feat(app): remove backend

src/lib/actions.ts

@@ -0,0 +1,31 @@
+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;
+};

src/lib/chains/imageSearchAgent.ts

@@ -0,0 +1,90 @@
+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;

src/lib/chains/suggestionGeneratorAgent.ts

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

src/lib/chains/videoSearchAgent.ts

@@ -0,0 +1,97 @@
+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;

src/lib/config.ts

@@ -0,0 +1,116 @@
+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 = () =>
+  loadConfig().API_ENDPOINTS.SEARXNG || process.env.SEARXNG_API_URL;
+
+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),
+  );
+};

src/lib/db/index.ts

@@ -0,0 +1,11 @@
+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;

src/lib/db/schema.ts

@@ -0,0 +1,28 @@
+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`'[]'`),
+});

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/prompts/academicSearch.ts

@@ -0,0 +1,65 @@
+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}.
+`;

src/lib/prompts/index.ts

@@ -0,0 +1,32 @@
+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,
+};

src/lib/prompts/redditSearch.ts

@@ -0,0 +1,65 @@
+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}.
+`;

src/lib/prompts/webSearch.ts

@@ -0,0 +1,106 @@
+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}.
+`;

src/lib/prompts/wolframAlpha.ts

@@ -0,0 +1,65 @@
+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}.
+`;

src/lib/prompts/writingAssistant.ts

@@ -0,0 +1,13 @@
+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>
+`;

src/lib/prompts/youtubeSearch.ts

@@ -0,0 +1,65 @@
+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}.
+`;

src/lib/providers/anthropic.ts

@@ -1,6 +1,38 @@
-import { ChatAnthropic } from '@langchain/anthropic';
-import { getAnthropicApiKey } from '../../config';
-import logger from '../../utils/logger';
+import { ChatOpenAI } from '@langchain/openai';
+import { ChatModel } from '.';
+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 = {
-      'claude-3-5-sonnet-20241022': {
-        displayName: 'Claude 3.5 Sonnet',
-        model: new ChatAnthropic({
+    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,
-          anthropicApiKey: anthropicApiKey,
-          model: 'claude-3-5-sonnet-20241022',
-        }),
-      },
-      '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',
-        }),
-      },
-    };
+          configuration: {
+            baseURL: 'https://api.anthropic.com/v1/',
+          },
+        }) as unknown as BaseChatModel,
+      };
+    });
 
     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 {
-  ChatGoogleGenerativeAI,
-  GoogleGenerativeAIEmbeddings,
-} from '@langchain/google-genai';
-import { getGeminiApiKey } from '../../config';
-import logger from '../../utils/logger';
+import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
+import { getGeminiApiKey } from '../config';
+import { ChatModel, EmbeddingModel } from '.';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import { Embeddings } from '@langchain/core/embeddings';
+
+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 = {
-      'gemini-1.5-flash': {
-        displayName: 'Gemini 1.5 Flash',
-        model: new ChatGoogleGenerativeAI({
-          modelName: 'gemini-1.5-flash',
+    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,
-          apiKey: geminiApiKey,
-        }),
-      },
-      'gemini-1.5-flash-8b': {
-        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,
-        }),
-      },
-    };
+          configuration: {
+            baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai/',
+          },
+        }) as unknown as BaseChatModel,
+      };
+    });
 
     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 = {
-      'text-embedding-004': {
-        displayName: 'Text Embedding',
-        model: new GoogleGenerativeAIEmbeddings({
-          apiKey: geminiApiKey,
-          modelName: 'text-embedding-004',
-        }),
-      },
-    };
+    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/',
+          },
+        }) 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 logger from '../../utils/logger';
+import { getGroqApiKey } from '../config';
+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 = {
-      'llama-3.3-70b-versatile': {
-        displayName: 'Llama 3.3 70B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.3-70b-versatile',
-            temperature: 0.7,
-          },
-          {
+    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',
           },
-        ),
-      },
-      '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',
-          },
-        ),
-      },
-    };
+        }) as unknown as BaseChatModel,
+      };
+    });
 
