feat(custom-openai): use apiKey instead of openAIApiKey

src/app/api/search/route.ts

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

src/app/api/weather/route.ts

@@ -1,10 +1,7 @@
 export const POST = async (req: Request) => {
   try {
-    const body: {
+    const body: { lat: number; lng: number; temperatureUnit: 'C' | 'F' } =
-      lat: number;
+      await req.json();
-      lng: number;
-      measureUnit: 'Imperial' | 'Metric';
-    } = await req.json();
 
     if (!body.lat || !body.lng) {
       return Response.json(
@@ -16,9 +13,7 @@ export const POST = async (req: Request) => {
     }
 
     const res = await fetch(
-      `https://api.open-meteo.com/v1/forecast?latitude=${body.lat}&longitude=${body.lng}&current=weather_code,temperature_2m,is_day,relative_humidity_2m,wind_speed_10m&timezone=auto${
+      `https://api.open-meteo.com/v1/forecast?latitude=${body.lat}&longitude=${body.lng}&current=weather_code,temperature_2m,is_day,relative_humidity_2m,wind_speed_10m&timezone=auto${body.temperatureUnit === 'C' ? '' : '&temperature_unit=fahrenheit'}`,
-        body.measureUnit === 'Metric' ? '' : '&temperature_unit=fahrenheit'
-      }${body.measureUnit === 'Metric' ? '' : '&wind_speed_unit=mph'}`,
     );
 
     const data = await res.json();
@@ -40,15 +35,13 @@ export const POST = async (req: Request) => {
       windSpeed: number;
       icon: string;
       temperatureUnit: 'C' | 'F';
-      windSpeedUnit: 'm/s' | 'mph';
     } = {
       temperature: data.current.temperature_2m,
       condition: '',
       humidity: data.current.relative_humidity_2m,
       windSpeed: data.current.wind_speed_10m,
       icon: '',
-      temperatureUnit: body.measureUnit === 'Metric' ? 'C' : 'F',
+      temperatureUnit: body.temperatureUnit,
-      windSpeedUnit: body.measureUnit === 'Metric' ? 'm/s' : 'mph',
     };
 
     const code = data.current.weather_code;

src/app/settings/page.tsx

@@ -148,9 +148,7 @@ const Page = () => {
   const [automaticImageSearch, setAutomaticImageSearch] = useState(false);
   const [automaticVideoSearch, setAutomaticVideoSearch] = useState(false);
   const [systemInstructions, setSystemInstructions] = useState<string>('');
-  const [measureUnit, setMeasureUnit] = useState<'Imperial' | 'Metric'>(
+  const [temperatureUnit, setTemperatureUnit] = useState<'C' | 'F'>('C');
-    'Metric',
-  );
   const [savingStates, setSavingStates] = useState<Record<string, boolean>>({});
 
   useEffect(() => {
@@ -213,9 +211,7 @@ const Page = () => {
 
       setSystemInstructions(localStorage.getItem('systemInstructions')!);
 
-      setMeasureUnit(
+      setTemperatureUnit(localStorage.getItem('temperatureUnit')! as 'C' | 'F');
-        localStorage.getItem('measureUnit')! as 'Imperial' | 'Metric',
-      );
 
       setIsLoading(false);
     };
@@ -375,8 +371,8 @@ const Page = () => {
         localStorage.setItem('embeddingModel', value);
       } else if (key === 'systemInstructions') {
         localStorage.setItem('systemInstructions', value);
-      } else if (key === 'measureUnit') {
+      } else if (key === 'temperatureUnit') {
-        localStorage.setItem('measureUnit', value.toString());
+        localStorage.setItem('temperatureUnit', value.toString());
       }
     } catch (err) {
       console.error('Failed to save:', err);
@@ -434,22 +430,22 @@ const Page = () => {
               </div>
               <div className="flex flex-col space-y-1">
                 <p className="text-black/70 dark:text-white/70 text-sm">
-                  Measurement Units
+                  Temperature Unit
                 </p>
                 <Select
-                  value={measureUnit ?? undefined}
+                  value={temperatureUnit ?? undefined}
                   onChange={(e) => {
-                    setMeasureUnit(e.target.value as 'Imperial' | 'Metric');
+                    setTemperatureUnit(e.target.value as 'C' | 'F');
-                    saveConfig('measureUnit', e.target.value);
+                    saveConfig('temperatureUnit', e.target.value);
                   }}
                   options={[
                     {
-                      label: 'Metric',
+                      label: 'Celsius',
-                      value: 'Metric',
+                      value: 'C',
                     },
                     {
-                      label: 'Imperial',
+                      label: 'Fahrenheit',
-                      value: 'Imperial',
+                      value: 'F',
                     },
                   ]}
                 />

src/components/ChatWindow.tsx

@@ -354,11 +354,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
     }
   }, [isMessagesLoaded, isConfigReady]);
 
