remi/Perplexica
1
0
Fork 0
mirror of https://github.com/ItzCrazyKns/Perplexica.git synced 2025-09-18 07:11:34 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat(app): remove backend

Browse Source
This commit is contained in:
ItzCrazyKns
2025-03-19 16:23:27 +05:30
parent 8a24572cd2
commit 217736d05a
145 changed files with 3546 additions and 10516 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
ui/.env.example
View File

@@ -1,2 +0,0 @@
NEXT_PUBLIC_WS_URL=ws://localhost:3001
NEXT_PUBLIC_API_URL=http://localhost:3001/api

3
ui/.eslintrc.json
View File

@@ -1,3 +0,0 @@
{
"extends": "next/core-web-vitals"
}

34
ui/.gitignore vendored
View File

@@ -1,34 +0,0 @@
# dependencies
/node_modules
/.pnp
.pnp.js
.yarn/install-state.gz
# testing
/coverage
# next.js
/.next/
/out/
# production
/build
# misc
.DS_Store
*.pem
# debug
npm-debug.log*
yarn-debug.log*
yarn-error.log*
# local env files
.env*.local
# vercel
.vercel
# typescript
*.tsbuildinfo
next-env.d.ts

41
ui/.prettierignore
View File

@@ -1,41 +0,0 @@
# Ignore all files in the node_modules directory
node_modules
# Ignore all files in the .next directory (Next.js build output)
.next
# Ignore all files in the .out directory (TypeScript build output)
.out
# Ignore all files in the .cache directory (Prettier cache)
.cache
# Ignore all files in the .vscode directory (Visual Studio Code settings)
.vscode
# Ignore all files in the .idea directory (IntelliJ IDEA settings)
.idea
# Ignore all files in the dist directory (build output)
dist
# Ignore all files in the build directory (build output)
build
# Ignore all files in the coverage directory (test coverage reports)
coverage
# Ignore all files with the .log extension
*.log
# Ignore all files with the .tmp extension
*.tmp
# Ignore all files with the .swp extension
*.swp
# Ignore all files with the .DS_Store extension (macOS specific)
.DS_Store
# Ignore all files in uploads directory
uploads

11
ui/.prettierrc.js
View File

@@ -1,11 +0,0 @@
/** @type {import("prettier").Config} */
const config = {
printWidth: 80,
trailingComma: 'all',
endOfLine: 'auto',
singleQuote: true,
tabWidth: 2,
};
module.exports = config;

346
ui/app/api/chat/route.ts
View File

@@ -1,346 +0,0 @@
import prompts from '@/lib/prompts';
import MetaSearchAgent from '@/lib/search/metaSearchAgent';
import crypto from 'crypto';
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
import { EventEmitter } from 'stream';
import { chatModelProviders, embeddingModelProviders } from '@/lib/providers';
import db from '@/lib/db';
import { chats, messages as messagesSchema } from '@/lib/db/schema';
import { and, eq, gt } from 'drizzle-orm';
import { getFileDetails } from '@/lib/utils/files';
export const runtime = 'nodejs';
export const dynamic = 'force-dynamic';
export const searchHandlers: Record<string, MetaSearchAgent> = {
webSearch: new MetaSearchAgent({
activeEngines: [],
queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
responsePrompt: prompts.webSearchResponsePrompt,
rerank: true,
rerankThreshold: 0.3,
searchWeb: true,
summarizer: true,
}),
academicSearch: new MetaSearchAgent({
activeEngines: ['arxiv', 'google scholar', 'pubmed'],
queryGeneratorPrompt: prompts.academicSearchRetrieverPrompt,
responsePrompt: prompts.academicSearchResponsePrompt,
rerank: true,
rerankThreshold: 0,
searchWeb: true,
summarizer: false,
}),
writingAssistant: new MetaSearchAgent({
activeEngines: [],
queryGeneratorPrompt: '',
responsePrompt: prompts.writingAssistantPrompt,
rerank: true,
rerankThreshold: 0,
searchWeb: false,
summarizer: false,
}),
wolframAlphaSearch: new MetaSearchAgent({
activeEngines: ['wolframalpha'],
queryGeneratorPrompt: prompts.wolframAlphaSearchRetrieverPrompt,
responsePrompt: prompts.wolframAlphaSearchResponsePrompt,
rerank: false,
rerankThreshold: 0,
searchWeb: true,
summarizer: false,
}),
youtubeSearch: new MetaSearchAgent({
activeEngines: ['youtube'],
queryGeneratorPrompt: prompts.youtubeSearchRetrieverPrompt,
responsePrompt: prompts.youtubeSearchResponsePrompt,
rerank: true,
rerankThreshold: 0.3,
searchWeb: true,
summarizer: false,
}),
redditSearch: new MetaSearchAgent({
activeEngines: ['reddit'],
queryGeneratorPrompt: prompts.redditSearchRetrieverPrompt,
responsePrompt: prompts.redditSearchResponsePrompt,
rerank: true,
rerankThreshold: 0.3,
searchWeb: true,
summarizer: false,
}),
};
type Message = {
messageId: string;
chatId: string;
content: string;
};
type ChatModel = {
provider: string;
name: string;
};
type EmbeddingModel = {
provider: string;
name: string;
};
type Body = {
message: Message;
optimizationMode: 'speed' | 'balanced' | 'quality';
focusMode: string;
history: Array<[string, string]>;
files: Array<string>;
chatModel: ChatModel;
embeddingModel: EmbeddingModel;
};
const handleEmitterEvents = async (
stream: EventEmitter,
writer: WritableStreamDefaultWriter,
encoder: TextEncoder,
aiMessageId: string,
chatId: string,
) => {
let recievedMessage = '';
let sources: any[] = [];
stream.on('data', (data) => {
const parsedData = JSON.parse(data);
if (parsedData.type === 'response') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'message',
data: parsedData.data,
messageId: aiMessageId,
}) + '\n',
),
);
recievedMessage += parsedData.data;
} else if (parsedData.type === 'sources') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'sources',
data: parsedData.data,
messageId: aiMessageId,
}) + '\n',
),
);
sources = parsedData.data;
}
});
stream.on('end', () => {
writer.write(
encoder.encode(
JSON.stringify({
type: 'messageEnd',
messageId: aiMessageId,
}) + '\n',
),
);
writer.close();
db.insert(messagesSchema)
.values({
content: recievedMessage,
chatId: chatId,
messageId: aiMessageId,
role: 'assistant',
metadata: JSON.stringify({
createdAt: new Date(),
...(sources && sources.length > 0 && { sources }),
}),
})
.execute();
});
stream.on('error', (data) => {
const parsedData = JSON.parse(data);
writer.write(
encoder.encode(
JSON.stringify({
type: 'error',
data: parsedData.data,
}),
),
);
writer.close();
});
};
const handleHistorySave = async (
message: Message,
humanMessageId: string,
focusMode: string,
files: string[],
) => {
const chat = await db.query.chats.findFirst({
where: eq(chats.id, message.chatId),
});
if (!chat) {
await db
.insert(chats)
.values({
id: message.chatId,
title: message.content,
createdAt: new Date().toString(),
focusMode: focusMode,
files: files.map(getFileDetails),
})
.execute();
}
const messageExists = await db.query.messages.findFirst({
where: eq(messagesSchema.messageId, humanMessageId),
});
if (!messageExists) {
await db
.insert(messagesSchema)
.values({
content: message.content,
chatId: message.chatId,
messageId: humanMessageId,
role: 'user',
metadata: JSON.stringify({
createdAt: new Date(),
}),
})
.execute();
} else {
await db
.delete(messagesSchema)
.where(
and(
gt(messagesSchema.id, messageExists.id),
eq(messagesSchema.chatId, message.chatId),
),
)
.execute();
}
};
export const POST = async (req: Request) => {
try {
const body = (await req.json()) as Body;
const { message, chatModel, embeddingModel } = body;
if (message.content === '') {
return Response.json(
{
message: 'Please provide a message to process',
},
{ status: 400 },
);
}
const getProviderChatModels = chatModelProviders[chatModel.provider];
if (!getProviderChatModels) {
return Response.json(
{
message: 'Invalid chat model provider',
},
{ status: 400 },
);
}
const chatModels = await getProviderChatModels();
const llm = chatModels[chatModel.name].model;
if (!llm) {
return Response.json(
{
message: 'Invalid chat model',
},
{ status: 400 },
);
}
const getProviderEmbeddingModels =
embeddingModelProviders[embeddingModel.provider];
if (!getProviderEmbeddingModels) {
return Response.json(
{
message: 'Invalid embedding model provider',
},
{ status: 400 },
);
}
const embeddingModels = await getProviderEmbeddingModels();
const embedding = embeddingModels[embeddingModel.name].model;
if (!embedding) {
return Response.json(
{
message: 'Invalid embedding model',
},
{ status: 400 },
);
}
const humanMessageId =
message.messageId ?? crypto.randomBytes(7).toString('hex');
const aiMessageId = crypto.randomBytes(7).toString('hex');
const history: BaseMessage[] = body.history.map((msg) => {
if (msg[0] === 'human') {
return new HumanMessage({
content: msg[1],
});
} else {
return new AIMessage({
content: msg[1],
});
}
});
const handler = searchHandlers[body.focusMode];
if (!handler) {
return Response.json(
{
message: 'Invalid focus mode',
},
{ status: 400 },
);
}
const stream = await handler.searchAndAnswer(
message.content,
history,
llm,
embedding,
body.optimizationMode,
body.files,
);
const responseStream = new TransformStream();
const writer = responseStream.writable.getWriter();
const encoder = new TextEncoder();
handleEmitterEvents(stream, writer, encoder, aiMessageId, message.chatId);
handleHistorySave(message, humanMessageId, body.focusMode, body.files);
return new Response(responseStream.readable, {
headers: {
'Content-Type': 'text/event-stream',
Connection: 'keep-alive',
'Cache-Control': 'no-cache, no-transform',
},
});
} catch (err) {
console.error('An error ocurred while processing chat request:', err);
return Response.json(
{ message: 'An error ocurred while processing chat request' },
{ status: 500 },
);
}
};

69
ui/app/api/chats/[id]/route.ts
View File

@@ -1,69 +0,0 @@
import db from '@/lib/db';
import { chats, messages } from '@/lib/db/schema';
import { eq } from 'drizzle-orm';
export const GET = async (
req: Request,
{ params }: { params: Promise<{ id: string }> },
) => {
try {
const { id } = await params;
const chatExists = await db.query.chats.findFirst({
where: eq(chats.id, id),
});
if (!chatExists) {
return Response.json({ message: 'Chat not found' }, { status: 404 });
}
const chatMessages = await db.query.messages.findMany({
where: eq(messages.chatId, id),
});
return Response.json(
{
chat: chatExists,
messages: chatMessages,
},
{ status: 200 },
);
} catch (err) {
console.error('Error in getting chat by id: ', err);
return Response.json(
{ message: 'An error has occurred.' },
{ status: 500 },
);
}
};
export const DELETE = async (
req: Request,
{ params }: { params: Promise<{ id: string }> },
) => {
try {
const { id } = await params;
const chatExists = await db.query.chats.findFirst({
where: eq(chats.id, id),
});
if (!chatExists) {
return Response.json({ message: 'Chat not found' }, { status: 404 });
}
await db.delete(chats).where(eq(chats.id, id)).execute();
await db.delete(messages).where(eq(messages.chatId, id)).execute();
return Response.json(
{ message: 'Chat deleted successfully' },
{ status: 200 },
);
} catch (err) {
console.error('Error in deleting chat by id: ', err);
return Response.json(
{ message: 'An error has occurred.' },
{ status: 500 },
);
}
};

15
ui/app/api/chats/route.ts
View File

@@ -1,15 +0,0 @@
import db from '@/lib/db';
export const GET = async (req: Request) => {
try {
let chats = await db.query.chats.findMany();
chats = chats.reverse();
return Response.json({ chats: chats }, { status: 200 });
} catch (err) {
console.error('Error in getting chats: ', err);
return Response.json(
{ message: 'An error has occurred.' },
{ status: 500 },
);
}
};

109
ui/app/api/config/route.ts
View File

@@ -1,109 +0,0 @@
import {
getAnthropicApiKey,
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
getGeminiApiKey,
getGroqApiKey,
getOllamaApiEndpoint,
getOpenaiApiKey,
updateConfig,
} from '@/lib/config';
import {
getAvailableChatModelProviders,
getAvailableEmbeddingModelProviders,
} from '@/lib/providers';
export const GET = async (req: Request) => {
try {
const config: Record<string, any> = {};
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
getAvailableChatModelProviders(),
getAvailableEmbeddingModelProviders(),
]);
config['chatModelProviders'] = {};
config['embeddingModelProviders'] = {};
for (const provider in chatModelProviders) {
config['chatModelProviders'][provider] = Object.keys(
chatModelProviders[provider],
).map((model) => {
return {
name: model,
displayName: chatModelProviders[provider][model].displayName,
};
});
}
for (const provider in embeddingModelProviders) {
config['embeddingModelProviders'][provider] = Object.keys(
embeddingModelProviders[provider],
).map((model) => {
return {
name: model,
displayName: embeddingModelProviders[provider][model].displayName,
};
});
}
config['openaiApiKey'] = getOpenaiApiKey();
config['ollamaApiUrl'] = getOllamaApiEndpoint();
config['anthropicApiKey'] = getAnthropicApiKey();
config['groqApiKey'] = getGroqApiKey();
config['geminiApiKey'] = getGeminiApiKey();
config['customOpenaiApiUrl'] = getCustomOpenaiApiUrl();
config['customOpenaiApiKey'] = getCustomOpenaiApiKey();
config['customOpenaiModelName'] = getCustomOpenaiModelName();
return Response.json({ ...config }, { status: 200 });
} catch (err) {
console.error('An error ocurred while getting config:', err);
return Response.json(
{ message: 'An error ocurred while getting config' },
{ status: 500 },
);
}
};
export const POST = async (req: Request) => {
try {
const config = await req.json();
const updatedConfig = {
MODELS: {
OPENAI: {
API_KEY: config.openaiApiKey,
},
GROQ: {
API_KEY: config.groqApiKey,
},
ANTHROPIC: {
API_KEY: config.anthropicApiKey,
},
GEMINI: {
API_KEY: config.geminiApiKey,
},
OLLAMA: {
API_URL: config.ollamaApiUrl,
},
CUSTOM_OPENAI: {
API_URL: config.customOpenaiApiUrl,
API_KEY: config.customOpenaiApiKey,
MODEL_NAME: config.customOpenaiModelName,
},
},
};
updateConfig(updatedConfig);
return Response.json({ message: 'Config updated' }, { status: 200 });
} catch (err) {
console.error('An error ocurred while updating config:', err);
return Response.json(
{ message: 'An error ocurred while updating config' },
{ status: 500 },
);
}
};

61
ui/app/api/discover/route.ts
View File

@@ -1,61 +0,0 @@
import { searchSearxng } from '@/lib/searxng';
const articleWebsites = [
'yahoo.com',
'www.exchangewire.com',
'businessinsider.com',
/* 'wired.com',
'mashable.com',
'theverge.com',
'gizmodo.com',
'cnet.com',
'venturebeat.com', */
];
const topics = ['AI', 'tech']; /* TODO: Add UI to customize this */
export const GET = async (req: Request) => {
try {
const data = (
await Promise.all([
...new Array(articleWebsites.length * topics.length)
.fill(0)
.map(async (_, i) => {
return (
await searchSearxng(
`site:${articleWebsites[i % articleWebsites.length]} ${
topics[i % topics.length]
}`,
{
engines: ['bing news'],
pageno: 1,
},
)
).results;
}),
])
)
.map((result) => result)
.flat()
.sort(() => Math.random() - 0.5);
return Response.json(
{
blogs: data,
},
{
status: 200,
},
);
} catch (err) {
console.error(`An error ocurred in discover route: ${err}`);
return Response.json(
{
message: 'An error has occurred',
},
{
status: 500,
},
);
}
};

83
ui/app/api/images/route.ts
View File

@@ -1,83 +0,0 @@
import handleImageSearch from '@/lib/chains/imageSearchAgent';
import {
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
} from '@/lib/config';
import { getAvailableChatModelProviders } from '@/lib/providers';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { AIMessage, HumanMessage } from '@langchain/core/messages';
import { ChatOpenAI } from '@langchain/openai';
interface ChatModel {
provider: string;
model: string;
}
interface ImageSearchBody {
query: string;
chatHistory: any[];
chatModel?: ChatModel;
}
export const POST = async (req: Request) => {
try {
const body: ImageSearchBody = await req.json();
const chatHistory = body.chatHistory
.map((msg: any) => {
if (msg.role === 'user') {
return new HumanMessage(msg.content);
} else if (msg.role === 'assistant') {
return new AIMessage(msg.content);
}
})
.filter((msg) => msg !== undefined);
const chatModelProviders = await getAvailableChatModelProviders();
const chatModelProvider =
chatModelProviders[
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
];
const chatModel =
chatModelProvider[
body.chatModel?.model || Object.keys(chatModelProvider)[0]
];
let llm: BaseChatModel | undefined;
if (body.chatModel?.provider === 'custom_openai') {
llm = new ChatOpenAI({
openAIApiKey: getCustomOpenaiApiKey(),
modelName: getCustomOpenaiModelName(),
temperature: 0.7,
configuration: {
baseURL: getCustomOpenaiApiUrl(),
},
});
} else if (chatModelProvider && chatModel) {
llm = chatModel.model;
}
if (!llm) {
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
}
const images = await handleImageSearch(
{
chat_history: chatHistory,
query: body.query,
},
llm,
);
return Response.json({ images }, { status: 200 });
} catch (err) {
console.error(`An error ocurred while searching images: ${err}`);
return Response.json(
{ message: 'An error ocurred while searching images' },
{ status: 500 },
);
}
};

47
ui/app/api/models/route.ts
View File

@@ -1,47 +0,0 @@
import {
getAvailableChatModelProviders,
getAvailableEmbeddingModelProviders,
} from '@/lib/providers';
export const GET = async (req: Request) => {
try {
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
getAvailableChatModelProviders(),
getAvailableEmbeddingModelProviders(),
]);
Object.keys(chatModelProviders).forEach((provider) => {
Object.keys(chatModelProviders[provider]).forEach((model) => {
delete (chatModelProviders[provider][model] as { model?: unknown })
.model;
});
});
Object.keys(embeddingModelProviders).forEach((provider) => {
Object.keys(embeddingModelProviders[provider]).forEach((model) => {
delete (embeddingModelProviders[provider][model] as { model?: unknown })
.model;
});
});
return Response.json(
{
chatModelProviders,
embeddingModelProviders,
},
{
status: 200,
},
);
} catch (err) {
console.error('An error ocurred while fetching models', err);
return Response.json(
{
message: 'An error has occurred.',
},
{
status: 500,
},
);
}
};

81
ui/app/api/suggestions/route.ts
View File

@@ -1,81 +0,0 @@
import generateSuggestions from '@/lib/chains/suggestionGeneratorAgent';
import {
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
} from '@/lib/config';
import { getAvailableChatModelProviders } from '@/lib/providers';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { AIMessage, HumanMessage } from '@langchain/core/messages';
import { ChatOpenAI } from '@langchain/openai';
interface ChatModel {
provider: string;
model: string;
}
interface SuggestionsGenerationBody {
chatHistory: any[];
chatModel?: ChatModel;
}
export const POST = async (req: Request) => {
try {
const body: SuggestionsGenerationBody = await req.json();
const chatHistory = body.chatHistory
.map((msg: any) => {
if (msg.role === 'user') {
return new HumanMessage(msg.content);
} else if (msg.role === 'assistant') {
return new AIMessage(msg.content);
}
})
.filter((msg) => msg !== undefined);
const chatModelProviders = await getAvailableChatModelProviders();
const chatModelProvider =
chatModelProviders[
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
];
const chatModel =
chatModelProvider[
body.chatModel?.model || Object.keys(chatModelProvider)[0]
];
let llm: BaseChatModel | undefined;
if (body.chatModel?.provider === 'custom_openai') {
llm = new ChatOpenAI({
openAIApiKey: getCustomOpenaiApiKey(),
modelName: getCustomOpenaiModelName(),
temperature: 0.7,
configuration: {
baseURL: getCustomOpenaiApiUrl(),
},
});
} else if (chatModelProvider && chatModel) {
llm = chatModel.model;
}
if (!llm) {
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
}
const suggestions = await generateSuggestions(
{
chat_history: chatHistory,
},
llm,
);
return Response.json({ suggestions }, { status: 200 });
} catch (err) {
console.error(`An error ocurred while generating suggestions: ${err}`);
return Response.json(
{ message: 'An error ocurred while generating suggestions' },
{ status: 500 },
);
}
};

