@ -1,59 +0,0 @@
-import MetaSearchAgent from '@/lib/search/metaSearchAgent';
-import prompts from '../prompts';
-
-export const searchHandlers: Record<string, MetaSearchAgent> = {
-  webSearch: new MetaSearchAgent({
-    activeEngines: [],
-    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
-    responsePrompt: prompts.webSearchResponsePrompt,
-    queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
-    rerank: true,
-    rerankThreshold: 0.3,
-    searchWeb: true,
-  }),
-  academicSearch: new MetaSearchAgent({
-    activeEngines: ['arxiv', 'google scholar', 'pubmed'],
-    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
-    responsePrompt: prompts.webSearchResponsePrompt,
-    queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
-    rerank: true,
-    rerankThreshold: 0,
-    searchWeb: true,
-  }),
-  writingAssistant: new MetaSearchAgent({
-    activeEngines: [],
-    queryGeneratorPrompt: '',
-    queryGeneratorFewShots: [],
-    responsePrompt: prompts.writingAssistantPrompt,
-    rerank: true,
-    rerankThreshold: 0,
-    searchWeb: false,
-  }),
-  wolframAlphaSearch: new MetaSearchAgent({
-    activeEngines: ['wolframalpha'],
-    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
-    responsePrompt: prompts.webSearchResponsePrompt,
-    queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
-    rerank: false,
-    rerankThreshold: 0,
-    searchWeb: true,
-  }),
-  youtubeSearch: new MetaSearchAgent({
-    activeEngines: ['youtube'],
-    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
-    responsePrompt: prompts.webSearchResponsePrompt,
-    queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
-    rerank: true,
-    rerankThreshold: 0.3,
-    searchWeb: true,
-  }),
-  redditSearch: new MetaSearchAgent({
-    activeEngines: ['reddit'],
-    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
-    responsePrompt: prompts.webSearchResponsePrompt,
-    queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
-    rerank: true,
-    rerankThreshold: 0.3,
-    searchWeb: true,
-  }),
-};

