feat(gemini): use model instead of modelName

Fix name of provider in embeddings models error message

package.json

@@ -15,11 +15,12 @@
     "@headlessui/react": "^2.2.0",
     "@iarna/toml": "^2.2.5",
     "@icons-pack/react-simple-icons": "^12.3.0",
-    "@langchain/anthropic": "^0.3.15",
-    "@langchain/community": "^0.3.36",
-    "@langchain/core": "^0.3.42",
-    "@langchain/google-genai": "^0.1.12",
-    "@langchain/openai": "^0.0.25",
+    "@langchain/anthropic": "^0.3.24",
+    "@langchain/community": "^0.3.49",
+    "@langchain/core": "^0.3.66",
+    "@langchain/google-genai": "^0.2.15",
+    "@langchain/ollama": "^0.2.3",
+    "@langchain/openai": "^0.6.2",
     "@langchain/textsplitters": "^0.1.0",
     "@tailwindcss/typography": "^0.5.12",
     "@xenova/transformers": "^2.17.2",
@@ -31,7 +32,7 @@
     "drizzle-orm": "^0.40.1",
     "html-to-text": "^9.0.5",
     "jspdf": "^3.0.1",
-    "langchain": "^0.1.30",
+    "langchain": "^0.3.30",
     "lucide-react": "^0.363.0",
     "mammoth": "^1.9.1",
     "markdown-to-jsx": "^7.7.2",

src/app/api/chat/route.ts

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

src/app/api/discover/route.ts

@@ -36,6 +36,7 @@ export const GET = async (req: Request) => {
                   {
                     engines: ['bing news'],
                     pageno: 1,
+                    language: 'en',
                   },
                 )
               ).results;
@@ -49,7 +50,11 @@ export const GET = async (req: Request) => {
       data = (
         await searchSearxng(
           `site:${articleWebsites[Math.floor(Math.random() * articleWebsites.length)]} ${topics[Math.floor(Math.random() * topics.length)]}`,
-          { engines: ['bing news'], pageno: 1 },
+          {
+            engines: ['bing news'],
+            pageno: 1,
+            language: 'en',
+          },
         )
       ).results;
     }

src/app/api/images/route.ts

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

src/app/api/search/route.ts

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

src/app/api/suggestions/route.ts

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

src/app/api/videos/route.ts

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

src/app/api/weather/route.ts

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

src/app/settings/page.tsx

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

src/components/ChatWindow.tsx

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

src/components/MessageBox.tsx

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

src/components/ThinkBox.tsx

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

src/components/WeatherWidget.tsx

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

src/lib/chains/imageSearchAgent.ts

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

src/lib/chains/videoSearchAgent.ts

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

src/lib/providers/aimlapi.ts

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

src/lib/providers/deepseek.ts

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

src/lib/providers/gemini.ts

@@ -75,7 +75,7 @@ export const loadGeminiChatModels = async () => {
         displayName: model.displayName,
         model: new ChatGoogleGenerativeAI({
           apiKey: geminiApiKey,
-          modelName: model.key,
+          model: model.key,
           temperature: 0.7,
         }) as unknown as BaseChatModel,
       };
@@ -108,7 +108,7 @@ export const loadGeminiEmbeddingModels = async () => {
 
     return embeddingModels;
   } catch (err) {
-    console.error(`Error loading OpenAI embeddings models: ${err}`);
+    console.error(`Error loading Gemini embeddings models: ${err}`);
     return {};
   }
 };

src/lib/providers/groq.ts

@@ -29,12 +29,15 @@ export const loadGroqChatModels = async () => {
       chatModels[model.id] = {
         displayName: model.id,
         model: new ChatOpenAI({
-          openAIApiKey: groqApiKey,
+          apiKey: groqApiKey,
           modelName: model.id,
           temperature: 0.7,
           configuration: {
             baseURL: 'https://api.groq.com/openai/v1',
           },
+          metadata: {
+            'model-type': 'groq',
+          },
         }) as unknown as BaseChatModel,
       };
     });

src/lib/providers/index.ts

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

src/lib/providers/lmstudio.ts

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

src/lib/providers/ollama.ts

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

src/lib/providers/openai.ts

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

src/lib/utils/formatHistory.ts

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