feat(providers): add optimization modes

src/agents/academicSearchAgent.ts

@@ -118,7 +118,6 @@ const createBasicAcademicSearchRetrieverChain = (llm: BaseChatModel) => {
         engines: [
           'arxiv',
           'google scholar',
-          'internetarchivescholar',
           'pubmed',
         ],
       });
@@ -143,6 +142,7 @@ const createBasicAcademicSearchRetrieverChain = (llm: BaseChatModel) => {
 const createBasicAcademicSearchAnsweringChain = (
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
   const basicAcademicSearchRetrieverChain =
     createBasicAcademicSearchRetrieverChain(llm);
@@ -168,26 +168,33 @@ const createBasicAcademicSearchAnsweringChain = (
       (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    const [docEmbeddings, queryEmbedding] = await Promise.all([
+    if (optimizationMode === 'speed') {
-      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      return docsWithContent.slice(0, 15);
-      embeddings.embedQuery(query),
+    } else if (optimizationMode === 'balanced') {
-    ]);
+      console.log('Balanced mode');
+      const [docEmbeddings, queryEmbedding] = await Promise.all([
+        embeddings.embedDocuments(
+          docsWithContent.map((doc) => doc.pageContent),
+        ),
+        embeddings.embedQuery(query),
+      ]);
 
-    const similarity = docEmbeddings.map((docEmbedding, i) => {
+      const similarity = docEmbeddings.map((docEmbedding, i) => {
-      const sim = computeSimilarity(queryEmbedding, docEmbedding);
+        const sim = computeSimilarity(queryEmbedding, docEmbedding);
 
-      return {
+        return {
-        index: i,
+          index: i,
-        similarity: sim,
+          similarity: sim,
-      };
+        };
-    });
+      });
 
-    const sortedDocs = similarity
+      const sortedDocs = similarity
-      .sort((a, b) => b.similarity - a.similarity)
+        .sort((a, b) => b.similarity - a.similarity)
-      .slice(0, 15)
+        .slice(0, 15)
-      .map((sim) => docsWithContent[sim.index]);
+        .map((sim) => docsWithContent[sim.index]);
 
-    return sortedDocs;
+      return sortedDocs;
+    }
   };
 
   return RunnableSequence.from([
@@ -224,12 +231,17 @@ const basicAcademicSearch = (
   history: BaseMessage[],
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
   const emitter = new eventEmitter();
 
   try {
     const basicAcademicSearchAnsweringChain =
-      createBasicAcademicSearchAnsweringChain(llm, embeddings);
+      createBasicAcademicSearchAnsweringChain(
+        llm,
+        embeddings,
+        optimizationMode,
+      );
 
     const stream = basicAcademicSearchAnsweringChain.streamEvents(
       {
@@ -258,8 +270,15 @@ const handleAcademicSearch = (
   history: BaseMessage[],
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
-  const emitter = basicAcademicSearch(message, history, llm, embeddings);
+  const emitter = basicAcademicSearch(
+    message,
+    history,
+    llm,
+    embeddings,
+    optimizationMode,
+  );
   return emitter;
 };
 

src/agents/redditSearchAgent.ts

@@ -138,6 +138,7 @@ const createBasicRedditSearchRetrieverChain = (llm: BaseChatModel) => {
 const createBasicRedditSearchAnsweringChain = (
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
   const basicRedditSearchRetrieverChain =
     createBasicRedditSearchRetrieverChain(llm);
@@ -163,27 +164,33 @@ const createBasicRedditSearchAnsweringChain = (
       (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    const [docEmbeddings, queryEmbedding] = await Promise.all([
+    if (optimizationMode === 'speed') {
-      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      return docsWithContent.slice(0, 15);
-      embeddings.embedQuery(query),
+    } else if (optimizationMode === 'balanced') {
-    ]);
+      const [docEmbeddings, queryEmbedding] = await Promise.all([
+        embeddings.embedDocuments(
+          docsWithContent.map((doc) => doc.pageContent),
+        ),
+        embeddings.embedQuery(query),
+      ]);
 
-    const similarity = docEmbeddings.map((docEmbedding, i) => {
+      const similarity = docEmbeddings.map((docEmbedding, i) => {
-      const sim = computeSimilarity(queryEmbedding, docEmbedding);
+        const sim = computeSimilarity(queryEmbedding, docEmbedding);
 
