diff --git a/src/search/metaSearchAgent.ts b/src/search/metaSearchAgent.ts
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--- /dev/null
+++ b/src/search/metaSearchAgent.ts
@@ -0,0 +1,486 @@
+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 '../lib/outputParsers/listLineOutputParser';
+import LineOutputParser from '../lib/outputParsers/lineOutputParser';
+import { getDocumentsFromLinks } from '../utils/documents';
+import { Document } from 'langchain/document';
+import { searchSearxng } from '../lib/searxng';
+import path from 'path';
+import fs from 'fs';
+import computeSimilarity from '../utils/computeSimilarity';
+import formatChatHistoryAsString from '../utils/formatHistory';
+import eventEmitter from 'events';
+import { StreamEvent } from '@langchain/core/tracers/log_stream';
+import { IterableReadableStream } from '@langchain/core/utils/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 = [];
+
+ 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,
+ 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,
+ 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) => {
+ 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;
+ }
+ }
+
+ private processDocs(docs: Document[]) {
+ return docs
+ .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
+ .join('\n');
+ }
+
+ private async handleStream(
+ stream: IterableReadableStream<StreamEvent>,
+ 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;