     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 { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
-import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
-import { loadAnthropicChatModels } from './anthropic';
-import { loadTransformersEmbeddingsModels } from './transformers';
-import { loadGeminiChatModels, loadGeminiEmbeddingsModels } from './gemini';
+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';
+} 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 = {
-  openai: loadOpenAIEmbeddingsModels,
-  local: loadTransformersEmbeddingsModels,
-  ollama: loadOllamaEmbeddingsModels,
-  gemini: loadGeminiEmbeddingsModels,
+export const embeddingModelProviders: Record<
+  string,
+  () => Promise<Record<string, EmbeddingModel>>
+> = {
+  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 keepAlive = getKeepAlive();
+  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 chatModels = ollamaModels.reduce((acc, model) => {
-      acc[model.model] = {
+    const chatModels: Record<string, ChatModel> = {};
+
+    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 () => {
-  const ollamaEndpoint = getOllamaApiEndpoint();
+export const loadOllamaEmbeddingModels = async () => {
+  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) => {
-      acc[model.model] = {
+    const embeddingModels: Record<string, EmbeddingModel> = {};
+
+    models.forEach((model: any) => {
+      embeddingModels[model.model] = {
         displayName: model.name,
         model: new OllamaEmbeddings({
-          baseUrl: ollamaEndpoint,
+          baseUrl: ollamaApiEndpoint,
           model: model.model,
         }),
       };
+    });
 
-      return acc;
-    }, {});
-
-    return embeddingsModels;
+    return embeddingModels;
   } 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 logger from '../../utils/logger';
+import { getOpenaiApiKey } from '../config';
+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 = {
-      'gpt-3.5-turbo': {
-        displayName: 'GPT-3.5 Turbo',
+    const chatModels: Record<string, ChatModel> = {};
+
+    openaiChatModels.forEach((model) => {
+      chatModels[model.key] = {
+        displayName: model.displayName,
         model: new ChatOpenAI({
-          openAIApiKey,
-          modelName: 'gpt-3.5-turbo',
+          openAIApiKey: openaiApiKey,
+          modelName: model.key,
           temperature: 0.7,
-        }),
-      },
-      '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,
-        }),
-      },
-    };
+        }) as unknown as BaseChatModel,
+      };
+    });
 
     return chatModels;
   } catch (err) {
-    logger.error(`Error loading OpenAI models: ${err}`);
+    console.error(`Error loading OpenAI models: ${err}`);
     return {};
   }
 };
 
-export const loadOpenAIEmbeddingsModels = async () => {
-  const openAIApiKey = getOpenaiApiKey();
+export const loadOpenAIEmbeddingModels = async () => {
+  const openaiApiKey = getOpenaiApiKey();
 
-  if (!openAIApiKey) return {};
+  if (!openaiApiKey) return {};
 
   try {
-    const embeddingModels = {
-      'text-embedding-3-small': {
-        displayName: 'Text Embedding 3 Small',
+    const embeddingModels: Record<string, EmbeddingModel> = {};
+
+    openaiEmbeddingModels.forEach((model) => {
+      embeddingModels[model.key] = {
+        displayName: model.displayName,
         model: new OpenAIEmbeddings({
-          openAIApiKey,
-          modelName: 'text-embedding-3-small',
-        }),
-      },
-      'text-embedding-3-large': {
-        displayName: 'Text Embedding 3 Large',
-        model: new OpenAIEmbeddings({
-          openAIApiKey,
-          modelName: 'text-embedding-3-large',
-        }),
-      },
-    };
+          openAIApiKey: openaiApiKey,
+          modelName: model.key,
+        }) as unknown as Embeddings,
+      };
+    });
 
     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/search/metaSearchAgent.ts

@@ -0,0 +1,495 @@
+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;

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])) {
-        url.searchParams.append(key, opts[key].join(','));
+      const value = opts[key as keyof SearxngSearchOptions];
+      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/lib/types/compute-dot.d.ts

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

src/lib/utils.ts

@@ -0,0 +1,27 @@
+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' : ''}`;
+};

src/lib/utils/computeSimilarity.ts

@@ -0,0 +1,17 @@
+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;

src/lib/utils/documents.ts

@@ -0,0 +1,99 @@
+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;
+};

src/lib/utils/files.ts

@@ -0,0 +1,17 @@
+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,
+  };
+};

src/lib/utils/formatHistory.ts

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