-  const sendMessage = async (
+  const sendMessage = async (message: string, messageId?: string) => {
-    message: string,
-    messageId?: string,
-    rewrite = false,
-  ) => {
     if (loading) return;
     if (!isConfigReady) {
       toast.error('Cannot send message before the configuration is ready');
@@ -486,8 +482,6 @@ const ChatWindow = ({ id }: { id?: string }) => {
       }
     };
 
-    const messageIndex = messages.findIndex((m) => m.messageId === messageId);
-
     const res = await fetch('/api/chat', {
       method: 'POST',
       headers: {
@@ -504,9 +498,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
         files: fileIds,
         focusMode: focusMode,
         optimizationMode: optimizationMode,
-        history: rewrite
+        history: chatHistory,
-          ? chatHistory.slice(0, messageIndex === -1 ? undefined : messageIndex)
-          : chatHistory,
         chatModel: {
           name: chatModelProvider.name,
           provider: chatModelProvider.provider,
@@ -560,7 +552,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
       return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
     });
 
-    sendMessage(message.content, message.messageId, true);
+    sendMessage(message.content, message.messageId);
   };
 
   useEffect(() => {

src/components/MessageBox.tsx

@@ -21,16 +21,8 @@ import SearchVideos from './SearchVideos';
 import { useSpeech } from 'react-text-to-speech';
 import ThinkBox from './ThinkBox';
 
-const ThinkTagProcessor = ({
+const ThinkTagProcessor = ({ children }: { children: React.ReactNode }) => {
-  children,
+  return <ThinkBox content={children as string} />;
-  thinkingEnded,
-}: {
-  children: React.ReactNode;
-  thinkingEnded: boolean;
-}) => {
-  return (
-    <ThinkBox content={children as string} thinkingEnded={thinkingEnded} />
-  );
 };
 
 const MessageBox = ({
@@ -54,7 +46,6 @@ const MessageBox = ({
 }) => {
   const [parsedMessage, setParsedMessage] = useState(message.content);
   const [speechMessage, setSpeechMessage] = useState(message.content);
-  const [thinkingEnded, setThinkingEnded] = useState(false);
 
   useEffect(() => {
     const citationRegex = /\[([^\]]+)\]/g;
@@ -70,10 +61,6 @@ const MessageBox = ({
       }
     }
 
-    if (message.role === 'assistant' && message.content.includes('</think>')) {
-      setThinkingEnded(true);
-    }
-
     if (
       message.role === 'assistant' &&
       message?.sources &&
@@ -101,7 +88,7 @@ const MessageBox = ({
                 if (url) {
                   return `<a href="${url}" target="_blank" className="bg-light-secondary dark:bg-dark-secondary px-1 rounded ml-1 no-underline text-xs text-black/70 dark:text-white/70 relative">${numStr}</a>`;
                 } else {
-                  return ``;
+                  return `[${numStr}]`;
                 }
               })
               .join('');
@@ -112,14 +99,6 @@ const MessageBox = ({
       );
       setSpeechMessage(message.content.replace(regex, ''));
       return;
-    } else if (
-      message.role === 'assistant' &&
-      message?.sources &&
-      message.sources.length === 0
-    ) {
-      setParsedMessage(processedMessage.replace(regex, ''));
-      setSpeechMessage(message.content.replace(regex, ''));
-      return;
     }
 
     setSpeechMessage(message.content.replace(regex, ''));
@@ -132,9 +111,6 @@ const MessageBox = ({
     overrides: {
       think: {
         component: ThinkTagProcessor,
-        props: {
-          thinkingEnded: thinkingEnded,
-        },
       },
     },
   };

src/components/ThinkBox.tsx

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

src/components/WeatherWidget.tsx

@@ -10,7 +10,6 @@ const WeatherWidget = () => {
     windSpeed: 0,
     icon: '',
     temperatureUnit: 'C',
-    windSpeedUnit: 'm/s',
   });
 
   const [loading, setLoading] = useState(true);
@@ -76,7 +75,7 @@ const WeatherWidget = () => {
         body: JSON.stringify({
           lat: location.latitude,
           lng: location.longitude,
-          measureUnit: localStorage.getItem('measureUnit') ?? 'Metric',
+          temperatureUnit: localStorage.getItem('temperatureUnit') ?? 'C',
         }),
       });
 
