diff --git a/docs/API/SEARCH.md b/docs/API/SEARCH.md index a573021..f87e788 100644 --- a/docs/API/SEARCH.md +++ b/docs/API/SEARCH.md @@ -26,6 +26,7 @@ The API accepts a JSON object in the request body, where you define the focus mo "provider": "openai", "model": "text-embedding-3-large" }, + "optimizationMode": "speed", "focusMode": "webSearch", "query": "What is Perplexica", "history": [ @@ -37,7 +38,7 @@ The API accepts a JSON object in the request body, where you define the focus mo ### Request Parameters -- **`chatModel`** (object, optional): Defines the chat model to be used for the query. For model details you can send a GET request at `http://localhost:3001/api/models`. +- **`chatModel`** (object, optional): Defines the chat model to be used for the query. For model details you can send a GET request at `http://localhost:3001/api/models`. Make sure to use the key value (For example "gpt-4o-mini" instead of the display name "GPT 4 omni mini"). - `provider`: Specifies the provider for the chat model (e.g., `openai`, `ollama`). - `model`: The specific model from the chosen provider (e.g., `gpt-4o-mini`). @@ -45,7 +46,7 @@ The API accepts a JSON object in the request body, where you define the focus mo - `customOpenAIBaseURL`: If you’re using a custom OpenAI instance, provide the base URL. - `customOpenAIKey`: The API key for a custom OpenAI instance. -- **`embeddingModel`** (object, optional): Defines the embedding model for similarity-based searching. For model details you can send a GET request at `http://localhost:3001/api/models`. +- **`embeddingModel`** (object, optional): Defines the embedding model for similarity-based searching. For model details you can send a GET request at `http://localhost:3001/api/models`. Make sure to use the key value (For example "text-embedding-3-large" instead of the display name "Text Embedding 3 Large"). - `provider`: The provider for the embedding model (e.g., `openai`). - `model`: The specific embedding model (e.g., `text-embedding-3-large`). @@ -54,9 +55,15 @@ The API accepts a JSON object in the request body, where you define the focus mo - `webSearch`, `academicSearch`, `writingAssistant`, `wolframAlphaSearch`, `youtubeSearch`, `redditSearch`. +- **`optimizationMode`** (string, optional): Specifies the optimization mode to control the balance between performance and quality. Available modes: + + - `speed`: Prioritize speed and return the fastest answer. + - `balanced`: Provide a balanced answer with good speed and reasonable quality. + - **`query`** (string, required): The search query or question. - **`history`** (array, optional): An array of message pairs representing the conversation history. Each pair consists of a role (either 'human' or 'assistant') and the message content. This allows the system to use the context of the conversation to refine results. Example: + ```json [ ["human", "What is Perplexica?"], diff --git a/src/agents/academicSearchAgent.ts b/src/agents/academicSearchAgent.ts index a72e3a2..4a10c98 100644 --- a/src/agents/academicSearchAgent.ts +++ b/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([ - embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)), - embeddings.embedQuery(query), - ]); + if (optimizationMode === 'speed') { + return docsWithContent.slice(0, 15); + } 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 sim = computeSimilarity(queryEmbedding, docEmbedding); + const similarity = docEmbeddings.map((docEmbedding, i) => { + const sim = computeSimilarity(queryEmbedding, docEmbedding); - return { - index: i, - similarity: sim, - }; - }); + return { + index: i, + similarity: sim, + }; + }); - const sortedDocs = similarity - .sort((a, b) => b.similarity - a.similarity) - .slice(0, 15) - .map((sim) => docsWithContent[sim.index]); + const sortedDocs = similarity + .sort((a, b) => b.similarity - a.similarity) + .slice(0, 15) + .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; }; diff --git a/src/agents/redditSearchAgent.ts b/src/agents/redditSearchAgent.ts index 9c2c443..2c44c13 100644 --- a/src/agents/redditSearchAgent.ts +++ b/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([ - embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)), - embeddings.embedQuery(query), - ]); + if (optimizationMode === 'speed') { + return docsWithContent.slice(0, 15); + } 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 sim = computeSimilarity(queryEmbedding, docEmbedding); + const similarity = docEmbeddings.map((docEmbedding, i) => { + const sim = computeSimilarity(queryEmbedding, docEmbedding); - return { - index: i, - similarity: sim, - }; - }); + return { + index: i, + similarity: sim, + }; + }); - const sortedDocs = similarity - .filter((sim) => sim.similarity > 0.3) - .sort((a, b) => b.similarity - a.similarity) - .slice(0, 15) - .map((sim) => docsWithContent[sim.index]); + const sortedDocs = similarity + .filter((sim) => sim.similarity > 0.3) + .sort((a, b) => b.similarity - a.similarity) + .slice(0, 15) + .