134
ui/app/api/uploads/route.ts
View File

@@ -1,134 +0,0 @@
import { NextResponse } from 'next/server';
import fs from 'fs';
import path from 'path';
import crypto from 'crypto';
import { getAvailableEmbeddingModelProviders } from '@/lib/providers';
import { PDFLoader } from '@langchain/community/document_loaders/fs/pdf';
import { DocxLoader } from '@langchain/community/document_loaders/fs/docx';
import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';
import { Document } from 'langchain/document';
interface FileRes {
fileName: string;
fileExtension: string;
fileId: string;
}
const uploadDir = path.join(process.cwd(), 'uploads');
if (!fs.existsSync(uploadDir)) {
fs.mkdirSync(uploadDir, { recursive: true });
}
const splitter = new RecursiveCharacterTextSplitter({
chunkSize: 500,
chunkOverlap: 100,
});
export async function POST(req: Request) {
try {
const formData = await req.formData();
const files = formData.getAll('files') as File[];
const embedding_model = formData.get('embedding_model');
const embedding_model_provider = formData.get('embedding_model_provider');
if (!embedding_model || !embedding_model_provider) {
return NextResponse.json(
{ message: 'Missing embedding model or provider' },
{ status: 400 },
);
}
const embeddingModels = await getAvailableEmbeddingModelProviders();
const provider =
embedding_model_provider ?? Object.keys(embeddingModels)[0];
const embeddingModel =
embedding_model ?? Object.keys(embeddingModels[provider as string])[0];
let embeddingsModel =
embeddingModels[provider as string]?.[embeddingModel as string]?.model;
if (!embeddingsModel) {
return NextResponse.json(
{ message: 'Invalid embedding model selected' },
{ status: 400 },
);
}
const processedFiles: FileRes[] = [];
await Promise.all(
files.map(async (file: any) => {
const fileExtension = file.name.split('.').pop();
if (!['pdf', 'docx', 'txt'].includes(fileExtension!)) {
return NextResponse.json(
{ message: 'File type not supported' },
{ status: 400 },
);
}
const uniqueFileName = `${crypto.randomBytes(16).toString('hex')}.${fileExtension}`;
const filePath = path.join(uploadDir, uniqueFileName);
const buffer = Buffer.from(await file.arrayBuffer());
fs.writeFileSync(filePath, new Uint8Array(buffer));
let docs: any[] = [];
if (fileExtension === 'pdf') {
const loader = new PDFLoader(filePath);
docs = await loader.load();
} else if (fileExtension === 'docx') {
const loader = new DocxLoader(filePath);
docs = await loader.load();
} else if (fileExtension === 'txt') {
const text = fs.readFileSync(filePath, 'utf-8');
docs = [
new Document({ pageContent: text, metadata: { title: file.name } }),
];
}
const splitted = await splitter.splitDocuments(docs);
const extractedDataPath = filePath.replace(/\.\w+$/, '-extracted.json');
fs.writeFileSync(
extractedDataPath,
JSON.stringify({
title: file.name,
contents: splitted.map((doc) => doc.pageContent),
}),
);
const embeddings = await embeddingsModel.embedDocuments(
splitted.map((doc) => doc.pageContent),
);
const embeddingsDataPath = filePath.replace(
/\.\w+$/,
'-embeddings.json',
);
fs.writeFileSync(
embeddingsDataPath,
JSON.stringify({
title: file.name,
embeddings,
}),
);
processedFiles.push({
fileName: file.name,
fileExtension: fileExtension,
fileId: uniqueFileName.replace(/\.\w+$/, ''),
});
}),
);
return NextResponse.json({
files: processedFiles,
});
} catch (error) {
console.error('Error uploading file:', error);
return NextResponse.json(
{ message: 'An error has occurred.' },
{ status: 500 },
);
}
}

83
ui/app/api/videos/route.ts
View File

@@ -1,83 +0,0 @@
import handleVideoSearch from '@/lib/chains/videoSearchAgent';
import {
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
} from '@/lib/config';
import { getAvailableChatModelProviders } from '@/lib/providers';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { AIMessage, HumanMessage } from '@langchain/core/messages';
import { ChatOpenAI } from '@langchain/openai';
interface ChatModel {
provider: string;
model: string;
}
interface VideoSearchBody {
query: string;
chatHistory: any[];
chatModel?: ChatModel;
}
export const POST = async (req: Request) => {
try {
const body: VideoSearchBody = await req.json();
const chatHistory = body.chatHistory
.map((msg: any) => {
if (msg.role === 'user') {
return new HumanMessage(msg.content);
} else if (msg.role === 'assistant') {
return new AIMessage(msg.content);
}
})
.filter((msg) => msg !== undefined);
const chatModelProviders = await getAvailableChatModelProviders();
const chatModelProvider =
chatModelProviders[
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
];
const chatModel =
chatModelProvider[
body.chatModel?.model || Object.keys(chatModelProvider)[0]
];
let llm: BaseChatModel | undefined;
if (body.chatModel?.provider === 'custom_openai') {
llm = new ChatOpenAI({
openAIApiKey: getCustomOpenaiApiKey(),
modelName: getCustomOpenaiModelName(),
temperature: 0.7,
configuration: {
baseURL: getCustomOpenaiApiUrl(),
},
});
} else if (chatModelProvider && chatModel) {
llm = chatModel.model;
}
if (!llm) {
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
}
const videos = await handleVideoSearch(
{
chat_history: chatHistory,
query: body.query,
},
llm,
);
return Response.json({ videos }, { status: 200 });
} catch (err) {
console.error(`An error ocurred while searching videos: ${err}`);
return Response.json(
{ message: 'An error ocurred while searching videos' },
{ status: 500 },
);
}
};

9
ui/app/c/[chatId]/page.tsx
View File

@@ -1,9 +0,0 @@
import ChatWindow from '@/components/ChatWindow';
import React from 'react';
const Page = ({ params }: { params: Promise<{ chatId: string }> }) => {
const { chatId } = React.use(params);
return <ChatWindow id={chatId} />;
};
export default Page;

113
ui/app/discover/page.tsx
View File

@@ -1,113 +0,0 @@
'use client';
import { Search } from 'lucide-react';
import { useEffect, useState } from 'react';
import Link from 'next/link';
import { toast } from 'sonner';
interface Discover {
title: string;
content: string;
url: string;
thumbnail: string;
}
const Page = () => {
const [discover, setDiscover] = useState<Discover[] | null>(null);
const [loading, setLoading] = useState(true);
useEffect(() => {
const fetchData = async () => {
try {
const res = await fetch(`/api/discover`, {
method: 'GET',
headers: {
'Content-Type': 'application/json',
},
});
const data = await res.json();
if (!res.ok) {
throw new Error(data.message);
}
data.blogs = data.blogs.filter((blog: Discover) => blog.thumbnail);
setDiscover(data.blogs);
} catch (err: any) {
console.error('Error fetching data:', err.message);
toast.error('Error fetching data');
} finally {
setLoading(false);
}
};
fetchData();
}, []);
return loading ? (
<div className="flex flex-row items-center justify-center min-h-screen">
<svg
aria-hidden="true"
className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
viewBox="0 0 100 101"
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<path
d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
fill="currentColor"
/>
<path
d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
fill="currentFill"
/>
</svg>
</div>
) : (
<>
<div>
<div className="flex flex-col pt-4">
<div className="flex items-center">
<Search />
<h1 className="text-3xl font-medium p-2">Discover</h1>
</div>
<hr className="border-t border-[#2B2C2C] my-4 w-full" />
</div>
<div className="grid lg:grid-cols-3 sm:grid-cols-2 grid-cols-1 gap-4 pb-28 lg:pb-8 w-full justify-items-center lg:justify-items-start">
{discover &&
discover?.map((item, i) => (
<Link
href={`/?q=Summary: ${item.url}`}
key={i}
className="max-w-sm rounded-lg overflow-hidden bg-light-secondary dark:bg-dark-secondary hover:-translate-y-[1px] transition duration-200"
target="_blank"
>
<img
className="object-cover w-full aspect-video"
src={
new URL(item.thumbnail).origin +
new URL(item.thumbnail).pathname +
`?id=${new URL(item.thumbnail).searchParams.get('id')}`
}
alt={item.title}
/>
<div className="px-6 py-4">
<div className="font-bold text-lg mb-2">
{item.title.slice(0, 100)}...
</div>
<p className="text-black-70 dark:text-white/70 text-sm">
{item.content.slice(0, 100)}...
</p>
</div>
</Link>
))}
</div>
</div>
</>
);
};
export default Page;

BIN
ui/app/favicon.ico
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 25 KiB

13
ui/app/globals.css
View File

@@ -1,13 +0,0 @@
@tailwind base;
@tailwind components;
@tailwind utilities;
@layer base {
.overflow-hidden-scrollable {
-ms-overflow-style: none;
}
.overflow-hidden-scrollable::-webkit-scrollbar {
display: none;
}
}

45
ui/app/layout.tsx
View File

@@ -1,45 +0,0 @@
import type { Metadata } from 'next';
import { Montserrat } from 'next/font/google';
import './globals.css';
import { cn } from '@/lib/utils';
import Sidebar from '@/components/Sidebar';
import { Toaster } from 'sonner';
import ThemeProvider from '@/components/theme/Provider';
const montserrat = Montserrat({
weight: ['300', '400', '500', '700'],
subsets: ['latin'],
display: 'swap',
fallback: ['Arial', 'sans-serif'],
});
export const metadata: Metadata = {
title: 'Perplexica - Chat with the internet',
description:
'Perplexica is an AI powered chatbot that is connected to the internet.',
};
export default function RootLayout({
children,
}: Readonly<{
children: React.ReactNode;
}>) {
return (
<html className="h-full" lang="en" suppressHydrationWarning>
<body className={cn('h-full', montserrat.className)}>
<ThemeProvider>
<Sidebar>{children}</Sidebar>
<Toaster
toastOptions={{
unstyled: true,
classNames: {
toast:
'bg-light-primary dark:bg-dark-secondary dark:text-white/70 text-black-70 rounded-lg p-4 flex flex-row items-center space-x-2',
},
}}
/>
</ThemeProvider>
</body>
</html>
);
}

12
ui/app/library/layout.tsx
View File

@@ -1,12 +0,0 @@
import { Metadata } from 'next';
import React from 'react';
export const metadata: Metadata = {
title: 'Library - Perplexica',
};
const Layout = ({ children }: { children: React.ReactNode }) => {
return <div>{children}</div>;
};
export default Layout;

114
ui/app/library/page.tsx
View File

@@ -1,114 +0,0 @@
'use client';
import DeleteChat from '@/components/DeleteChat';
import { cn, formatTimeDifference } from '@/lib/utils';
import { BookOpenText, ClockIcon, Delete, ScanEye } from 'lucide-react';
import Link from 'next/link';
import { useEffect, useState } from 'react';
export interface Chat {
id: string;
title: string;
createdAt: string;
focusMode: string;
}
const Page = () => {
const [chats, setChats] = useState<Chat[]>([]);
const [loading, setLoading] = useState(true);
useEffect(() => {
const fetchChats = async () => {
setLoading(true);
const res = await fetch(`/api/chats`, {
method: 'GET',
headers: {
'Content-Type': 'application/json',
},
});
const data = await res.json();
setChats(data.chats);
setLoading(false);
};
fetchChats();
}, []);
return loading ? (
<div className="flex flex-row items-center justify-center min-h-screen">
<svg
aria-hidden="true"
className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
viewBox="0 0 100 101"
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<path
d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
fill="currentColor"
/>
<path
d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
fill="currentFill"
/>
</svg>
</div>
) : (
<div>
<div className="flex flex-col pt-4">
<div className="flex items-center">
<BookOpenText />
<h1 className="text-3xl font-medium p-2">Library</h1>
</div>
<hr className="border-t border-[#2B2C2C] my-4 w-full" />
</div>
{chats.length === 0 && (
<div className="flex flex-row items-center justify-center min-h-screen">
<p className="text-black/70 dark:text-white/70 text-sm">
No chats found.
</p>
</div>
)}
{chats.length > 0 && (
<div className="flex flex-col pb-20 lg:pb-2">
{chats.map((chat, i) => (
<div
className={cn(
'flex flex-col space-y-4 py-6',
i !== chats.length - 1
? 'border-b border-white-200 dark:border-dark-200'
: '',
)}
key={i}
>
<Link
href={`/c/${chat.id}`}
className="text-black dark:text-white lg:text-xl font-medium truncate transition duration-200 hover:text-[#24A0ED] dark:hover:text-[#24A0ED] cursor-pointer"
>
{chat.title}
</Link>
<div className="flex flex-row items-center justify-between w-full">
<div className="flex flex-row items-center space-x-1 lg:space-x-1.5 text-black/70 dark:text-white/70">
<ClockIcon size={15} />
<p className="text-xs">
{formatTimeDifference(new Date(), chat.createdAt)} Ago
</p>
</div>
<DeleteChat
chatId={chat.id}
chats={chats}
setChats={setChats}
/>
</div>
</div>
))}
</div>
)}
</div>
);
};
export default Page;

20
ui/app/page.tsx
View File

@@ -1,20 +0,0 @@
import ChatWindow from '@/components/ChatWindow';
import { Metadata } from 'next';
import { Suspense } from 'react';
export const metadata: Metadata = {
title: 'Chat - Perplexica',
description: 'Chat with the internet, chat with Perplexica.',
};
const Home = () => {
return (
<div>
<Suspense>
<ChatWindow />
</Suspense>
</div>
);
};
export default Home;

800
ui/app/settings/page.tsx
View File

@@ -1,800 +0,0 @@
'use client';
import { Settings as SettingsIcon, ArrowLeft, Loader2 } from 'lucide-react';
import { useEffect, useState } from 'react';
import { cn } from '@/lib/utils';
import { Switch } from '@headlessui/react';
import ThemeSwitcher from '@/components/theme/Switcher';
import { ImagesIcon, VideoIcon } from 'lucide-react';
import Link from 'next/link';
interface SettingsType {
chatModelProviders: {
[key: string]: [Record<string, any>];
};
embeddingModelProviders: {
[key: string]: [Record<string, any>];
};
openaiApiKey: string;
groqApiKey: string;
anthropicApiKey: string;
geminiApiKey: string;
ollamaApiUrl: string;
customOpenaiApiKey: string;
customOpenaiApiUrl: string;
customOpenaiModelName: string;
}
interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {
isSaving?: boolean;
onSave?: (value: string) => void;
}
const Input = ({ className, isSaving, onSave, ...restProps }: InputProps) => {
return (
<div className="relative">
<input
{...restProps}
className={cn(
'bg-light-secondary dark:bg-dark-secondary w-full px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
isSaving && 'pr-10',
className,
)}
onBlur={(e) => onSave?.(e.target.value)}
/>
{isSaving && (
<div className="absolute right-3 top-1/2 -translate-y-1/2">
<Loader2
size={16}
className="animate-spin text-black/70 dark:text-white/70"
/>
</div>
)}
</div>
);
};
const Select = ({
className,
options,
...restProps
}: React.SelectHTMLAttributes<HTMLSelectElement> & {
options: { value: string; label: string; disabled?: boolean }[];
}) => {
return (
<select
{...restProps}
className={cn(
'bg-light-secondary dark:bg-dark-secondary px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
className,
)}
>
{options.map(({ label, value, disabled }) => (
<option key={value} value={value} disabled={disabled}>
{label}
</option>
))}
</select>
);
};
const SettingsSection = ({
title,
children,
}: {
title: string;
children: React.ReactNode;
}) => (
<div className="flex flex-col space-y-4 p-4 bg-light-secondary/50 dark:bg-dark-secondary/50 rounded-xl border border-light-200 dark:border-dark-200">
<h2 className="text-black/90 dark:text-white/90 font-medium">{title}</h2>
{children}
</div>
);
const Page = () => {
const [config, setConfig] = useState<SettingsType | null>(null);
const [chatModels, setChatModels] = useState<Record<string, any>>({});
const [embeddingModels, setEmbeddingModels] = useState<Record<string, any>>(
{},
);
const [selectedChatModelProvider, setSelectedChatModelProvider] = useState<
string | null
>(null);
const [selectedChatModel, setSelectedChatModel] = useState<string | null>(
null,
);
const [selectedEmbeddingModelProvider, setSelectedEmbeddingModelProvider] =
useState<string | null>(null);
const [selectedEmbeddingModel, setSelectedEmbeddingModel] = useState<
string | null
>(null);
const [isLoading, setIsLoading] = useState(false);
const [automaticImageSearch, setAutomaticImageSearch] = useState(false);
const [automaticVideoSearch, setAutomaticVideoSearch] = useState(false);
const [savingStates, setSavingStates] = useState<Record<string, boolean>>({});
useEffect(() => {
const fetchConfig = async () => {
setIsLoading(true);
const res = await fetch(`/api/config`, {
headers: {
'Content-Type': 'application/json',
},
});
const data = (await res.json()) as SettingsType;
setConfig(data);
const chatModelProvidersKeys = Object.keys(data.chatModelProviders || {});
const embeddingModelProvidersKeys = Object.keys(
data.embeddingModelProviders || {},
);
const defaultChatModelProvider =
chatModelProvidersKeys.length > 0 ? chatModelProvidersKeys[0] : '';
const defaultEmbeddingModelProvider =
embeddingModelProvidersKeys.length > 0
? embeddingModelProvidersKeys[0]
: '';
const chatModelProvider =
localStorage.getItem('chatModelProvider') ||
defaultChatModelProvider ||
'';
const chatModel =
localStorage.getItem('chatModel') ||
(data.chatModelProviders &&
data.chatModelProviders[chatModelProvider]?.length > 0
? data.chatModelProviders[chatModelProvider][0].name
: undefined) ||
'';
const embeddingModelProvider =
localStorage.getItem('embeddingModelProvider') ||
defaultEmbeddingModelProvider ||
'';
const embeddingModel =
localStorage.getItem('embeddingModel') ||
(data.embeddingModelProviders &&
data.embeddingModelProviders[embeddingModelProvider]?.[0].name) ||
'';
setSelectedChatModelProvider(chatModelProvider);
setSelectedChatModel(chatModel);
setSelectedEmbeddingModelProvider(embeddingModelProvider);
setSelectedEmbeddingModel(embeddingModel);
setChatModels(data.chatModelProviders || {});
setEmbeddingModels(data.embeddingModelProviders || {});
setAutomaticImageSearch(
localStorage.getItem('autoImageSearch') === 'true',
);
setAutomaticVideoSearch(
localStorage.getItem('autoVideoSearch') === 'true',
);
setIsLoading(false);
};
fetchConfig();
}, []);
const saveConfig = async (key: string, value: any) => {
setSavingStates((prev) => ({ ...prev, [key]: true }));
try {
const updatedConfig = {
...config,
[key]: value,
} as SettingsType;
const response = await fetch(`/api/config`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify(updatedConfig),
});
if (!response.ok) {
throw new Error('Failed to update config');
}
setConfig(updatedConfig);
if (
key.toLowerCase().includes('api') ||
key.toLowerCase().includes('url')
) {
const res = await fetch(`/api/config`, {
headers: {
'Content-Type': 'application/json',
},
});
if (!res.ok) {
throw new Error('Failed to fetch updated config');
}
const data = await res.json();
setChatModels(data.chatModelProviders || {});
setEmbeddingModels(data.embeddingModelProviders || {});
const currentChatProvider = selectedChatModelProvider;
const newChatProviders = Object.keys(data.chatModelProviders || {});
if (!currentChatProvider && newChatProviders.length > 0) {
const firstProvider = newChatProviders[0];
const firstModel = data.chatModelProviders[firstProvider]?.[0]?.name;
if (firstModel) {
setSelectedChatModelProvider(firstProvider);
setSelectedChatModel(firstModel);
localStorage.setItem('chatModelProvider', firstProvider);
localStorage.setItem('chatModel', firstModel);
}
} else if (
currentChatProvider &&
(!data.chatModelProviders ||
!data.chatModelProviders[currentChatProvider] ||
!Array.isArray(data.chatModelProviders[currentChatProvider]) ||
data.chatModelProviders[currentChatProvider].length === 0)
) {
const firstValidProvider = Object.entries(
data.chatModelProviders || {},
).find(
([_, models]) => Array.isArray(models) && models.length > 0,
)?.[0];
if (firstValidProvider) {
setSelectedChatModelProvider(firstValidProvider);
setSelectedChatModel(
data.chatModelProviders[firstValidProvider][0].name,
);
localStorage.setItem('chatModelProvider', firstValidProvider);
localStorage.setItem(
'chatModel',
data.chatModelProviders[firstValidProvider][0].name,
);
} else {
setSelectedChatModelProvider(null);
setSelectedChatModel(null);
localStorage.removeItem('chatModelProvider');
localStorage.removeItem('chatModel');
}
}
const currentEmbeddingProvider = selectedEmbeddingModelProvider;
const newEmbeddingProviders = Object.keys(
data.embeddingModelProviders || {},
);
if (!currentEmbeddingProvider && newEmbeddingProviders.length > 0) {
const firstProvider = newEmbeddingProviders[0];
const firstModel =
data.embeddingModelProviders[firstProvider]?.[0]?.name;
if (firstModel) {
setSelectedEmbeddingModelProvider(firstProvider);
setSelectedEmbeddingModel(firstModel);
localStorage.setItem('embeddingModelProvider', firstProvider);
localStorage.setItem('embeddingModel', firstModel);
}
} else if (
currentEmbeddingProvider &&
(!data.embeddingModelProviders ||
!data.embeddingModelProviders[currentEmbeddingProvider] ||
!Array.isArray(
data.embeddingModelProviders[currentEmbeddingProvider],
) ||
data.embeddingModelProviders[currentEmbeddingProvider].length === 0)
) {
const firstValidProvider = Object.entries(
data.embeddingModelProviders || {},
).find(
([_, models]) => Array.isArray(models) && models.length > 0,
)?.[0];
if (firstValidProvider) {
setSelectedEmbeddingModelProvider(firstValidProvider);
setSelectedEmbeddingModel(
data.embeddingModelProviders[firstValidProvider][0].name,
);
localStorage.setItem('embeddingModelProvider', firstValidProvider);
localStorage.setItem(
'embeddingModel',
data.embeddingModelProviders[firstValidProvider][0].name,
);
} else {
setSelectedEmbeddingModelProvider(null);
setSelectedEmbeddingModel(null);
localStorage.removeItem('embeddingModelProvider');
localStorage.removeItem('embeddingModel');
}
}
setConfig(data);
}
if (key === 'automaticImageSearch') {
localStorage.setItem('autoImageSearch', value.toString());
} else if (key === 'automaticVideoSearch') {
localStorage.setItem('autoVideoSearch', value.toString());
} else if (key === 'chatModelProvider') {
localStorage.setItem('chatModelProvider', value);
} else if (key === 'chatModel') {
localStorage.setItem('chatModel', value);
} else if (key === 'embeddingModelProvider') {
localStorage.setItem('embeddingModelProvider', value);
} else if (key === 'embeddingModel') {
localStorage.setItem('embeddingModel', value);
}
} catch (err) {
console.error('Failed to save:', err);
setConfig((prev) => ({ ...prev! }));
} finally {
setTimeout(() => {
setSavingStates((prev) => ({ ...prev, [key]: false }));
}, 500);
}
};
return (
<div className="max-w-3xl mx-auto">
<div className="flex flex-col pt-4">
<div className="flex items-center space-x-2">
<Link href="/" className="lg:hidden">
<ArrowLeft className="text-black/70 dark:text-white/70" />
</Link>
<div className="flex flex-row space-x-0.5 items-center">
<SettingsIcon size={23} />
<h1 className="text-3xl font-medium p-2">Settings</h1>
</div>
</div>
<hr className="border-t border-[#2B2C2C] my-4 w-full" />
</div>
{isLoading ? (
<div className="flex flex-row items-center justify-center min-h-[50vh]">
<svg
aria-hidden="true"
className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
viewBox="0 0 100 101"
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<path
d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
fill="currentColor"
/>
<path
d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
fill="currentFill"
/>
</svg>
</div>
) : (
config && (
<div className="flex flex-col space-y-6 pb-28 lg:pb-8">
<SettingsSection title="Appearance">
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Theme
</p>
<ThemeSwitcher />
</div>
</SettingsSection>
<SettingsSection title="Automatic Search">
<div className="flex flex-col space-y-4">
<div className="flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors">
<div className="flex items-center space-x-3">
<div className="p-2 bg-light-200 dark:bg-dark-200 rounded-lg">
<ImagesIcon
size={18}
className="text-black/70 dark:text-white/70"
/>
</div>
<div>
<p className="text-sm text-black/90 dark:text-white/90 font-medium">
Automatic Image Search
</p>
<p className="text-xs text-black/60 dark:text-white/60 mt-0.5">
Automatically search for relevant images in chat
responses
</p>
</div>
</div>
<Switch
checked={automaticImageSearch}
onChange={(checked) => {
setAutomaticImageSearch(checked);
saveConfig('automaticImageSearch', checked);
}}
className={cn(
automaticImageSearch
? 'bg-[#24A0ED]'
: 'bg-light-200 dark:bg-dark-200',
'relative inline-flex h-6 w-11 items-center rounded-full transition-colors focus:outline-none',
)}
>
<span
className={cn(
automaticImageSearch
? 'translate-x-6'
: 'translate-x-1',
'inline-block h-4 w-4 transform rounded-full bg-white transition-transform',
)}
/>
</Switch>
</div>
<div className="flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors">
<div className="flex items-center space-x-3">
<div className="p-2 bg-light-200 dark:bg-dark-200 rounded-lg">
<VideoIcon
size={18}
className="text-black/70 dark:text-white/70"
/>
</div>
<div>
<p className="text-sm text-black/90 dark:text-white/90 font-medium">
Automatic Video Search
</p>
<p className="text-xs text-black/60 dark:text-white/60 mt-0.5">
Automatically search for relevant videos in chat
responses
</p>
</div>
</div>
<Switch
checked={automaticVideoSearch}
onChange={(checked) => {
setAutomaticVideoSearch(checked);
saveConfig('automaticVideoSearch', checked);
}}
className={cn(
automaticVideoSearch
? 'bg-[#24A0ED]'
: 'bg-light-200 dark:bg-dark-200',
'relative inline-flex h-6 w-11 items-center rounded-full transition-colors focus:outline-none',
)}
>
<span
className={cn(
automaticVideoSearch
? 'translate-x-6'
: 'translate-x-1',
'inline-block h-4 w-4 transform rounded-full bg-white transition-transform',
)}
/>
</Switch>
</div>
</div>
</SettingsSection>
<SettingsSection title="Model Settings">
{config.chatModelProviders && (
<div className="flex flex-col space-y-4">
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Chat Model Provider
</p>
<Select
value={selectedChatModelProvider ?? undefined}
onChange={(e) => {
const value = e.target.value;
setSelectedChatModelProvider(value);
saveConfig('chatModelProvider', value);
const firstModel =
config.chatModelProviders[value]?.[0]?.name;
if (firstModel) {
setSelectedChatModel(firstModel);
saveConfig('chatModel', firstModel);
}
}}
options={Object.keys(config.chatModelProviders).map(
(provider) => ({
value: provider,
label:
provider.charAt(0).toUpperCase() +
provider.slice(1),
}),
)}
/>
</div>
{selectedChatModelProvider &&
selectedChatModelProvider != 'custom_openai' && (
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Chat Model
</p>
<Select
value={selectedChatModel ?? undefined}
onChange={(e) => {
const value = e.target.value;
setSelectedChatModel(value);
saveConfig('chatModel', value);
}}
options={(() => {
const chatModelProvider =
config.chatModelProviders[
selectedChatModelProvider
];
return chatModelProvider
? chatModelProvider.length > 0
? chatModelProvider.map((model) => ({
value: model.name,
label: model.displayName,
}))
: [
{
value: '',
label: 'No models available',
disabled: true,
},
]
: [
{
value: '',
label:
'Invalid provider, please check backend logs',
disabled: true,
},
];
})()}
/>
</div>
)}
</div>
)}
{selectedChatModelProvider &&
selectedChatModelProvider === 'custom_openai' && (
<div className="flex flex-col space-y-4">
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Model Name
</p>
<Input
type="text"
placeholder="Model name"
value={config.customOpenaiModelName}
isSaving={savingStates['customOpenaiModelName']}
onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
setConfig((prev) => ({
...prev!,
customOpenaiModelName: e.target.value,
}));
}}
onSave={(value) =>
saveConfig('customOpenaiModelName', value)
}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Custom OpenAI API Key
</p>
<Input
type="text"
placeholder="Custom OpenAI API Key"
value={config.customOpenaiApiKey}
isSaving={savingStates['customOpenaiApiKey']}
onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
setConfig((prev) => ({
...prev!,
customOpenaiApiKey: e.target.value,
}));
}}
onSave={(value) =>
saveConfig('customOpenaiApiKey', value)
}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Custom OpenAI Base URL
</p>
<Input
type="text"
placeholder="Custom OpenAI Base URL"
value={config.customOpenaiApiUrl}
isSaving={savingStates['customOpenaiApiUrl']}
onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
setConfig((prev) => ({
...prev!,
customOpenaiApiUrl: e.target.value,
}));
}}
onSave={(value) =>
saveConfig('customOpenaiApiUrl', value)
}
/>
</div>
</div>
)}
{config.embeddingModelProviders && (
<div className="flex flex-col space-y-4 mt-4 pt-4 border-t border-light-200 dark:border-dark-200">
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Embedding Model Provider
</p>
<Select
value={selectedEmbeddingModelProvider ?? undefined}
onChange={(e) => {
const value = e.target.value;
setSelectedEmbeddingModelProvider(value);
saveConfig('embeddingModelProvider', value);
const firstModel =
config.embeddingModelProviders[value]?.[0]?.name;
if (firstModel) {
setSelectedEmbeddingModel(firstModel);
saveConfig('embeddingModel', firstModel);
}
}}
options={Object.keys(config.embeddingModelProviders).map(
(provider) => ({
value: provider,
label:
provider.charAt(0).toUpperCase() +
provider.slice(1),
}),
)}
/>
</div>
{selectedEmbeddingModelProvider && (
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Embedding Model
</p>
<Select
value={selectedEmbeddingModel ?? undefined}
onChange={(e) => {
const value = e.target.value;
setSelectedEmbeddingModel(value);
saveConfig('embeddingModel', value);
}}
options={(() => {
const embeddingModelProvider =
config.embeddingModelProviders[
selectedEmbeddingModelProvider
];
return embeddingModelProvider
? embeddingModelProvider.length > 0
? embeddingModelProvider.map((model) => ({
value: model.name,
label: model.displayName,
}))
: [
{
value: '',
label: 'No models available',
disabled: true,
},
]
: [
{
value: '',
label:
'Invalid provider, please check backend logs',
disabled: true,
},
];
})()}
/>
</div>
)}
</div>
)}
</SettingsSection>
<SettingsSection title="API Keys">
<div className="flex flex-col space-y-4">
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
OpenAI API Key
</p>
<Input
type="text"
placeholder="OpenAI API Key"
value={config.openaiApiKey}
isSaving={savingStates['openaiApiKey']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
openaiApiKey: e.target.value,
}));
}}
onSave={(value) => saveConfig('openaiApiKey', value)}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Ollama API URL
</p>
<Input
type="text"
placeholder="Ollama API URL"
value={config.ollamaApiUrl}
isSaving={savingStates['ollamaApiUrl']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
ollamaApiUrl: e.target.value,
}));
}}
onSave={(value) => saveConfig('ollamaApiUrl', value)}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
GROQ API Key
</p>
<Input
type="text"
placeholder="GROQ API Key"
value={config.groqApiKey}
isSaving={savingStates['groqApiKey']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
groqApiKey: e.target.value,
}));
}}
onSave={(value) => saveConfig('groqApiKey', value)}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Anthropic API Key
</p>
<Input
type="text"
placeholder="Anthropic API key"
value={config.anthropicApiKey}
isSaving={savingStates['anthropicApiKey']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
anthropicApiKey: e.target.value,
}));
}}
onSave={(value) => saveConfig('anthropicApiKey', value)}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Gemini API Key
</p>
<Input
type="text"
placeholder="Gemini API key"
value={config.geminiApiKey}
isSaving={savingStates['geminiApiKey']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
geminiApiKey: e.target.value,
}));
}}
onSave={(value) => saveConfig('geminiApiKey', value)}
/>
</div>
</div>
</SettingsSection>
</div>
)
)}
</div>
);
};
export default Page;

