feat(app): remove backend

.gitignore

@@ -4,9 +4,9 @@ npm-debug.log
 yarn-error.log
 
 # Build output
-/.next/
+.next/
-/out/
+out/
-/dist/
+dist/
 
 # IDE/Editor specific
 .vscode/

.prettierrc.js

@@ -6,7 +6,6 @@ const config = {
   endOfLine: 'auto',
   singleQuote: true,
   tabWidth: 2,
-  semi: true,
 };
 
 module.exports = config;

app.dockerfile

@@ -1,13 +1,20 @@
 FROM node:20.18.0-alpine
 
-ARG NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
-ARG NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
-ENV NEXT_PUBLIC_WS_URL=${NEXT_PUBLIC_WS_URL}
-ENV NEXT_PUBLIC_API_URL=${NEXT_PUBLIC_API_URL}
-
 WORKDIR /home/perplexica
 
-COPY ui /home/perplexica/
+COPY src /home/perplexica/src
+COPY public /home/perplexica/public
+COPY package.json /home/perplexica/package.json
+COPY yarn.lock /home/perplexica/yarn.lock
+COPY tsconfig.json /home/perplexica/tsconfig.json
+COPY next.config.mjs /home/perplexica/next.config.mjs
+COPY next-env.d.ts /home/perplexica/next-env.d.ts
+COPY postcss.config.js /home/perplexica/postcss.config.js
+COPY drizzle.config.ts /home/perplexica/drizzle.config.ts
+COPY tailwind.config.ts /home/perplexica/tailwind.config.ts
+
+RUN mkdir /home/perplexica/data
+RUN mkdir /home/perplexica/uploads
 
 RUN yarn install --frozen-lockfile
 RUN yarn build

backend.dockerfile

@@ -1,17 +0,0 @@
-FROM node:18-slim
-
-WORKDIR /home/perplexica
-
-COPY src /home/perplexica/src
-COPY tsconfig.json /home/perplexica/
-COPY drizzle.config.ts /home/perplexica/
-COPY package.json /home/perplexica/
-COPY yarn.lock /home/perplexica/
-
-RUN mkdir /home/perplexica/data
-RUN mkdir /home/perplexica/uploads
-
-RUN yarn install --frozen-lockfile --network-timeout 600000
-RUN yarn build
-
-CMD ["yarn", "start"]

docker-compose.yaml

@@ -9,41 +9,20 @@ services:
       - perplexica-network
     restart: unless-stopped
 
-  perplexica-backend:
+  app:
-    build:
-      context: .
-      dockerfile: backend.dockerfile
-    image: itzcrazykns1337/perplexica-backend:main
-    environment:
-      - SEARXNG_API_URL=http://searxng:8080
-    depends_on:
-      - searxng
-    ports:
-      - 3001:3001
-    volumes:
-      - backend-dbstore:/home/perplexica/data
-      - uploads:/home/perplexica/uploads
-      - ./config.toml:/home/perplexica/config.toml
-    extra_hosts:
-      - 'host.docker.internal:host-gateway'
-    networks:
-      - perplexica-network
-    restart: unless-stopped
-
-  perplexica-frontend:
     build:
       context: .
       dockerfile: app.dockerfile
-      args:
+    environment:
-        - NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
+      - SEARXNG_API_URL=http://searxng:8080
-        - NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
-    image: itzcrazykns1337/perplexica-frontend:main
-    depends_on:
-      - perplexica-backend
     ports:
       - 3000:3000
     networks:
       - perplexica-network
+    volumes:
+      - backend-dbstore:/home/perplexica/data
+      - uploads:/home/perplexica/uploads
+      - ./config.toml:/home/perplexica/config.toml
     restart: unless-stopped
 
 networks:

drizzle.config.ts

@@ -2,7 +2,7 @@ import { defineConfig } from 'drizzle-kit';
 
 export default defineConfig({
   dialect: 'sqlite',
-  schema: './src/db/schema.ts',
+  schema: './src/lib/db/schema.ts',
   out: './drizzle',
   dbCredentials: {
     url: './data/db.sqlite',

next-env.d.ts

@@ -0,0 +1,5 @@
+/// <reference types="next" />
+/// <reference types="next/image-types/global" />
+
+// NOTE: This file should not be edited
+// see https://nextjs.org/docs/app/api-reference/config/typescript for more information.

package.json

@@ -1,53 +1,62 @@
 {
-  "name": "perplexica-backend",
+  "name": "perplexica-frontend",
   "version": "1.10.0-rc3",
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {
-    "start": "npm run db:push && node dist/app.js",
+    "dev": "next dev",
-    "build": "tsc",
+    "build": "next build",
-    "dev": "nodemon --ignore uploads/ src/app.ts ",
+    "start": "next start",
-    "db:push": "drizzle-kit push sqlite",
+    "lint": "next lint",
-    "format": "prettier . --check",
+    "format:write": "prettier . --write",
-    "format:write": "prettier . --write"
+    "db:push": "drizzle-kit push sqlite"
-  },
-  "devDependencies": {
-    "@types/better-sqlite3": "^7.6.10",
-    "@types/cors": "^2.8.17",
-    "@types/express": "^4.17.21",
-    "@types/html-to-text": "^9.0.4",
-    "@types/multer": "^1.4.12",
-    "@types/pdf-parse": "^1.1.4",
-    "@types/readable-stream": "^4.0.11",
-    "@types/ws": "^8.5.12",
-    "drizzle-kit": "^0.22.7",
-    "nodemon": "^3.1.0",
-    "prettier": "^3.2.5",
-    "ts-node": "^10.9.2",
-    "typescript": "^5.4.3"
   },
   "dependencies": {
+    "@headlessui/react": "^2.2.0",
     "@iarna/toml": "^2.2.5",
-    "@langchain/anthropic": "^0.2.3",
+    "@icons-pack/react-simple-icons": "^12.3.0",
-    "@langchain/community": "^0.2.16",
+    "@langchain/community": "^0.3.36",
+    "@langchain/core": "^0.3.42",
     "@langchain/openai": "^0.0.25",
-    "@langchain/google-genai": "^0.0.23",
+    "@langchain/textsplitters": "^0.1.0",
-    "@xenova/transformers": "^2.17.1",
+    "@tailwindcss/typography": "^0.5.12",
-    "axios": "^1.6.8",
+    "axios": "^1.8.3",
-    "better-sqlite3": "^11.0.0",
+    "better-sqlite3": "^11.9.1",
+    "clsx": "^2.1.0",
     "compute-cosine-similarity": "^1.1.0",
     "compute-dot": "^1.1.0",
-    "cors": "^2.8.5",
+    "drizzle-orm": "^0.40.1",
-    "dotenv": "^16.4.5",
-    "drizzle-orm": "^0.31.2",
-    "express": "^4.19.2",
     "html-to-text": "^9.0.5",
     "langchain": "^0.1.30",
-    "mammoth": "^1.8.0",
+    "lucide-react": "^0.363.0",
-    "multer": "^1.4.5-lts.1",
+    "markdown-to-jsx": "^7.7.2",
+    "next": "^15.2.2",
+    "next-themes": "^0.3.0",
     "pdf-parse": "^1.1.1",
-    "winston": "^3.13.0",
+    "react": "^18",
-    "ws": "^8.17.1",
+    "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"
   }
 }

sample.config.toml

@@ -24,4 +24,4 @@ MODEL_NAME = ""
 API_URL = "" # Ollama API URL - http://host.docker.internal:11434
 
