feat(app): lint & beautify

src/lib/db/schema.ts

@@ -6,14 +6,18 @@ 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`),
+  createdAt: text('createdAt')
+    .notNull()
+    .default(sql`CURRENT_TIMESTAMP`),
   messageId: text('messageId').notNull(),
 
   content: text('content'),
 
   sources: text('sources', {
     mode: 'json',
-  }).$type<Document[]>().default(sql`'[]'`),
+  })
+    .$type<Document[]>()
+    .default(sql`'[]'`),
 });
 
 interface File {

src/lib/hooks/useChat.tsx

@@ -694,7 +694,11 @@ export const ChatProvider = ({
           (msg, i) => i > userMessageIndex && msg.role === 'suggestion',
         );
 
-        if (sourceMessage && sourceMessage.sources.length > 0 && suggestionMessageIndex == -1) {
+        if (
+          sourceMessage &&
+          sourceMessage.sources.length > 0 &&
+          suggestionMessageIndex == -1
+        ) {
           const suggestions = await getSuggestions(messagesRef.current);
           setMessages((prev) => {
             return [

src/lib/prompts/index.ts

@@ -1,7 +1,10 @@
-import { webSearchResponsePrompt, webSearchRetrieverFewShots, webSearchRetrieverPrompt } from './webSearch';
+import {
+  webSearchResponsePrompt,
+  webSearchRetrieverFewShots,
+  webSearchRetrieverPrompt,
+} from './webSearch';
 import { writingAssistantPrompt } from './writingAssistant';
 
-
 export default {
   webSearchResponsePrompt,
   webSearchRetrieverPrompt,

src/lib/prompts/webSearch.ts

@@ -1,4 +1,4 @@
-import { BaseMessageLike } from "@langchain/core/messages";
+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.
@@ -16,13 +16,13 @@ export const webSearchRetrieverFewShots: BaseMessageLike[] = [
 </conversation>
 <query>
 What is the capital of France
-</query>`
+</query>`,
   ],
   [
     'assistant',
     `<question>
 Capital of france
-</question>`
+</question>`,
   ],
   [
     'user',
@@ -30,13 +30,13 @@ Capital of france
 </conversation>
 <query>
 Hi, how are you?
-</query>`
+</query>`,
   ],
   [
     'assistant',
     `<question>
 not_needed
-</question>`
+</question>`,
   ],
   [
     'user',
@@ -44,13 +44,13 @@ not_needed
 </conversation>
 <query>
 What is Docker?
-</query>`
+</query>`,
   ],
   [
     'assistant',
     `<question>
 What is Docker
-</question>`
+</question>`,
   ],
   [
     'user',
@@ -58,7 +58,7 @@ What is Docker
 </conversation>
 <query>
 Can you tell me what is X from https://example.com
-</query>`
+</query>`,
   ],
   [
     'assistant',
@@ -67,7 +67,7 @@ What is X?
 </question>
 <links>
 https://example.com
-</links>`
+</links>`,
   ],
   [
     'user',
@@ -75,7 +75,7 @@ https://example.com
 </conversation>
 <query>
 Summarize the content from https://example.com
-</query>`
+</query>`,
   ],
   [
     'assistant',
@@ -84,8 +84,8 @@ summarize
 </question>
 <links>
 https://example.com
-</links>`
+</links>`,
-  ]
+  ],
 ];
 
 export const webSearchResponsePrompt = `

src/lib/search/metaSearchAgent.ts

@@ -67,7 +67,9 @@ class MetaSearchAgent implements MetaSearchAgentType {
       ChatPromptTemplate.fromMessages([
         ['system', this.config.queryGeneratorPrompt],
         ...this.config.queryGeneratorFewShots,
-        ['user', `
+        [
+          'user',
+          `
         <conversation>
         {chat_history}
         </conversation>
@@ -75,7 +77,8 @@ class MetaSearchAgent implements MetaSearchAgentType {
         <query>
         {query}
         </query>
-       `]
+       `,
+        ],
       ]),
       llm,
       this.strParser,
@@ -89,7 +92,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
         });
 
         const links = await linksOutputParser.parse(input);
-        let question = await questionOutputParser.parse(input) ?? input;
+        let question = (await questionOutputParser.parse(input)) ?? input;
 
         if (question === 'not_needed') {
           return { query: '', docs: [] };