Merge branch 'pr/830'

src/app/api/weather/route.ts

@@ -1,7 +1,10 @@
 export const POST = async (req: Request) => {
   try {
-    const body: { lat: number; lng: number; temperatureUnit: 'C' | 'F' } =
-      await req.json();
+    const body: {
+      lat: number;
+      lng: number;
+      measureUnit: 'Imperial' | 'Metric';
+    } = await req.json();
 
     if (!body.lat || !body.lng) {
       return Response.json(
@@ -13,7 +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${body.temperatureUnit === 'C' ? '' : '&temperature_unit=fahrenheit'}`,
+      `https://api.open-meteo.com/v1/forecast?latitude=${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();
@@ -35,13 +40,15 @@ 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.temperatureUnit,
+      temperatureUnit: body.measureUnit === 'Metric' ? 'C' : 'F',
+      windSpeedUnit: body.measureUnit === 'Metric' ? 'm/s' : 'mph',
     };
 
     const code = data.current.weather_code;

src/app/settings/page.tsx

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

src/components/WeatherWidget.tsx

@@ -10,6 +10,7 @@ const WeatherWidget = () => {
     windSpeed: 0,
     icon: '',
     temperatureUnit: 'C',
+    windSpeedUnit: 'm/s',
   });
 
   const [loading, setLoading] = useState(true);
@@ -75,7 +76,7 @@ const WeatherWidget = () => {
         body: JSON.stringify({
           lat: location.latitude,
           lng: location.longitude,
-          temperatureUnit: localStorage.getItem('temperatureUnit') ?? 'C',
+          measureUnit: localStorage.getItem('measureUnit') ?? 'Metric',
         }),
       });
 
@@ -95,6 +96,7 @@ const WeatherWidget = () => {
         windSpeed: data.windSpeed,
         icon: data.icon,
         temperatureUnit: data.temperatureUnit,
+        windSpeedUnit: data.windSpeedUnit,
       });
       setLoading(false);
     });
@@ -139,7 +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;