src/lib/search/metaSearchAgent.ts

@@ -1,514 +0,0 @@
-import { ChatOpenAI } from '@langchain/openai';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import type { Embeddings } from '@langchain/core/embeddings';
-import {
-  ChatPromptTemplate,
-  MessagesPlaceholder,
-  PromptTemplate,
-} from '@langchain/core/prompts';
-import {
-  RunnableLambda,
-  RunnableMap,
-  RunnableSequence,
-} from '@langchain/core/runnables';
-import { BaseMessage, BaseMessageLike } from '@langchain/core/messages';
-import { StringOutputParser } from '@langchain/core/output_parsers';
-import LineListOutputParser from '../outputParsers/listLineOutputParser';
-import LineOutputParser from '../outputParsers/lineOutputParser';
-import { getDocumentsFromLinks } from '../utils/documents';
-import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../searxng';
-import path from 'node:path';
-import fs from 'node:fs';
-import computeSimilarity from '../utils/computeSimilarity';
-import formatChatHistoryAsString from '../utils/formatHistory';
-import eventEmitter from 'events';
-import { StreamEvent } from '@langchain/core/tracers/log_stream';
-
-export interface MetaSearchAgentType {
-  searchAndAnswer: (
-    message: string,
-    history: BaseMessage[],
-    llm: BaseChatModel,
-    embeddings: Embeddings,
-    optimizationMode: 'speed' | 'balanced' | 'quality',
-    fileIds: string[],
-    systemInstructions: string,
-  ) => Promise<eventEmitter>;
-}
-
-interface Config {
-  searchWeb: boolean;
-  rerank: boolean;
-  rerankThreshold: number;
-  queryGeneratorPrompt: string;
-  queryGeneratorFewShots: BaseMessageLike[];
-  responsePrompt: string;
-  activeEngines: string[];
-}
-
-type BasicChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-class MetaSearchAgent implements MetaSearchAgentType {
-  private config: Config;
-  private strParser = new StringOutputParser();
-
-  constructor(config: Config) {
-    this.config = config;
-  }
-
-  private async createSearchRetrieverChain(llm: BaseChatModel) {
-    (llm as unknown as ChatOpenAI).temperature = 0;
-
-    return RunnableSequence.from([
-      ChatPromptTemplate.fromMessages([
-        ['system', this.config.queryGeneratorPrompt],
-        ...this.config.queryGeneratorFewShots,
-        [
-          'user',
-          `
-        <conversation>
-        {chat_history}
-        </conversation>
-
-        <query>
-        {query}
-        </query>
-       `,
-        ],
-      ]),
-      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 = (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[],
-    fileIds: string[],
-    embeddings: Embeddings,
-    optimizationMode: 'speed' | 'balanced' | 'quality',
-  ) {
-    if (docs.length === 0 && fileIds.length === 0) {
-      return docs;
-    }
-
-    const filesData = fileIds
-      .map((file) => {
-        const filePath = path.join(process.cwd(), 'uploads', file);
-
-        const contentPath = filePath + '-extracted.json';
-        const embeddingsPath = filePath + '-embeddings.json';
-
-        const content = JSON.parse(fs.readFileSync(contentPath, 'utf8'));
-        const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
-
-        const fileSimilaritySearchObject = content.contents.map(
-          (c: string, i: number) => {
-            return {
-              fileName: content.title,
-              content: c,
-              embeddings: embeddings.embeddings[i],
-            };
-          },
-        );
-
-        return fileSimilaritySearchObject;
-      })
-      .flat();
-
-    if (query.toLocaleLowerCase() === 'summarize') {
-      return docs.slice(0, 15);
-    }
-
-    const docsWithContent = docs.filter(
-      (doc) => doc.pageContent && doc.pageContent.length > 0,
-    );
-
-    if (optimizationMode === 'speed' || this.config.rerank === false) {
-      if (filesData.length > 0) {
-        const [queryEmbedding] = await Promise.all([
-          embeddings.embedQuery(query),
-        ]);
-
-        const fileDocs = filesData.map((fileData) => {
-          return new Document({
-            pageContent: fileData.content,
-            metadata: {
-              title: fileData.fileName,
-              url: `File`,
-            },
-          });
-        });
-
-        const similarity = filesData.map((fileData, i) => {
-          const sim = computeSimilarity(queryEmbedding, fileData.embeddings);
-
-          return {
-            index: i,
-            similarity: sim,
-          };
-        });
-
-        let sortedDocs = similarity
-          .filter(
-            (sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3),
-          )
-          .sort((a, b) => b.similarity - a.similarity)
-          .slice(0, 15)
-          .map((sim) => fileDocs[sim.index]);
-
-        sortedDocs =
-          docsWithContent.length > 0 ? sortedDocs.slice(0, 8) : sortedDocs;
-
-        return [
-          ...sortedDocs,
-          ...docsWithContent.slice(0, 15 - sortedDocs.length),
-        ];
-      } else {
-        return docsWithContent.slice(0, 15);
-      }
-    } else if (optimizationMode === 'balanced') {
-      const [docEmbeddings, queryEmbedding] = await Promise.all([
-        embeddings.embedDocuments(
-          docsWithContent.map((doc) => doc.pageContent),
-        ),
-        embeddings.embedQuery(query),
-      ]);
-
-      docsWithContent.push(
-        ...filesData.map((fileData) => {
-          return new Document({
-            pageContent: fileData.content,
-            metadata: {
-              title: fileData.fileName,
-              url: `File`,
-            },
-          });
-        }),
-      );
-
-      docEmbeddings.push(...filesData.map((fileData) => fileData.embeddings));
-
-      const similarity = docEmbeddings.map((docEmbedding, i) => {
-        const sim = computeSimilarity(queryEmbedding, docEmbedding);
-
-        return {
-          index: i,
-          similarity: sim,
-        };
-      });
-
-      const sortedDocs = similarity
-        .filter((sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3))
-        .sort((a, b) => b.similarity - a.similarity)
-        .slice(0, 15)
-        .map((sim) => docsWithContent[sim.index]);
-
-      return sortedDocs;
-    }
-
-    return [];
-  }
-
-  private processDocs(docs: Document[]) {
-    return docs
-      .map(
-        (_, index) =>
-          `${index + 1}. ${docs[index].metadata.title} ${docs[index].pageContent}`,
-      )
-      .join('\n');
-  }
-
-  private async handleStream(
-    stream: AsyncGenerator<StreamEvent, any, any>,
-    emitter: eventEmitter,
-  ) {
-    for await (const event of stream) {
-      if (
-        event.event === 'on_chain_end' &&
-        event.name === 'FinalSourceRetriever'
-      ) {
-        emitter.emit(
-          'data',
-          JSON.stringify({ type: 'sources', data: event.data.output }),
-        );
-      }
-      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');
-      }
-    }
-  }
-
-  async searchAndAnswer(
-    message: string,
-    history: BaseMessage[],
-    llm: BaseChatModel,
-    embeddings: Embeddings,
-    optimizationMode: 'speed' | 'balanced' | 'quality',
-    fileIds: string[],
-    systemInstructions: string,
-  ) {
-    const emitter = new eventEmitter();
-
-    const answeringChain = await this.createAnsweringChain(
-      llm,
-      fileIds,
-      embeddings,
-      optimizationMode,
-      systemInstructions,
-    );
-
-    const stream = answeringChain.streamEvents(
-      {
-        chat_history: history,
-        query: message,
-      },
-      {
-        version: 'v1',
-      },
-    );
-
-    this.handleStream(stream, emitter);
-
-    return emitter;
-  }
-}
-
-export default MetaSearchAgent;