109
ui/components/Chat.tsx
View File

@@ -1,109 +0,0 @@
'use client';
import { Fragment, useEffect, useRef, useState } from 'react';
import MessageInput from './MessageInput';
import { File, Message } from './ChatWindow';
import MessageBox from './MessageBox';
import MessageBoxLoading from './MessageBoxLoading';
const Chat = ({
loading,
messages,
sendMessage,
messageAppeared,
rewrite,
fileIds,
setFileIds,
files,
setFiles,
}: {
messages: Message[];
sendMessage: (message: string) => void;
loading: boolean;
messageAppeared: boolean;
rewrite: (messageId: string) => void;
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
files: File[];
setFiles: (files: File[]) => void;
}) => {
const [dividerWidth, setDividerWidth] = useState(0);
const dividerRef = useRef<HTMLDivElement | null>(null);
const messageEnd = useRef<HTMLDivElement | null>(null);
useEffect(() => {
const updateDividerWidth = () => {
if (dividerRef.current) {
setDividerWidth(dividerRef.current.scrollWidth);
}
};
updateDividerWidth();
window.addEventListener('resize', updateDividerWidth);
return () => {
window.removeEventListener('resize', updateDividerWidth);
};
});
useEffect(() => {
const scroll = () => {
messageEnd.current?.scrollIntoView({ behavior: 'smooth' });
};
if (messages.length === 1) {
document.title = `${messages[0].content.substring(0, 30)} - Perplexica`;
}
if (messages[messages.length - 1]?.role == 'user') {
scroll();
}
}, [messages]);
return (
<div className="flex flex-col space-y-6 pt-8 pb-44 lg:pb-32 sm:mx-4 md:mx-8">
{messages.map((msg, i) => {
const isLast = i === messages.length - 1;
return (
<Fragment key={msg.messageId}>
<MessageBox
key={i}
message={msg}
messageIndex={i}
history={messages}
loading={loading}
dividerRef={isLast ? dividerRef : undefined}
isLast={isLast}
rewrite={rewrite}
sendMessage={sendMessage}
/>
{!isLast && msg.role === 'assistant' && (
<div className="h-px w-full bg-light-secondary dark:bg-dark-secondary" />
)}
</Fragment>
);
})}
{loading && !messageAppeared && <MessageBoxLoading />}
<div ref={messageEnd} className="h-0" />
{dividerWidth > 0 && (
<div
className="bottom-24 lg:bottom-10 fixed z-40"
style={{ width: dividerWidth }}
>
<MessageInput
loading={loading}
sendMessage={sendMessage}
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
</div>
)}
</div>
);
};
export default Chat;

653
ui/components/ChatWindow.tsx
View File

@@ -1,653 +0,0 @@
'use client';
import { useEffect, useRef, useState } from 'react';
import { Document } from '@langchain/core/documents';
import Navbar from './Navbar';
import Chat from './Chat';
import EmptyChat from './EmptyChat';
import crypto from 'crypto';
import { toast } from 'sonner';
import { useSearchParams } from 'next/navigation';
import { getSuggestions } from '@/lib/actions';
import { Settings } from 'lucide-react';
import Link from 'next/link';
import NextError from 'next/error';
export type Message = {
messageId: string;
chatId: string;
createdAt: Date;
content: string;
role: 'user' | 'assistant';
suggestions?: string[];
sources?: Document[];
};
export interface File {
fileName: string;
fileExtension: string;
fileId: string;
}
interface ChatModelProvider {
name: string;
provider: string;
}
interface EmbeddingModelProvider {
name: string;
provider: string;
}
const checkConfig = async (
setChatModelProvider: (provider: ChatModelProvider) => void,
setEmbeddingModelProvider: (provider: EmbeddingModelProvider) => void,
setIsConfigReady: (ready: boolean) => void,
setHasError: (hasError: boolean) => void,
) => {
useEffect(() => {
const checkConfig = async () => {
try {
let chatModel = localStorage.getItem('chatModel');
let chatModelProvider = localStorage.getItem('chatModelProvider');
let embeddingModel = localStorage.getItem('embeddingModel');
let embeddingModelProvider = localStorage.getItem(
'embeddingModelProvider',
);
const autoImageSearch = localStorage.getItem('autoImageSearch');
const autoVideoSearch = localStorage.getItem('autoVideoSearch');
if (!autoImageSearch) {
localStorage.setItem('autoImageSearch', 'true');
}
if (!autoVideoSearch) {
localStorage.setItem('autoVideoSearch', 'false');
}
const providers = await fetch(`/api/models`, {
headers: {
'Content-Type': 'application/json',
},
}).then(async (res) => {
if (!res.ok)
throw new Error(
`Failed to fetch models: ${res.status} ${res.statusText}`,
);
return res.json();
});
if (
!chatModel ||
!chatModelProvider ||
!embeddingModel ||
!embeddingModelProvider
) {
if (!chatModel || !chatModelProvider) {
const chatModelProviders = providers.chatModelProviders;
chatModelProvider =
chatModelProvider || Object.keys(chatModelProviders)[0];
chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
if (
!chatModelProviders ||
Object.keys(chatModelProviders).length === 0
)
return toast.error('No chat models available');
}
if (!embeddingModel || !embeddingModelProvider) {
const embeddingModelProviders = providers.embeddingModelProviders;
if (
!embeddingModelProviders ||
Object.keys(embeddingModelProviders).length === 0
)
return toast.error('No embedding models available');
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
embeddingModel = Object.keys(
embeddingModelProviders[embeddingModelProvider],
)[0];
}
localStorage.setItem('chatModel', chatModel!);
localStorage.setItem('chatModelProvider', chatModelProvider);
localStorage.setItem('embeddingModel', embeddingModel!);
localStorage.setItem(
'embeddingModelProvider',
embeddingModelProvider,
);
} else {
const chatModelProviders = providers.chatModelProviders;
const embeddingModelProviders = providers.embeddingModelProviders;
if (
Object.keys(chatModelProviders).length > 0 &&
!chatModelProviders[chatModelProvider]
) {
const chatModelProvidersKeys = Object.keys(chatModelProviders);
chatModelProvider =
chatModelProvidersKeys.find(
(key) => Object.keys(chatModelProviders[key]).length > 0,
) || chatModelProvidersKeys[0];
localStorage.setItem('chatModelProvider', chatModelProvider);
}
if (
chatModelProvider &&
!chatModelProviders[chatModelProvider][chatModel]
) {
chatModel = Object.keys(
chatModelProviders[
Object.keys(chatModelProviders[chatModelProvider]).length > 0
? chatModelProvider
: Object.keys(chatModelProviders)[0]
],
)[0];
localStorage.setItem('chatModel', chatModel);
}
if (
Object.keys(embeddingModelProviders).length > 0 &&
!embeddingModelProviders[embeddingModelProvider]
) {
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
localStorage.setItem(
'embeddingModelProvider',
embeddingModelProvider,
);
}
if (
embeddingModelProvider &&
!embeddingModelProviders[embeddingModelProvider][embeddingModel]
) {
embeddingModel = Object.keys(
embeddingModelProviders[embeddingModelProvider],
)[0];
localStorage.setItem('embeddingModel', embeddingModel);
}
}
setChatModelProvider({
name: chatModel!,
provider: chatModelProvider,
});
setEmbeddingModelProvider({
name: embeddingModel!,
provider: embeddingModelProvider,
});
setIsConfigReady(true);
} catch (err) {
console.error(
'An error occurred while checking the configuration:',
err,
);
setIsConfigReady(false);
setHasError(true);
}
};
checkConfig();
// eslint-disable-next-line react-hooks/exhaustive-deps
}, []);
};
const loadMessages = async (
chatId: string,
setMessages: (messages: Message[]) => void,
setIsMessagesLoaded: (loaded: boolean) => void,
setChatHistory: (history: [string, string][]) => void,
setFocusMode: (mode: string) => void,
setNotFound: (notFound: boolean) => void,
setFiles: (files: File[]) => void,
setFileIds: (fileIds: string[]) => void,
) => {
const res = await fetch(`/api/chats/${chatId}`, {
method: 'GET',
headers: {
'Content-Type': 'application/json',
},
});
if (res.status === 404) {
setNotFound(true);
setIsMessagesLoaded(true);
return;
}
const data = await res.json();
const messages = data.messages.map((msg: any) => {
return {
...msg,
...JSON.parse(msg.metadata),
};
}) as Message[];
setMessages(messages);
const history = messages.map((msg) => {
return [msg.role, msg.content];
}) as [string, string][];
console.debug(new Date(), 'app:messages_loaded');
document.title = messages[0].content;
const files = data.chat.files.map((file: any) => {
return {
fileName: file.name,
fileExtension: file.name.split('.').pop(),
fileId: file.fileId,
};
});
setFiles(files);
setFileIds(files.map((file: File) => file.fileId));
setChatHistory(history);
setFocusMode(data.chat.focusMode);
setIsMessagesLoaded(true);
};
const ChatWindow = ({ id }: { id?: string }) => {
const searchParams = useSearchParams();
const initialMessage = searchParams.get('q');
const [chatId, setChatId] = useState<string | undefined>(id);
const [newChatCreated, setNewChatCreated] = useState(false);
const [chatModelProvider, setChatModelProvider] = useState<ChatModelProvider>(
{
name: '',
provider: '',
},
);
const [embeddingModelProvider, setEmbeddingModelProvider] =
useState<EmbeddingModelProvider>({
name: '',
provider: '',
});
const [isConfigReady, setIsConfigReady] = useState(false);
const [hasError, setHasError] = useState(false);
const [isReady, setIsReady] = useState(false);
checkConfig(
setChatModelProvider,
setEmbeddingModelProvider,
setIsConfigReady,
setHasError,
);
const [loading, setLoading] = useState(false);
const [messageAppeared, setMessageAppeared] = useState(false);
const [chatHistory, setChatHistory] = useState<[string, string][]>([]);
const [messages, setMessages] = useState<Message[]>([]);
const [files, setFiles] = useState<File[]>([]);
const [fileIds, setFileIds] = useState<string[]>([]);
const [focusMode, setFocusMode] = useState('webSearch');
const [optimizationMode, setOptimizationMode] = useState('speed');
const [isMessagesLoaded, setIsMessagesLoaded] = useState(false);
const [notFound, setNotFound] = useState(false);
useEffect(() => {
if (
chatId &&
!newChatCreated &&
!isMessagesLoaded &&
messages.length === 0
) {
loadMessages(
chatId,
setMessages,
setIsMessagesLoaded,
setChatHistory,
setFocusMode,
setNotFound,
setFiles,
setFileIds,
);
} else if (!chatId) {
setNewChatCreated(true);
setIsMessagesLoaded(true);
setChatId(crypto.randomBytes(20).toString('hex'));
}
// eslint-disable-next-line react-hooks/exhaustive-deps
}, []);
const messagesRef = useRef<Message[]>([]);
useEffect(() => {
messagesRef.current = messages;
}, [messages]);
useEffect(() => {
if (isMessagesLoaded && isConfigReady) {
setIsReady(true);
console.debug(new Date(), 'app:ready');
} else {
setIsReady(false);
}
}, [isMessagesLoaded, isConfigReady]);
const sendMessage = async (message: string, messageId?: string) => {
if (loading) return;
if (!isConfigReady) {
toast.error('Cannot send message before the configuration is ready');
return;
}
setLoading(true);
setMessageAppeared(false);
let sources: Document[] | undefined = undefined;
let recievedMessage = '';
let added = false;
messageId = messageId ?? crypto.randomBytes(7).toString('hex');
console.log(
JSON.stringify({
content: message,
message: {
messageId: messageId,
chatId: chatId!,
content: message,
},
chatId: chatId!,
files: fileIds,
focusMode: focusMode,
optimizationMode: optimizationMode,
history: chatHistory,
chatModel: {
name: chatModelProvider.name,
provider: chatModelProvider.provider,
},
embeddingModel: {
name: embeddingModelProvider.name,
provider: embeddingModelProvider.provider,
},
}),
);
setMessages((prevMessages) => [
...prevMessages,
{
content: message,
messageId: messageId,
chatId: chatId!,
role: 'user',
createdAt: new Date(),
},
]);
const messageHandler = async (data: any) => {
if (data.type === 'error') {
toast.error(data.data);
setLoading(false);
return;
}
if (data.type === 'sources') {
sources = data.data;
if (!added) {
setMessages((prevMessages) => [
...prevMessages,
{
content: '',
messageId: data.messageId,
chatId: chatId!,
role: 'assistant',
sources: sources,
createdAt: new Date(),
},
]);
added = true;
}
setMessageAppeared(true);
}
if (data.type === 'message') {
if (!added) {
setMessages((prevMessages) => [
...prevMessages,
{
content: data.data,
messageId: data.messageId,
chatId: chatId!,
role: 'assistant',
sources: sources,
createdAt: new Date(),
},
]);
added = true;
}
setMessages((prev) =>
prev.map((message) => {
if (message.messageId === data.messageId) {
return { ...message, content: message.content + data.data };
}
return message;
}),
);
recievedMessage += data.data;
setMessageAppeared(true);
}
if (data.type === 'messageEnd') {
setChatHistory((prevHistory) => [
...prevHistory,
['human', message],
['assistant', recievedMessage],
]);
setLoading(false);
const lastMsg = messagesRef.current[messagesRef.current.length - 1];
if (
lastMsg.role === 'assistant' &&
lastMsg.sources &&
lastMsg.sources.length > 0 &&
!lastMsg.suggestions
) {
const suggestions = await getSuggestions(messagesRef.current);
setMessages((prev) =>
prev.map((msg) => {
if (msg.messageId === lastMsg.messageId) {
return { ...msg, suggestions: suggestions };
}
return msg;
}),
);
}
const autoImageSearch = localStorage.getItem('autoImageSearch');
const autoVideoSearch = localStorage.getItem('autoVideoSearch');
if (autoImageSearch === 'true') {
document
.getElementById(`search-images-${lastMsg.messageId}`)
?.click();
}
if (autoVideoSearch === 'true') {
document
.getElementById(`search-videos-${lastMsg.messageId}`)
?.click();
}
}
};
const res = await fetch('/api/chat', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
content: message,
message: {
messageId: messageId,
chatId: chatId!,
content: message,
},
chatId: chatId!,
files: fileIds,
focusMode: focusMode,
optimizationMode: optimizationMode,
history: chatHistory,
chatModel: {
name: chatModelProvider.name,
provider: chatModelProvider.provider,
},
embeddingModel: {
name: embeddingModelProvider.name,
provider: embeddingModelProvider.provider,
},
}),
});
if (!res.body) throw new Error('No response body');
const reader = res.body?.getReader();
const decoder = new TextDecoder('utf-8');
let partialChunk = '';
while (true) {
const { value, done } = await reader.read();
if (done) break;
partialChunk += decoder.decode(value, { stream: true });
try {
const messages = partialChunk.split('\n');
for (const msg of messages) {
if (!msg.trim()) continue;
const json = JSON.parse(msg);
messageHandler(json);
}
partialChunk = '';
} catch (error) {
console.warn('Incomplete JSON, waiting for next chunk...');
}
}
};
const rewrite = (messageId: string) => {
const index = messages.findIndex((msg) => msg.messageId === messageId);
if (index === -1) return;
const message = messages[index - 1];
setMessages((prev) => {
return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
});
setChatHistory((prev) => {
return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
});
sendMessage(message.content, message.messageId);
};
useEffect(() => {
if (isReady && initialMessage && isConfigReady) {
sendMessage(initialMessage);
}
// eslint-disable-next-line react-hooks/exhaustive-deps
}, [isConfigReady, isReady, initialMessage]);
if (hasError) {
return (
<div className="relative">
<div className="absolute w-full flex flex-row items-center justify-end mr-5 mt-5">
<Link href="/settings">
<Settings className="cursor-pointer lg:hidden" />
</Link>
</div>
<div className="flex flex-col items-center justify-center min-h-screen">
<p className="dark:text-white/70 text-black/70 text-sm">
Failed to connect to the server. Please try again later.
</p>
</div>
</div>
);
}
return isReady ? (
notFound ? (
<NextError statusCode={404} />
) : (
<div>
{messages.length > 0 ? (
<>
<Navbar chatId={chatId!} messages={messages} />
<Chat
loading={loading}
messages={messages}
sendMessage={sendMessage}
messageAppeared={messageAppeared}
rewrite={rewrite}
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
</>
) : (
<EmptyChat
sendMessage={sendMessage}
focusMode={focusMode}
setFocusMode={setFocusMode}
optimizationMode={optimizationMode}
setOptimizationMode={setOptimizationMode}
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
)}
</div>
)
) : (
<div className="flex flex-row items-center justify-center min-h-screen">
<svg
aria-hidden="true"
className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
viewBox="0 0 100 101"
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<path
d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
fill="currentColor"
/>
<path
d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
fill="currentFill"
/>
</svg>
</div>
);
};
export default ChatWindow;