 [API_ENDPOINTS]
-SEARXNG = "http://localhost:32768" # SearxNG API URL
+SEARXNG = "" # SearxNG API URL - http://localhost:32768

src/app.ts

@@ -1,38 +0,0 @@
-import { startWebSocketServer } from './websocket';
-import express from 'express';
-import cors from 'cors';
-import http from 'http';
-import routes from './routes';
-import { getPort } from './config';
-import logger from './utils/logger';
-
-const port = getPort();
-
-const app = express();
-const server = http.createServer(app);
-
-const corsOptions = {
-  origin: '*',
-};
-
-app.use(cors(corsOptions));
-app.use(express.json());
-
-app.use('/api', routes);
-app.get('/api', (_, res) => {
-  res.status(200).json({ status: 'ok' });
-});
-
-server.listen(port, () => {
-  logger.info(`Server is running on port ${port}`);
-});
-
-startWebSocketServer(server);
-
-process.on('uncaughtException', (err, origin) => {
-  logger.error(`Uncaught Exception at ${origin}: ${err}`);
-});
-
-process.on('unhandledRejection', (reason, promise) => {
-  logger.error(`Unhandled Rejection at: ${promise}, reason: ${reason}`);
-});

src/app/api/chat/route.ts

@@ -3,16 +3,28 @@ 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 {
+  chatModelProviders,
+  embeddingModelProviders,
+  getAvailableChatModelProviders,
+  getAvailableEmbeddingModelProviders,
+} 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';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { ChatOpenAI } from '@langchain/openai';
+import {
+  getCustomOpenaiApiKey,
+  getCustomOpenaiApiUrl,
+  getCustomOpenaiModelName,
+} from '@/lib/config';
 
 export const runtime = 'nodejs';
 export const dynamic = 'force-dynamic';
 
-export const searchHandlers: Record<string, MetaSearchAgent> = {
+const searchHandlers: Record<string, MetaSearchAgent> = {
   webSearch: new MetaSearchAgent({
     activeEngines: [],
     queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
@@ -227,7 +239,7 @@ const handleHistorySave = async (
 export const POST = async (req: Request) => {
   try {
     const body = (await req.json()) as Body;
-    const { message, chatModel, embeddingModel } = body;
+    const { message } = body;
 
     if (message.content === '') {
       return Response.json(
@@ -238,50 +250,52 @@ export const POST = async (req: Request) => {
       );
     }
 
-    const getProviderChatModels = chatModelProviders[chatModel.provider];
+    const [chatModelProviders, embeddingModelProviders] = await Promise.all([
+      getAvailableChatModelProviders(),
+      getAvailableEmbeddingModelProviders(),
+    ]);
 
-    if (!getProviderChatModels) {
+    const chatModelProvider =
-      return Response.json(
+      chatModelProviders[
-        {
+        body.chatModel?.provider || Object.keys(chatModelProviders)[0]
-          message: 'Invalid chat model provider',
+      ];
+    const chatModel =
+      chatModelProvider[
+        body.chatModel?.name || Object.keys(chatModelProvider)[0]
+      ];
+
+    const embeddingProvider =
+      embeddingModelProviders[
+        body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0]
+      ];
+    const embeddingModel =
+      embeddingProvider[
+        body.embeddingModel?.name || Object.keys(embeddingProvider)[0]
+      ];
+
+    let llm: BaseChatModel | undefined;
+    let embedding = embeddingModel.model;
+
+    if (body.chatModel?.provider === 'custom_openai') {
+      llm = new ChatOpenAI({
+        openAIApiKey: getCustomOpenaiApiKey(),
+        modelName: getCustomOpenaiModelName(),
+        temperature: 0.7,
+        configuration: {
+          baseURL: getCustomOpenaiApiUrl(),
         },
-        { status: 400 },
+      }) as unknown as BaseChatModel;
-      );
+    } else if (chatModelProvider && chatModel) {
+      llm = chatModel.model;
     }
 
-    const chatModels = await getProviderChatModels();
-
-    const llm = chatModels[chatModel.name].model;
-
     if (!llm) {
-      return Response.json(
+      return Response.json({ error: 'Invalid chat model' }, { status: 400 });
-        {
-          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(
-        {
+        { error: 'Invalid embedding model' },
-          message: 'Invalid embedding model',
-        },
         { status: 400 },
       );
     }

src/app/api/images/route.ts

@@ -6,7 +6,7 @@ import {
 } 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 { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
 import { ChatOpenAI } from '@langchain/openai';
 
 interface ChatModel {
@@ -32,7 +32,7 @@ export const POST = async (req: Request) => {
           return new AIMessage(msg.content);
         }
       })
-      .filter((msg) => msg !== undefined);
+      .filter((msg) => msg !== undefined) as BaseMessage[];
 
     const chatModelProviders = await getAvailableChatModelProviders();
 
@@ -55,7 +55,7 @@ export const POST = async (req: Request) => {
         configuration: {
           baseURL: getCustomOpenaiApiUrl(),
         },
-      });
+      }) as unknown as BaseChatModel;
     } else if (chatModelProvider && chatModel) {
       llm = chatModel.model;
     }

src/app/api/suggestions/route.ts

@@ -6,7 +6,7 @@ import {
 } 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 { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
 import { ChatOpenAI } from '@langchain/openai';
 
 interface ChatModel {
@@ -31,7 +31,7 @@ export const POST = async (req: Request) => {
           return new AIMessage(msg.content);
         }
       })
-      .filter((msg) => msg !== undefined);
+      .filter((msg) => msg !== undefined) as BaseMessage[];
 
     const chatModelProviders = await getAvailableChatModelProviders();
 