src/lib/session.ts

@@ -0,0 +1,82 @@
+import { EventEmitter } from 'stream';
+import { applyPatch } from 'rfc6902';
+import { Block } from './types';
+
+class SessionManager {
+  private static sessions = new Map<string, SessionManager>();
+  readonly id: string;
+  private blocks = new Map<string, Block>();
+  private events: { event: string; data: any }[] = [];
+  private emitter = new EventEmitter();
+  private TTL_MS = 30 * 60 * 1000;
+
+  constructor(id?: string) {
+    this.id = id ?? crypto.randomUUID();
+
+    setTimeout(() => {
+      SessionManager.sessions.delete(this.id);
+    }, this.TTL_MS);
+  }
+
+  static getSession(id: string): SessionManager | undefined {
+    return this.sessions.get(id);
+  }
+
+  static getAllSessions(): SessionManager[] {
+    return Array.from(this.sessions.values());
+  }
+
+  static createSession(): SessionManager {
+    const session = new SessionManager();
+    this.sessions.set(session.id, session);
+    return session;
+  }
+
+  removeAllListeners() {
+    this.emitter.removeAllListeners();
+  }
+
+  emit(event: string, data: any) {
+    this.emitter.emit(event, data);
+    this.events.push({ event, data });
+  }
+
+  emitBlock(block: Block) {
+    this.blocks.set(block.id, block);
+    this.emit('data', {
+      type: 'block',
+      block: block,
+    });
+  }
+
+  getBlock(blockId: string): Block | undefined {
+    return this.blocks.get(blockId);
+  }
+
+  updateBlock(blockId: string, patch: any[]) {
+    const block = this.blocks.get(blockId);
+
+    if (block) {
+      applyPatch(block, patch);
+      this.blocks.set(blockId, block);
+      this.emit('data', {
+        type: 'updateBlock',
+        blockId: blockId,
+        patch: patch,
+      });
+    }
+  }
+
+  addListener(event: string, listener: (data: any) => void) {
+    this.emitter.addListener(event, listener);
+  }
+
+  replay() {
+    for (const { event, data } of this.events) {
+      /* Using emitter directly to avoid infinite loop */
+      this.emitter.emit(event, data);
+    }
+  }
+}
+
+export default SessionManager;