125
ui/components/DeleteChat.tsx
View File

@@ -1,125 +0,0 @@
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';
const DeleteChat = ({
chatId,
chats,
setChats,
redirect = false,
}: {
chatId: string;
chats: Chat[];
setChats: (chats: Chat[]) => void;
redirect?: boolean;
}) => {
const [confirmationDialogOpen, setConfirmationDialogOpen] = useState(false);
const [loading, setLoading] = useState(false);
const handleDelete = async () => {
setLoading(true);
try {
const res = await fetch(`/api/chats/${chatId}`, {
method: 'DELETE',
headers: {
'Content-Type': 'application/json',
},
});
if (res.status != 200) {
throw new Error('Failed to delete chat');
}
const newChats = chats.filter((chat) => chat.id !== chatId);
setChats(newChats);
if (redirect) {
window.location.href = '/';
}
} catch (err: any) {
toast.error(err.message);
} finally {
setConfirmationDialogOpen(false);
setLoading(false);
}
};
return (
<>
<button
onClick={() => {
setConfirmationDialogOpen(true);
}}
className="bg-transparent text-red-400 hover:scale-105 transition duration-200"
>
<Trash size={17} />
</button>
<Transition appear show={confirmationDialogOpen} as={Fragment}>
<Dialog
as="div"
className="relative z-50"
onClose={() => {
if (!loading) {
setConfirmationDialogOpen(false);
}
}}
>
<DialogBackdrop className="fixed inset-0 bg-black/30" />
<div className="fixed inset-0 overflow-y-auto">
<div className="flex min-h-full items-center justify-center p-4 text-center">
<TransitionChild
as={Fragment}
enter="ease-out duration-200"
enterFrom="opacity-0 scale-95"
enterTo="opacity-100 scale-100"
leave="ease-in duration-100"
leaveFrom="opacity-100 scale-200"
leaveTo="opacity-0 scale-95"
>
<DialogPanel className="w-full max-w-md transform rounded-2xl bg-light-secondary dark:bg-dark-secondary border border-light-200 dark:border-dark-200 p-6 text-left align-middle shadow-xl transition-all">
<DialogTitle className="text-lg font-medium leading-6 dark:text-white">
Delete Confirmation
</DialogTitle>
<Description className="text-sm dark:text-white/70 text-black/70">
Are you sure you want to delete this chat?
</Description>
<div className="flex flex-row items-end justify-end space-x-4 mt-6">
<button
onClick={() => {
if (!loading) {
setConfirmationDialogOpen(false);
}
}}
className="text-black/50 dark:text-white/50 text-sm hover:text-black/70 hover:dark:text-white/70 transition duration-200"
>
Cancel
</button>
<button
onClick={handleDelete}
className="text-red-400 text-sm hover:text-red-500 transition duration200"
>
Delete
</button>
</div>
</DialogPanel>
</TransitionChild>
</div>
</div>
</Dialog>
</Transition>
</>
);
};
export default DeleteChat;

57
ui/components/EmptyChat.tsx
View File

@@ -1,57 +0,0 @@
import { Settings } from 'lucide-react';
import EmptyChatMessageInput from './EmptyChatMessageInput';
import { useState } from 'react';
import { File } from './ChatWindow';
import Link from 'next/link';
const EmptyChat = ({
sendMessage,
focusMode,
setFocusMode,
optimizationMode,
setOptimizationMode,
fileIds,
setFileIds,
files,
setFiles,
}: {
sendMessage: (message: string) => void;
focusMode: string;
setFocusMode: (mode: string) => void;
optimizationMode: string;
setOptimizationMode: (mode: string) => void;
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
files: File[];
setFiles: (files: File[]) => void;
}) => {
const [isSettingsOpen, setIsSettingsOpen] = useState(false);
return (
<div className="relative">
<div className="absolute w-full flex flex-row items-center justify-end mr-5 mt-5">
<Link href="/settings">
<Settings className="cursor-pointer lg:hidden" />
</Link>
</div>
<div className="flex flex-col items-center justify-center min-h-screen max-w-screen-sm mx-auto p-2 space-y-8">
<h2 className="text-black/70 dark:text-white/70 text-3xl font-medium -mt-8">
Research begins here.
</h2>
<EmptyChatMessageInput
sendMessage={sendMessage}
focusMode={focusMode}
setFocusMode={setFocusMode}
optimizationMode={optimizationMode}
setOptimizationMode={setOptimizationMode}
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
</div>
</div>
);
};
export default EmptyChat;

114
ui/components/EmptyChatMessageInput.tsx
View File

@@ -1,114 +0,0 @@
import { ArrowRight } from 'lucide-react';
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';
import Attach from './MessageInputActions/Attach';
import { File } from './ChatWindow';
const EmptyChatMessageInput = ({
sendMessage,
focusMode,
setFocusMode,
optimizationMode,
setOptimizationMode,
fileIds,
setFileIds,
files,
setFiles,
}: {
sendMessage: (message: string) => void;
focusMode: string;
setFocusMode: (mode: string) => void;
optimizationMode: string;
setOptimizationMode: (mode: string) => void;
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
files: File[];
setFiles: (files: File[]) => void;
}) => {
const [copilotEnabled, setCopilotEnabled] = useState(false);
const [message, setMessage] = useState('');
const inputRef = useRef<HTMLTextAreaElement | null>(null);
useEffect(() => {
const handleKeyDown = (e: KeyboardEvent) => {
const activeElement = document.activeElement;
const isInputFocused =
activeElement?.tagName === 'INPUT' ||
activeElement?.tagName === 'TEXTAREA' ||
activeElement?.hasAttribute('contenteditable');
if (e.key === '/' && !isInputFocused) {
e.preventDefault();
inputRef.current?.focus();
}
};
document.addEventListener('keydown', handleKeyDown);
inputRef.current?.focus();
return () => {
document.removeEventListener('keydown', handleKeyDown);
};
}, []);
return (
<form
onSubmit={(e) => {
e.preventDefault();
sendMessage(message);
setMessage('');
}}
onKeyDown={(e) => {
if (e.key === 'Enter' && !e.shiftKey) {
e.preventDefault();
sendMessage(message);
setMessage('');
}
}}
className="w-full"
>
<div className="flex flex-col bg-light-secondary dark:bg-dark-secondary px-5 pt-5 pb-2 rounded-lg w-full border border-light-200 dark:border-dark-200">
<TextareaAutosize
ref={inputRef}
value={message}
onChange={(e) => setMessage(e.target.value)}
minRows={2}
className="bg-transparent placeholder:text-black/50 dark:placeholder:text-white/50 text-sm text-black dark:text-white resize-none focus:outline-none w-full max-h-24 lg:max-h-36 xl:max-h-48"
placeholder="Ask anything..."
/>
<div className="flex flex-row items-center justify-between mt-4">
<div className="flex flex-row items-center space-x-2 lg:space-x-4">
<Focus focusMode={focusMode} setFocusMode={setFocusMode} />
<Attach
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
showText
/>
</div>
<div className="flex flex-row items-center space-x-1 sm:space-x-4">
<Optimization
optimizationMode={optimizationMode}
setOptimizationMode={setOptimizationMode}
/>
<button
disabled={message.trim().length === 0}
className="bg-[#24A0ED] text-white disabled:text-black/50 dark:disabled:text-white/50 disabled:bg-[#e0e0dc] dark:disabled:bg-[#ececec21] hover:bg-opacity-85 transition duration-100 rounded-full p-2"
>
<ArrowRight className="bg-background" size={17} />
</button>
</div>
</div>
</div>
</form>
);
};
export default EmptyChatMessageInput;

9
ui/components/Layout.tsx
View File

@@ -1,9 +0,0 @@
const Layout = ({ children }: { children: React.ReactNode }) => {
return (
<main className="lg:pl-20 bg-light-primary dark:bg-dark-primary min-h-screen">
<div className="max-w-screen-lg lg:mx-auto mx-4">{children}</div>
</main>
);
};
export default Layout;

29
ui/components/MessageActions/Copy.tsx
View File

@@ -1,29 +0,0 @@
import { Check, ClipboardList } from 'lucide-react';
import { Message } from '../ChatWindow';
import { useState } from 'react';
const Copy = ({
message,
initialMessage,
}: {
message: Message;
initialMessage: string;
}) => {
const [copied, setCopied] = useState(false);
return (
<button
onClick={() => {
const contentToCopy = `${initialMessage}${message.sources && message.sources.length > 0 && `\n\nCitations:\n${message.sources?.map((source: any, i: any) => `[${i + 1}] ${source.metadata.url}`).join(`\n`)}`}`;
navigator.clipboard.writeText(contentToCopy);
setCopied(true);
setTimeout(() => setCopied(false), 1000);
}}
className="p-2 text-black/70 dark:text-white/70 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white"
>
{copied ? <Check size={18} /> : <ClipboardList size={18} />}
</button>
);
};
export default Copy;

21
ui/components/MessageActions/Rewrite.tsx
View File

@@ -1,21 +0,0 @@
import { ArrowLeftRight } from 'lucide-react';
const Rewrite = ({
rewrite,
messageId,
}: {
rewrite: (messageId: string) => void;
messageId: string;
}) => {
return (
<button
onClick={() => rewrite(messageId)}
className="py-2 px-3 text-black/70 dark:text-white/70 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white flex flex-row items-center space-x-1"
>
<ArrowLeftRight size={18} />
<p className="text-xs font-medium">Rewrite</p>
</button>
);
};
export default Rewrite;

210
ui/components/MessageBox.tsx
View File

@@ -1,210 +0,0 @@
'use client';
/* eslint-disable @next/next/no-img-element */
import React, { MutableRefObject, useEffect, useState } from 'react';
import { Message } from './ChatWindow';
import { cn } from '@/lib/utils';
import {
BookCopy,
Disc3,
Volume2,
StopCircle,
Layers3,
Plus,
} from 'lucide-react';
import Markdown from 'markdown-to-jsx';
import Copy from './MessageActions/Copy';
import Rewrite from './MessageActions/Rewrite';
import MessageSources from './MessageSources';
import SearchImages from './SearchImages';
import SearchVideos from './SearchVideos';
import { useSpeech } from 'react-text-to-speech';
const MessageBox = ({
message,
messageIndex,
history,
loading,
dividerRef,
isLast,
rewrite,
sendMessage,
}: {
message: Message;
messageIndex: number;
history: Message[];
loading: boolean;
dividerRef?: MutableRefObject<HTMLDivElement | null>;
isLast: boolean;
rewrite: (messageId: string) => void;
sendMessage: (message: string) => void;
}) => {
const [parsedMessage, setParsedMessage] = useState(message.content);
const [speechMessage, setSpeechMessage] = useState(message.content);
useEffect(() => {
const regex = /\[(\d+)\]/g;
if (
message.role === 'assistant' &&
message?.sources &&
message.sources.length > 0
) {
return setParsedMessage(
message.content.replace(
regex,
(_, number) =>
`<a href="${message.sources?.[number - 1]?.metadata?.url}" target="_blank" className="bg-light-secondary dark:bg-dark-secondary px-1 rounded ml-1 no-underline text-xs text-black/70 dark:text-white/70 relative">${number}</a>`,
),
);
}
setSpeechMessage(message.content.replace(regex, ''));
setParsedMessage(message.content);
}, [message.content, message.sources, message.role]);
const { speechStatus, start, stop } = useSpeech({ text: speechMessage });
return (
<div>
{message.role === 'user' && (
<div
className={cn(
'w-full',
messageIndex === 0 ? 'pt-16' : 'pt-8',
'break-words',
)}
>
<h2 className="text-black dark:text-white font-medium text-3xl lg:w-9/12">
{message.content}
</h2>
</div>
)}
{message.role === 'assistant' && (
<div className="flex flex-col space-y-9 lg:space-y-0 lg:flex-row lg:justify-between lg:space-x-9">
<div
ref={dividerRef}
className="flex flex-col space-y-6 w-full lg:w-9/12"
>
{message.sources && message.sources.length > 0 && (
<div className="flex flex-col space-y-2">
<div className="flex flex-row items-center space-x-2">
<BookCopy className="text-black dark:text-white" size={20} />
<h3 className="text-black dark:text-white font-medium text-xl">
Sources
</h3>
</div>
<MessageSources sources={message.sources} />
</div>
)}
<div className="flex flex-col space-y-2">
<div className="flex flex-row items-center space-x-2">
<Disc3
className={cn(
'text-black dark:text-white',
isLast && loading ? 'animate-spin' : 'animate-none',
)}
size={20}
/>
<h3 className="text-black dark:text-white font-medium text-xl">
Answer
</h3>
</div>
<Markdown
className={cn(
'prose prose-h1:mb-3 prose-h2:mb-2 prose-h2:mt-6 prose-h2:font-[800] prose-h3:mt-4 prose-h3:mb-1.5 prose-h3:font-[600] dark:prose-invert prose-p:leading-relaxed prose-pre:p-0 font-[400]',
'max-w-none break-words text-black dark:text-white',
)}
>
{parsedMessage}
</Markdown>
{loading && isLast ? null : (
<div className="flex flex-row items-center justify-between w-full text-black dark:text-white py-4 -mx-2">
<div className="flex flex-row items-center space-x-1">
{/* <button className="p-2 text-black/70 dark:text-white/70 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black text-black dark:hover:text-white">
<Share size={18} />
</button> */}
<Rewrite rewrite={rewrite} messageId={message.messageId} />
</div>
<div className="flex flex-row items-center space-x-1">
<Copy initialMessage={message.content} message={message} />
<button
onClick={() => {
if (speechStatus === 'started') {
stop();
} else {
start();
}
}}
className="p-2 text-black/70 dark:text-white/70 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white"
>
{speechStatus === 'started' ? (
<StopCircle size={18} />
) : (
<Volume2 size={18} />
)}
</button>
</div>
</div>
)}
{isLast &&
message.suggestions &&
message.suggestions.length > 0 &&
message.role === 'assistant' &&
!loading && (
<>
<div className="h-px w-full bg-light-secondary dark:bg-dark-secondary" />
<div className="flex flex-col space-y-3 text-black dark:text-white">
<div className="flex flex-row items-center space-x-2 mt-4">
<Layers3 />
<h3 className="text-xl font-medium">Related</h3>
</div>
<div className="flex flex-col space-y-3">
{message.suggestions.map((suggestion, i) => (
<div
className="flex flex-col space-y-3 text-sm"
key={i}
>
<div className="h-px w-full bg-light-secondary dark:bg-dark-secondary" />
<div
onClick={() => {
sendMessage(suggestion);
}}
className="cursor-pointer flex flex-row justify-between font-medium space-x-2 items-center"
>
<p className="transition duration-200 hover:text-[#24A0ED]">
{suggestion}
</p>
<Plus
size={20}
className="text-[#24A0ED] flex-shrink-0"
/>
</div>
</div>
))}
</div>
</div>
</>
)}
</div>
</div>
<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={history[messageIndex - 1].content}
chatHistory={history.slice(0, messageIndex - 1)}
messageId={message.messageId}
/>
<SearchVideos
chatHistory={history.slice(0, messageIndex - 1)}
query={history[messageIndex - 1].content}
messageId={message.messageId}
/>
</div>
</div>
)}
</div>
);
};
export default MessageBox;

11
ui/components/MessageBoxLoading.tsx
View File

@@ -1,11 +0,0 @@
const MessageBoxLoading = () => {
return (
<div className="flex flex-col space-y-2 w-full lg:w-9/12 bg-light-primary dark:bg-dark-primary animate-pulse rounded-lg py-3">
<div className="h-2 rounded-full w-full bg-light-secondary dark:bg-dark-secondary" />
<div className="h-2 rounded-full w-9/12 bg-light-secondary dark:bg-dark-secondary" />
<div className="h-2 rounded-full w-10/12 bg-light-secondary dark:bg-dark-secondary" />
</div>
);
};
export default MessageBoxLoading;

140
ui/components/MessageInput.tsx
View File

@@ -1,140 +0,0 @@
import { cn } from '@/lib/utils';
import { ArrowUp } from 'lucide-react';
import { useEffect, useRef, useState } from 'react';
import TextareaAutosize from 'react-textarea-autosize';
import Attach from './MessageInputActions/Attach';
import CopilotToggle from './MessageInputActions/Copilot';
import { File } from './ChatWindow';
import AttachSmall from './MessageInputActions/AttachSmall';
const MessageInput = ({
sendMessage,
loading,
fileIds,
setFileIds,
files,
setFiles,
}: {
sendMessage: (message: string) => void;
loading: boolean;
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
files: File[];
setFiles: (files: File[]) => void;
}) => {
const [copilotEnabled, setCopilotEnabled] = useState(false);
const [message, setMessage] = useState('');
const [textareaRows, setTextareaRows] = useState(1);
const [mode, setMode] = useState<'multi' | 'single'>('single');
useEffect(() => {
if (textareaRows >= 2 && message && mode === 'single') {
setMode('multi');
} else if (!message && mode === 'multi') {
setMode('single');
}
}, [textareaRows, mode, message]);
const inputRef = useRef<HTMLTextAreaElement | null>(null);
useEffect(() => {
const handleKeyDown = (e: KeyboardEvent) => {
const activeElement = document.activeElement;
const isInputFocused =
activeElement?.tagName === 'INPUT' ||
activeElement?.tagName === 'TEXTAREA' ||
activeElement?.hasAttribute('contenteditable');
if (e.key === '/' && !isInputFocused) {
e.preventDefault();
inputRef.current?.focus();
}
};
document.addEventListener('keydown', handleKeyDown);
return () => {
document.removeEventListener('keydown', handleKeyDown);
};
}, []);
return (
<form
onSubmit={(e) => {
if (loading) return;
e.preventDefault();
sendMessage(message);
setMessage('');
}}
onKeyDown={(e) => {
if (e.key === 'Enter' && !e.shiftKey && !loading) {
e.preventDefault();
sendMessage(message);
setMessage('');
}
}}
className={cn(
'bg-light-secondary dark:bg-dark-secondary p-4 flex items-center overflow-hidden border border-light-200 dark:border-dark-200',
mode === 'multi' ? 'flex-col rounded-lg' : 'flex-row rounded-full',
)}
>
{mode === 'single' && (
<AttachSmall
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
)}
<TextareaAutosize
ref={inputRef}
value={message}
onChange={(e) => setMessage(e.target.value)}
onHeightChange={(height, props) => {
setTextareaRows(Math.ceil(height / props.rowHeight));
}}
className="transition bg-transparent dark:placeholder:text-white/50 placeholder:text-sm text-sm dark:text-white resize-none focus:outline-none w-full px-2 max-h-24 lg:max-h-36 xl:max-h-48 flex-grow flex-shrink"
placeholder="Ask a follow-up"
/>
{mode === 'single' && (
<div className="flex flex-row items-center space-x-4">
<CopilotToggle
copilotEnabled={copilotEnabled}
setCopilotEnabled={setCopilotEnabled}
/>
<button
disabled={message.trim().length === 0 || loading}
className="bg-[#24A0ED] text-white disabled:text-black/50 dark:disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#e0e0dc79] dark:disabled:bg-[#ececec21] rounded-full p-2"
>
<ArrowUp className="bg-background" size={17} />
</button>
</div>
)}
{mode === 'multi' && (
<div className="flex flex-row items-center justify-between w-full pt-2">
<AttachSmall
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
<div className="flex flex-row items-center space-x-4">
<CopilotToggle
copilotEnabled={copilotEnabled}
setCopilotEnabled={setCopilotEnabled}
/>
<button
disabled={message.trim().length === 0 || loading}
className="bg-[#24A0ED] text-white text-black/50 dark:disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#e0e0dc79] dark:disabled:bg-[#ececec21] rounded-full p-2"
>
<ArrowUp className="bg-background" size={17} />
</button>
</div>
</div>
)}
</form>
);
};
export default MessageInput;