@@ -54,7 +54,7 @@ export const POST = async (req: Request) => {
         configuration: {
           baseURL: getCustomOpenaiApiUrl(),
         },
-      });
+      }) as unknown as BaseChatModel;
     } else if (chatModelProvider && chatModel) {
       llm = chatModel.model;
     }

src/app/api/videos/route.ts

@@ -6,7 +6,7 @@ import {
 } 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 { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
 import { ChatOpenAI } from '@langchain/openai';
 
 interface ChatModel {
@@ -32,7 +32,7 @@ export const POST = async (req: Request) => {
           return new AIMessage(msg.content);
         }
       })
-      .filter((msg) => msg !== undefined);
+      .filter((msg) => msg !== undefined) as BaseMessage[];
 
     const chatModelProviders = await getAvailableChatModelProviders();
 
@@ -55,7 +55,7 @@ export const POST = async (req: Request) => {
         configuration: {
           baseURL: getCustomOpenaiApiUrl(),
         },
-      });
+      }) as unknown as BaseChatModel;
     } else if (chatModelProvider && chatModel) {
       llm = chatModel.model;
     }

src/chains/imageSearchAgent.ts

@@ -1,84 +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 '../lib/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;
-};
-
-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 = [];
-
-      res.results.forEach((result) => {
-        if (result.img_src && result.url && result.title) {
-          images.push({
-            img_src: result.img_src,
-            url: result.url,
-            title: result.title,
-          });
-        }
-      });
-
-      return images.slice(0, 10);
-    }),
-  ]);
-};
-
-const handleImageSearch = (
-  input: ImageSearchChainInput,
-  llm: BaseChatModel,
-) => {
-  const imageSearchChain = createImageSearchChain(llm);
-  return imageSearchChain.invoke(input);
-};
-
-export default handleImageSearch;

src/chains/suggestionGeneratorAgent.ts

@@ -1,55 +0,0 @@
-import { RunnableSequence, RunnableMap } from '@langchain/core/runnables';
-import ListLineOutputParser from '../lib/outputParsers/listLineOutputParser';
-import { PromptTemplate } from '@langchain/core/prompts';
-import formatChatHistoryAsString from '../utils/formatHistory';
-import { BaseMessage } from '@langchain/core/messages';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { ChatOpenAI } from '@langchain/openai';
-
-const suggestionGeneratorPrompt = `
-You are an AI suggestion generator for an AI powered search engine. You will be given a conversation below. You need to generate 4-5 suggestions based on the conversation. The suggestion should be relevant to the conversation that can be used by the user to ask the chat model for more information.
-You need to make sure the suggestions are relevant to the conversation and are helpful to the user. Keep a note that the user might use these suggestions to ask a chat model for more information. 
-Make sure the suggestions are medium in length and are informative and relevant to the conversation.
-
-Provide these suggestions separated by newlines between the XML tags <suggestions> and </suggestions>. For example:
-
-<suggestions>
-Tell me more about SpaceX and their recent projects
-What is the latest news on SpaceX?
-Who is the CEO of SpaceX?
-</suggestions>
-
-Conversation:
-{chat_history}
-`;
-
-type SuggestionGeneratorInput = {
-  chat_history: BaseMessage[];
-};
-
-const outputParser = new ListLineOutputParser({
-  key: 'suggestions',
-});
-
-const createSuggestionGeneratorChain = (llm: BaseChatModel) => {
-  return RunnableSequence.from([
-    RunnableMap.from({
-      chat_history: (input: SuggestionGeneratorInput) =>
-        formatChatHistoryAsString(input.chat_history),
-    }),
-    PromptTemplate.fromTemplate(suggestionGeneratorPrompt),
-    llm,
-    outputParser,
-  ]);
-};
-
-const generateSuggestions = (
-  input: SuggestionGeneratorInput,
-  llm: BaseChatModel,
-) => {
-  (llm as unknown as ChatOpenAI).temperature = 0;
-  const suggestionGeneratorChain = createSuggestionGeneratorChain(llm);
-  return suggestionGeneratorChain.invoke(input);
-};
-
-export default generateSuggestions;

src/chains/videoSearchAgent.ts

@@ -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 '../lib/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;
-};
-
-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 = [];
-
-      res.results.forEach((result) => {
-        if (
-          result.thumbnail &&
-          result.url &&
-          result.title &&
-          result.iframe_src
-        ) {
-          videos.push({
-            img_src: result.thumbnail,
-            url: result.url,
-            title: result.title,
-            iframe_src: result.iframe_src,
-          });
-        }
-      });
-
-      return videos.slice(0, 10);
-    }),
-  ]);
-};
-
-const handleVideoSearch = (
-  input: VideoSearchChainInput,
-  llm: BaseChatModel,
-) => {
-  const VideoSearchChain = createVideoSearchChain(llm);
-  return VideoSearchChain.invoke(input);
-};
-
-export default handleVideoSearch;

src/components/ChatWindow.tsx

@@ -45,159 +45,138 @@ const checkConfig = async (
   setIsConfigReady: (ready: boolean) => void,
   setHasError: (hasError: boolean) => void,
 ) => {
-  useEffect(() => {
+  try {
-    const checkConfig = async () => {
+    let chatModel = localStorage.getItem('chatModel');
-      try {
+    let chatModelProvider = localStorage.getItem('chatModelProvider');
-        let chatModel = localStorage.getItem('chatModel');
+    let embeddingModel = localStorage.getItem('embeddingModel');
-        let chatModelProvider = localStorage.getItem('chatModelProvider');
+    let embeddingModelProvider = localStorage.getItem('embeddingModelProvider');
-        let embeddingModel = localStorage.getItem('embeddingModel');
+
-        let embeddingModelProvider = localStorage.getItem(
+    const autoImageSearch = localStorage.getItem('autoImageSearch');
-          'embeddingModelProvider',
+    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();
+    });
 
-        const autoImageSearch = localStorage.getItem('autoImageSearch');
+    if (
-        const autoVideoSearch = localStorage.getItem('autoVideoSearch');
+      !chatModel ||
+      !chatModelProvider ||
+      !embeddingModel ||
+      !embeddingModelProvider
+    ) {
+      if (!chatModel || !chatModelProvider) {
+        const chatModelProviders = providers.chatModelProviders;
 
-        if (!autoImageSearch) {
+        chatModelProvider =
-          localStorage.setItem('autoImageSearch', 'true');
+          chatModelProvider || Object.keys(chatModelProviders)[0];
-        }
 
-        if (!autoVideoSearch) {
+        chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
-          localStorage.setItem('autoVideoSearch', 'false');
-        }
 