@@ -96,7 +95,6 @@ const WeatherWidget = () => {
         windSpeed: data.windSpeed,
         icon: data.icon,
         temperatureUnit: data.temperatureUnit,
-        windSpeedUnit: data.windSpeedUnit,
       });
       setLoading(false);
     });
@@ -141,7 +139,7 @@ const WeatherWidget = () => {
               </span>
               <span className="flex items-center text-xs text-black/60 dark:text-white/60">
                 <Wind className="w-3 h-3 mr-1" />
-                {data.windSpeed} {data.windSpeedUnit}
+                {data.windSpeed} km/h
               </span>
             </div>
             <span className="text-xs text-black/60 dark:text-white/60 mt-1">

src/lib/chains/imageSearchAgent.ts

@@ -3,18 +3,32 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatPromptTemplate } from '@langchain/core/prompts';
+import { PromptTemplate } 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.
+
+Example:
+1. Follow up question: What is a cat?
+Rephrased: A cat
+
+2. Follow up question: What is a car? How does it works?
+Rephrased: Car working
+
+3. Follow up question: How does an AC work?
+Rephrased: AC working
+
+Conversation:
+{chat_history}
+
+Follow up question: {query}
+Rephrased question:
 `;
 
 type ImageSearchChainInput = {
@@ -40,39 +54,12 @@ const createImageSearchChain = (llm: BaseChatModel) => {
         return input.query;
       },
     }),
-    ChatPromptTemplate.fromMessages([
+    PromptTemplate.fromTemplate(imageSearchChainPrompt),
-      ['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({
+      input = input.replace(/<think>.*?<\/think>/g, '');
-        key: 'query',
-      });
 
-      return await queryParser.parse(input);
-    }),
-    RunnableLambda.from(async (input: string) => {
       const res = await searchSearxng(input, {
         engines: ['bing images', 'google images'],
       });

src/lib/chains/videoSearchAgent.ts

@@ -3,19 +3,33 @@ import {
   RunnableMap,
   RunnableLambda,
 } from '@langchain/core/runnables';
-import { ChatPromptTemplate } from '@langchain/core/prompts';
+import { PromptTemplate } 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 = `
+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 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.
+  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.
+  
-`;
+  Example:
+  1. Follow up question: How does a car work?
+  Rephrased: How does a car work?
+  
+  2. Follow up question: What is the theory of relativity?
+  Rephrased: What is theory of relativity
+  
+  3. Follow up question: How does an AC work?
+  Rephrased: How does an AC work
+  
+  Conversation:
+  {chat_history}
+  
+  Follow up question: {query}
+  Rephrased question:
+  `;
 
 type VideoSearchChainInput = {
   chat_history: BaseMessage[];
@@ -41,37 +55,12 @@ const createVideoSearchChain = (llm: BaseChatModel) => {
         return input.query;
       },
     }),
-    ChatPromptTemplate.fromMessages([
+    PromptTemplate.fromTemplate(VideoSearchChainPrompt),
-      ['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({
+      input = input.replace(/<think>.*?<\/think>/g, '');
-        key: 'query',
+
-      });
-      return await queryParser.parse(input);
-    }),
-    RunnableLambda.from(async (input: string) => {
       const res = await searchSearxng(input, {
         engines: ['youtube'],
       });
@@ -103,8 +92,8 @@ const handleVideoSearch = (
   input: VideoSearchChainInput,
   llm: BaseChatModel,
 ) => {
-  const videoSearchChain = createVideoSearchChain(llm);
+  const VideoSearchChain = createVideoSearchChain(llm);
-  return videoSearchChain.invoke(input);
+  return VideoSearchChain.invoke(input);
 };
 
 export default handleVideoSearch;

src/lib/prompts/webSearch.ts

@@ -1,63 +1,41 @@
 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.
+You are an AI question rephraser. You will be given a conversation and a follow-up question; rephrase it into a standalone question that another LLM can use to search the web.
-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.
 
-There are several examples attached for your reference inside the below \`examples\` XML block
+Return ONLY a JSON object that matches this schema:
+query: string   // the standalone question (or "summarize")
+links: string[] // URLs extracted from the user query (empty if none)
+searchRequired: boolean // true if web search is needed, false for greetings/simple writing tasks
+searchMode: "" | "normal" | "news" // "" when searchRequired is false; "news" if the user asks for news/articles, otherwise "normal"
 
-<examples>
+Rules
-1. Follow up question: What is the capital of France
+- Greetings / simple writing tasks → query:"", links:[], searchRequired:false, searchMode:""
-Rephrased question:\`
+- Summarizing a URL → query:"summarize", links:[url...], searchRequired:true, searchMode:"normal"
-<question>
+- Asking for news/articles → searchMode:"news"
-Capital of france
+
-</question>
+Examples
-\`
+1. Follow-up: What is the capital of France?
+"query":"capital of France","links":[],"searchRequired":true,"searchMode":"normal"
 