src/lib/types.ts

@@ -0,0 +1,123 @@
+import { ToolCall } from './models/types';
+
+export type SystemMessage = {
+  role: 'system';
+  content: string;
+};
+
+export type AssistantMessage = {
+  role: 'assistant';
+  content: string;
+  tool_calls?: ToolCall[];
+};
+
+export type UserMessage = {
+  role: 'user';
+  content: string;
+};
+
+export type ToolMessage = {
+  role: 'tool';
+  id: string;
+  name: string;
+  content: string;
+};
+
+export type ChatTurnMessage = UserMessage | AssistantMessage;
+
+export type Message =
+  | UserMessage
+  | AssistantMessage
+  | SystemMessage
+  | ToolMessage;
+
+export type Chunk = {
+  content: string;
+  metadata: Record<string, any>;
+};
+
+export type TextBlock = {
+  id: string;
+  type: 'text';
+  data: string;
+};
+
+export type SourceBlock = {
+  id: string;
+  type: 'source';
+  data: Chunk[];
+};
+
+export type SuggestionBlock = {
+  id: string;
+  type: 'suggestion';
+  data: string[];
+};
+
+export type WidgetBlock = {
+  id: string;
+  type: 'widget';
+  data: {
+    widgetType: string;
+    params: Record<string, any>;
+  };
+};
+
+export type ReasoningResearchBlock = {
+  id: string;
+  type: 'reasoning';
+  reasoning: string;
+};
+
+export type SearchingResearchBlock = {
+  id: string;
+  type: 'searching';
+  searching: string[];
+};
+
+export type SearchResultsResearchBlock = {
+  id: string;
+  type: 'search_results';
+  reading: Chunk[];
+};
+
+export type ReadingResearchBlock = {
+  id: string;
+  type: 'reading';
+  reading: Chunk[];
+};
+
+export type UploadSearchingResearchBlock = {
+  id: string;
+  type: 'upload_searching';
+  queries: string[];
+};
+
+export type UploadSearchResultsResearchBlock = {
+  id: string;
+  type: 'upload_search_results';
+  results: Chunk[];
+};
+
+export type ResearchBlockSubStep =
+  | ReasoningResearchBlock
+  | SearchingResearchBlock
+  | SearchResultsResearchBlock
+  | ReadingResearchBlock
+  | UploadSearchingResearchBlock
+  | UploadSearchResultsResearchBlock;
+
+export type ResearchBlock = {
+  id: string;
+  type: 'research';
+  data: {
+    subSteps: ResearchBlockSubStep[];
+  };
+};
+
+export type Block =
+  | TextBlock
+  | SourceBlock
+  | SuggestionBlock
+  | WidgetBlock
+  | ResearchBlock;