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; }; diff --git a/src/agents/webSearchAgent.ts b/src/agents/webSearchAgent.ts index 77ec181..51653a0 100644 --- a/src/agents/webSearchAgent.ts +++ b/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 need to summarize the text into 1 or 2 sentences capturing the main idea of the text. - You need to make sure that you don't miss any point while summarizing the text. - 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. - Only return the summarized text without any other messages, text or 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 + text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query. + 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. + + - **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague. + - **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. + + + + 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. + + + + What is Docker and how does it work? + + + 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. + + + Everything below is the actual data you will be working with. Good luck! ${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([ - embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)), - embeddings.embedQuery(query), - ]); + if (optimizationMode === 'speed') { + return docsWithContent.slice(0, 15); + } 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 sim = computeSimilarity(queryEmbedding, docEmbedding); + const similarity = docEmbeddings.map((docEmbedding, i) => { + const sim = computeSimilarity(queryEmbedding, docEmbedding); - return { - index: i, - similarity: sim, - }; - }); + return { + index: i, + similarity: sim, + }; + }); - const sortedDocs = similarity - .filter((sim) => sim.similarity > 0.3) - .sort((a, b) => b.similarity - a.similarity) - .slice(0, 15) - .map((sim) => docsWithContent[sim.index]); + const sortedDocs = similarity + .filter((sim) => sim.similarity > 0.3) + .sort((a, b) => b.similarity - a.similarity) + .slice(0, 15) + .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; }; diff --git a/src/agents/youtubeSearchAgent.ts b/src/agents/youtubeSearchAgent.ts index e9b6553..2f53bc9 100644 --- a/src/agents/youtubeSearchAgent.ts +++ b/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([ - embeddings.embedDocuments(docsWithContent.map((doc) => doc.pageContent)), - embeddings.embedQuery(query), - ]); + if (optimizationMode === 'speed') { + return docsWithContent.slice(0, 15); + } else { + const [docEmbeddings, queryEmbedding] = await Promise.all([ + embeddings.embedDocuments( + docsWithContent.map((doc) => doc.pageContent), + ), + embeddings.embedQuery(query), + ]); - const similarity = docEmbeddings.map((docEmbedding, i) => { - const sim = computeSimilarity(queryEmbedding, docEmbedding); + const similarity = docEmbeddings.map((docEmbedding, i) => { + const sim = computeSimilarity(queryEmbedding, docEmbedding); - return { - index: i, - similarity: sim, - }; - }); + return { + index: i, + similarity: sim, + }; + }); - const sortedDocs = similarity - .filter((sim) => sim.similarity > 0.3) - .sort((a, b) => b.similarity - a.similarity) - .slice(0, 15) - .map((sim) => docsWithContent[sim.index]); + const sortedDocs = similarity + .filter((sim) => sim.similarity > 0.3) + .sort((a, b) => b.similarity - a.similarity) + .slice(0, 15) + .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; }; diff --git a/src/routes/models.ts b/src/routes/models.ts index 36df25a..c4f5d40 100644 --- a/src/routes/models.ts +++ b/src/routes/models.ts @@ -12,7 +12,19 @@ router.get('/', async (req, res) => { const [chatModelProviders, embeddingModelProviders] = await Promise.all([ getAvailableChatModelProviders(), getAvailableEmbeddingModelProviders(), - ]); + ]); + + Object.keys(chatModelProviders).forEach((provider) => { + Object.keys(chatModelProviders[provider]).forEach((model) => { + delete chatModelProviders[provider][model].model; + }); + }); + + Object.keys(embeddingModelProviders).forEach((provider) => { + Object.keys(embeddingModelProviders[provider]).forEach((model) => { + delete embeddingModelProviders[provider][model].model; + }); + }); res.status(200).json({ chatModelProviders, embeddingModelProviders }); } catch (err) { diff --git a/src/routes/search.ts b/src/routes/search.ts index 9eec29f..6684632 100644 --- a/src/routes/search.ts +++ b/src/routes/search.ts @@ -25,6 +25,7 @@ interface embeddingModel { } interface ChatRequestBody { + optimizationMode: 'speed' | 'balanced'; focusMode: string; chatModel?: chatModel; embeddingModel?: embeddingModel; @@ -41,6 +42,7 @@ router.post('/', async (req, res) => { } body.history = body.history || []; + body.optimizationMode = body.optimizationMode || 'balanced'; const history: BaseMessage[] = body.history.map((msg) => { if (msg[0] === 'human') { @@ -119,7 +121,7 @@ router.post('/', async (req, res) => { return res.status(400).json({ message: 'Invalid focus mode' }); } - const emitter = searchHandler(body.query, history, llm, embeddings); + const emitter = searchHandler(body.query, history, llm, embeddings, body.optimizationMode); let message = ''; let sources = []; diff --git a/src/websocket/messageHandler.ts b/src/websocket/messageHandler.ts index 332910c..d230386 100644 --- a/src/websocket/messageHandler.ts +++ b/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); diff --git a/ui/components/ChatWindow.tsx b/ui/components/ChatWindow.tsx index 96fe859..624cdd3 100644 --- a/ui/components/ChatWindow.tsx +++ b/ui/components/ChatWindow.tsx @@ -315,6 +315,7 @@ const ChatWindow = ({ id }: { id?: string }) => { const [messages, setMessages] = useState([]); const [focusMode, setFocusMode] = useState('webSearch'); + const [optimizationMode, setOptimizationMode] = useState('speed'); const [isMessagesLoaded, setIsMessagesLoaded] = useState(false); @@ -386,6 +387,7 @@ const ChatWindow = ({ id }: { id?: string }) => { content: message, }, focusMode: focusMode, + optimizationMode: optimizationMode, history: [...chatHistory, ['human', message]], }), ); @@ -548,6 +550,8 @@ const ChatWindow = ({ id }: { id?: string }) => { sendMessage={sendMessage} focusMode={focusMode} setFocusMode={setFocusMode} + optimizationMode={optimizationMode} + setOptimizationMode={setOptimizationMode} /> )} diff --git a/ui/components/DeleteChat.tsx b/ui/components/DeleteChat.tsx index 165f86e..f981e32 100644 --- a/ui/components/DeleteChat.tsx +++ b/ui/components/DeleteChat.tsx @@ -1,5 +1,13 @@ -import { Delete, Trash } from 'lucide-react'; -import { Dialog, Transition } from '@headlessui/react'; +import { Trash } from 'lucide-react'; +import { + Description, + Dialog, + DialogBackdrop, + DialogPanel, + DialogTitle, + Transition, + TransitionChild, +} from '@headlessui/react'; import { Fragment, useState } from 'react'; import { toast } from 'sonner'; import { Chat } from '@/app/library/page'; @@ -64,10 +72,10 @@ const DeleteChat = ({ } }} > - +
- - - + + Delete Confirmation - - + + Are you sure you want to delete this chat? - +
- - + +
diff --git a/ui/components/EmptyChat.tsx b/ui/components/EmptyChat.tsx index ea3642b..63f186c 100644 --- a/ui/components/EmptyChat.tsx +++ b/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 (
@@ -19,6 +23,8 @@ const EmptyChat = ({ sendMessage={sendMessage} focusMode={focusMode} setFocusMode={setFocusMode} + optimizationMode={optimizationMode} + setOptimizationMode={setOptimizationMode} />
diff --git a/ui/components/EmptyChatMessageInput.tsx b/ui/components/EmptyChatMessageInput.tsx index 39d3f16..845bed9 100644 --- a/ui/components/EmptyChatMessageInput.tsx +++ b/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..." />
-
+
- {/* */}
-
- +
- - + +
diff --git a/ui/package.json b/ui/package.json index 04512b6..e5dc677 100644 --- a/ui/package.json +++ b/ui/package.json @@ -11,7 +11,7 @@ "format:write": "prettier . --write" }, "dependencies": { - "@headlessui/react": "^1.7.18", + "@headlessui/react": "^2.1.9", "@icons-pack/react-simple-icons": "^9.4.0", "@langchain/openai": "^0.0.25", "@tailwindcss/typography": "^0.5.12", diff --git a/ui/yarn.lock b/ui/yarn.lock index d348a8c..fad2596 100644 --- a/ui/yarn.lock +++ b/ui/yarn.lock @@ -66,13 +66,51 @@ resolved "https://registry.yarnpkg.com/@eslint/js/-/js-8.57.0.tgz#a5417ae8427873f1dd08b70b3574b453e67b5f7f" integrity sha512-Ys+3g2TaW7gADOJzPt83SJtCDhMjndcDMFVQ/Tj9iA1BfJzFKD9mAUXT3OenpuPHbI6P/myECxRJrofUsDx/5g== -"@headlessui/react@^1.7.18": - version "1.7.18" - resolved "https://registry.yarnpkg.com/@headlessui/react/-/react-1.7.18.tgz#30af4634d2215b2ca1aa29d07f33d02bea82d9d7" - integrity sha512-4i5DOrzwN4qSgNsL4Si61VMkUcWbcSKueUV7sFhpHzQcSShdlHENE5+QBntMSRvHt8NyoFO2AGG8si9lq+w4zQ== 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