-        const providers = await fetch(`/api/models`, {
+        if (!chatModelProviders || Object.keys(chatModelProviders).length === 0)
-          headers: {
+          return toast.error('No chat models available');
-            'Content-Type': 'application/json',
+      }
-          },
+
-        }).then(async (res) => {
+      if (!embeddingModel || !embeddingModelProvider) {
-          if (!res.ok)
+        const embeddingModelProviders = providers.embeddingModelProviders;
-            throw new Error(
-              `Failed to fetch models: ${res.status} ${res.statusText}`,
-            );
-          return res.json();
-        });
 
         if (
-          !chatModel ||
+          !embeddingModelProviders ||
-          !chatModelProvider ||
+          Object.keys(embeddingModelProviders).length === 0
-          !embeddingModel ||
+        )
-          !embeddingModelProvider
+          return toast.error('No embedding models available');
-        ) {
-          if (!chatModel || !chatModelProvider) {
-            const chatModelProviders = providers.chatModelProviders;
 
-            chatModelProvider =
+        embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
-              chatModelProvider || Object.keys(chatModelProviders)[0];
+        embeddingModel = Object.keys(
-
+          embeddingModelProviders[embeddingModelProvider],
-            chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
+        )[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();
+      localStorage.setItem('chatModel', chatModel!);
-    // eslint-disable-next-line react-hooks/exhaustive-deps
+      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);
+  }
 };
 
 const loadMessages = async (
@@ -282,12 +261,15 @@ const ChatWindow = ({ id }: { id?: string }) => {
   const [hasError, setHasError] = useState(false);
   const [isReady, setIsReady] = useState(false);
 
-  checkConfig(
+  useEffect(() => {
-    setChatModelProvider,
+    checkConfig(
-    setEmbeddingModelProvider,
+      setChatModelProvider,
-    setIsConfigReady,
+      setEmbeddingModelProvider,
-    setHasError,
+      setIsConfigReady,
-  );
+      setHasError,
+    );
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
 
   const [loading, setLoading] = useState(false);
   const [messageAppeared, setMessageAppeared] = useState(false);

src/config.ts

@@ -1,117 +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(__dirname, `../${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(__dirname, `../${configFileName}`),
-    toml.stringify(mergedConfig),
-  );
-};

src/db/index.ts

@@ -1,10 +0,0 @@
-import { drizzle } from 'drizzle-orm/better-sqlite3';
-import Database from 'better-sqlite3';
-import * as schema from './schema';
-
-const sqlite = new Database('data/db.sqlite');
-const db = drizzle(sqlite, {
-  schema: schema,
-});
-
-export default db;

src/lib/config.ts

@@ -62,7 +62,7 @@ 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;
+  loadConfig().API_ENDPOINTS.SEARXNG || process.env.SEARXNG_API_URL;
 
 export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
 

src/lib/huggingfaceTransformer.ts

@@ -1,82 +0,0 @@
-import { Embeddings, type EmbeddingsParams } from '@langchain/core/embeddings';
-import { chunkArray } from '@langchain/core/utils/chunk_array';
-
-export interface HuggingFaceTransformersEmbeddingsParams
-  extends EmbeddingsParams {
-  modelName: string;
-
-  model: string;
-
-  timeout?: number;
-
-  batchSize?: number;
-
-  stripNewLines?: boolean;
-}
-
-export class HuggingFaceTransformersEmbeddings
-  extends Embeddings
-  implements HuggingFaceTransformersEmbeddingsParams
-{
-  modelName = 'Xenova/all-MiniLM-L6-v2';
-
-  model = 'Xenova/all-MiniLM-L6-v2';
-
-  batchSize = 512;
-
-  stripNewLines = true;
-
-  timeout?: number;
-
-  private pipelinePromise: Promise<any>;
-
-  constructor(fields?: Partial<HuggingFaceTransformersEmbeddingsParams>) {
-    super(fields ?? {});
-
-    this.modelName = fields?.model ?? fields?.modelName ?? this.model;
-    this.model = this.modelName;
-    this.stripNewLines = fields?.stripNewLines ?? this.stripNewLines;
-    this.timeout = fields?.timeout;
-  }
-
-  async embedDocuments(texts: string[]): Promise<number[][]> {
-    const batches = chunkArray(
-      this.stripNewLines ? texts.map((t) => t.replace(/\n/g, ' ')) : texts,
-      this.batchSize,
-    );
-
-    const batchRequests = batches.map((batch) => this.runEmbedding(batch));
-    const batchResponses = await Promise.all(batchRequests);
-    const embeddings: number[][] = [];
-
-    for (let i = 0; i < batchResponses.length; i += 1) {
-      const batchResponse = batchResponses[i];
-      for (let j = 0; j < batchResponse.length; j += 1) {
-        embeddings.push(batchResponse[j]);
-      }
-    }
-
-    return embeddings;
-  }
-
-  async embedQuery(text: string): Promise<number[]> {
-    const data = await this.runEmbedding([
-      this.stripNewLines ? text.replace(/\n/g, ' ') : text,
-    ]);
-    return data[0];
-  }
-
-  private async runEmbedding(texts: string[]) {
-    const { pipeline } = await import('@xenova/transformers');
-
-    const pipe = await (this.pipelinePromise ??= pipeline(
-      'feature-extraction',
-      this.model,
-    ));
-
-    return this.caller.call(async () => {
-      const output = await pipe(texts, { pooling: 'mean', normalize: true });
-      return output.tolist();
-    });
-  }
-}

src/lib/outputParsers/lineOutputParser.ts

@@ -9,7 +9,7 @@ class LineOutputParser extends BaseOutputParser<string> {
 
   constructor(args?: LineOutputParserArgs) {
     super();
-    this.key = args.key ?? this.key;
+    this.key = args?.key ?? this.key;
   }
 
   static lc_name() {

src/lib/outputParsers/listLineOutputParser.ts

@@ -9,7 +9,7 @@ class LineListOutputParser extends BaseOutputParser<string[]> {
 
   constructor(args?: LineListOutputParserArgs) {
     super();
-    this.key = args.key ?? this.key;
+    this.key = args?.key ?? this.key;
   }
 
   static lc_name() {

src/lib/providers/anthropic.ts

@@ -1,6 +1,38 @@
-import { ChatAnthropic } from '@langchain/anthropic';
+import { ChatOpenAI } from '@langchain/openai';
-import { getAnthropicApiKey } from '../../config';
+import { ChatModel } from '.';
-import logger from '../../utils/logger';
+import { getAnthropicApiKey } from '../config';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+
+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();
@@ -8,52 +40,25 @@ export const loadAnthropicChatModels = async () => {
   if (!anthropicApiKey) return {};
 
