Update README.md

README.md

@@ -49,10 +49,29 @@ Perplexica's development is powered by the generous support of our sponsors. The
   <img alt="Warp Terminal" src=".assets/sponsers/warp.png" width="100%">
 </a>
 
-**[Warp](https://www.warp.dev/perplexica)** - The AI-powered terminal revolutionizing development workflows
+### **✨ [Try Warp - The AI-Powered Terminal →](https://www.warp.dev/perplexica)**
+
+Warp is revolutionizing development workflows with AI-powered features, modern UX, and blazing-fast performance. Used by developers at top companies worldwide.
 
 </div>
 
+---
+
+We'd also like to thank the following partners for their generous support:
+
+<table>
+  <tr>
+    <td width="100" align="center">
+      <a href="https://dashboard.exa.ai" target="_blank">
+        <img src=".assets/sponsers/exa.png" alt="Exa" width="80" height="80" style="border-radius: .75rem;" />
+      </a>
+    </td>
+    <td>
+      <a href="https://dashboard.exa.ai">Exa</a> • The Perfect Web Search API for LLMs - web search, crawling, deep research, and answer APIs
+    </td>
+  </tr>
+</table>
+
 ## Installation
 
 There are mainly 2 ways of installing Perplexica - With Docker, Without Docker. Using Docker is highly recommended.

src/app/api/images/route.ts

@@ -1,4 +1,4 @@
-import searchImages from '@/lib/agents/media/image';
+import handleImageSearch from '@/lib/chains/imageSearchAgent';
 import ModelRegistry from '@/lib/models/registry';
 import { ModelWithProvider } from '@/lib/models/types';
 import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
@@ -13,13 +13,6 @@ export const POST = async (req: Request) => {
   try {
     const body: ImageSearchBody = await req.json();
 
-    const registry = new ModelRegistry();
-
-    const llm = await registry.loadChatModel(
-      body.chatModel.providerId,
-      body.chatModel.key,
-    );
-
     const chatHistory = body.chatHistory
       .map((msg: any) => {
         if (msg.role === 'user') {
@@ -30,9 +23,16 @@ export const POST = async (req: Request) => {
       })
       .filter((msg) => msg !== undefined) as BaseMessage[];
 
-    const images = await searchImages(
+    const registry = new ModelRegistry();
+
+    const llm = await registry.loadChatModel(
+      body.chatModel.providerId,
+      body.chatModel.key,
+    );
+
+    const images = await handleImageSearch(
       {
-        chatHistory: chatHistory,
+        chat_history: chatHistory,
         query: body.query,
       },
       llm,

src/app/api/search/route.ts

@@ -30,6 +30,12 @@ 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([
@@ -40,12 +46,6 @@ export const POST = async (req: Request) => {
       ),
     ]);
 
-    const history: BaseMessage[] = body.history.map((msg) => {
-      return msg[0] === 'human'
-        ? new HumanMessage({ content: msg[1] })
-        : new AIMessage({ content: msg[1] });
-    });
-
     const searchHandler: MetaSearchAgentType = searchHandlers[body.focusMode];
 
     if (!searchHandler) {
@@ -128,7 +128,7 @@ export const POST = async (req: Request) => {
 
           try {
             controller.close();
-          } catch (error) { }
+          } catch (error) {}
         });
 
         emitter.on('data', (data: string) => {

src/app/api/suggestions/route.ts

@@ -1,6 +1,7 @@
-import generateSuggestions from '@/lib/agents/suggestions';
+import generateSuggestions from '@/lib/chains/suggestionGeneratorAgent';
 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 {
@@ -12,13 +13,6 @@ export const POST = async (req: Request) => {
   try {
     const body: SuggestionsGenerationBody = await req.json();
 