-      return {
+        return {
-        index: i,
+          index: i,
-        similarity: sim,
+          similarity: sim,
-      };
+        };
-    });
+      });
 
-    const sortedDocs = similarity
+      const sortedDocs = similarity
-      .filter((sim) => sim.similarity > 0.3)
+        .filter((sim) => sim.similarity > 0.3)
-      .sort((a, b) => b.similarity - a.similarity)
+        .sort((a, b) => b.similarity - a.similarity)
-      .slice(0, 15)
+        .slice(0, 15)
-      .map((sim) => docsWithContent[sim.index]);
+        .map((sim) => docsWithContent[sim.index]);
 
-    return sortedDocs;
+      return sortedDocs;
+    }
   };
 
   return RunnableSequence.from([
@@ -220,12 +227,13 @@ const basicRedditSearch = (
   history: BaseMessage[],
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
   const emitter = new eventEmitter();
 
   try {
     const basicRedditSearchAnsweringChain =
-      createBasicRedditSearchAnsweringChain(llm, embeddings);
+      createBasicRedditSearchAnsweringChain(llm, embeddings, optimizationMode);
     const stream = basicRedditSearchAnsweringChain.streamEvents(
       {
         chat_history: history,
@@ -253,8 +261,15 @@ const handleRedditSearch = (
   history: BaseMessage[],
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
-  const emitter = basicRedditSearch(message, history, llm, embeddings);
+  const emitter = basicRedditSearch(
+    message,
+    history,
+    llm,
+    embeddings,
+    optimizationMode,
+  );
   return emitter;
 };
 

src/agents/webSearchAgent.ts

@@ -216,12 +216,34 @@ const createBasicWebSearchRetrieverChain = (llm: BaseChatModel) => {
         await Promise.all(
           docGroups.map(async (doc) => {
             const res = await llm.invoke(`
-            You are a text summarizer. You need to summarize the text provided inside the \`text\` XML block. 
+            You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the 
-            You need to summarize the text into 1 or 2 sentences capturing the main idea of the text.
+            text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
-            You need to make sure that you don't miss any point while summarizing the text.
+            If the query is \"summarize\", you should provide a detailed summary of the text. If the query is a specific question, you should answer it in the summary.
-            You will also be given a \`query\` XML block which will contain the query of the user. Try to answer the query in the summary from the text provided.
+            
-            If the query says Summarize then you just need to summarize the text without answering the query.
+            - **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague.
-            Only return the summarized text without any other messages, text or XML block.
+            - **Thorough and detailed**: Ensure that every key point from the text is captured and that the summary directly answers the query.
+            - **Not too lengthy, but detailed**: The summary should be informative but not excessively long. Focus on providing detailed information in a concise format.
+
+            The text will be shared inside the \`text\` XML tag, and the query inside the \`query\` XML tag.
+
+            <example>
+            <text>
+            Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers. 
+            It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications 
+            by using containers.
+            </text>
+
+            <query>
+            What is Docker and how does it work?
+            </query>
+
+            Response:
+            Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application 
+            deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in 
+            any environment. Released in 2013, Docker has transformed the way applications are built, deployed, and managed.
+            </example>
+
+            Everything below is the actual data you will be working with. Good luck!
 
             <query>
             ${question}
@@ -273,6 +295,7 @@ const createBasicWebSearchRetrieverChain = (llm: BaseChatModel) => {
 const createBasicWebSearchAnsweringChain = (
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
   const basicWebSearchRetrieverChain = createBasicWebSearchRetrieverChain(llm);
 
@@ -301,27 +324,33 @@ const createBasicWebSearchAnsweringChain = (
       (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    const [docEmbeddings, queryEmbedding] = await Promise.all([
+    if (optimizationMode === 'speed') {
-      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      return docsWithContent.slice(0, 15);
-      embeddings.embedQuery(query),
+    } else if (optimizationMode === 'balanced') {
-    ]);
+      const [docEmbeddings, queryEmbedding] = await Promise.all([
+        embeddings.embedDocuments(
+          docsWithContent.map((doc) => doc.pageContent),
+        ),
+        embeddings.embedQuery(query),
+      ]);
 