   try {
-    const chatModels = {
+    const chatModels: Record<string, ChatModel> = {};
-      'claude-3-5-sonnet-20241022': {
+
-        displayName: 'Claude 3.5 Sonnet',
+    anthropicChatModels.forEach((model) => {
-        model: new ChatAnthropic({
+      chatModels[model.key] = {
+        displayName: model.displayName,
+        model: new ChatOpenAI({
+          openAIApiKey: anthropicApiKey,
+          modelName: model.key,
           temperature: 0.7,
-          anthropicApiKey: anthropicApiKey,
+          configuration: {
-          model: 'claude-3-5-sonnet-20241022',
+            baseURL: 'https://api.anthropic.com/v1/',
-        }),
+          },
-      },
+        }) as unknown as BaseChatModel,
-      'claude-3-5-haiku-20241022': {
+      };
-        displayName: 'Claude 3.5 Haiku',
+    });
-        model: new ChatAnthropic({
-          temperature: 0.7,
-          anthropicApiKey: anthropicApiKey,
-          model: 'claude-3-5-haiku-20241022',
-        }),
-      },
-      'claude-3-opus-20240229': {
-        displayName: 'Claude 3 Opus',
-        model: new ChatAnthropic({
-          temperature: 0.7,
-          anthropicApiKey: anthropicApiKey,
-          model: 'claude-3-opus-20240229',
-        }),
-      },
-      'claude-3-sonnet-20240229': {
-        displayName: 'Claude 3 Sonnet',
-        model: new ChatAnthropic({
-          temperature: 0.7,
-          anthropicApiKey: anthropicApiKey,
-          model: 'claude-3-sonnet-20240229',
-        }),
-      },
-      'claude-3-haiku-20240307': {
-        displayName: 'Claude 3 Haiku',
-        model: new ChatAnthropic({
-          temperature: 0.7,
-          anthropicApiKey: anthropicApiKey,
-          model: 'claude-3-haiku-20240307',
-        }),
-      },
-    };
 
     return chatModels;
   } catch (err) {
-    logger.error(`Error loading Anthropic models: ${err}`);
+    console.error(`Error loading Anthropic models: ${err}`);
     return {};
   }
 };

src/lib/providers/gemini.ts

@@ -1,9 +1,42 @@
-import {
+import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
-  ChatGoogleGenerativeAI,
+import { getGeminiApiKey } from '../config';
-  GoogleGenerativeAIEmbeddings,
+import { ChatModel, EmbeddingModel } from '.';
-} from '@langchain/google-genai';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { getGeminiApiKey } from '../../config';
+import { Embeddings } from '@langchain/core/embeddings';
-import logger from '../../utils/logger';
+
+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();
@@ -11,75 +44,53 @@ export const loadGeminiChatModels = async () => {
   if (!geminiApiKey) return {};
 
   try {
-    const chatModels = {
+    const chatModels: Record<string, ChatModel> = {};
-      'gemini-1.5-flash': {
+
-        displayName: 'Gemini 1.5 Flash',
+    geminiChatModels.forEach((model) => {
-        model: new ChatGoogleGenerativeAI({
+      chatModels[model.key] = {
-          modelName: 'gemini-1.5-flash',
+        displayName: model.displayName,
+        model: new ChatOpenAI({
+          openAIApiKey: geminiApiKey,
+          modelName: model.key,
           temperature: 0.7,
-          apiKey: geminiApiKey,
+          configuration: {
-        }),
+            baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai/',
-      },
+          },
-      'gemini-1.5-flash-8b': {
+        }) as unknown as BaseChatModel,
-        displayName: 'Gemini 1.5 Flash 8B',
+      };
-        model: new ChatGoogleGenerativeAI({
+    });
-          modelName: 'gemini-1.5-flash-8b',
-          temperature: 0.7,
-          apiKey: geminiApiKey,
-        }),
-      },
-      'gemini-1.5-pro': {
-        displayName: 'Gemini 1.5 Pro',
-        model: new ChatGoogleGenerativeAI({
-          modelName: 'gemini-1.5-pro',
-          temperature: 0.7,
-          apiKey: geminiApiKey,
-        }),
-      },
-      'gemini-2.0-flash-exp': {
-        displayName: 'Gemini 2.0 Flash Exp',
-        model: new ChatGoogleGenerativeAI({
-          modelName: 'gemini-2.0-flash-exp',
-          temperature: 0.7,
-          apiKey: geminiApiKey,
-        }),
-      },
-      'gemini-2.0-flash-thinking-exp-01-21': {
-        displayName: 'Gemini 2.0 Flash Thinking Exp 01-21',
-        model: new ChatGoogleGenerativeAI({
-          modelName: 'gemini-2.0-flash-thinking-exp-01-21',
-          temperature: 0.7,
-          apiKey: geminiApiKey,
-        }),
-      },
-    };
 
     return chatModels;
   } catch (err) {
-    logger.error(`Error loading Gemini models: ${err}`);
+    console.error(`Error loading Gemini models: ${err}`);
     return {};
   }
 };
 
-export const loadGeminiEmbeddingsModels = async () => {
+export const loadGeminiEmbeddingModels = async () => {
   const geminiApiKey = getGeminiApiKey();
 
   if (!geminiApiKey) return {};
 
   try {
-    const embeddingModels = {
+    const embeddingModels: Record<string, EmbeddingModel> = {};
-      'text-embedding-004': {
+
-        displayName: 'Text Embedding',
+    geminiEmbeddingModels.forEach((model) => {
-        model: new GoogleGenerativeAIEmbeddings({
+      embeddingModels[model.key] = {
-          apiKey: geminiApiKey,
+        displayName: model.displayName,
-          modelName: 'text-embedding-004',
+        model: new OpenAIEmbeddings({
-        }),
+          openAIApiKey: geminiApiKey,
-      },
+          modelName: model.key,
-    };
+          configuration: {
+            baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai/',
+          },
+        }) as unknown as Embeddings,
+      };
+    });
 
     return embeddingModels;
   } catch (err) {
-    logger.error(`Error loading Gemini embeddings model: ${err}`);
+    console.error(`Error loading OpenAI embeddings models: ${err}`);
     return {};
   }
 };

src/lib/providers/groq.ts

@@ -1,6 +1,82 @@
 import { ChatOpenAI } from '@langchain/openai';
-import { getGroqApiKey } from '../../config';
+import { getGroqApiKey } from '../config';
-import logger from '../../utils/logger';
+import { ChatModel } from '.';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+
+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();
@@ -8,129 +84,25 @@ export const loadGroqChatModels = async () => {
   if (!groqApiKey) return {};
 