-    const registry = new ModelRegistry();
-
-    const llm = await registry.loadChatModel(
-      body.chatModel.providerId,
-      body.chatModel.key,
-    );
-
     const chatHistory = body.chatHistory
       .map((msg: any) => {
         if (msg.role === 'user') {
@@ -29,9 +23,16 @@ export const POST = async (req: Request) => {
       })
       .filter((msg) => msg !== undefined) as BaseMessage[];
 
+    const registry = new ModelRegistry();
+
+    const llm = await registry.loadChatModel(
+      body.chatModel.providerId,
+      body.chatModel.key,
+    );
+
     const suggestions = await generateSuggestions(
       {
-        chatHistory,
+        chat_history: chatHistory,
       },
       llm,
     );

src/app/api/videos/route.ts

@@ -1,4 +1,4 @@
-import handleVideoSearch from '@/lib/agents/media/video';
+import handleVideoSearch from '@/lib/chains/videoSearchAgent';
 import ModelRegistry from '@/lib/models/registry';
 import { ModelWithProvider } from '@/lib/models/types';
 import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
@@ -13,13 +13,6 @@ export const POST = async (req: Request) => {
   try {
     const body: VideoSearchBody = await req.json();
 
-    const registry = new ModelRegistry();
-
-    const llm = await registry.loadChatModel(
-      body.chatModel.providerId,
-      body.chatModel.key,
-    );
-
     const chatHistory = body.chatHistory
       .map((msg: any) => {
         if (msg.role === 'user') {
@@ -30,9 +23,16 @@ export const POST = async (req: Request) => {
       })
       .filter((msg) => msg !== undefined) as BaseMessage[];
 
+    const registry = new ModelRegistry();
+
+    const llm = await registry.loadChatModel(
+      body.chatModel.providerId,
+      body.chatModel.key,
+    );
+
     const videos = await handleVideoSearch(
       {
-        chatHistory: chatHistory,
+        chat_history: chatHistory,
         query: body.query,
       },
       llm,

src/components/MessageBox.tsx

@@ -205,11 +205,11 @@ const MessageBox = ({
           <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}
+              chatHistory={chatTurns.slice(0, sectionIndex * 2)}
               messageId={section.assistantMessage.messageId}
             />
             <SearchVideos
-              chatHistory={chatTurns}
+              chatHistory={chatTurns.slice(0, sectionIndex * 2)}
               query={section.userMessage.content}
               messageId={section.assistantMessage.messageId}
             />

src/lib/agents/media/image.ts

@@ -1,65 +0,0 @@
-/* I don't think can be classified as agents but to keep the structure consistent i guess ill keep it here */
-
-import {
-  RunnableSequence,
-  RunnableMap,
-  RunnableLambda,
-} from '@langchain/core/runnables';
-import { ChatPromptTemplate } from '@langchain/core/prompts';
-import formatChatHistoryAsString from '@/lib/utils/formatHistory';
-import { BaseMessage, HumanMessage, SystemMessage } from '@langchain/core/messages';
-import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '@/lib/searxng';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import LineOutputParser from '@/lib/outputParsers/lineOutputParser';
-import { imageSearchFewShots, imageSearchPrompt } from '@/lib/prompts/media/image';
-
-type ImageSearchChainInput = {
-  chatHistory: BaseMessage[];
-  query: string;
-};
-
-type ImageSearchResult = {
-  img_src: string;
-  url: string;
-  title: string;
-}
-
-const outputParser = new LineOutputParser({
-  key: 'query',
-})
-
-const searchImages = async (
-  input: ImageSearchChainInput,
-  llm: BaseChatModel,
-) => {
-  const chatPrompt = await ChatPromptTemplate.fromMessages([
-    new SystemMessage(imageSearchPrompt),
-    ...imageSearchFewShots,
-    new HumanMessage(`<conversation>\n${formatChatHistoryAsString(input.chatHistory)}\n</conversation>\n<follow_up>\n${input.query}\n</follow_up>`)
-  ]).formatMessages({})
-
-  const res = await llm.invoke(chatPrompt)
-
-  const query = await outputParser.invoke(res)
-
-  const searchRes = await searchSearxng(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