-    const similarity = docEmbeddings.map((docEmbedding, i) => {
+      const similarity = docEmbeddings.map((docEmbedding, i) => {
-      const sim = computeSimilarity(queryEmbedding, docEmbedding);
+        const sim = computeSimilarity(queryEmbedding, docEmbedding);
 
-      return {
+        return {
-        index: i,
+          index: i,
-        similarity: sim,
+          similarity: sim,
-      };
+        };
-    });
+      });
 
-    const sortedDocs = similarity
+      const sortedDocs = similarity
-      .filter((sim) => sim.similarity > 0.3)
+        .filter((sim) => sim.similarity > 0.3)
-      .sort((a, b) => b.similarity - a.similarity)
+        .sort((a, b) => b.similarity - a.similarity)
-      .slice(0, 15)
+        .slice(0, 15)
-      .map((sim) => docsWithContent[sim.index]);
+        .map((sim) => docsWithContent[sim.index]);
 
-    return sortedDocs;
+      return sortedDocs;
+    }
   };
 
   return RunnableSequence.from([
@@ -358,6 +387,7 @@ const basicWebSearch = (
   history: BaseMessage[],
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
   const emitter = new eventEmitter();
 
@@ -365,6 +395,7 @@ const basicWebSearch = (
     const basicWebSearchAnsweringChain = createBasicWebSearchAnsweringChain(
       llm,
       embeddings,
+      optimizationMode,
     );
 
     const stream = basicWebSearchAnsweringChain.streamEvents(
@@ -394,8 +425,15 @@ const handleWebSearch = (
   history: BaseMessage[],
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
-  const emitter = basicWebSearch(message, history, llm, embeddings);
+  const emitter = basicWebSearch(
+    message,
+    history,
+    llm,
+    embeddings,
+    optimizationMode,
+  );
   return emitter;
 };
 

src/agents/youtubeSearchAgent.ts

@@ -138,6 +138,7 @@ const createBasicYoutubeSearchRetrieverChain = (llm: BaseChatModel) => {
 const createBasicYoutubeSearchAnsweringChain = (
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
   const basicYoutubeSearchRetrieverChain =
     createBasicYoutubeSearchRetrieverChain(llm);
@@ -163,27 +164,33 @@ const createBasicYoutubeSearchAnsweringChain = (
       (doc) => doc.pageContent && doc.pageContent.length > 0,
     );
 
-    const [docEmbeddings, queryEmbedding] = await Promise.all([
+    if (optimizationMode === 'speed') {
-      embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)),
+      return docsWithContent.slice(0, 15);
-      embeddings.embedQuery(query),
+    } else {
-    ]);
+      const [docEmbeddings, queryEmbedding] = await Promise.all([
+        embeddings.embedDocuments(
+          docsWithContent.map((doc) => doc.pageContent),
+        ),
+        embeddings.embedQuery(query),
+      ]);
 
-    const similarity = docEmbeddings.map((docEmbedding, i) => {
+      const similarity = docEmbeddings.map((docEmbedding, i) => {
-      const sim = computeSimilarity(queryEmbedding, docEmbedding);
+        const sim = computeSimilarity(queryEmbedding, docEmbedding);
 
-      return {
+        return {
-        index: i,
+          index: i,
-        similarity: sim,
+          similarity: sim,
-      };
+        };
-    });
+      });
 
-    const sortedDocs = similarity
+      const sortedDocs = similarity
-      .filter((sim) => sim.similarity > 0.3)
+        .filter((sim) => sim.similarity > 0.3)
-      .sort((a, b) => b.similarity - a.similarity)
+        .sort((a, b) => b.similarity - a.similarity)
-      .slice(0, 15)
+        .slice(0, 15)
-      .map((sim) => docsWithContent[sim.index]);
+        .map((sim) => docsWithContent[sim.index]);
 