src/lib/uploads/manager.ts

@@ -0,0 +1,217 @@
+import path from "path";
+import BaseEmbedding from "../models/base/embedding"
+import crypto from "crypto"
+import fs from 'fs';
+import { splitText } from "../utils/splitText";
+import { PDFParse } from 'pdf-parse';
+import officeParser from 'officeparser'
+import { Chunk } from "../types";
+
+const supportedMimeTypes = ['application/pdf', 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'text/plain'] as const
+
+type SupportedMimeType = typeof supportedMimeTypes[number];
+
+type UploadManagerParams = {
+    embeddingModel: BaseEmbedding<any>;
+}
+
+type RecordedFile = {
+    id: string;
+    name: string;
+    filePath: string;
+    contentPath: string;
+    uploadedAt: string;
+}
+
+type FileRes = {
+    fileName: string;
+    fileExtension: string;
+    fileId: string;
+}
+
+class UploadManager {
+    private embeddingModel: BaseEmbedding<any>;
+    static uploadsDir = path.join(process.cwd(), 'data', 'uploads');
+    static uploadedFilesRecordPath = path.join(this.uploadsDir, 'uploaded_files.json');
+
+    constructor(private params: UploadManagerParams) {
+        this.embeddingModel = params.embeddingModel;
+
+        if (!fs.existsSync(UploadManager.uploadsDir)) {
+            fs.mkdirSync(UploadManager.uploadsDir, { recursive: true });
+        }
+
+        if (!fs.existsSync(UploadManager.uploadedFilesRecordPath)) {
+            const data = {
+                files: []
+            }
+
+            fs.writeFileSync(UploadManager.uploadedFilesRecordPath, JSON.stringify(data, null, 2));
+        }
+    }
+
+    private static getRecordedFiles(): RecordedFile[] {
+        const data = fs.readFileSync(UploadManager.uploadedFilesRecordPath, 'utf-8');
+        return JSON.parse(data).files;
+    }
+
+    private static addNewRecordedFile(fileRecord: RecordedFile) {
+        const currentData = this.getRecordedFiles()
+
+        currentData.push(fileRecord);
+
+        fs.writeFileSync(UploadManager.uploadedFilesRecordPath, JSON.stringify({ files: currentData }, null, 2));
+    }
+
+    static getFile(fileId: string): RecordedFile | null {
+        const recordedFiles = this.getRecordedFiles();
+
+        return recordedFiles.find(f => f.id === fileId) || null;
+    }
+
+    static getFileChunks(fileId: string): { content: string; embedding: number[] }[] {
+        try {
+            const recordedFile = this.getFile(fileId);
+
+            if (!recordedFile) {
+                throw new Error(`File with ID ${fileId} not found`);
+            }
+
+            const contentData = JSON.parse(fs.readFileSync(recordedFile.contentPath, 'utf-8'))
+
+            return contentData.chunks;
+        } catch (err) {
+            console.log('Error getting file chunks:', err);
+            return [];
+        }
+    }
+
+    private async extractContentAndEmbed(filePath: string, fileType: SupportedMimeType): Promise<string> {
+        switch (fileType) {
+            case 'text/plain':
+                const content = fs.readFileSync(filePath, 'utf-8');
+
+                const splittedText = splitText(content, 512, 128)
+                const embeddings = await this.embeddingModel.embedText(splittedText)
+
+                if (embeddings.length !== splittedText.length) {
+                    throw new Error('Embeddings and text chunks length mismatch');
+                }
+
+                const contentPath = filePath.split('.').slice(0, -1).join('.') + '.content.json';
+
+                const data = {
+                    chunks: splittedText.map((text, i) => {
+                        return {
+                            content: text,
+                            embedding: embeddings[i],
+                        }
+                    })
+                }
+
+                fs.writeFileSync(contentPath, JSON.stringify(data, null, 2));
+
+                return contentPath;
+            case 'application/pdf':
+                const pdfBuffer = fs.readFileSync(filePath);
+
+                const parser = new PDFParse({
+                    data: pdfBuffer
+                })
+
+                const pdfText = await parser.getText().then(res => res.text)
+
+                const pdfSplittedText = splitText(pdfText, 512, 128)
+                const pdfEmbeddings = await this.embeddingModel.embedText(pdfSplittedText)
+
+                if (pdfEmbeddings.length !== pdfSplittedText.length) {
+                    throw new Error('Embeddings and text chunks length mismatch');
+                }
+
+                const pdfContentPath = filePath.split('.').slice(0, -1).join('.') + '.content.json';
+
+                const pdfData = {
+                    chunks: pdfSplittedText.map((text, i) => {
+                        return {
+                            content: text,
+                            embedding: pdfEmbeddings[i],
+                        }
+                    })
+                }
+
+                fs.writeFileSync(pdfContentPath, JSON.stringify(pdfData, null, 2));
+
+                return pdfContentPath;
+            case 'application/vnd.openxmlformats-officedocument.wordprocessingml.document':
+                const docBuffer = fs.readFileSync(filePath);
+
+                const docText = await officeParser.parseOfficeAsync(docBuffer)
+
+                const docSplittedText = splitText(docText, 512, 128)
+                const docEmbeddings = await this.embeddingModel.embedText(docSplittedText)
+
+                if (docEmbeddings.length !== docSplittedText.length) {
+                    throw new Error('Embeddings and text chunks length mismatch');
+                }
+
+                const docContentPath = filePath.split('.').slice(0, -1).join('.') + '.content.json';
+
+                const docData = {
+                    chunks: docSplittedText.map((text, i) => {
+                        return {
+                            content: text,
+                            embedding: docEmbeddings[i],
+                        }
+                    })
+                }
+
+                fs.writeFileSync(docContentPath, JSON.stringify(docData, null, 2));
+
+                return docContentPath;
+            default:
+                throw new Error(`Unsupported file type: ${fileType}`);
+        }
+    }
+
+    async processFiles(files: File[]): Promise<FileRes[]> {
+        const processedFiles: FileRes[] = [];
+
+        await Promise.all(files.map(async (file) => {
+            if (!(supportedMimeTypes as unknown as string[]).includes(file.type)) {
+                throw new Error(`File type ${file.type} not supported`);
+            }
+
+            const fileId = crypto.randomBytes(16).toString('hex');
+
+            const fileExtension = file.name.split('.').pop();
+            const fileName = `${crypto.randomBytes(16).toString('hex')}.${fileExtension}`;
+            const filePath = path.join(UploadManager.uploadsDir, fileName);
+
+            const buffer = Buffer.from(await file.arrayBuffer())
+
+            fs.writeFileSync(filePath, buffer);
+
+            const contentFilePath = await this.extractContentAndEmbed(filePath, file.type as SupportedMimeType);
+
+            const fileRecord: RecordedFile = {
+                id: fileId,
+                name: file.name,
+                filePath: filePath,
+                contentPath: contentFilePath,
+                uploadedAt: new Date().toISOString(),
+            }
+
+            UploadManager.addNewRecordedFile(fileRecord);
+
+            processedFiles.push({
+                fileExtension: fileExtension || '',
+                fileId,
+                fileName: file.name
+            });
+        }))
+
+        return processedFiles;
+    }
+}
+
+export default UploadManager;