   try {
-    const chatModels = {
+    const chatModels: Record<string, ChatModel> = {};
-      'llama-3.3-70b-versatile': {
+
-        displayName: 'Llama 3.3 70B',
+    groqChatModels.forEach((model) => {
-        model: new ChatOpenAI(
+      chatModels[model.key] = {
-          {
+        displayName: model.displayName,
-            openAIApiKey: groqApiKey,
+        model: new ChatOpenAI({
-            modelName: 'llama-3.3-70b-versatile',
+          openAIApiKey: groqApiKey,
-            temperature: 0.7,
+          modelName: model.key,
-          },
+          temperature: 0.7,
-          {
+          configuration: {
             baseURL: 'https://api.groq.com/openai/v1',
           },
-        ),
+        }) as unknown as BaseChatModel,
-      },
+      };
-      'llama-3.2-3b-preview': {
+    });
-        displayName: 'Llama 3.2 3B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.2-3b-preview',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama-3.2-11b-vision-preview': {
-        displayName: 'Llama 3.2 11B Vision',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.2-11b-vision-preview',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama-3.2-90b-vision-preview': {
-        displayName: 'Llama 3.2 90B Vision',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.2-90b-vision-preview',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama-3.1-8b-instant': {
-        displayName: 'Llama 3.1 8B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.1-8b-instant',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama3-8b-8192': {
-        displayName: 'LLaMA3 8B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama3-8b-8192',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama3-70b-8192': {
-        displayName: 'LLaMA3 70B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama3-70b-8192',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'mixtral-8x7b-32768': {
-        displayName: 'Mixtral 8x7B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'mixtral-8x7b-32768',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'gemma2-9b-it': {
-        displayName: 'Gemma2 9B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'gemma2-9b-it',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-    };
 
     return chatModels;
   } catch (err) {
-    logger.error(`Error loading Groq models: ${err}`);
+    console.error(`Error loading Groq models: ${err}`);
     return {};
   }
 };

src/lib/providers/index.ts

@@ -1,33 +1,49 @@
-import { loadGroqChatModels } from './groq';
+import { Embeddings } from '@langchain/core/embeddings';
-import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
+import { loadOpenAIChatModels, loadOpenAIEmbeddingModels } from './openai';
-import { loadAnthropicChatModels } from './anthropic';
-import { loadTransformersEmbeddingsModels } from './transformers';
-import { loadGeminiChatModels, loadGeminiEmbeddingsModels } from './gemini';
 import {
   getCustomOpenaiApiKey,
   getCustomOpenaiApiUrl,
   getCustomOpenaiModelName,
-} from '../../config';
+} 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';
 
-const chatModelProviders = {
+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,
-  groq: loadGroqChatModels,
   ollama: loadOllamaChatModels,
+  groq: loadGroqChatModels,
   anthropic: loadAnthropicChatModels,
   gemini: loadGeminiChatModels,
 };
 
-const embeddingModelProviders = {
+export const embeddingModelProviders: Record<
-  openai: loadOpenAIEmbeddingsModels,
+  string,
-  local: loadTransformersEmbeddingsModels,
+  () => Promise<Record<string, EmbeddingModel>>
-  ollama: loadOllamaEmbeddingsModels,
+> = {
-  gemini: loadGeminiEmbeddingsModels,
+  openai: loadOpenAIEmbeddingModels,
+  ollama: loadOllamaEmbeddingModels,
+  gemini: loadGeminiEmbeddingModels,
 };
 
 export const getAvailableChatModelProviders = async () => {
-  const models = {};
+  const models: Record<string, Record<string, ChatModel>> = {};
 
   for (const provider in chatModelProviders) {
     const providerModels = await chatModelProviders[provider]();
@@ -52,7 +68,7 @@ export const getAvailableChatModelProviders = async () => {
               configuration: {
                 baseURL: customOpenAiApiUrl,
               },
-            }),
+            }) as unknown as BaseChatModel,
           },
         }
       : {}),
@@ -62,7 +78,7 @@ export const getAvailableChatModelProviders = async () => {
 };
 
 export const getAvailableEmbeddingModelProviders = async () => {
-  const models = {};
+  const models: Record<string, Record<string, EmbeddingModel>> = {};
 
   for (const provider in embeddingModelProviders) {
     const providerModels = await embeddingModelProviders[provider]();

src/lib/providers/ollama.ts

@@ -1,74 +1,73 @@
-import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
-import { getKeepAlive, getOllamaApiEndpoint } from '../../config';
-import logger from '../../utils/logger';
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
 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 ollamaEndpoint = getOllamaApiEndpoint();
+  const ollamaApiEndpoint = getOllamaApiEndpoint();
-  const keepAlive = getKeepAlive();
 
-  if (!ollamaEndpoint) return {};
+  if (!ollamaApiEndpoint) return {};
 
   try {
-    const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
+    const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
     });
 
-    const { models: ollamaModels } = response.data;
+    const { models } = res.data;
 
-    const chatModels = ollamaModels.reduce((acc, model) => {
+    const chatModels: Record<string, ChatModel> = {};
-      acc[model.model] = {
+
+    models.forEach((model: any) => {
+      chatModels[model.model] = {
         displayName: model.name,
         model: new ChatOllama({
-          baseUrl: ollamaEndpoint,
+          baseUrl: ollamaApiEndpoint,
           model: model.model,
           temperature: 0.7,
-          keepAlive: keepAlive,
+          keepAlive: getKeepAlive(),
         }),
       };
-
+    });
-      return acc;
-    }, {});
 
     return chatModels;
   } catch (err) {
-    logger.error(`Error loading Ollama models: ${err}`);
+    console.error(`Error loading Ollama models: ${err}`);
     return {};
   }
 };
 
-export const loadOllamaEmbeddingsModels = async () => {
+export const loadOllamaEmbeddingModels = async () => {
-  const ollamaEndpoint = getOllamaApiEndpoint();
+  const ollamaApiEndpoint = getOllamaApiEndpoint();
 
-  if (!ollamaEndpoint) return {};
+  if (!ollamaApiEndpoint) return {};
 
   try {
-    const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
+    const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
     });
 
-    const { models: ollamaModels } = response.data;
+    const { models } = res.data;
 
-    const embeddingsModels = ollamaModels.reduce((acc, model) => {
+    const embeddingModels: Record<string, EmbeddingModel> = {};
-      acc[model.model] = {
+
+    models.forEach((model: any) => {
+      embeddingModels[model.model] = {
         displayName: model.name,
         model: new OllamaEmbeddings({
-          baseUrl: ollamaEndpoint,
+          baseUrl: ollamaApiEndpoint,
           model: model.model,
         }),
       };
+    });
 