-    return sortedDocs;
+      return sortedDocs;
+    }
   };
 
   return RunnableSequence.from([
@@ -220,12 +227,13 @@ const basicYoutubeSearch = (
   history: BaseMessage[],
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
   const emitter = new eventEmitter();
 
   try {
     const basicYoutubeSearchAnsweringChain =
-      createBasicYoutubeSearchAnsweringChain(llm, embeddings);
+      createBasicYoutubeSearchAnsweringChain(llm, embeddings, optimizationMode);
 
     const stream = basicYoutubeSearchAnsweringChain.streamEvents(
       {
@@ -254,8 +262,15 @@ const handleYoutubeSearch = (
   history: BaseMessage[],
   llm: BaseChatModel,
   embeddings: Embeddings,
+  optimizationMode: 'speed' | 'balanced' | 'quality',
 ) => {
-  const emitter = basicYoutubeSearch(message, history, llm, embeddings);
+  const emitter = basicYoutubeSearch(
+    message,
+    history,
+    llm,
+    embeddings,
+    optimizationMode,
+  );
   return emitter;
 };
 

src/websocket/messageHandler.ts

@@ -22,7 +22,7 @@ type Message = {
 
 type WSMessage = {
   message: Message;
-  copilot: boolean;
+  optimizationMode: string;
   type: string;
   focusMode: string;
   history: Array<[string, string]>;
@@ -138,6 +138,7 @@ export const handleMessage = async (
           history,
           llm,
           embeddings,
+          parsedWSMessage.optimizationMode,
         );
 
         handleEmitterEvents(emitter, ws, id, parsedMessage.chatId);

ui/components/ChatWindow.tsx

@@ -278,6 +278,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
   const [messages, setMessages] = useState<Message[]>([]);
 
   const [focusMode, setFocusMode] = useState('webSearch');
+  const [optimizationMode, setOptimizationMode] = useState('speed');
 
   const [isMessagesLoaded, setIsMessagesLoaded] = useState(false);
 
@@ -346,6 +347,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
           content: message,
         },
         focusMode: focusMode,
+        optimizationMode: optimizationMode,
         history: [...chatHistory, ['human', message]],
       }),
     );
@@ -508,6 +510,8 @@ const ChatWindow = ({ id }: { id?: string }) => {
             sendMessage={sendMessage}
             focusMode={focusMode}
             setFocusMode={setFocusMode}
+            optimizationMode={optimizationMode}
+            setOptimizationMode={setOptimizationMode}
           />
         )}
       </div>

ui/components/EmptyChat.tsx

@@ -4,10 +4,14 @@ const EmptyChat = ({
   sendMessage,
   focusMode,
   setFocusMode,
+  optimizationMode,
+  setOptimizationMode,
 }: {
   sendMessage: (message: string) => void;
   focusMode: string;
   setFocusMode: (mode: string) => void;
+  optimizationMode: string;
+  setOptimizationMode: (mode: string) => void;
 }) => {
   return (
     <div className="relative">
@@ -19,6 +23,8 @@ const EmptyChat = ({
           sendMessage={sendMessage}
           focusMode={focusMode}
           setFocusMode={setFocusMode}
+          optimizationMode={optimizationMode}
+          setOptimizationMode={setOptimizationMode}
         />
       </div>
     </div>

ui/components/EmptyChatMessageInput.tsx

@@ -3,15 +3,20 @@ import { useEffect, useRef, useState } from 'react';
 import TextareaAutosize from 'react-textarea-autosize';
 import CopilotToggle from './MessageInputActions/Copilot';
 import Focus from './MessageInputActions/Focus';
+import Optimization from './MessageInputActions/Optimization';
 
 const EmptyChatMessageInput = ({
   sendMessage,
   focusMode,
   setFocusMode,
+  optimizationMode,
+  setOptimizationMode,
 }: {
   sendMessage: (message: string) => void;
   focusMode: string;
   setFocusMode: (mode: string) => void;
+  optimizationMode: string;
+  setOptimizationMode: (mode: string) => void;
 }) => {
   const [copilotEnabled, setCopilotEnabled] = useState(false);
   const [message, setMessage] = useState('');
@@ -66,14 +71,13 @@ const EmptyChatMessageInput = ({
           placeholder="Ask anything..."
         />
         <div className="flex flex-row items-center justify-between mt-4">
-          <div className="flex flex-row items-center space-x-1 -mx-2">
+          <div className="flex flex-row items-center space-x-4">
             <Focus focusMode={focusMode} setFocusMode={setFocusMode} />
-            {/* <Attach /> */}
           </div>
-          <div className="flex flex-row items-center space-x-4 -mx-2">
+          <div className="flex flex-row items-center space-x-1 sm:space-x-4">
-            <CopilotToggle
+            <Optimization
-              copilotEnabled={copilotEnabled}
+              optimizationMode={optimizationMode}
-              setCopilotEnabled={setCopilotEnabled}
+              setOptimizationMode={setOptimizationMode}
             />
             <button
               disabled={message.trim().length === 0}