185
ui/components/MessageInputActions/Attach.tsx
View File

@@ -1,185 +0,0 @@
import { cn } from '@/lib/utils';
import {
Popover,
PopoverButton,
PopoverPanel,
Transition,
} from '@headlessui/react';
import { CopyPlus, File, LoaderCircle, Plus, Trash } from 'lucide-react';
import { Fragment, useRef, useState } from 'react';
import { File as FileType } from '../ChatWindow';
const Attach = ({
fileIds,
setFileIds,
showText,
files,
setFiles,
}: {
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
showText?: boolean;
files: FileType[];
setFiles: (files: FileType[]) => void;
}) => {
const [loading, setLoading] = useState(false);
const fileInputRef = useRef<any>();
const handleChange = async (e: React.ChangeEvent<HTMLInputElement>) => {
setLoading(true);
const data = new FormData();
for (let i = 0; i < e.target.files!.length; i++) {
data.append('files', e.target.files![i]);
}
const embeddingModelProvider = localStorage.getItem(
'embeddingModelProvider',
);
const embeddingModel = localStorage.getItem('embeddingModel');
data.append('embedding_model_provider', embeddingModelProvider!);
data.append('embedding_model', embeddingModel!);
const res = await fetch(`/api/uploads`, {
method: 'POST',
body: data,
});
const resData = await res.json();
setFiles([...files, ...resData.files]);
setFileIds([...fileIds, ...resData.files.map((file: any) => file.fileId)]);
setLoading(false);
};
return loading ? (
<div className="flex flex-row items-center justify-between space-x-1">
<LoaderCircle size={18} className="text-sky-400 animate-spin" />
<p className="text-sky-400 inline whitespace-nowrap text-xs font-medium">
Uploading..
</p>
</div>
) : files.length > 0 ? (
<Popover className="relative w-full max-w-[15rem] md:max-w-md lg:max-w-lg">
<PopoverButton
type="button"
className={cn(
'flex flex-row items-center justify-between space-x-1 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',
files.length > 0 ? '-ml-2 lg:-ml-3' : '',
)}
>
{files.length > 1 && (
<>
<File size={19} className="text-sky-400" />
<p className="text-sky-400 inline whitespace-nowrap text-xs font-medium">
{files.length} files
</p>
</>
)}
{files.length === 1 && (
<>
<File size={18} className="text-sky-400" />
<p className="text-sky-400 text-xs font-medium">
{files[0].fileName.length > 10
? files[0].fileName.replace(/\.\w+$/, '').substring(0, 3) +
'...' +
files[0].fileExtension
: files[0].fileName}
</p>
</>
)}
</PopoverButton>
<Transition
as={Fragment}
enter="transition ease-out duration-150"
enterFrom="opacity-0 translate-y-1"
enterTo="opacity-100 translate-y-0"
leave="transition ease-in duration-150"
leaveFrom="opacity-100 translate-y-0"
leaveTo="opacity-0 translate-y-1"
>
<PopoverPanel className="absolute z-10 w-64 md:w-[350px] right-0">
<div className="bg-light-primary dark:bg-dark-primary border rounded-md border-light-200 dark:border-dark-200 w-full max-h-[200px] md:max-h-none overflow-y-auto flex flex-col">
<div className="flex flex-row items-center justify-between px-3 py-2">
<h4 className="text-black dark:text-white font-medium text-sm">
Attached files
</h4>
<div className="flex flex-row items-center space-x-4">
<button
type="button"
onClick={() => fileInputRef.current.click()}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
>
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<Plus size={18} />
<p className="text-xs">Add</p>
</button>
<button
onClick={() => {
setFiles([]);
setFileIds([]);
}}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
>
<Trash size={14} />
<p className="text-xs">Clear</p>
</button>
</div>
</div>
<div className="h-[0.5px] mx-2 bg-white/10" />
<div className="flex flex-col items-center">
{files.map((file, i) => (
<div
key={i}
className="flex flex-row items-center justify-start w-full space-x-3 p-3"
>
<div className="bg-dark-100 flex items-center justify-center w-10 h-10 rounded-md">
<File size={16} className="text-white/70" />
</div>
<p className="text-black/70 dark:text-white/70 text-sm">
{file.fileName.length > 25
? file.fileName.replace(/\.\w+$/, '').substring(0, 25) +
'...' +
file.fileExtension
: file.fileName}
</p>
</div>
))}
</div>
</div>
</PopoverPanel>
</Transition>
</Popover>
) : (
<button
type="button"
onClick={() => fileInputRef.current.click()}
className={cn(
'flex flex-row items-center space-x-1 text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white',
showText ? '' : 'p-2',
)}
>
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<CopyPlus size={showText ? 18 : undefined} />
{showText && <p className="text-xs font-medium pl-[1px]">Attach</p>}
</button>
);
};
export default Attach;

153
ui/components/MessageInputActions/AttachSmall.tsx
View File

@@ -1,153 +0,0 @@
import { cn } from '@/lib/utils';
import {
Popover,
PopoverButton,
PopoverPanel,
Transition,
} from '@headlessui/react';
import { CopyPlus, File, LoaderCircle, Plus, Trash } from 'lucide-react';
import { Fragment, useRef, useState } from 'react';
import { File as FileType } from '../ChatWindow';
const AttachSmall = ({
fileIds,
setFileIds,
files,
setFiles,
}: {
fileIds: string[];
setFileIds: (fileIds: string[]) => void;
files: FileType[];
setFiles: (files: FileType[]) => void;
}) => {
const [loading, setLoading] = useState(false);
const fileInputRef = useRef<any>();
const handleChange = async (e: React.ChangeEvent<HTMLInputElement>) => {
setLoading(true);
const data = new FormData();
for (let i = 0; i < e.target.files!.length; i++) {
data.append('files', e.target.files![i]);
}
const embeddingModelProvider = localStorage.getItem(
'embeddingModelProvider',
);
const embeddingModel = localStorage.getItem('embeddingModel');
data.append('embedding_model_provider', embeddingModelProvider!);
data.append('embedding_model', embeddingModel!);
const res = await fetch(`/api/uploads`, {
method: 'POST',
body: data,
});
const resData = await res.json();
setFiles([...files, ...resData.files]);
setFileIds([...fileIds, ...resData.files.map((file: any) => file.fileId)]);
setLoading(false);
};
return loading ? (
<div className="flex flex-row items-center justify-between space-x-1 p-1">
<LoaderCircle size={20} className="text-sky-400 animate-spin" />
</div>
) : files.length > 0 ? (
<Popover className="max-w-[15rem] md:max-w-md lg:max-w-lg">
<PopoverButton
type="button"
className="flex flex-row items-center justify-between space-x-1 p-1 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"
>
<File size={20} className="text-sky-400" />
</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-[350px] bottom-14 -ml-3">
<div className="bg-light-primary dark:bg-dark-primary border rounded-md border-light-200 dark:border-dark-200 w-full max-h-[200px] md:max-h-none overflow-y-auto flex flex-col">
<div className="flex flex-row items-center justify-between px-3 py-2">
<h4 className="text-black dark:text-white font-medium text-sm">
Attached files
</h4>
<div className="flex flex-row items-center space-x-4">
<button
type="button"
onClick={() => fileInputRef.current.click()}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
>
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<Plus size={18} />
<p className="text-xs">Add</p>
</button>
<button
onClick={() => {
setFiles([]);
setFileIds([]);
}}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
>
<Trash size={14} />
<p className="text-xs">Clear</p>
</button>
</div>
</div>
<div className="h-[0.5px] mx-2 bg-white/10" />
<div className="flex flex-col items-center">
{files.map((file, i) => (
<div
key={i}
className="flex flex-row items-center justify-start w-full space-x-3 p-3"
>
<div className="bg-dark-100 flex items-center justify-center w-10 h-10 rounded-md">
<File size={16} className="text-white/70" />
</div>
<p className="text-black/70 dark:text-white/70 text-sm">
{file.fileName.length > 25
? file.fileName.replace(/\.\w+$/, '').substring(0, 25) +
'...' +
file.fileExtension
: file.fileName}
</p>
</div>
))}
</div>
</div>
</PopoverPanel>
</Transition>
</Popover>
) : (
<button
type="button"
onClick={() => fileInputRef.current.click()}
className="flex flex-row items-center space-x-1 text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary transition duration-200 hover:text-black dark:hover:text-white p-1"
>
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<CopyPlus size={20} />
</button>
);
};
export default AttachSmall;

43
ui/components/MessageInputActions/Copilot.tsx
View File

@@ -1,43 +0,0 @@
import { cn } from '@/lib/utils';
import { Switch } from '@headlessui/react';
const CopilotToggle = ({
copilotEnabled,
setCopilotEnabled,
}: {
copilotEnabled: boolean;
setCopilotEnabled: (enabled: boolean) => void;
}) => {
return (
<div className="group flex flex-row items-center space-x-1 active:scale-95 duration-200 transition cursor-pointer">
<Switch
checked={copilotEnabled}
onChange={setCopilotEnabled}
className="bg-light-secondary dark:bg-dark-secondary border border-light-200/70 dark:border-dark-200 relative inline-flex h-5 w-10 sm:h-6 sm:w-11 items-center rounded-full"
>
<span className="sr-only">Copilot</span>
<span
className={cn(
copilotEnabled
? 'translate-x-6 bg-[#24A0ED]'
: 'translate-x-1 bg-black/50 dark:bg-white/50',
'inline-block h-3 w-3 sm:h-4 sm:w-4 transform rounded-full transition-all duration-200',
)}
/>
</Switch>
<p
onClick={() => setCopilotEnabled(!copilotEnabled)}
className={cn(
'text-xs font-medium transition-colors duration-150 ease-in-out',
copilotEnabled
? 'text-[#24A0ED]'
: 'text-black/50 dark:text-white/50 group-hover:text-black dark:group-hover:text-white',
)}
>
Copilot
</p>
</div>
);
};
export default CopilotToggle;

131
ui/components/MessageInputActions/Focus.tsx
View File

@@ -1,131 +0,0 @@
import {
BadgePercent,
ChevronDown,
Globe,
Pencil,
ScanEye,
SwatchBook,
} from 'lucide-react';
import { cn } from '@/lib/utils';
import {
Popover,
PopoverButton,
PopoverPanel,
Transition,
} from '@headlessui/react';
import { SiReddit, SiYoutube } from '@icons-pack/react-simple-icons';
import { Fragment } from 'react';
const focusModes = [
{
key: 'webSearch',
title: 'All',
description: 'Searches across all of the internet',
icon: <Globe size={20} />,
},
{
key: 'academicSearch',
title: 'Academic',
description: 'Search in published academic papers',
icon: <SwatchBook size={20} />,
},
{
key: 'writingAssistant',
title: 'Writing',
description: 'Chat without searching the web',
icon: <Pencil size={16} />,
},
{
key: 'wolframAlphaSearch',
title: 'Wolfram Alpha',
description: 'Computational knowledge engine',
icon: <BadgePercent size={20} />,
},
{
key: 'youtubeSearch',
title: 'Youtube',
description: 'Search and watch videos',
icon: <SiYoutube className="h-5 w-auto mr-0.5" />,
},
{
key: 'redditSearch',
title: 'Reddit',
description: 'Search for discussions and opinions',
icon: <SiReddit className="h-5 w-auto mr-0.5" />,
},
];
const Focus = ({
focusMode,
setFocusMode,
}: {
focusMode: string;
setFocusMode: (mode: string) => void;
}) => {
return (
<Popover className="relative w-full max-w-[15rem] md:max-w-md lg:max-w-lg mt-[6.5px]">
<PopoverButton
type="button"
className=" 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"
>
{focusMode !== 'webSearch' ? (
<div className="flex flex-row items-center space-x-1">
{focusModes.find((mode) => mode.key === focusMode)?.icon}
<p className="text-xs font-medium hidden lg:block">
{focusModes.find((mode) => mode.key === focusMode)?.title}
</p>
<ChevronDown size={20} className="-translate-x-1" />
</div>
) : (
<div className="flex flex-row items-center space-x-1">
<ScanEye size={20} />
<p className="text-xs font-medium hidden lg:block">Focus</p>
</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-[500px] left-0">
<div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 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">
{focusModes.map((mode, i) => (
<PopoverButton
onClick={() => setFocusMode(mode.key)}
key={i}
className={cn(
'p-2 rounded-lg flex flex-col items-start justify-start text-start space-y-2 duration-200 cursor-pointer transition',
focusMode === mode.key
? 'bg-light-secondary dark:bg-dark-secondary'
: 'hover:bg-light-secondary dark:hover:bg-dark-secondary',
)}
>
<div
className={cn(
'flex flex-row items-center space-x-1',
focusMode === mode.key
? 'text-[#24A0ED]'
: '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 Focus;

104
ui/components/MessageInputActions/Optimization.tsx
View File

@@ -1,104 +0,0 @@
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;

163
ui/components/MessageSources.tsx
View File

@@ -1,163 +0,0 @@
/* eslint-disable @next/next/no-img-element */
import {
Dialog,
DialogPanel,
DialogTitle,
Transition,
TransitionChild,
} from '@headlessui/react';
import { Document } from '@langchain/core/documents';
import { File } from 'lucide-react';
import { Fragment, useState } from 'react';
const MessageSources = ({ sources }: { sources: Document[] }) => {
const [isDialogOpen, setIsDialogOpen] = useState(false);
const closeModal = () => {
setIsDialogOpen(false);
document.body.classList.remove('overflow-hidden-scrollable');
};
const openModal = () => {
setIsDialogOpen(true);
document.body.classList.add('overflow-hidden-scrollable');
};
return (
<div className="grid grid-cols-2 lg:grid-cols-4 gap-2">
{sources.slice(0, 3).map((source, i) => (
<a
className="bg-light-100 hover:bg-light-200 dark:bg-dark-100 dark:hover:bg-dark-200 transition duration-200 rounded-lg p-3 flex flex-col space-y-2 font-medium"
key={i}
href={source.metadata.url}
target="_blank"
>
<p className="dark:text-white text-xs overflow-hidden whitespace-nowrap text-ellipsis">
{source.metadata.title}
</p>
<div className="flex flex-row items-center justify-between">
<div className="flex flex-row items-center space-x-1">
{source.metadata.url === 'File' ? (
<div className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full">
<File size={12} className="text-white/70" />
</div>
) : (
<img
src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
width={16}
height={16}
alt="favicon"
className="rounded-lg h-4 w-4"
/>
)}
<p className="text-xs text-black/50 dark:text-white/50 overflow-hidden whitespace-nowrap text-ellipsis">
{source.metadata.url.replace(/.+\/\/|www.|\..+/g, '')}
</p>
</div>
<div className="flex flex-row items-center space-x-1 text-black/50 dark:text-white/50 text-xs">
<div className="bg-black/50 dark:bg-white/50 h-[4px] w-[4px] rounded-full" />
<span>{i + 1}</span>
</div>
</div>
</a>
))}
{sources.length > 3 && (
<button
onClick={openModal}
className="bg-light-100 hover:bg-light-200 dark:bg-dark-100 dark:hover:bg-dark-200 transition duration-200 rounded-lg p-3 flex flex-col space-y-2 font-medium"
>
<div className="flex flex-row items-center space-x-1">
{sources.slice(3, 6).map((source, i) => {
return source.metadata.url === 'File' ? (
<div
key={i}
className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full"
>
<File size={12} className="text-white/70" />
</div>
) : (
<img
key={i}
src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
width={16}
height={16}
alt="favicon"
className="rounded-lg h-4 w-4"
/>
);
})}
</div>
<p className="text-xs text-black/50 dark:text-white/50">
View {sources.length - 3} more
</p>
</button>
)}
<Transition appear show={isDialogOpen} as={Fragment}>
<Dialog as="div" className="relative z-50" onClose={closeModal}>
<div className="fixed inset-0 overflow-y-auto">
<div className="flex min-h-full items-center justify-center p-4 text-center">
<TransitionChild
as={Fragment}
enter="ease-out duration-200"
enterFrom="opacity-0 scale-95"
enterTo="opacity-100 scale-100"
leave="ease-in duration-100"
leaveFrom="opacity-100 scale-200"
leaveTo="opacity-0 scale-95"
>
<DialogPanel className="w-full max-w-md transform rounded-2xl bg-light-secondary dark:bg-dark-secondary border border-light-200 dark:border-dark-200 p-6 text-left align-middle shadow-xl transition-all">
<DialogTitle className="text-lg font-medium leading-6 dark:text-white">
Sources
</DialogTitle>
<div className="grid grid-cols-2 gap-2 overflow-auto max-h-[300px] mt-2 pr-2">
{sources.map((source, i) => (
<a
className="bg-light-secondary hover:bg-light-200 dark:bg-dark-secondary dark:hover:bg-dark-200 border border-light-200 dark:border-dark-200 transition duration-200 rounded-lg p-3 flex flex-col space-y-2 font-medium"
key={i}
href={source.metadata.url}
target="_blank"
>
<p className="dark:text-white text-xs overflow-hidden whitespace-nowrap text-ellipsis">
{source.metadata.title}
</p>
<div className="flex flex-row items-center justify-between">
<div className="flex flex-row items-center space-x-1">
{source.metadata.url === 'File' ? (
<div className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full">
<File size={12} className="text-white/70" />
</div>
) : (
<img
src={`https://s2.googleusercontent.com/s2/favicons?domain_url=${source.metadata.url}`}
width={16}
height={16}
alt="favicon"
className="rounded-lg h-4 w-4"
/>
)}
<p className="text-xs text-black/50 dark:text-white/50 overflow-hidden whitespace-nowrap text-ellipsis">
{source.metadata.url.replace(
/.+\/\/|www.|\..+/g,
'',
)}
</p>
</div>
<div className="flex flex-row items-center space-x-1 text-black/50 dark:text-white/50 text-xs">
<div className="bg-black/50 dark:bg-white/50 h-[4px] w-[4px] rounded-full" />
<span>{i + 1}</span>
</div>
</div>
</a>
))}
</div>
</DialogPanel>
</TransitionChild>
</div>
</div>
</Dialog>
</Transition>
</div>
);
};
export default MessageSources;

72
ui/components/Navbar.tsx
View File

@@ -1,72 +0,0 @@
import { Clock, Edit, Share, Trash } from 'lucide-react';
import { Message } from './ChatWindow';
import { useEffect, useState } from 'react';
import { formatTimeDifference } from '@/lib/utils';
import DeleteChat from './DeleteChat';
const Navbar = ({
chatId,
messages,
}: {
messages: Message[];
chatId: string;
}) => {
const [title, setTitle] = useState<string>('');
const [timeAgo, setTimeAgo] = useState<string>('');
useEffect(() => {
if (messages.length > 0) {
const newTitle =
messages[0].content.length > 20
? `${messages[0].content.substring(0, 20).trim()}...`
: messages[0].content;
setTitle(newTitle);
const newTimeAgo = formatTimeDifference(
new Date(),
messages[0].createdAt,
);
setTimeAgo(newTimeAgo);
}
}, [messages]);
useEffect(() => {
const intervalId = setInterval(() => {
if (messages.length > 0) {
const newTimeAgo = formatTimeDifference(
new Date(),
messages[0].createdAt,
);
setTimeAgo(newTimeAgo);
}
}, 1000);
return () => clearInterval(intervalId);
// eslint-disable-next-line react-hooks/exhaustive-deps
}, []);
return (
<div className="fixed z-40 top-0 left-0 right-0 px-4 lg:pl-[104px] lg:pr-6 lg:px-8 flex flex-row items-center justify-between w-full py-4 text-sm text-black dark:text-white/70 border-b bg-light-primary dark:bg-dark-primary border-light-100 dark:border-dark-200">
<a
href="/"
className="active:scale-95 transition duration-100 cursor-pointer lg:hidden"
>
<Edit size={17} />
</a>
<div className="hidden lg:flex flex-row items-center justify-center space-x-2">
<Clock size={17} />
<p className="text-xs">{timeAgo} ago</p>
</div>
<p className="hidden lg:flex">{title}</p>
<div className="flex flex-row items-center space-x-4">
<Share
size={17}
className="active:scale-95 transition duration-100 cursor-pointer"
/>
<DeleteChat redirect chatId={chatId} chats={[]} setChats={() => {}} />
</div>
</div>
);
};
export default Navbar;