-      return acc;
+    return embeddingModels;
-    }, {});
-
-    return embeddingsModels;
   } catch (err) {
-    logger.error(`Error loading Ollama embeddings model: ${err}`);
+    console.error(`Error loading Ollama embeddings models: ${err}`);
     return {};
   }
 };

src/lib/providers/openai.ts

@@ -1,89 +1,90 @@
 import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
-import { getOpenaiApiKey } from '../../config';
+import { getOpenaiApiKey } from '../config';
-import logger from '../../utils/logger';
+import { ChatModel, EmbeddingModel } from '.';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { Embeddings } from '@langchain/core/embeddings';
+
+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();
+  const openaiApiKey = getOpenaiApiKey();
 
-  if (!openAIApiKey) return {};
+  if (!openaiApiKey) return {};
 
   try {
-    const chatModels = {
+    const chatModels: Record<string, ChatModel> = {};
-      'gpt-3.5-turbo': {
+
-        displayName: 'GPT-3.5 Turbo',
+    openaiChatModels.forEach((model) => {
+      chatModels[model.key] = {
+        displayName: model.displayName,
         model: new ChatOpenAI({
-          openAIApiKey,
+          openAIApiKey: openaiApiKey,
-          modelName: 'gpt-3.5-turbo',
+          modelName: model.key,
           temperature: 0.7,
-        }),
+        }) as unknown as BaseChatModel,
-      },
+      };
-      'gpt-4': {
+    });
-        displayName: 'GPT-4',
-        model: new ChatOpenAI({
-          openAIApiKey,
-          modelName: 'gpt-4',
-          temperature: 0.7,
-        }),
-      },
-      'gpt-4-turbo': {
-        displayName: 'GPT-4 turbo',
-        model: new ChatOpenAI({
-          openAIApiKey,
-          modelName: 'gpt-4-turbo',
-          temperature: 0.7,
-        }),
-      },
-      'gpt-4o': {
-        displayName: 'GPT-4 omni',
-        model: new ChatOpenAI({
-          openAIApiKey,
-          modelName: 'gpt-4o',
-          temperature: 0.7,
-        }),
-      },
-      'gpt-4o-mini': {
-        displayName: 'GPT-4 omni mini',
-        model: new ChatOpenAI({
-          openAIApiKey,
-          modelName: 'gpt-4o-mini',
-          temperature: 0.7,
-        }),
-      },
-    };
 
     return chatModels;
   } catch (err) {
-    logger.error(`Error loading OpenAI models: ${err}`);
+    console.error(`Error loading OpenAI models: ${err}`);
     return {};
   }
 };
 
-export const loadOpenAIEmbeddingsModels = async () => {
+export const loadOpenAIEmbeddingModels = async () => {
-  const openAIApiKey = getOpenaiApiKey();
+  const openaiApiKey = getOpenaiApiKey();
 
-  if (!openAIApiKey) return {};
+  if (!openaiApiKey) return {};
 
   try {
-    const embeddingModels = {
+    const embeddingModels: Record<string, EmbeddingModel> = {};
-      'text-embedding-3-small': {
+
-        displayName: 'Text Embedding 3 Small',
+    openaiEmbeddingModels.forEach((model) => {
+      embeddingModels[model.key] = {
+        displayName: model.displayName,
         model: new OpenAIEmbeddings({
-          openAIApiKey,
+          openAIApiKey: openaiApiKey,
-          modelName: 'text-embedding-3-small',
+          modelName: model.key,
-        }),
+        }) as unknown as Embeddings,
-      },
+      };
-      'text-embedding-3-large': {
+    });
-        displayName: 'Text Embedding 3 Large',
-        model: new OpenAIEmbeddings({
-          openAIApiKey,
-          modelName: 'text-embedding-3-large',
-        }),
-      },
-    };
 
     return embeddingModels;
   } catch (err) {
-    logger.error(`Error loading OpenAI embeddings model: ${err}`);
+    console.error(`Error loading OpenAI embeddings models: ${err}`);
     return {};
   }
 };

src/lib/providers/transformers.ts

@@ -1,32 +0,0 @@
-import logger from '../../utils/logger';
-import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer';
-
-export const loadTransformersEmbeddingsModels = async () => {
-  try {
-    const embeddingModels = {
-      'xenova-bge-small-en-v1.5': {
-        displayName: 'BGE Small',
-        model: new HuggingFaceTransformersEmbeddings({
-          modelName: 'Xenova/bge-small-en-v1.5',
-        }),
-      },
-      'xenova-gte-small': {
-        displayName: 'GTE Small',
-        model: new HuggingFaceTransformersEmbeddings({
-          modelName: 'Xenova/gte-small',
-        }),
-      },
-      'xenova-bert-base-multilingual-uncased': {
-        displayName: 'Bert Multilingual',
-        model: new HuggingFaceTransformersEmbeddings({
-          modelName: 'Xenova/bert-base-multilingual-uncased',
-        }),
-      },
-    };
-
-    return embeddingModels;
-  } catch (err) {
-    logger.error(`Error loading Transformers embeddings model: ${err}`);
-    return {};
-  }
-};

src/lib/searxng.ts

@@ -1,5 +1,5 @@
 import axios from 'axios';
-import { getSearxngApiEndpoint } from '../config';
+import { getSearxngApiEndpoint } from './config';
 
 interface SearxngSearchOptions {
   categories?: string[];
@@ -30,11 +30,12 @@ export const searchSearxng = async (
 
   if (opts) {
     Object.keys(opts).forEach((key) => {
-      if (Array.isArray(opts[key])) {
+      const value = opts[key as keyof SearxngSearchOptions];
-        url.searchParams.append(key, opts[key].join(','));
+      if (Array.isArray(value)) {
+        url.searchParams.append(key, value.join(','));
         return;
       }
-      url.searchParams.append(key, opts[key]);
+      url.searchParams.append(key, value as string);
     });
   }
 

src/routes/chats.ts

@@ -1,66 +0,0 @@
-import express from 'express';
-import logger from '../utils/logger';
-import db from '../db/index';
-import { eq } from 'drizzle-orm';
-import { chats, messages } from '../db/schema';
-
-const router = express.Router();
-
-router.get('/', async (_, res) => {
-  try {
-    let chats = await db.query.chats.findMany();
-
-    chats = chats.reverse();
-
-    return res.status(200).json({ chats: chats });
-  } catch (err) {
-    res.status(500).json({ message: 'An error has occurred.' });
-    logger.error(`Error in getting chats: ${err.message}`);
-  }
-});
-
-router.get('/:id', async (req, res) => {
-  try {
-    const chatExists = await db.query.chats.findFirst({
-      where: eq(chats.id, req.params.id),
-    });
-
-    if (!chatExists) {
-      return res.status(404).json({ message: 'Chat not found' });
-    }
-
-    const chatMessages = await db.query.messages.findMany({
-      where: eq(messages.chatId, req.params.id),
-    });
-
-    return res.status(200).json({ chat: chatExists, messages: chatMessages });
-  } catch (err) {
-    res.status(500).json({ message: 'An error has occurred.' });
-    logger.error(`Error in getting chat: ${err.message}`);
-  }
-});
-
-router.delete(`/:id`, async (req, res) => {
-  try {
-    const chatExists = await db.query.chats.findFirst({
-      where: eq(chats.id, req.params.id),
-    });
-
-    if (!chatExists) {
-      return res.status(404).json({ message: 'Chat not found' });
-    }
-
-    await db.delete(chats).where(eq(chats.id, req.params.id)).execute();
-    await db
-      .delete(messages)
-      .where(eq(messages.chatId, req.params.id))
-      .execute();
-
-    return res.status(200).json({ message: 'Chat deleted successfully' });
-  } catch (err) {
-    res.status(500).json({ message: 'An error has occurred.' });
-    logger.error(`Error in deleting chat: ${err.message}`);
-  }
-});
-
-export default router;