ui/components/MessageInputActions/Optimization.tsx

@@ -0,0 +1,104 @@
+import { ChevronDown, Sliders, Star, Zap } from 'lucide-react';
+import { cn } from '@/lib/utils';
+import {
+  Popover,
+  PopoverButton,
+  PopoverPanel,
+  Transition,
+} from '@headlessui/react';
+import { Fragment } from 'react';
+
+const OptimizationModes = [
+  {
+    key: 'speed',
+    title: 'Speed',
+    description: 'Prioritize speed and get the quickest possible answer.',
+    icon: <Zap size={20} className="text-[#FF9800]" />,
+  },
+  {
+    key: 'balanced',
+    title: 'Balanced',
+    description: 'Find the right balance between speed and accuracy',
+    icon: <Sliders size={20} className="text-[#4CAF50]" />,
+  },
+  {
+    key: 'quality',
+    title: 'Quality (Soon)',
+    description: 'Get the most thorough and accurate answer',
+    icon: (
+      <Star
+        size={16}
+        className="text-[#2196F3] dark:text-[#BBDEFB] fill-[#BBDEFB] dark:fill-[#2196F3]"
+      />
+    ),
+  },
+];
+
+const Optimization = ({
+  optimizationMode,
+  setOptimizationMode,
+}: {
+  optimizationMode: string;
+  setOptimizationMode: (mode: string) => void;
+}) => {
+  return (
+    <Popover className="relative w-full max-w-[15rem] md:max-w-md lg:max-w-lg">
+      <PopoverButton
+        type="button"
+        className="p-2 text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary active:scale-95 transition duration-200 hover:text-black dark:hover:text-white"
+      >
+        <div className="flex flex-row items-center space-x-1">
+          {
+            OptimizationModes.find((mode) => mode.key === optimizationMode)
+              ?.icon
+          }
+          <p className="text-xs font-medium">
+            {
+              OptimizationModes.find((mode) => mode.key === optimizationMode)
+                ?.title
+            }
+          </p>
+          <ChevronDown size={20} />
+        </div>
+      </PopoverButton>
+      <Transition
+        as={Fragment}
+        enter="transition ease-out duration-150"
+        enterFrom="opacity-0 translate-y-1"
+        enterTo="opacity-100 translate-y-0"
+        leave="transition ease-in duration-150"
+        leaveFrom="opacity-100 translate-y-0"
+        leaveTo="opacity-0 translate-y-1"
+      >
+        <PopoverPanel className="absolute z-10 w-64 md:w-[250px] right-0">
+          <div className="flex flex-col gap-2 bg-light-primary dark:bg-dark-primary border rounded-lg border-light-200 dark:border-dark-200 w-full p-4 max-h-[200px] md:max-h-none overflow-y-auto">
+            {OptimizationModes.map((mode, i) => (
+              <PopoverButton
+                onClick={() => setOptimizationMode(mode.key)}
+                key={i}
+                disabled={mode.key === 'quality'}
+                className={cn(
+                  'p-2 rounded-lg flex flex-col items-start justify-start text-start space-y-1 duration-200 cursor-pointer transition',
+                  optimizationMode === mode.key
+                    ? 'bg-light-secondary dark:bg-dark-secondary'
+                    : 'hover:bg-light-secondary dark:hover:bg-dark-secondary',
+                  mode.key === 'quality' && 'opacity-50 cursor-not-allowed',
+                )}
+              >
+                <div className="flex flex-row items-center space-x-1 text-black dark:text-white">
+                  {mode.icon}
+                  <p className="text-sm font-medium">{mode.title}</p>
+                </div>
+                <p className="text-black/70 dark:text-white/70 text-xs">
+                  {mode.description}
+                </p>
+              </PopoverButton>
+            ))}
+          </div>
+        </PopoverPanel>
+      </Transition>
+    </Popover>
+  );
+};
+
+export default Optimization;