157
ui/components/SearchImages.tsx
View File

@@ -1,157 +0,0 @@
/* eslint-disable @next/next/no-img-element */
import { ImagesIcon, PlusIcon } from 'lucide-react';
import { useState } from 'react';
import Lightbox from 'yet-another-react-lightbox';
import 'yet-another-react-lightbox/styles.css';
import { Message } from './ChatWindow';
type Image = {
url: string;
img_src: string;
title: string;
};
const SearchImages = ({
query,
chatHistory,
messageId,
}: {
query: string;
chatHistory: Message[];
messageId: string;
}) => {
const [images, setImages] = useState<Image[] | null>(null);
const [loading, setLoading] = useState(false);
const [open, setOpen] = useState(false);
const [slides, setSlides] = useState<any[]>([]);
return (
<>
{!loading && images === null && (
<button
id={`search-images-${messageId}`}
onClick={async () => {
setLoading(true);
const chatModelProvider = localStorage.getItem('chatModelProvider');
const chatModel = localStorage.getItem('chatModel');
const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
const customOpenAIKey = localStorage.getItem('openAIApiKey');
const res = await fetch(`/api/images`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
query: query,
chatHistory: chatHistory,
chatModel: {
provider: chatModelProvider,
model: chatModel,
...(chatModelProvider === 'custom_openai' && {
customOpenAIBaseURL: customOpenAIBaseURL,
customOpenAIKey: customOpenAIKey,
}),
},
}),
});
const data = await res.json();
const images = data.images ?? [];
setImages(images);
setSlides(
images.map((image: Image) => {
return {
src: image.img_src,
};
}),
);
setLoading(false);
}}
className="border border-dashed border-light-200 dark:border-dark-200 hover:bg-light-200 dark:hover:bg-dark-200 active:scale-95 duration-200 transition px-4 py-2 flex flex-row items-center justify-between rounded-lg dark:text-white text-sm w-full"
>
<div className="flex flex-row items-center space-x-2">
<ImagesIcon size={17} />
<p>Search images</p>
</div>
<PlusIcon className="text-[#24A0ED]" size={17} />
</button>
)}
{loading && (
<div className="grid grid-cols-2 gap-2">
{[...Array(4)].map((_, i) => (
<div
key={i}
className="bg-light-secondary dark:bg-dark-secondary h-32 w-full rounded-lg animate-pulse aspect-video object-cover"
/>
))}
</div>
)}
{images !== null && images.length > 0 && (
<>
<div className="grid grid-cols-2 gap-2">
{images.length > 4
? images.slice(0, 3).map((image, i) => (
<img
onClick={() => {
setOpen(true);
setSlides([
slides[i],
...slides.slice(0, i),
...slides.slice(i + 1),
]);
}}
key={i}
src={image.img_src}
alt={image.title}
className="h-full w-full aspect-video object-cover rounded-lg transition duration-200 active:scale-95 hover:scale-[1.02] cursor-zoom-in"
/>
))
: images.map((image, i) => (
<img
onClick={() => {
setOpen(true);
setSlides([
slides[i],
...slides.slice(0, i),
...slides.slice(i + 1),
]);
}}
key={i}
src={image.img_src}
alt={image.title}
className="h-full w-full aspect-video object-cover rounded-lg transition duration-200 active:scale-95 hover:scale-[1.02] cursor-zoom-in"
/>
))}
{images.length > 4 && (
<button
onClick={() => setOpen(true)}
className="bg-light-100 hover:bg-light-200 dark:bg-dark-100 dark:hover:bg-dark-200 transition duration-200 active:scale-95 hover:scale-[1.02] h-auto w-full rounded-lg flex flex-col justify-between text-white p-2"
>
<div className="flex flex-row items-center space-x-1">
{images.slice(3, 6).map((image, i) => (
<img
key={i}
src={image.img_src}
alt={image.title}
className="h-6 w-12 rounded-md lg:h-3 lg:w-6 lg:rounded-sm aspect-video object-cover"
/>
))}
</div>
<p className="text-black/70 dark:text-white/70 text-xs">
View {images.length - 3} more
</p>
</button>
)}
</div>
<Lightbox open={open} close={() => setOpen(false)} slides={slides} />
</>
)}
</>
);
};
export default SearchImages;

228
ui/components/SearchVideos.tsx
View File

@@ -1,228 +0,0 @@
/* eslint-disable @next/next/no-img-element */
import { PlayCircle, PlayIcon, PlusIcon, VideoIcon } from 'lucide-react';
import { useRef, useState } from 'react';
import Lightbox, { GenericSlide, VideoSlide } from 'yet-another-react-lightbox';
import 'yet-another-react-lightbox/styles.css';
import { Message } from './ChatWindow';
type Video = {
url: string;
img_src: string;
title: string;
iframe_src: string;
};
declare module 'yet-another-react-lightbox' {
export interface VideoSlide extends GenericSlide {
type: 'video-slide';
src: string;
iframe_src: string;
}
interface SlideTypes {
'video-slide': VideoSlide;
}
}
const Searchvideos = ({
query,
chatHistory,
messageId,
}: {
query: string;
chatHistory: Message[];
messageId: string;
}) => {
const [videos, setVideos] = useState<Video[] | null>(null);
const [loading, setLoading] = useState(false);
const [open, setOpen] = useState(false);
const [slides, setSlides] = useState<VideoSlide[]>([]);
const [currentIndex, setCurrentIndex] = useState(0);
const videoRefs = useRef<(HTMLIFrameElement | null)[]>([]);
return (
<>
{!loading && videos === null && (
<button
id={`search-videos-${messageId}`}
onClick={async () => {
setLoading(true);
const chatModelProvider = localStorage.getItem('chatModelProvider');
const chatModel = localStorage.getItem('chatModel');
const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
const customOpenAIKey = localStorage.getItem('openAIApiKey');
const res = await fetch(`/api/videos`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
query: query,
chatHistory: chatHistory,
chatModel: {
provider: chatModelProvider,
model: chatModel,
...(chatModelProvider === 'custom_openai' && {
customOpenAIBaseURL: customOpenAIBaseURL,
customOpenAIKey: customOpenAIKey,
}),
},
}),
});
const data = await res.json();
const videos = data.videos ?? [];
setVideos(videos);
setSlides(
videos.map((video: Video) => {
return {
type: 'video-slide',
iframe_src: video.iframe_src,
src: video.img_src,
};
}),
);
setLoading(false);
}}
className="border border-dashed border-light-200 dark:border-dark-200 hover:bg-light-200 dark:hover:bg-dark-200 active:scale-95 duration-200 transition px-4 py-2 flex flex-row items-center justify-between rounded-lg dark:text-white text-sm w-full"
>
<div className="flex flex-row items-center space-x-2">
<VideoIcon size={17} />
<p>Search videos</p>
</div>
<PlusIcon className="text-[#24A0ED]" size={17} />
</button>
)}
{loading && (
<div className="grid grid-cols-2 gap-2">
{[...Array(4)].map((_, i) => (
<div
key={i}
className="bg-light-secondary dark:bg-dark-secondary h-32 w-full rounded-lg animate-pulse aspect-video object-cover"
/>
))}
</div>
)}
{videos !== null && videos.length > 0 && (
<>
<div className="grid grid-cols-2 gap-2">
{videos.length > 4
? videos.slice(0, 3).map((video, i) => (
<div
onClick={() => {
setOpen(true);
setSlides([
slides[i],
...slides.slice(0, i),
...slides.slice(i + 1),
]);
}}
className="relative transition duration-200 active:scale-95 hover:scale-[1.02] cursor-pointer"
key={i}
>
<img
src={video.img_src}
alt={video.title}
className="relative h-full w-full aspect-video object-cover rounded-lg"
/>
<div className="absolute bg-white/70 dark:bg-black/70 text-black/70 dark:text-white/70 px-2 py-1 flex flex-row items-center space-x-1 bottom-1 right-1 rounded-md">
<PlayCircle size={15} />
<p className="text-xs">Video</p>
</div>
</div>
))
: videos.map((video, i) => (
<div
onClick={() => {
setOpen(true);
setSlides([
slides[i],
...slides.slice(0, i),
...slides.slice(i + 1),
]);
}}
className="relative transition duration-200 active:scale-95 hover:scale-[1.02] cursor-pointer"
key={i}
>
<img
src={video.img_src}
alt={video.title}
className="relative h-full w-full aspect-video object-cover rounded-lg"
/>
<div className="absolute bg-white/70 dark:bg-black/70 text-black/70 dark:text-white/70 px-2 py-1 flex flex-row items-center space-x-1 bottom-1 right-1 rounded-md">
<PlayCircle size={15} />
<p className="text-xs">Video</p>
</div>
</div>
))}
{videos.length > 4 && (
<button
onClick={() => setOpen(true)}
className="bg-light-100 hover:bg-light-200 dark:bg-dark-100 dark:hover:bg-dark-200 transition duration-200 active:scale-95 hover:scale-[1.02] h-auto w-full rounded-lg flex flex-col justify-between text-white p-2"
>
<div className="flex flex-row items-center space-x-1">
{videos.slice(3, 6).map((video, i) => (
<img
key={i}
src={video.img_src}
alt={video.title}
className="h-6 w-12 rounded-md lg:h-3 lg:w-6 lg:rounded-sm aspect-video object-cover"
/>
))}
</div>
<p className="text-black/70 dark:text-white/70 text-xs">
View {videos.length - 3} more
</p>
</button>
)}
</div>
<Lightbox
open={open}
close={() => setOpen(false)}
slides={slides}
index={currentIndex}
on={{
view: ({ index }) => {
const previousIframe = videoRefs.current[currentIndex];
if (previousIframe?.contentWindow) {
previousIframe.contentWindow.postMessage(
'{"event":"command","func":"pauseVideo","args":""}',
'*',
);
}
setCurrentIndex(index);
},
}}
render={{
slide: ({ slide }) => {
const index = slides.findIndex((s) => s === slide);
return slide.type === 'video-slide' ? (
<div className="h-full w-full flex flex-row items-center justify-center">
<iframe
src={`${slide.iframe_src}${slide.iframe_src.includes('?') ? '&' : '?'}enablejsapi=1`}
ref={(el) => {
if (el) {
videoRefs.current[index] = el;
}
}}
className="aspect-video max-h-[95vh] w-[95vw] rounded-2xl md:w-[80vw]"
allowFullScreen
allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"
/>
</div>
) : null;
},
}}
/>
</>
)}
</>
);
};
export default Searchvideos;

101
ui/components/Sidebar.tsx
View File

@@ -1,101 +0,0 @@
'use client';
import { cn } from '@/lib/utils';
import { BookOpenText, Home, Search, SquarePen, Settings } from 'lucide-react';
import Link from 'next/link';
import { useSelectedLayoutSegments } from 'next/navigation';
import React, { useState, type ReactNode } from 'react';
import Layout from './Layout';
const VerticalIconContainer = ({ children }: { children: ReactNode }) => {
return (
<div className="flex flex-col items-center gap-y-3 w-full">{children}</div>
);
};
const Sidebar = ({ children }: { children: React.ReactNode }) => {
const segments = useSelectedLayoutSegments();
const [isSettingsOpen, setIsSettingsOpen] = useState(false);
const navLinks = [
{
icon: Home,
href: '/',
active: segments.length === 0 || segments.includes('c'),
label: 'Home',
},
{
icon: Search,
href: '/discover',
active: segments.includes('discover'),
label: 'Discover',
},
{
icon: BookOpenText,
href: '/library',
active: segments.includes('library'),
label: 'Library',
},
];
return (
<div>
<div className="hidden lg:fixed lg:inset-y-0 lg:z-50 lg:flex lg:w-20 lg:flex-col">
<div className="flex grow flex-col items-center justify-between gap-y-5 overflow-y-auto bg-light-secondary dark:bg-dark-secondary px-2 py-8">
<a href="/">
<SquarePen className="cursor-pointer" />
</a>
<VerticalIconContainer>
{navLinks.map((link, i) => (
<Link
key={i}
href={link.href}
className={cn(
'relative flex flex-row items-center justify-center cursor-pointer hover:bg-black/10 dark:hover:bg-white/10 duration-150 transition w-full py-2 rounded-lg',
link.active
? 'text-black dark:text-white'
: 'text-black/70 dark:text-white/70',
)}
>
<link.icon />
{link.active && (
<div className="absolute right-0 -mr-2 h-full w-1 rounded-l-lg bg-black dark:bg-white" />
)}
</Link>
))}
</VerticalIconContainer>
<Link href="/settings">
<Settings className="cursor-pointer" />
</Link>
</div>
</div>
<div className="fixed bottom-0 w-full z-50 flex flex-row items-center gap-x-6 bg-light-primary dark:bg-dark-primary px-4 py-4 shadow-sm lg:hidden">
{navLinks.map((link, i) => (
<Link
href={link.href}
key={i}
className={cn(
'relative flex flex-col items-center space-y-1 text-center w-full',
link.active
? 'text-black dark:text-white'
: 'text-black dark:text-white/70',
)}
>
{link.active && (
<div className="absolute top-0 -mt-4 h-1 w-full rounded-b-lg bg-black dark:bg-white" />
)}
<link.icon />
<p className="text-xs">{link.label}</p>
</Link>
))}
</div>
<Layout>{children}</Layout>
</div>
);
};
export default Sidebar;

16
ui/components/theme/Provider.tsx
View File

@@ -1,16 +0,0 @@
'use client';
import { ThemeProvider } from 'next-themes';
const ThemeProviderComponent = ({
children,
}: {
children: React.ReactNode;
}) => {
return (
<ThemeProvider attribute="class" enableSystem={false} defaultTheme="dark">
{children}
</ThemeProvider>
);
};
export default ThemeProviderComponent;

60
ui/components/theme/Switcher.tsx
View File

@@ -1,60 +0,0 @@
'use client';
import { useTheme } from 'next-themes';
import { useCallback, useEffect, useState } from 'react';
import Select from '../ui/Select';
type Theme = 'dark' | 'light' | 'system';
const ThemeSwitcher = ({ className }: { className?: string }) => {
const [mounted, setMounted] = useState(false);
const { theme, setTheme } = useTheme();
const isTheme = useCallback((t: Theme) => t === theme, [theme]);
const handleThemeSwitch = (theme: Theme) => {
setTheme(theme);
};
useEffect(() => {
setMounted(true);
}, []);
useEffect(() => {
if (isTheme('system')) {
const preferDarkScheme = window.matchMedia(
'(prefers-color-scheme: dark)',
);
const detectThemeChange = (event: MediaQueryListEvent) => {
const theme: Theme = event.matches ? 'dark' : 'light';
setTheme(theme);
};
preferDarkScheme.addEventListener('change', detectThemeChange);
return () => {
preferDarkScheme.removeEventListener('change', detectThemeChange);
};
}
}, [isTheme, setTheme, theme]);
// Avoid Hydration Mismatch
if (!mounted) {
return null;
}
return (
<Select
className={className}
value={theme}
onChange={(e) => handleThemeSwitch(e.target.value as Theme)}
options={[
{ value: 'light', label: 'Light' },
{ value: 'dark', label: 'Dark' },
]}
/>
);
};
export default ThemeSwitcher;

28
ui/components/ui/Select.tsx
View File

@@ -1,28 +0,0 @@
import { cn } from '@/lib/utils';
import { SelectHTMLAttributes } from 'react';
interface SelectProps extends SelectHTMLAttributes<HTMLSelectElement> {
options: { value: string; label: string; disabled?: boolean }[];
}
export const Select = ({ className, options, ...restProps }: SelectProps) => {
return (
<select
{...restProps}
className={cn(
'bg-light-secondary dark:bg-dark-secondary px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
className,
)}
>
{options.map(({ label, value, disabled }) => {
return (
<option key={value} value={value} disabled={disabled}>
{label}
</option>
);
})}
</select>
);
};
export default Select;

2
ui/data/.gitignore vendored
View File

@@ -1,2 +0,0 @@
*
!.gitignore

10
ui/drizzle.config.ts
View File

@@ -1,10 +0,0 @@
import { defineConfig } from 'drizzle-kit';
export default defineConfig({
dialect: 'sqlite',
schema: './lib/db/schema.ts',
out: './drizzle',
dbCredentials: {
url: './data/db.sqlite',
},
});

31
ui/lib/actions.ts
View File

@@ -1,31 +0,0 @@
import { Message } from '@/components/ChatWindow';
export const getSuggestions = async (chatHisory: Message[]) => {
const chatModel = localStorage.getItem('chatModel');
const chatModelProvider = localStorage.getItem('chatModelProvider');
const customOpenAIKey = localStorage.getItem('openAIApiKey');
const customOpenAIBaseURL = localStorage.getItem('openAIBaseURL');
const res = await fetch(`/api/suggestions`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
chatHistory: chatHisory,
chatModel: {
provider: chatModelProvider,
model: chatModel,
...(chatModelProvider === 'custom_openai' && {
customOpenAIKey,
customOpenAIBaseURL,
}),
},
}),
});
const data = (await res.json()) as { suggestions: string[] };
return data.suggestions;
};

90
ui/lib/chains/imageSearchAgent.ts
View File

@@ -1,90 +0,0 @@
import {
RunnableSequence,
RunnableMap,
RunnableLambda,
} from '@langchain/core/runnables';
import { PromptTemplate } 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';
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:
`;
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;
},
}),
PromptTemplate.fromTemplate(imageSearchChainPrompt),
llm,
strParser,
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;

55
ui/lib/chains/suggestionGeneratorAgent.ts
View File

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

97
ui/lib/chains/videoSearchAgent.ts
View File

@@ -1,97 +0,0 @@
import {
RunnableSequence,
RunnableMap,
RunnableLambda,
} from '@langchain/core/runnables';
import { PromptTemplate } 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';
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:
`;
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;
},
}),
PromptTemplate.fromTemplate(VideoSearchChainPrompt),
llm,
strParser,
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;

116
ui/lib/config.ts
View File

@@ -1,116 +0,0 @@
import fs from 'fs';
import path from 'path';
import toml from '@iarna/toml';
const configFileName = 'config.toml';
interface Config {
GENERAL: {
PORT: number;
SIMILARITY_MEASURE: string;
KEEP_ALIVE: string;
};
MODELS: {
OPENAI: {
API_KEY: string;
};
GROQ: {
API_KEY: string;
};
ANTHROPIC: {
API_KEY: string;
};
GEMINI: {
API_KEY: string;
};
OLLAMA: {
API_URL: string;
};
CUSTOM_OPENAI: {
API_URL: string;
API_KEY: string;
MODEL_NAME: string;
};
};
API_ENDPOINTS: {
SEARXNG: string;
};
}
type RecursivePartial<T> = {
[P in keyof T]?: RecursivePartial<T[P]>;
};
const loadConfig = () =>
toml.parse(
fs.readFileSync(path.join(process.cwd(), `${configFileName}`), 'utf-8'),
) as any as Config;
export const getPort = () => loadConfig().GENERAL.PORT;
export const getSimilarityMeasure = () =>
loadConfig().GENERAL.SIMILARITY_MEASURE;
export const getKeepAlive = () => loadConfig().GENERAL.KEEP_ALIVE;
export const getOpenaiApiKey = () => loadConfig().MODELS.OPENAI.API_KEY;
export const getGroqApiKey = () => loadConfig().MODELS.GROQ.API_KEY;
export const getAnthropicApiKey = () => loadConfig().MODELS.ANTHROPIC.API_KEY;
export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
export const getSearxngApiEndpoint = () =>
process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
export const getCustomOpenaiApiKey = () =>
loadConfig().MODELS.CUSTOM_OPENAI.API_KEY;
export const getCustomOpenaiApiUrl = () =>
loadConfig().MODELS.CUSTOM_OPENAI.API_URL;
export const getCustomOpenaiModelName = () =>
loadConfig().MODELS.CUSTOM_OPENAI.MODEL_NAME;
const mergeConfigs = (current: any, update: any): any => {
if (update === null || update === undefined) {
return current;
}
if (typeof current !== 'object' || current === null) {
return update;
}
const result = { ...current };
for (const key in update) {
if (Object.prototype.hasOwnProperty.call(update, key)) {
const updateValue = update[key];
if (
typeof updateValue === 'object' &&
updateValue !== null &&
typeof result[key] === 'object' &&
result[key] !== null
) {
result[key] = mergeConfigs(result[key], updateValue);
} else if (updateValue !== undefined) {
result[key] = updateValue;
}
}
}
return result;
};
export const updateConfig = (config: RecursivePartial<Config>) => {
const currentConfig = loadConfig();
const mergedConfig = mergeConfigs(currentConfig, config);
fs.writeFileSync(
path.join(path.join(process.cwd(), `${configFileName}`)),
toml.stringify(mergedConfig),
);
};

11
ui/lib/db/index.ts
View File

@@ -1,11 +0,0 @@
import { drizzle } from 'drizzle-orm/better-sqlite3';
import Database from 'better-sqlite3';
import * as schema from './schema';
import path from 'path';
const sqlite = new Database(path.join(process.cwd(), 'data/db.sqlite'));
const db = drizzle(sqlite, {
schema: schema,
});
export default db;

28
ui/lib/db/schema.ts
View File

@@ -1,28 +0,0 @@
import { sql } from 'drizzle-orm';
import { text, integer, sqliteTable } from 'drizzle-orm/sqlite-core';
export const messages = sqliteTable('messages', {
id: integer('id').primaryKey(),
content: text('content').notNull(),
chatId: text('chatId').notNull(),
messageId: text('messageId').notNull(),
role: text('type', { enum: ['assistant', 'user'] }),
metadata: text('metadata', {
mode: 'json',
}),
});
interface File {
name: string;
fileId: string;
}
export const chats = sqliteTable('chats', {
id: text('id').primaryKey(),
title: text('title').notNull(),
createdAt: text('createdAt').notNull(),
focusMode: text('focusMode').notNull(),
files: text('files', { mode: 'json' })
.$type<File[]>()
.default(sql`'[]'`),
});

48
ui/lib/outputParsers/lineOutputParser.ts
View File

@@ -1,48 +0,0 @@
import { BaseOutputParser } from '@langchain/core/output_parsers';
interface LineOutputParserArgs {
key?: string;
}
class LineOutputParser extends BaseOutputParser<string> {
private key = 'questions';
constructor(args?: LineOutputParserArgs) {
super();
this.key = args?.key ?? this.key;
}
static lc_name() {
return 'LineOutputParser';
}
lc_namespace = ['langchain', 'output_parsers', 'line_output_parser'];
async parse(text: string): Promise<string> {
text = text.trim() || '';
const regex = /^(\s*(-|\*|\d+\.\s|\d+\)\s|\u2022)\s*)+/;
const startKeyIndex = text.indexOf(`<${this.key}>`);
const endKeyIndex = text.indexOf(`</${this.key}>`);
if (startKeyIndex === -1 || endKeyIndex === -1) {
return '';
}
const questionsStartIndex =
startKeyIndex === -1 ? 0 : startKeyIndex + `<${this.key}>`.length;
const questionsEndIndex = endKeyIndex === -1 ? text.length : endKeyIndex;
const line = text
.slice(questionsStartIndex, questionsEndIndex)
.trim()
.replace(regex, '');
return line;
}
getFormatInstructions(): string {
throw new Error('Not implemented.');
}
}
export default LineOutputParser;

50
ui/lib/outputParsers/listLineOutputParser.ts
View File

@@ -1,50 +0,0 @@
import { BaseOutputParser } from '@langchain/core/output_parsers';
interface LineListOutputParserArgs {
key?: string;
}
class LineListOutputParser extends BaseOutputParser<string[]> {
private key = 'questions';
constructor(args?: LineListOutputParserArgs) {
super();
this.key = args?.key ?? this.key;
}
static lc_name() {
return 'LineListOutputParser';
}
lc_namespace = ['langchain', 'output_parsers', 'line_list_output_parser'];
async parse(text: string): Promise<string[]> {
text = text.trim() || '';
const regex = /^(\s*(-|\*|\d+\.\s|\d+\)\s|\u2022)\s*)+/;
const startKeyIndex = text.indexOf(`<${this.key}>`);
const endKeyIndex = text.indexOf(`</${this.key}>`);
if (startKeyIndex === -1 || endKeyIndex === -1) {
return [];
}
const questionsStartIndex =
startKeyIndex === -1 ? 0 : startKeyIndex + `<${this.key}>`.length;
const questionsEndIndex = endKeyIndex === -1 ? text.length : endKeyIndex;
const lines = text
.slice(questionsStartIndex, questionsEndIndex)
.trim()
.split('\n')
.filter((line) => line.trim() !== '')
.map((line) => line.replace(regex, ''));
return lines;
}
getFormatInstructions(): string {
throw new Error('Not implemented.');
}
}
export default LineListOutputParser;