src/lib/uploads/store.ts

@@ -0,0 +1,122 @@
+import BaseEmbedding from "../models/base/embedding";
+import UploadManager from "./manager";
+import computeSimilarity from "../utils/computeSimilarity";
+import { Chunk } from "../types";
+import { hashObj } from "../serverUtils";
+import fs from 'fs';
+
+type UploadStoreParams = {
+    embeddingModel: BaseEmbedding<any>;
+    fileIds: string[];
+}
+
+type StoreRecord = {
+    embedding: number[];
+    content: string;
+    fileId: string;
+    metadata: Record<string, any>
+}
+
+class UploadStore {
+    embeddingModel: BaseEmbedding<any>;
+    fileIds: string[];
+    records: StoreRecord[] = [];
+
+    constructor(private params: UploadStoreParams) {
+        this.embeddingModel = params.embeddingModel;
+        this.fileIds = params.fileIds;
+        this.initializeStore()
+    }
+
+    initializeStore() {
+        this.fileIds.forEach((fileId) => {
+            const file = UploadManager.getFile(fileId)
+
+            if (!file) {
+                throw new Error(`File with ID ${fileId} not found`);
+            }
+
+            const chunks = UploadManager.getFileChunks(fileId);
+
+            this.records.push(...chunks.map((chunk) => ({
+                embedding: chunk.embedding,
+                content: chunk.content,
+                fileId: fileId,
+                metadata: {
+                    fileName: file.name,
+                    title: file.name,
+                    url: `file_id://${file.id}`,
+                }
+            })))
+        })
+    }
+
+    async query(queries: string[], topK: number): Promise<Chunk[]> {
+        const queryEmbeddings = await this.embeddingModel.embedText(queries)
+
+        const results: { chunk: Chunk; score: number; }[][] = [];
+        const hashResults: string[][] = []
+
+        await Promise.all(queryEmbeddings.map(async (query) => {
+            const similarities = this.records.map((record, idx) => {
+                return {
+                    chunk: {
+                        content: record.content,
+                        metadata: {
+                            ...record.metadata,
+                            fileId: record.fileId,
+                        }
+                    },
+                    score: computeSimilarity(query, record.embedding)
+                } as { chunk: Chunk; score: number; };
+            }).sort((a, b) => b.score - a.score)
+
+            results.push(similarities)
+            hashResults.push(similarities.map(s => hashObj(s)))
+        }))
+
+        const chunkMap: Map<string, Chunk> = new Map();
+        const scoreMap: Map<string, number> = new Map();
+        const k = 60;
+
+        for (let i = 0; i < results.length; i++) {
+            for (let j = 0; j < results[i].length; j++) {
+                const chunkHash = hashResults[i][j]
+
+                chunkMap.set(chunkHash, results[i][j].chunk);
+                scoreMap.set(chunkHash, (scoreMap.get(chunkHash) || 0) + results[i][j].score / (j + 1 + k));
+            }
+        }
+
+        const finalResults = Array.from(scoreMap.entries())
+            .sort((a, b) => b[1] - a[1])
+            .map(([chunkHash, _score]) => {
+                return chunkMap.get(chunkHash)!;
+            })
+
+        return finalResults.slice(0, topK);
+    }
+
+    static getFileData(fileIds: string[]): { fileName: string; initialContent: string }[] {
+        const filesData: { fileName: string; initialContent: string }[] = [];
+
+        fileIds.forEach((fileId) => {
+            const file = UploadManager.getFile(fileId)
+
+            if (!file) {
+                throw new Error(`File with ID ${fileId} not found`);
+            }
+
+            const chunks = UploadManager.getFileChunks(fileId);
+
+            filesData.push({
+                fileName: file.name,
+                initialContent: chunks.slice(0, 3).map(c => c.content).join('\n---\n'),
+            })
+        })
+
+        return filesData
+    }
+}
+
+export default UploadStore