src/routes/config.ts

@@ -1,104 +0,0 @@
-import express from 'express';
-import {
-  getAvailableChatModelProviders,
-  getAvailableEmbeddingModelProviders,
-} from '../lib/providers';
-import {
-  getGroqApiKey,
-  getOllamaApiEndpoint,
-  getAnthropicApiKey,
-  getGeminiApiKey,
-  getOpenaiApiKey,
-  updateConfig,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiApiKey,
-  getCustomOpenaiModelName,
-} from '../config';
-import logger from '../utils/logger';
-
-const router = express.Router();
-
-router.get('/', async (_, res) => {
-  try {
-    const config = {};
-
-    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();
-
-    res.status(200).json(config);
-  } catch (err: any) {
-    res.status(500).json({ message: 'An error has occurred.' });
-    logger.error(`Error getting config: ${err.message}`);
-  }
-});
-
-router.post('/', async (req, res) => {
-  const config = req.body;
-
-  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);
-
-  res.status(200).json({ message: 'Config updated' });
-});
-
-export default router;

src/routes/discover.ts

@@ -1,48 +0,0 @@
-import express from 'express';
-import { searchSearxng } from '../lib/searxng';
-import logger from '../utils/logger';
-
-const router = express.Router();
-
-router.get('/', async (req, res) => {
-  try {
-    const data = (
-      await Promise.all([
-        searchSearxng('site:businessinsider.com AI', {
-          engines: ['bing news'],
-          pageno: 1,
-        }),
-        searchSearxng('site:www.exchangewire.com AI', {
-          engines: ['bing news'],
-          pageno: 1,
-        }),
-        searchSearxng('site:yahoo.com AI', {
-          engines: ['bing news'],
-          pageno: 1,
-        }),
-        searchSearxng('site:businessinsider.com tech', {
-          engines: ['bing news'],
-          pageno: 1,
-        }),
-        searchSearxng('site:www.exchangewire.com tech', {
-          engines: ['bing news'],
-          pageno: 1,
-        }),
-        searchSearxng('site:yahoo.com tech', {
-          engines: ['bing news'],
-          pageno: 1,
-        }),
-      ])
-    )
-      .map((result) => result.results)
-      .flat()
-      .sort(() => Math.random() - 0.5);
-
-    return res.json({ blogs: data });
-  } catch (err: any) {
-    logger.error(`Error in discover route: ${err.message}`);
-    return res.status(500).json({ message: 'An error has occurred' });
-  }
-});
-
-export default router;

src/routes/images.ts

@@ -1,82 +0,0 @@
-import express from 'express';
-import handleImageSearch from '../chains/imageSearchAgent';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { getAvailableChatModelProviders } from '../lib/providers';
-import { HumanMessage, AIMessage } from '@langchain/core/messages';
-import logger from '../utils/logger';
-import { ChatOpenAI } from '@langchain/openai';
-import {
-  getCustomOpenaiApiKey,
-  getCustomOpenaiApiUrl,
-  getCustomOpenaiModelName,
-} from '../config';
-
-const router = express.Router();
-
-interface ChatModel {
-  provider: string;
-  model: string;
-}
-
-interface ImageSearchBody {
-  query: string;
-  chatHistory: any[];
-  chatModel?: ChatModel;
-}
-
-router.post('/', async (req, res) => {
-  try {
-    let body: ImageSearchBody = req.body;
-
-    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);
-      }
-    });
-
-    const chatModelProviders = await getAvailableChatModelProviders();
-
-    const chatModelProvider =
-      body.chatModel?.provider || Object.keys(chatModelProviders)[0];
-    const chatModel =
-      body.chatModel?.model ||
-      Object.keys(chatModelProviders[chatModelProvider])[0];
-
-    let llm: BaseChatModel | undefined;
-
-    if (body.chatModel?.provider === 'custom_openai') {
-      llm = new ChatOpenAI({
-        modelName: getCustomOpenaiModelName(),
-        openAIApiKey: getCustomOpenaiApiKey(),
-        temperature: 0.7,
-        configuration: {
-          baseURL: getCustomOpenaiApiUrl(),
-        },
-      }) as unknown as BaseChatModel;
-    } else if (
-      chatModelProviders[chatModelProvider] &&
-      chatModelProviders[chatModelProvider][chatModel]
-    ) {
-      llm = chatModelProviders[chatModelProvider][chatModel]
-        .model as unknown as BaseChatModel | undefined;
-    }
-
-    if (!llm) {
-      return res.status(400).json({ message: 'Invalid model selected' });
-    }
-
-    const images = await handleImageSearch(
-      { query: body.query, chat_history: chatHistory },
-      llm,
-    );
-
-    res.status(200).json({ images });
-  } catch (err) {
-    res.status(500).json({ message: 'An error has occurred.' });
-    logger.error(`Error in image search: ${err.message}`);
-  }
-});
-
-export default router;

src/routes/index.ts

@@ -1,24 +0,0 @@
-import express from 'express';
-import imagesRouter from './images';
-import videosRouter from './videos';
-import configRouter from './config';
-import modelsRouter from './models';
-import suggestionsRouter from './suggestions';
-import chatsRouter from './chats';
-import searchRouter from './search';
-import discoverRouter from './discover';
-import uploadsRouter from './uploads';
-
-const router = express.Router();
-
-router.use('/images', imagesRouter);
-router.use('/videos', videosRouter);
-router.use('/config', configRouter);
-router.use('/models', modelsRouter);
-router.use('/suggestions', suggestionsRouter);
-router.use('/chats', chatsRouter);
-router.use('/search', searchRouter);
-router.use('/discover', discoverRouter);
-router.use('/uploads', uploadsRouter);
-
-export default router;