65
ui/lib/prompts/academicSearch.ts
View File

@@ -1,65 +0,0 @@
export const academicSearchRetrieverPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: How does stable diffusion work?
Rephrased: Stable diffusion working
2. Follow up question: What is linear algebra?
Rephrased: Linear algebra
3. Follow up question: What is the third law of thermodynamics?
Rephrased: Third law of thermodynamics
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
export const academicSearchResponsePrompt = `
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
- You are set on focus mode 'Academic', this means you will be searching for academic papers and articles on the web.
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
<context>
{context}
</context>
Current date & time in ISO format (UTC timezone) is: {date}.
`;

32
ui/lib/prompts/index.ts
View File

@@ -1,32 +0,0 @@
import {
academicSearchResponsePrompt,
academicSearchRetrieverPrompt,
} from './academicSearch';
import {
redditSearchResponsePrompt,
redditSearchRetrieverPrompt,
} from './redditSearch';
import { webSearchResponsePrompt, webSearchRetrieverPrompt } from './webSearch';
import {
wolframAlphaSearchResponsePrompt,
wolframAlphaSearchRetrieverPrompt,
} from './wolframAlpha';
import { writingAssistantPrompt } from './writingAssistant';
import {
youtubeSearchResponsePrompt,
youtubeSearchRetrieverPrompt,
} from './youtubeSearch';
export default {
webSearchResponsePrompt,
webSearchRetrieverPrompt,
academicSearchResponsePrompt,
academicSearchRetrieverPrompt,
redditSearchResponsePrompt,
redditSearchRetrieverPrompt,
wolframAlphaSearchResponsePrompt,
wolframAlphaSearchRetrieverPrompt,
writingAssistantPrompt,
youtubeSearchResponsePrompt,
youtubeSearchRetrieverPrompt,
};

65
ui/lib/prompts/redditSearch.ts
View File

@@ -1,65 +0,0 @@
export const redditSearchRetrieverPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: Which company is most likely to create an AGI
Rephrased: Which company is most likely to create an AGI
2. Follow up question: Is Earth flat?
Rephrased: Is Earth flat?
3. Follow up question: Is there life on Mars?
Rephrased: Is there life on Mars?
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
export const redditSearchResponsePrompt = `
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
- You are set on focus mode 'Reddit', this means you will be searching for information, opinions and discussions on the web using Reddit.
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
<context>
{context}
</context>
Current date & time in ISO format (UTC timezone) is: {date}.
`;

106
ui/lib/prompts/webSearch.ts
View File

@@ -1,106 +0,0 @@
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.
If it is a smple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
If the user asks some question from some URL or wants you to summarize a PDF or a webpage (via URL) you need to return the links inside the \`links\` XML block and the question inside the \`question\` XML block. If the user wants to you to summarize the webpage or the PDF you need to return \`summarize\` inside the \`question\` XML block in place of a question and the link to summarize in the \`links\` XML block.
You must always return the rephrased question inside the \`question\` XML block, if there are no links in the follow-up question then don't insert a \`links\` XML block in your response.
There are several examples attached for your reference inside the below \`examples\` XML block
<examples>
1. Follow up question: What is the capital of France
Rephrased question:\`
<question>
Capital of france
</question>
\`
2. Hi, how are you?
Rephrased question\`
<question>
not_needed
</question>
\`
3. Follow up question: What is Docker?
Rephrased question: \`
<question>
What is Docker
</question>
\`
4. Follow up question: Can you tell me what is X from https://example.com
Rephrased question: \`
<question>
Can you tell me what is X?
</question>
<links>
https://example.com
</links>
\`
5. Follow up question: Summarize the content from https://example.com
Rephrased question: \`
<question>
summarize
</question>
<links>
https://example.com
</links>
\`
</examples>
Anything below is the part of the actual conversation and you need to use conversation and the follow-up question to rephrase the follow-up question as a standalone question based on the guidelines shared above.
<conversation>
{chat_history}
</conversation>
Follow up question: {query}
Rephrased question:
`;
export const webSearchResponsePrompt = `
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
<context>
{context}
</context>
Current date & time in ISO format (UTC timezone) is: {date}.
`;

65
ui/lib/prompts/wolframAlpha.ts
View File

@@ -1,65 +0,0 @@
export const wolframAlphaSearchRetrieverPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: What is the atomic radius of S?
Rephrased: Atomic radius of S
2. Follow up question: What is linear algebra?
Rephrased: Linear algebra
3. Follow up question: What is the third law of thermodynamics?
Rephrased: Third law of thermodynamics
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
export const wolframAlphaSearchResponsePrompt = `
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
- You are set on focus mode 'Wolfram Alpha', this means you will be searching for information on the web using Wolfram Alpha. It is a computational knowledge engine that can answer factual queries and perform computations.
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
<context>
{context}
</context>
Current date & time in ISO format (UTC timezone) is: {date}.
`;

13
ui/lib/prompts/writingAssistant.ts
View File

@@ -1,13 +0,0 @@
export const writingAssistantPrompt = `
You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query.
Since you are a writing assistant, you would not perform web searches. If you think you lack information to answer the query, you can ask the user for more information or suggest them to switch to a different focus mode.
You will be shared a context that can contain information from files user has uploaded to get answers from. You will have to generate answers upon that.
You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
<context>
{context}
</context>
`;

65
ui/lib/prompts/youtubeSearch.ts
View File

@@ -1,65 +0,0 @@
export const youtubeSearchRetrieverPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
Example:
1. Follow up question: How does an A.C work?
Rephrased: A.C working
2. Follow up question: Linear algebra explanation video
Rephrased: What is linear algebra?
3. Follow up question: What is theory of relativity?
Rephrased: What is theory of relativity?
Conversation:
{chat_history}
Follow up question: {query}
Rephrased question:
`;
export const youtubeSearchResponsePrompt = `
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
- You are set on focus mode 'Youtube', this means you will be searching for videos on the web using Youtube and providing information based on the video's transcrip
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
<context>
{context}
</context>
Current date & time in ISO format (UTC timezone) is: {date}.
`;

63
ui/lib/providers/anthropic.ts
View File

@@ -1,63 +0,0 @@
import { ChatOpenAI } from '@langchain/openai';
import { ChatModel } from '.';
import { getAnthropicApiKey } from '../config';
const anthropicChatModels: Record<string, string>[] = [
{
displayName: 'Claude 3.7 Sonnet',
key: 'claude-3-7-sonnet-20250219',
},
{
displayName: 'Claude 3.5 Haiku',
key: 'claude-3-5-haiku-20241022',
},
{
displayName: 'Claude 3.5 Sonnet v2',
key: 'claude-3-5-sonnet-20241022',
},
{
displayName: 'Claude 3.5 Sonnet',
key: 'claude-3-5-sonnet-20240620',
},
{
displayName: 'Claude 3 Opus',
key: 'claude-3-opus-20240229',
},
{
displayName: 'Claude 3 Sonnet',
key: 'claude-3-sonnet-20240229',
},
{
displayName: 'Claude 3 Haiku',
key: 'claude-3-haiku-20240307',
},
];
export const loadAnthropicChatModels = async () => {
const anthropicApiKey = getAnthropicApiKey();
if (!anthropicApiKey) return {};
try {
const chatModels: Record<string, ChatModel> = {};
anthropicChatModels.forEach((model) => {
chatModels[model.key] = {
displayName: model.displayName,
model: new ChatOpenAI({
openAIApiKey: anthropicApiKey,
modelName: model.key,
temperature: 0.7,
configuration: {
baseURL: 'https://api.anthropic.com/v1/',
},
}),
};
});
return chatModels;
} catch (err) {
console.error(`Error loading Anthropic models: ${err}`);
return {};
}
};

94
ui/lib/providers/gemini.ts
View File

@@ -1,94 +0,0 @@
import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
import { getGeminiApiKey } from '../config';
import { ChatModel, EmbeddingModel } from '.';
const geminiChatModels: Record<string, string>[] = [
{
displayName: 'Gemini 2.0 Flash',
key: 'gemini-2.0-flash',
},
{
displayName: 'Gemini 2.0 Flash-Lite',
key: 'gemini-2.0-flash-lite',
},
{
displayName: 'Gemini 2.0 Pro Experimental',
key: 'gemini-2.0-pro-exp-02-05',
},
{
displayName: 'Gemini 1.5 Flash',
key: 'gemini-1.5-flash',
},
{
displayName: 'Gemini 1.5 Flash-8B',
key: 'gemini-1.5-flash-8b',
},
{
displayName: 'Gemini 1.5 Pro',
key: 'gemini-1.5-pro',
},
];
const geminiEmbeddingModels: Record<string, string>[] = [
{
displayName: 'Gemini Embedding',
key: 'gemini-embedding-exp',
},
];
export const loadGeminiChatModels = async () => {
const geminiApiKey = getGeminiApiKey();
if (!geminiApiKey) return {};
try {
const chatModels: Record<string, ChatModel> = {};
geminiChatModels.forEach((model) => {
chatModels[model.key] = {
displayName: model.displayName,
model: new ChatOpenAI({
openAIApiKey: geminiApiKey,
modelName: model.key,
temperature: 0.7,
configuration: {
baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai/',
},
}),
};
});
return chatModels;
} catch (err) {
console.error(`Error loading Gemini models: ${err}`);
return {};
}
};
export const loadGeminiEmbeddingModels = async () => {
const geminiApiKey = getGeminiApiKey();
if (!geminiApiKey) return {};
try {
const embeddingModels: Record<string, EmbeddingModel> = {};
geminiEmbeddingModels.forEach((model) => {
embeddingModels[model.key] = {
displayName: model.displayName,
model: new OpenAIEmbeddings({
openAIApiKey: geminiApiKey,
modelName: model.key,
configuration: {
baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai/',
},
}),
};
});
return embeddingModels;
} catch (err) {
console.error(`Error loading OpenAI embeddings models: ${err}`);
return {};
}
};

107
ui/lib/providers/groq.ts
View File

@@ -1,107 +0,0 @@
import { ChatOpenAI } from '@langchain/openai';
import { getGroqApiKey } from '../config';
import { ChatModel } from '.';
const groqChatModels: Record<string, string>[] = [
{
displayName: 'Gemma2 9B IT',
key: 'gemma2-9b-it',
},
{
displayName: 'Llama 3.3 70B Versatile',
key: 'llama-3.3-70b-versatile',
},
{
displayName: 'Llama 3.1 8B Instant',
key: 'llama-3.1-8b-instant',
},
{
displayName: 'Llama3 70B 8192',
key: 'llama3-70b-8192',
},
{
displayName: 'Llama3 8B 8192',
key: 'llama3-8b-8192',
},
{
displayName: 'Mixtral 8x7B 32768',
key: 'mixtral-8x7b-32768',
},
{
displayName: 'Qwen QWQ 32B (Preview)',
key: 'qwen-qwq-32b',
},
{
displayName: 'Mistral Saba 24B (Preview)',
key: 'mistral-saba-24b',
},
{
displayName: 'Qwen 2.5 Coder 32B (Preview)',
key: 'qwen-2.5-coder-32b',
},
{
displayName: 'Qwen 2.5 32B (Preview)',
key: 'qwen-2.5-32b',
},
{
displayName: 'DeepSeek R1 Distill Qwen 32B (Preview)',
key: 'deepseek-r1-distill-qwen-32b',
},
{
displayName: 'DeepSeek R1 Distill Llama 70B SpecDec (Preview)',
key: 'deepseek-r1-distill-llama-70b-specdec',
},
{
displayName: 'DeepSeek R1 Distill Llama 70B (Preview)',
key: 'deepseek-r1-distill-llama-70b',
},
{
displayName: 'Llama 3.3 70B SpecDec (Preview)',
key: 'llama-3.3-70b-specdec',
},
{
displayName: 'Llama 3.2 1B Preview (Preview)',
key: 'llama-3.2-1b-preview',
},
{
displayName: 'Llama 3.2 3B Preview (Preview)',
key: 'llama-3.2-3b-preview',
},
{
displayName: 'Llama 3.2 11B Vision Preview (Preview)',
key: 'llama-3.2-11b-vision-preview',
},
{
displayName: 'Llama 3.2 90B Vision Preview (Preview)',
key: 'llama-3.2-90b-vision-preview',
},
];
export const loadGroqChatModels = async () => {
const groqApiKey = getGroqApiKey();
if (!groqApiKey) return {};
try {
const chatModels: Record<string, ChatModel> = {};
groqChatModels.forEach((model) => {
chatModels[model.key] = {
displayName: model.displayName,
model: new ChatOpenAI({
openAIApiKey: groqApiKey,
modelName: model.key,
temperature: 0.7,
configuration: {
baseURL: 'https://api.groq.com/openai/v1',
},
}),
};
});
return chatModels;
} catch (err) {
console.error(`Error loading Groq models: ${err}`);
return {};
}
};

91
ui/lib/providers/index.ts
View File

@@ -1,91 +0,0 @@
import { Embeddings } from '@langchain/core/embeddings';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { loadOpenAIChatModels, loadOpenAIEmbeddingModels } from './openai';
import {
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
} from '../config';
import { ChatOpenAI } from '@langchain/openai';
import { loadOllamaChatModels, loadOllamaEmbeddingModels } from './ollama';
import { loadGroqChatModels } from './groq';
import { loadAnthropicChatModels } from './anthropic';
import { loadGeminiChatModels, loadGeminiEmbeddingModels } from './gemini';
export interface ChatModel {
displayName: string;
model: BaseChatModel;
}
export interface EmbeddingModel {
displayName: string;
model: Embeddings;
}
export const chatModelProviders: Record<
string,
() => Promise<Record<string, ChatModel>>
> = {
openai: loadOpenAIChatModels,
ollama: loadOllamaChatModels,
groq: loadGroqChatModels,
anthropic: loadAnthropicChatModels,
gemini: loadGeminiChatModels,
};
export const embeddingModelProviders: Record<
string,
() => Promise<Record<string, EmbeddingModel>>
> = {
openai: loadOpenAIEmbeddingModels,
ollama: loadOllamaEmbeddingModels,
gemini: loadGeminiEmbeddingModels,
};
export const getAvailableChatModelProviders = async () => {
const models: Record<string, Record<string, ChatModel>> = {};
for (const provider in chatModelProviders) {
const providerModels = await chatModelProviders[provider]();
if (Object.keys(providerModels).length > 0) {
models[provider] = providerModels;
}
}
const customOpenAiApiKey = getCustomOpenaiApiKey();
const customOpenAiApiUrl = getCustomOpenaiApiUrl();
const customOpenAiModelName = getCustomOpenaiModelName();
models['custom_openai'] = {
...(customOpenAiApiKey && customOpenAiApiUrl && customOpenAiModelName
? {
[customOpenAiModelName]: {
displayName: customOpenAiModelName,
model: new ChatOpenAI({
openAIApiKey: customOpenAiApiKey,
modelName: customOpenAiModelName,
temperature: 0.7,
configuration: {
baseURL: customOpenAiApiUrl,
},
}),
},
}
: {}),
};
return models;
};
export const getAvailableEmbeddingModelProviders = async () => {
const models: Record<string, Record<string, EmbeddingModel>> = {};
for (const provider in embeddingModelProviders) {
const providerModels = await embeddingModelProviders[provider]();
if (Object.keys(providerModels).length > 0) {
models[provider] = providerModels;
}
}
return models;
};

73
ui/lib/providers/ollama.ts
View File

@@ -1,73 +0,0 @@
import axios from 'axios';
import { getKeepAlive, getOllamaApiEndpoint } from '../config';
import { ChatModel, EmbeddingModel } from '.';
import { ChatOllama } from '@langchain/community/chat_models/ollama';
import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
export const loadOllamaChatModels = async () => {
const ollamaApiEndpoint = getOllamaApiEndpoint();
if (!ollamaApiEndpoint) return {};
try {
const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
headers: {
'Content-Type': 'application/json',
},
});
const { models } = res.data;
const chatModels: Record<string, ChatModel> = {};
models.forEach((model: any) => {
chatModels[model.model] = {
displayName: model.name,
model: new ChatOllama({
baseUrl: ollamaApiEndpoint,
model: model.model,
temperature: 0.7,
keepAlive: getKeepAlive(),
}),
};
});
return chatModels;
} catch (err) {
console.error(`Error loading Ollama models: ${err}`);
return {};
}
};
export const loadOllamaEmbeddingModels = async () => {
const ollamaApiEndpoint = getOllamaApiEndpoint();
if (!ollamaApiEndpoint) return {};
try {
const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
headers: {
'Content-Type': 'application/json',
},
});
const { models } = res.data;
const embeddingModels: Record<string, EmbeddingModel> = {};
models.forEach((model: any) => {
embeddingModels[model.model] = {
displayName: model.name,
model: new OllamaEmbeddings({
baseUrl: ollamaApiEndpoint,
model: model.model,
}),
};
});
return embeddingModels;
} catch (err) {
console.error(`Error loading Ollama embeddings models: ${err}`);
return {};
}
};

88
ui/lib/providers/openai.ts
View File

@@ -1,88 +0,0 @@
import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
import { getOpenaiApiKey } from '../config';
import { ChatModel, EmbeddingModel } from '.';
const openaiChatModels: Record<string, string>[] = [
{
displayName: 'GPT-3.5 Turbo',
key: 'gpt-3.5-turbo',
},
{
displayName: 'GPT-4',
key: 'gpt-4',
},
{
displayName: 'GPT-4 turbo',
key: 'gpt-4-turbo',
},
{
displayName: 'GPT-4 omni',
key: 'gpt-4o',
},
{
displayName: 'GPT-4 omni mini',
key: 'gpt-4o-mini',
},
];
const openaiEmbeddingModels: Record<string, string>[] = [
{
displayName: 'Text Embedding 3 Small',
key: 'text-embedding-3-small',
},
{
displayName: 'Text Embedding 3 Large',
key: 'text-embedding-3-large',
},
];
export const loadOpenAIChatModels = async () => {
const openaiApiKey = getOpenaiApiKey();
if (!openaiApiKey) return {};
try {
const chatModels: Record<string, ChatModel> = {};
openaiChatModels.forEach((model) => {
chatModels[model.key] = {
displayName: model.displayName,
model: new ChatOpenAI({
openAIApiKey: openaiApiKey,
modelName: model.key,
temperature: 0.7,
}),
};
});
return chatModels;
} catch (err) {
console.error(`Error loading OpenAI models: ${err}`);
return {};
}
};
export const loadOpenAIEmbeddingModels = async () => {
const openaiApiKey = getOpenaiApiKey();
if (!openaiApiKey) return {};
try {
const embeddingModels: Record<string, EmbeddingModel> = {};
openaiEmbeddingModels.forEach((model) => {
embeddingModels[model.key] = {
displayName: model.displayName,
model: new OpenAIEmbeddings({
openAIApiKey: openaiApiKey,
modelName: model.key,
}),
};
});
return embeddingModels;
} catch (err) {
console.error(`Error loading OpenAI embeddings models: ${err}`);
return {};
}
};