@@ -1,65 +0,0 @@
-import { ChatPromptTemplate } from '@langchain/core/prompts';
-import formatChatHistoryAsString from '@/lib/utils/formatHistory';
-import { BaseMessage, HumanMessage, SystemMessage } from '@langchain/core/messages';
-import { searchSearxng } from '@/lib/searxng';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import LineOutputParser from '@/lib/outputParsers/lineOutputParser';
-import { videoSearchFewShots, videoSearchPrompt } from '@/lib/prompts/media/videos';
-
-type VideoSearchChainInput = {
-  chatHistory: BaseMessage[];
-  query: string;
-};
-
-type VideoSearchResult = {
-  img_src: string;
-  url: string;
-  title: string;
-  iframe_src: string;
-}
-
-const outputParser = new LineOutputParser({
-  key: 'query',
-});
-
-const searchVideos = async (
-  input: VideoSearchChainInput,
-  llm: BaseChatModel,
-) => {
-  const chatPrompt = await ChatPromptTemplate.fromMessages([
-    new SystemMessage(videoSearchPrompt),
-    ...videoSearchFewShots,
-    new HumanMessage(`<conversation>${formatChatHistoryAsString(input.chatHistory)}\n</conversation>\n<follow_up>\n${input.query}\n</follow_up>`)
-  ]).formatMessages({})
-
-  const res = await llm.invoke(chatPrompt)
-
-  const query = await outputParser.invoke(res)
-
-  const searchRes = await searchSearxng(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/suggestions/index.ts

@@ -1,32 +0,0 @@
-import ListLineOutputParser from '@/lib/outputParsers/listLineOutputParser';
-import { ChatPromptTemplate, PromptTemplate } from '@langchain/core/prompts';
-import formatChatHistoryAsString from '@/lib/utils/formatHistory';
-import { BaseMessage, HumanMessage, SystemMessage } from '@langchain/core/messages';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { suggestionGeneratorPrompt } from '@/lib/prompts/suggestions';
-
-type SuggestionGeneratorInput = {
-  chatHistory: BaseMessage[];
-};
-
-const outputParser = new ListLineOutputParser({
-  key: 'suggestions',
-});
-
-const generateSuggestions = async (
-  input: SuggestionGeneratorInput,
-  llm: BaseChatModel,
-) => {
-  const chatPrompt = await ChatPromptTemplate.fromMessages([
-    new SystemMessage(suggestionGeneratorPrompt),
-    new HumanMessage(`<conversation>${formatChatHistoryAsString(input.chatHistory)}</conversation>`)
-  ]).formatMessages({})
-
-  const res = await llm.invoke(chatPrompt)
-
-  const suggestions = await outputParser.invoke(res)
-
-  return suggestions
-};
-
-export default generateSuggestions;

src/lib/chains/imageSearchAgent.ts

@@ -0,0 +1,105 @@
+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

@@ -0,0 +1,55 @@
+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

@@ -0,0 +1,110 @@
+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/prompts/media/image.ts

@@ -1,26 +0,0 @@
-import { BaseMessageLike } from "@langchain/core/messages";
-
-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 wrapped in an XML <query> element. Do not include any explanation or additional text.
-`;
-
-export const imageSearchFewShots: BaseMessageLike[] = [
-    [
-        '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>']
-]

src/lib/prompts/media/videos.ts

@@ -1,25 +0,0 @@
-import { BaseMessageLike } from "@langchain/core/messages";
-
-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 wrapped in an XML <query> element. Do not include any explanation or additional text.
-`;
-
-export const videoSearchFewShots: BaseMessageLike[] = [
-    [
-        '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>'],
-]

src/lib/prompts/suggestions/index.ts

@@ -1,15 +0,0 @@
-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.
-
-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>
-
-Today's date is ${new Date().toISOString()}
-`;