src/lib/utils/computeSimilarity.ts

@@ -1,7 +1,22 @@
-import cosineSimilarity from 'compute-cosine-similarity';
-
 const computeSimilarity = (x: number[], y: number[]): number => {
-  return cosineSimilarity(x, y) as number;
+  if (x.length !== y.length)
+    throw new Error('Vectors must be of the same length');
+
+  let dotProduct = 0;
+  let normA = 0;
+  let normB = 0;
+
+  for (let i = 0; i < x.length; i++) {
+    dotProduct += x[i] * y[i];
+    normA += x[i] * x[i];
+    normB += y[i] * y[i];
+  }
+
+  if (normA === 0 || normB === 0) {
+    return 0;
+  }
+
+  return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
 };
 
 export default computeSimilarity;

src/lib/utils/documents.ts

@@ -1,99 +0,0 @@
-import axios from 'axios';
-import { htmlToText } from 'html-to-text';
-import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';
-import { Document } from '@langchain/core/documents';
-import pdfParse from 'pdf-parse';
-
-export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
-  const splitter = new RecursiveCharacterTextSplitter();
-
-  let docs: Document[] = [];
-
-  await Promise.all(
-    links.map(async (link) => {
-      link =
-        link.startsWith('http://') || link.startsWith('https://')
-          ? link
-          : `https://${link}`;
-
-      try {
-        const res = await axios.get(link, {
-          responseType: 'arraybuffer',
-        });
-
-        const isPdf = res.headers['content-type'] === 'application/pdf';
-
-        if (isPdf) {
-          const pdfText = await pdfParse(res.data);
-          const parsedText = pdfText.text
-            .replace(/(\r\n|\n|\r)/gm, ' ')
-            .replace(/\s+/g, ' ')
-            .trim();
-
-          const splittedText = await splitter.splitText(parsedText);
-          const title = 'PDF Document';
-
-          const linkDocs = splittedText.map((text) => {
-            return new Document({
-              pageContent: text,
-              metadata: {
-                title: title,
-                url: link,
-              },
-            });
-          });
-
-          docs.push(...linkDocs);
-          return;
-        }
-
-        const parsedText = htmlToText(res.data.toString('utf8'), {
-          selectors: [
-            {
-              selector: 'a',
-              options: {
-                ignoreHref: true,
-              },
-            },
-          ],
-        })
-          .replace(/(\r\n|\n|\r)/gm, ' ')
-          .replace(/\s+/g, ' ')
-          .trim();
-
-        const splittedText = await splitter.splitText(parsedText);
-        const title = res.data
-          .toString('utf8')
-          .match(/<title.*>(.*?)<\/title>/)?.[1];
-
-        const linkDocs = splittedText.map((text) => {
-          return new Document({
-            pageContent: text,
-            metadata: {
-              title: title || link,
-              url: link,
-            },
-          });
-        });
-
-        docs.push(...linkDocs);
-      } catch (err) {
-        console.error(
-          'An error occurred while getting documents from links: ',
-          err,
-        );
-        docs.push(
-          new Document({
-            pageContent: `Failed to retrieve content from the link: ${err}`,
-            metadata: {
-              title: 'Failed to retrieve content',
-              url: link,
-            },
-          }),
-        );
-      }
-    }),
-  );
-
-  return docs;
-};