495
ui/lib/search/metaSearchAgent.ts
View File

@@ -1,495 +0,0 @@
import { ChatOpenAI } from '@langchain/openai';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import {
ChatPromptTemplate,
MessagesPlaceholder,
PromptTemplate,
} from '@langchain/core/prompts';
import {
RunnableLambda,
RunnableMap,
RunnableSequence,
} from '@langchain/core/runnables';
import { BaseMessage } from '@langchain/core/messages';
import { StringOutputParser } from '@langchain/core/output_parsers';
import LineListOutputParser from '../outputParsers/listLineOutputParser';
import LineOutputParser from '../outputParsers/lineOutputParser';
import { getDocumentsFromLinks } from '../utils/documents';
import { Document } from 'langchain/document';
import { searchSearxng } from '../searxng';
import path from 'node:path';
import fs from 'node:fs';
import computeSimilarity from '../utils/computeSimilarity';
import formatChatHistoryAsString from '../utils/formatHistory';
import eventEmitter from 'events';
import { StreamEvent } from '@langchain/core/tracers/log_stream';
export interface MetaSearchAgentType {
searchAndAnswer: (
message: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
fileIds: string[],
) => Promise<eventEmitter>;
}
interface Config {
searchWeb: boolean;
rerank: boolean;
summarizer: boolean;
rerankThreshold: number;
queryGeneratorPrompt: string;
responsePrompt: string;
activeEngines: string[];
}
type BasicChainInput = {
chat_history: BaseMessage[];
query: string;
};
class MetaSearchAgent implements MetaSearchAgentType {
private config: Config;
private strParser = new StringOutputParser();
constructor(config: Config) {
this.config = config;
}
private async createSearchRetrieverChain(llm: BaseChatModel) {
(llm as unknown as ChatOpenAI).temperature = 0;
return RunnableSequence.from([
PromptTemplate.fromTemplate(this.config.queryGeneratorPrompt),
llm,
this.strParser,
RunnableLambda.from(async (input: string) => {
const linksOutputParser = new LineListOutputParser({
key: 'links',
});
const questionOutputParser = new LineOutputParser({
key: 'question',
});
const links = await linksOutputParser.parse(input);
let question = this.config.summarizer
? await questionOutputParser.parse(input)
: input;
if (question === 'not_needed') {
return { query: '', docs: [] };
}
if (links.length > 0) {
if (question.length === 0) {
question = 'summarize';
}
let docs: Document[] = [];
const linkDocs = await getDocumentsFromLinks({ links });
const docGroups: Document[] = [];
linkDocs.map((doc) => {
const URLDocExists = docGroups.find(
(d) =>
d.metadata.url === doc.metadata.url &&
d.metadata.totalDocs < 10,
);
if (!URLDocExists) {
docGroups.push({
...doc,
metadata: {
...doc.metadata,
totalDocs: 1,
},
});
}
const docIndex = docGroups.findIndex(
(d) =>
d.metadata.url === doc.metadata.url &&
d.metadata.totalDocs < 10,
);
if (docIndex !== -1) {
docGroups[docIndex].pageContent =
docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
docGroups[docIndex].metadata.totalDocs += 1;
}
});
await Promise.all(
docGroups.map(async (doc) => {
const res = await llm.invoke(`
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.
<example>
1. \`<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.
\`
2. \`<text>
The theory of relativity, or simply relativity, encompasses two interrelated theories of Albert Einstein: special relativity and general
relativity. However, the word "relativity" is sometimes used in reference to Galilean invariance. The term "theory of relativity" was based
on the expression "relative theory" used by Max Planck in 1906. The theory of relativity usually encompasses two interrelated theories by
Albert Einstein: special relativity and general relativity. Special relativity applies to all physical phenomena in the absence of gravity.
General relativity explains the law of gravitation and its relation to other forces of nature. It applies to the cosmological and astrophysical
realm, including astronomy.
</text>
<query>
summarize
</query>
Response:
The theory of relativity, developed by Albert Einstein, encompasses two main theories: special relativity and general relativity. Special
relativity applies to all physical phenomena in the absence of gravity, while general relativity explains the law of gravitation and its
relation to other forces of nature. The theory of relativity is based on the concept of "relative theory," as introduced by Max Planck in
1906. It is a fundamental theory in physics that has revolutionized our understanding of the universe.
\`
</example>
Everything below is the actual data you will be working with. Good luck!
<query>
${question}
</query>
<text>
${doc.pageContent}
</text>
Make sure to answer the query in the summary.
`);
const document = new Document({
pageContent: res.content as string,
metadata: {
title: doc.metadata.title,
url: doc.metadata.url,
},
});
docs.push(document);
}),
);
return { query: question, docs: docs };
} else {
const res = await searchSearxng(question, {
language: 'en',
engines: this.config.activeEngines,
});
const documents = res.results.map(
(result) =>
new Document({
pageContent:
result.content ||
(this.config.activeEngines.includes('youtube')
? result.title
: '') /* Todo: Implement transcript grabbing using Youtubei (source: https://www.npmjs.com/package/youtubei) */,
metadata: {
title: result.title,
url: result.url,
...(result.img_src && { img_src: result.img_src }),
},
}),
);
return { query: question, docs: documents };
}
}),
]);
}
private async createAnsweringChain(
llm: BaseChatModel,
fileIds: string[],
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) {
return RunnableSequence.from([
RunnableMap.from({
query: (input: BasicChainInput) => input.query,
chat_history: (input: BasicChainInput) => input.chat_history,
date: () => new Date().toISOString(),
context: RunnableLambda.from(async (input: BasicChainInput) => {
const processedHistory = formatChatHistoryAsString(
input.chat_history,
);
let docs: Document[] | null = null;
let query = input.query;
if (this.config.searchWeb) {
const searchRetrieverChain =
await this.createSearchRetrieverChain(llm);
const searchRetrieverResult = await searchRetrieverChain.invoke({
chat_history: processedHistory,
query,
});
query = searchRetrieverResult.query;
docs = searchRetrieverResult.docs;
}
const sortedDocs = await this.rerankDocs(
query,
docs ?? [],
fileIds,
embeddings,
optimizationMode,
);
return sortedDocs;
})
.withConfig({
runName: 'FinalSourceRetriever',
})
.pipe(this.processDocs),
}),
ChatPromptTemplate.fromMessages([
['system', this.config.responsePrompt],
new MessagesPlaceholder('chat_history'),
['user', '{query}'],
]),
llm,
this.strParser,
]).withConfig({
runName: 'FinalResponseGenerator',
});
}
private async rerankDocs(
query: string,
docs: Document[],
fileIds: string[],
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
) {
if (docs.length === 0 && fileIds.length === 0) {
return docs;
}
const filesData = fileIds
.map((file) => {
const filePath = path.join(process.cwd(), 'uploads', file);
const contentPath = filePath + '-extracted.json';
const embeddingsPath = filePath + '-embeddings.json';
const content = JSON.parse(fs.readFileSync(contentPath, 'utf8'));
const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
const fileSimilaritySearchObject = content.contents.map(
(c: string, i: number) => {
return {
fileName: content.title,
content: c,
embeddings: embeddings.embeddings[i],
};
},
);
return fileSimilaritySearchObject;
})
.flat();
if (query.toLocaleLowerCase() === 'summarize') {
return docs.slice(0, 15);
}
const docsWithContent = docs.filter(
(doc) => doc.pageContent && doc.pageContent.length > 0,
);
if (optimizationMode === 'speed' || this.config.rerank === false) {
if (filesData.length > 0) {
const [queryEmbedding] = await Promise.all([
embeddings.embedQuery(query),
]);
const fileDocs = filesData.map((fileData) => {
return new Document({
pageContent: fileData.content,
metadata: {
title: fileData.fileName,
url: `File`,
},
});
});
const similarity = filesData.map((fileData, i) => {
const sim = computeSimilarity(queryEmbedding, fileData.embeddings);
return {
index: i,
similarity: sim,
};
});
let sortedDocs = similarity
.filter(
(sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3),
)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 15)
.map((sim) => fileDocs[sim.index]);
sortedDocs =
docsWithContent.length > 0 ? sortedDocs.slice(0, 8) : sortedDocs;
return [
...sortedDocs,
...docsWithContent.slice(0, 15 - sortedDocs.length),
];
} else {
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),
]);
docsWithContent.push(
...filesData.map((fileData) => {
return new Document({
pageContent: fileData.content,
metadata: {
title: fileData.fileName,
url: `File`,
},
});
}),
);
docEmbeddings.push(...filesData.map((fileData) => fileData.embeddings));
const similarity = docEmbeddings.map((docEmbedding, i) => {
const sim = computeSimilarity(queryEmbedding, docEmbedding);
return {
index: i,
similarity: sim,
};
});
const sortedDocs = similarity
.filter((sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3))
.sort((a, b) => b.similarity - a.similarity)
.slice(0, 15)
.map((sim) => docsWithContent[sim.index]);
return sortedDocs;
}
return [];
}
private processDocs(docs: Document[]) {
return docs
.map(
(_, index) =>
`${index + 1}. ${docs[index].metadata.title} ${docs[index].pageContent}`,
)
.join('\n');
}
private async handleStream(
stream: AsyncGenerator<StreamEvent, any, any>,
emitter: eventEmitter,
) {
for await (const event of stream) {
if (
event.event === 'on_chain_end' &&
event.name === 'FinalSourceRetriever'
) {
``;
emitter.emit(
'data',
JSON.stringify({ type: 'sources', data: event.data.output }),
);
}
if (
event.event === 'on_chain_stream' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit(
'data',
JSON.stringify({ type: 'response', data: event.data.chunk }),
);
}
if (
event.event === 'on_chain_end' &&
event.name === 'FinalResponseGenerator'
) {
emitter.emit('end');
}
}
}
async searchAndAnswer(
message: string,
history: BaseMessage[],
llm: BaseChatModel,
embeddings: Embeddings,
optimizationMode: 'speed' | 'balanced' | 'quality',
fileIds: string[],
) {
const emitter = new eventEmitter();
const answeringChain = await this.createAnsweringChain(
llm,
fileIds,
embeddings,
optimizationMode,
);
const stream = answeringChain.streamEvents(
{
chat_history: history,
query: message,
},
{
version: 'v1',
},
);
this.handleStream(stream, emitter);
return emitter;
}
}
export default MetaSearchAgent;

48
ui/lib/searxng.ts
View File

@@ -1,48 +0,0 @@
import axios from 'axios';
import { getSearxngApiEndpoint } from './config';
interface SearxngSearchOptions {
categories?: string[];
engines?: string[];
language?: string;
pageno?: number;
}
interface SearxngSearchResult {
title: string;
url: string;
img_src?: string;
thumbnail_src?: string;
thumbnail?: string;
content?: string;
author?: string;
iframe_src?: string;
}
export const searchSearxng = async (
query: string,
opts?: SearxngSearchOptions,
) => {
const searxngURL = getSearxngApiEndpoint();
const url = new URL(`${searxngURL}/search?format=json`);
url.searchParams.append('q', query);
if (opts) {
Object.keys(opts).forEach((key) => {
const value = opts[key as keyof SearxngSearchOptions];
if (Array.isArray(value)) {
url.searchParams.append(key, value.join(','));
return;
}
url.searchParams.append(key, value as string);
});
}
const res = await axios.get(url.toString());
const results: SearxngSearchResult[] = res.data.results;
const suggestions: string[] = res.data.suggestions;
return { results, suggestions };
};

5
ui/lib/types/compute-dot.d.ts vendored
View File

@@ -1,5 +0,0 @@
declare function computeDot(vectorA: number[], vectorB: number[]): number;
declare module 'compute-dot' {
export default computeDot;
}

27
ui/lib/utils.ts
View File

@@ -1,27 +0,0 @@
import clsx, { ClassValue } from 'clsx';
import { twMerge } from 'tailwind-merge';
export const cn = (...classes: ClassValue[]) => twMerge(clsx(...classes));
export const formatTimeDifference = (
date1: Date | string,
date2: Date | string,
): string => {
date1 = new Date(date1);
date2 = new Date(date2);
const diffInSeconds = Math.floor(
Math.abs(date2.getTime() - date1.getTime()) / 1000,
);
if (diffInSeconds < 60)
return `${diffInSeconds} second${diffInSeconds !== 1 ? 's' : ''}`;
else if (diffInSeconds < 3600)
return `${Math.floor(diffInSeconds / 60)} minute${Math.floor(diffInSeconds / 60) !== 1 ? 's' : ''}`;
else if (diffInSeconds < 86400)
return `${Math.floor(diffInSeconds / 3600)} hour${Math.floor(diffInSeconds / 3600) !== 1 ? 's' : ''}`;
else if (diffInSeconds < 31536000)
return `${Math.floor(diffInSeconds / 86400)} day${Math.floor(diffInSeconds / 86400) !== 1 ? 's' : ''}`;
else
return `${Math.floor(diffInSeconds / 31536000)} year${Math.floor(diffInSeconds / 31536000) !== 1 ? 's' : ''}`;
};

17
ui/lib/utils/computeSimilarity.ts
View File

@@ -1,17 +0,0 @@
import dot from 'compute-dot';
import cosineSimilarity from 'compute-cosine-similarity';
import { getSimilarityMeasure } from '../config';
const computeSimilarity = (x: number[], y: number[]): number => {
const similarityMeasure = getSimilarityMeasure();
if (similarityMeasure === 'cosine') {
return cosineSimilarity(x, y) as number;
} else if (similarityMeasure === 'dot') {
return dot(x, y);
}
throw new Error('Invalid similarity measure');
};
export default computeSimilarity;

99
ui/lib/utils/documents.ts
View File

@@ -1,99 +0,0 @@
import axios from 'axios';
import { htmlToText } from 'html-to-text';
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
import { Document } from '@langchain/core/documents';
import pdfParse from 'pdf-parse';
export const getDocumentsFromLinks = async ({ links }: { links: string[] }) => {
const splitter = new RecursiveCharacterTextSplitter();
let docs: Document[] = [];
await Promise.all(
links.map(async (link) => {
link =
link.startsWith('http://') || link.startsWith('https://')
? link
: `https://${link}`;
try {
const res = await axios.get(link, {
responseType: 'arraybuffer',
});
const isPdf = res.headers['content-type'] === 'application/pdf';
if (isPdf) {
const pdfText = await pdfParse(res.data);
const parsedText = pdfText.text
.replace(/(\r\n|\n|\r)/gm, ' ')
.replace(/\s+/g, ' ')
.trim();
const splittedText = await splitter.splitText(parsedText);
const title = 'PDF Document';
const linkDocs = splittedText.map((text) => {
return new Document({
pageContent: text,
metadata: {
title: title,
url: link,
},
});
});
docs.push(...linkDocs);
return;
}
const parsedText = htmlToText(res.data.toString('utf8'), {
selectors: [
{
selector: 'a',
options: {
ignoreHref: true,
},
},
],
})
.replace(/(\r\n|\n|\r)/gm, ' ')
.replace(/\s+/g, ' ')
.trim();
const splittedText = await splitter.splitText(parsedText);
const title = res.data
.toString('utf8')
.match(/<title>(.*?)<\/title>/)?.[1];
const linkDocs = splittedText.map((text) => {
return new Document({
pageContent: text,
metadata: {
title: title || link,
url: link,
},
});
});
docs.push(...linkDocs);
} catch (err) {
console.error(
'An error occurred while getting documents from links: ',
err,
);
docs.push(
new Document({
pageContent: `Failed to retrieve content from the link: ${err}`,
metadata: {
title: 'Failed to retrieve content',
url: link,
},
}),
);
}
}),
);
return docs;
};

17
ui/lib/utils/files.ts
View File

@@ -1,17 +0,0 @@
import path from 'path';
import fs from 'fs';
export const getFileDetails = (fileId: string) => {
const fileLoc = path.join(
process.cwd(),
'./uploads',
fileId + '-extracted.json',
);
const parsedFile = JSON.parse(fs.readFileSync(fileLoc, 'utf8'));
return {
name: parsedFile.title,
fileId: fileId,
};
};

9
ui/lib/utils/formatHistory.ts
View File

@@ -1,9 +0,0 @@
import { BaseMessage } from '@langchain/core/messages';
const formatChatHistoryAsString = (history: BaseMessage[]) => {
return history
.map((message) => `${message._getType()}: ${message.content}`)
.join('\n');
};
export default formatChatHistoryAsString;

13
ui/next.config.mjs
View File

@@ -1,13 +0,0 @@
/** @type {import('next').NextConfig} */
const nextConfig = {
images: {
remotePatterns: [
{
hostname: 's2.googleusercontent.com',
},
],
},
serverExternalPackages: ['pdf-parse'],
};
export default nextConfig;

62
ui/package.json
View File

@@ -1,62 +0,0 @@
{
"name": "perplexica-frontend",
"version": "1.10.0-rc3",
"license": "MIT",
"author": "ItzCrazyKns",
"scripts": {
"dev": "next dev",
"build": "next build",
"start": "next start",
"lint": "next lint",
"format:write": "prettier . --write",
"db:push": "drizzle-kit push sqlite"
},
"dependencies": {
"@headlessui/react": "^2.2.0",
"@iarna/toml": "^2.2.5",
"@icons-pack/react-simple-icons": "^12.3.0",
"@langchain/community": "^0.3.36",
"@langchain/core": "^0.3.42",
"@langchain/openai": "^0.0.25",
"@langchain/textsplitters": "^0.1.0",
"@tailwindcss/typography": "^0.5.12",
"axios": "^1.8.3",
"better-sqlite3": "^11.9.1",
"clsx": "^2.1.0",
"compute-cosine-similarity": "^1.1.0",
"compute-dot": "^1.1.0",
"drizzle-orm": "^0.40.1",
"html-to-text": "^9.0.5",
"langchain": "^0.1.30",
"lucide-react": "^0.363.0",
"markdown-to-jsx": "^7.7.2",
"next": "^15.2.2",
"next-themes": "^0.3.0",
"pdf-parse": "^1.1.1",
"react": "^18",
"react-dom": "^18",
"react-text-to-speech": "^0.14.5",
"react-textarea-autosize": "^8.5.3",
"sonner": "^1.4.41",
"tailwind-merge": "^2.2.2",
"winston": "^3.17.0",
"yet-another-react-lightbox": "^3.17.2",
"zod": "^3.22.4"
},
"devDependencies": {
"@types/better-sqlite3": "^7.6.12",
"@types/html-to-text": "^9.0.4",
"@types/node": "^20",
"@types/pdf-parse": "^1.1.4",
"@types/react": "^18",
"@types/react-dom": "^18",
"autoprefixer": "^10.0.1",
"drizzle-kit": "^0.30.5",
"eslint": "^8",
"eslint-config-next": "14.1.4",
"postcss": "^8",
"prettier": "^3.2.5",
"tailwindcss": "^3.3.0",
"typescript": "^5"
}
}

6
ui/postcss.config.js
View File

@@ -1,6 +0,0 @@
module.exports = {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
};

1
ui/public/next.svg
View File

@@ -1 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 394 80"><path fill="#000" d="M262 0h68.5v12.7h-27.2v66.6h-13.6V12.7H262V0ZM149 0v12.7H94v20.4h44.3v12.6H94v21h55v12.6H80.5V0h68.7zm34.3 0h-17.8l63.8 79.4h17.9l-32-39.7 32-39.6h-17.9l-23 28.6-23-28.6zm18.3 56.7-9-11-27.1 33.7h17.8l18.3-22.7z"/><path fill="#000" d="M81 79.3 17 0H0v79.3h13.6V17l50.2 62.3H81Zm252.6-.4c-1 0-1.8-.4-2.5-1s-1.1-1.6-1.1-2.6.3-1.8 1-2.5 1.6-1 2.6-1 1.8.3 2.5 1a3.4 3.4 0 0 1 .6 4.3 3.7 3.7 0 0 1-3 1.8zm23.2-33.5h6v23.3c0 2.1-.4 4-1.3 5.5a9.1 9.1 0 0 1-3.8 3.5c-1.6.8-3.5 1.3-5.7 1.3-2 0-3.7-.4-5.3-1s-2.8-1.8-3.7-3.2c-.9-1.3-1.4-3-1.4-5h6c.1.8.3 1.6.7 2.2s1 1.2 1.6 1.5c.7.4 1.5.5 2.4.5 1 0 1.8-.2 2.4-.6a4 4 0 0 0 1.6-1.8c.3-.8.5-1.8.5-3V45.5zm30.9 9.1a4.4 4.4 0 0 0-2-3.3 7.5 7.5 0 0 0-4.3-1.1c-1.3 0-2.4.2-3.3.5-.9.4-1.6 1-2 1.6a3.5 3.5 0 0 0-.3 4c.3.5.7.9 1.3 1.2l1.8 1 2 .5 3.2.8c1.3.3 2.5.7 3.7 1.2a13 13 0 0 1 3.2 1.8 8.1 8.1 0 0 1 3 6.5c0 2-.5 3.7-1.5 5.1a10 10 0 0 1-4.4 3.5c-1.8.8-4.1 1.2-6.8 1.2-2.6 0-4.9-.4-6.8-1.2-2-.8-3.4-2-4.5-3.5a10 10 0 0 1-1.7-5.6h6a5 5 0 0 0 3.5 4.6c1 .4 2.2.6 3.4.6 1.3 0 2.5-.2 3.5-.6 1-.4 1.8-1 2.4-1.7a4 4 0 0 0 .8-2.4c0-.9-.2-1.6-.7-2.2a11 11 0 0 0-2.1-1.4l-3.2-1-3.8-1c-2.8-.7-5-1.7-6.6-3.2a7.2 7.2 0 0 1-2.4-5.7 8 8 0 0 1 1.7-5 10 10 0 0 1 4.3-3.5c2-.8 4-1.2 6.4-1.2 2.3 0 4.4.4 6.2 1.2 1.8.8 3.2 2 4.3 3.4 1 1.4 1.5 3 1.5 5h-5.8z"/></svg>
Side by Side

Before

Width:  |  Height:  |  Size: 1.3 KiB

1
ui/public/vercel.svg
View File

@@ -1 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 283 64"><path fill="black" d="M141 16c-11 0-19 7-19 18s9 18 20 18c7 0 13-3 16-7l-7-5c-2 3-6 4-9 4-5 0-9-3-10-7h28v-3c0-11-8-18-19-18zm-9 15c1-4 4-7 9-7s8 3 9 7h-18zm117-15c-11 0-19 7-19 18s9 18 20 18c6 0 12-3 16-7l-8-5c-2 3-5 4-8 4-5 0-9-3-11-7h28l1-3c0-11-8-18-19-18zm-10 15c2-4 5-7 10-7s8 3 9 7h-19zm-39 3c0 6 4 10 10 10 4 0 7-2 9-5l8 5c-3 5-9 8-17 8-11 0-19-7-19-18s8-18 19-18c8 0 14 3 17 8l-8 5c-2-3-5-5-9-5-6 0-10 4-10 10zm83-29v46h-9V5h9zM37 0l37 64H0L37 0zm92 5-27 48L74 5h10l18 30 17-30h10zm59 12v10l-3-1c-6 0-10 4-10 10v15h-9V17h9v9c0-5 6-9 13-9z"/></svg>
Side by Side

Before

Width:  |  Height:  |  Size: 629 B

52
ui/tailwind.config.ts
View File

@@ -1,52 +0,0 @@
import type { Config } from 'tailwindcss';
import type { DefaultColors } from 'tailwindcss/types/generated/colors';
const themeDark = (colors: DefaultColors) => ({
50: '#0a0a0a',
100: '#111111',
200: '#1c1c1c',
});
const themeLight = (colors: DefaultColors) => ({
50: '#fcfcf9',
100: '#f3f3ee',
200: '#e8e8e3',
});
const config: Config = {
content: [
'./pages/**/*.{js,ts,jsx,tsx,mdx}',
'./components/**/*.{js,ts,jsx,tsx,mdx}',
'./app/**/*.{js,ts,jsx,tsx,mdx}',
],
darkMode: 'class',
theme: {
extend: {
borderColor: ({ colors }) => {
return {
light: themeLight(colors),
dark: themeDark(colors),
};
},
colors: ({ colors }) => {
const colorsDark = themeDark(colors);
const colorsLight = themeLight(colors);
return {
dark: {
primary: colorsDark[50],
secondary: colorsDark[100],
...colorsDark,
},
light: {
primary: colorsLight[50],
secondary: colorsLight[100],
...colorsLight,
},
};
},
},
},
plugins: [require('@tailwindcss/typography')],
};
export default config;

27
ui/tsconfig.json
View File

@@ -1,27 +0,0 @@
{
"compilerOptions": {
"lib": ["dom", "dom.iterable", "esnext"],
"allowJs": true,
"skipLibCheck": true,
"strict": true,
"noEmit": true,
"esModuleInterop": true,
"module": "esnext",
"moduleResolution": "bundler",
"resolveJsonModule": true,
"isolatedModules": true,
"jsx": "preserve",
"incremental": true,
"plugins": [
{
"name": "next"
}
],
"paths": {
"@/*": ["./*"]
},
"target": "ES2017"
},
"include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
"exclude": ["node_modules"]
}

2
ui/uploads/.gitignore vendored
View File

@@ -1,2 +0,0 @@
*
!.gitignore

4883
ui/yarn.lock
View File

File diff suppressed because it is too large Load Diff