 2. Hi, how are you?
-Rephrased question\`
+"query":"","links":[],"searchRequired":false,"searchMode":""
-<question>
-not_needed
-</question>
-\`
 
-3. Follow up question: What is Docker?
+3. Follow-up: What is Docker?
-Rephrased question: \`
+"query":"what is Docker","links":[],"searchRequired":true,"searchMode":"normal"
-<question>
-What is Docker
-</question>
-\`
 
-4. Follow up question: Can you tell me what is X from https://example.com
+4. Follow-up: Can you tell me what is X from https://example.com?
-Rephrased question: \`
+"query":"what is X","links":["https://example.com"],"searchRequired":true,"searchMode":"normal"
-<question>
-Can you tell me what is X?
-</question>
 
-<links>
+5. Follow-up: Summarize the content from https://example.com
-https://example.com
+"query":"summarize","links":["https://example.com"],"searchRequired":true,"searchMode":"normal"
-</links>
-\`
 
-5. Follow up question: Summarize the content from https://example.com
+6. Follow-up: Latest news about AI
-Rephrased question: \`
+"query":"latest news about AI","links":[],"searchRequired":true,"searchMode":"news"
-<question>
-summarize
-</question>
-
-<links>
-https://example.com
-</links>
-\`
-</examples>
-
-Anything below is the part of the actual conversation and you need to use conversation and the follow-up question to rephrase the follow-up question as a standalone question based on the guidelines shared above.
 
 <conversation>
 {chat_history}
 </conversation>
 
-Follow up question: {query}
+Follow-up question: {query}
 Rephrased question:
 `;
 

src/lib/providers/gemini.ts

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

src/lib/search/metaSearchAgent.ts

@@ -24,6 +24,7 @@ import computeSimilarity from '../utils/computeSimilarity';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import { StreamEvent } from '@langchain/core/tracers/log_stream';
+import { z } from 'zod';
 
 export interface MetaSearchAgentType {
   searchAndAnswer: (
@@ -52,6 +53,17 @@ type BasicChainInput = {
   query: string;
 };
 
+const retrieverLLMOutputSchema = z.object({
+  query: z.string().describe('The query to search the web for.'),
+  links: z
+    .array(z.string())
+    .describe('The links to search/summarize if present'),
+  searchRequired: z
+    .boolean()
+    .describe('Wether there is a need to search the web'),
+  searchMode: z.enum(['', 'normal', 'news']).describe('The search mode.'),
+});
+
 class MetaSearchAgent implements MetaSearchAgentType {
   private config: Config;
   private strParser = new StringOutputParser();
@@ -62,26 +74,24 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
   private async createSearchRetrieverChain(llm: BaseChatModel) {
     (llm as unknown as ChatOpenAI).temperature = 0;
-
     return RunnableSequence.from([
       PromptTemplate.fromTemplate(this.config.queryGeneratorPrompt),
+      Object.assign(
+        Object.create(Object.getPrototypeOf(llm)),
         llm,
-      this.strParser,
+      ).withStructuredOutput(retrieverLLMOutputSchema, {
-      RunnableLambda.from(async (input: string) => {
+        ...(llm.metadata?.['model-type'] === 'groq'
-        const linksOutputParser = new LineListOutputParser({
+          ? {
-          key: 'links',
+              method: 'json-object',
-        });
+            }
+          : {}),
+      }),
+      RunnableLambda.from(
+        async (input: z.infer<typeof retrieverLLMOutputSchema>) => {
+          let question = input.query;
+          const links = input.links;
 
-        const questionOutputParser = new LineOutputParser({
+          if (!input.searchRequired) {
-          key: 'question',
-        });
-
-        const links = await linksOutputParser.parse(input);
-        let question = this.config.summarizer
-          ? await questionOutputParser.parse(input)
-          : input;
-
-        if (question === 'not_needed') {
             return { query: '', docs: [] };
           }
 
@@ -207,7 +217,10 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
             const res = await searchSearxng(question, {
               language: 'en',
-            engines: this.config.activeEngines,
+              engines:
+                input.searchMode === 'normal'
+                  ? this.config.activeEngines
+                  : ['bing news'],
             });
 
             const documents = res.results.map(
@@ -228,7 +241,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
             return { query: question, docs: documents };
           }
-      }),
+        },
+      ),
     ]);
   }
 

src/lib/utils/formatHistory.ts

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