src/lib/utils/formatHistory.ts

@@ -1,10 +1,10 @@
-import { BaseMessage, isAIMessage } from '@langchain/core/messages';
+import { ChatTurnMessage } from '../types';
 
-const formatChatHistoryAsString = (history: BaseMessage[]) => {
+const formatChatHistoryAsString = (history: ChatTurnMessage[]) => {
   return history
     .map(
       (message) =>
-        `${isAIMessage(message) ? 'AI' : 'User'}: ${message.content}`,
+        `${message.role === 'assistant' ? 'AI' : 'User'}: ${message.content}`,
     )
     .join('\n');
 };

src/lib/utils/splitText.ts

@@ -0,0 +1,74 @@
+import { getEncoding } from 'js-tiktoken';
+
+const splitRegex = /(?<=\. |\n|! |\? |; |:\s|\d+\.\s|- |\* )/g;
+
+const enc = getEncoding('cl100k_base');
+
+const getTokenCount = (text: string): number => {
+  try {
+    return enc.encode(text).length;
+  } catch {
+    return Math.ceil(text.length / 4);
+  }
+};
+
+export const splitText = (
+  text: string,
+  maxTokens = 512,
+  overlapTokens = 64,
+): string[] => {
+  const segments = text.split(splitRegex).filter(Boolean);
+
+  if (segments.length === 0) {
+    return [];
+  }
+
+  const segmentTokenCounts = segments.map(getTokenCount);
+
+  const result: string[] = [];
+
+  let chunkStart = 0;
+
+  while (chunkStart < segments.length) {
+    let chunkEnd = chunkStart;
+    let currentTokenCount = 0;
+
+    while (chunkEnd < segments.length && currentTokenCount < maxTokens) {
+      if (currentTokenCount + segmentTokenCounts[chunkEnd] > maxTokens) {
+        break;
+      }
+
+      currentTokenCount += segmentTokenCounts[chunkEnd];
+      chunkEnd++;
+    }
+
+    let overlapBeforeStart = Math.max(0, chunkStart - 1);
+    let overlapBeforeTokenCount = 0;
+
+    while (overlapBeforeStart >= 0 && overlapBeforeTokenCount < overlapTokens) {
+      if (
+        overlapBeforeTokenCount + segmentTokenCounts[overlapBeforeStart] >
+        overlapTokens
+      ) {
+        break;
+      }
+
+      overlapBeforeTokenCount += segmentTokenCounts[overlapBeforeStart];
+      overlapBeforeStart--;
+    }
+
+    const overlapStartIndex = Math.max(0, overlapBeforeStart + 1);
+
+    const overlapBeforeContent = segments
+      .slice(overlapStartIndex, chunkStart)
+      .join('');
+
+    const chunkContent = segments.slice(chunkStart, chunkEnd).join('');
+
+    result.push(overlapBeforeContent + chunkContent);
+
+    chunkStart = chunkEnd;
+  }
+
+  return result;
+};

tsconfig.json

@@ -10,7 +10,7 @@
     "moduleResolution": "bundler",
     "resolveJsonModule": true,
     "isolatedModules": true,
-    "jsx": "preserve",
+    "jsx": "react-jsx",
     "incremental": true,
     "plugins": [
       {
@@ -22,6 +22,12 @@
     },
     "target": "ES2017"
   },
-  "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
+  "include": [
+    "next-env.d.ts",
+    "**/*.ts",
+    "**/*.tsx",
+    ".next/types/**/*.ts",
+    ".next/dev/types/**/*.ts"
+  ],
   "exclude": ["node_modules"]
 }