PORT=3001GROQ_API_KEY=<GROQ API KEY>GROQ_MODEL=gemma2-9b-itGEMINI_API_KEY=<GEMINI API KEY>GEMINI_TEXT_EMBEDDING_MODEL=text-embedding-004SWAGGER_TITLE='Langchain Search Agent'SWAGGER_DESCRIPTION='Use Langchain tools and agent to search information on the Internet.'SWAGGER_VERSION='1.0'SWAGGER_TAG='Gemma 2, Langchain.js, Agent Tools'DUCK_DUCK_GO_MAX_RESULTS=1

npm i -save-exact @google/generative-ai @langchain/community@langchain/core @langchain/google-genai @langchain/groq @nestjs/axios @nestjs/config @nestjs/swagger @nestjs/throttler axios cheerio class-transformer class-validator compression duck-duck-scrape hbs langchain zod

export default () => ({ port: parseInt(process.env.PORT || '3001', 10), groq: { apiKey: process.env.GROQ_API_KEY || '', model: process.env.GROQ_MODEL || 'gemma2-9b-it', }, gemini: { apiKey: process.env.GEMINI_API_KEY || '', embeddingModel: process.env.GEMINI_TEXT_EMBEDDING_MODEL || 'text-embedding-004', }, swagger: { title: process.env.SWAGGER_TITLE || '', description: process.env.SWAGGER_DESCRIPTION || '', version: process.env.SWAGGER_VERSION || '', tag: process.env.SWAGGER_TAG || '', }, duckDuckGo: { maxResults: parseInt(process.env.DUCK_DUCK_GO_MAX_RESULTS || '1', 10), },});

// duck-config.type.ts
export type DuckDuckGoConfig = { maxResults: number;};

// groq-config.type.ts
export type GroqConfig = { model: string; apiKey: string;};

nest g mo angularDoc

// application/types/embedding-model-config.type.ts
export type EmbeddingModelConfig = { apiKey: string; embeddingModel: string;};

// application/embeddings/create-embedding-model.ts
import { TaskType } from '@google/generative-ai';import { GoogleGenerativeAIEmbeddings } from '@langchain/google-genai';import { ConfigService } from '@nestjs/config';import { EmbeddingModelConfig } from '../types/embedding-model-config.type';
export function createTextEmbeddingModel(configService: ConfigService, title = 'Angular') { const { apiKey, embeddingModel: model } = configService.get<EmbeddingModelConfig>('gemini'); return new GoogleGenerativeAIEmbeddings({ apiKey, model, taskType: TaskType.RETRIEVAL_DOCUMENT, title, });}

// application/loaders/web-page-loader.ts
import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';import { CheerioWebBaseLoader } from '@langchain/community/document_loaders/web/cheerio';
async function loadWebPages(webPages: string[]) { const loaders = webPages.map((page) => new CheerioWebBaseLoader(page)); const docs = await Promise.all(loaders.map((loader) => loader.load())); const signalDocs = docs.flat(); return splitter.splitDocuments(signalDocs);}

export async function loadSignalWebPages() { const webPages = [ 'https://angular.dev/guide/signals', 'https://angular.dev/guide/signals/rxjs-interop', 'https://angular.dev/guide/signals/inputs', 'https://angular.dev/guide/signals/model', 'https://angular.dev/guide/signals/queries', 'https://angular.dev/guide/components/output-fn', ];
 return loadWebPages(webPages);}

export async function loadFormWebPages() { const webPages = [ 'https://angular.dev/guide/forms', 'https://angular.dev/guide/forms/reactive-forms', 'https://angular.dev/guide/forms/typed-forms', 'https://angular.dev/guide/forms/template-driven-forms', 'https://angular.dev/guide/forms/form-validation', 'https://angular.dev/guide/forms/dynamic-forms', ];
 return loadWebPages(webPages);}

private async createSignalRetriever() { const docs = await loadSignalWebPages(); this.logger.log(`number of signal docs -> ${docs.length}`); const embeddings = createTextEmbeddingModel(this.configService, 'Angular Signal'); const vectorStore = await MemoryVectorStore.fromDocuments(docs, embeddings); return vectorStore.asRetriever(); }
 private async createFormRetriever() { const docs = await loadFormWebPages(); this.logger.log(`number of form docs -> ${docs.length}`); const embeddings = createTextEmbeddingModel(this.configService, 'Angular Forms'); const vectorStore = await MemoryVectorStore.fromDocuments(docs, embeddings); return vectorStore.asRetriever();}

private async createSignalRetrieverTool(): Promise<DynamicStructuredTool<any>> { const retriever = await this.createSignalRetriever(); return createRetrieverTool(retriever, { name: 'angular_signal_search', description: `Search for information about Angular Signal. For any questions about Angular Signal API, you must use this tool! Please Return the answer in markdown If you do not know the answer, please say you don't know. `, });}
private async createFormRetrieverTool(): Promise<DynamicStructuredTool<any>> { const retriever = await this.createFormRetriever(); return createRetrieverTool(retriever, { name: 'angular_form_search', description: `Search for information about Angular reactive, typed reactive, template-drive, and dynamic forms. For any questions about Angular Forms, you must use this tool! Please return the answer in markdown. If you do not know the answer, please say you don't know.`, });}
async createRetrieverTools(): Promise<DynamicStructuredTool<any>[]> { return Promise.all([this.createSignalRetrieverTool(), this.createFormRetrieverTool()]);}

nest g mo agentnest g s agent/application/agentExecutor --flatnest g s agent/application/dragonBall --flatnest g s agent/presenters/http/agent --flat

// agent.constant.ts
export const AGENT_EXECUTOR = 'AGENT_EXECUTOR';

// groq-chat-model.constant.ts
export const GROQ_CHAT_MODEL = 'GROQ_CHAT_MODEL';

// tools.constant.ts
export const TOOLS = 'TOOLS';

// groq-chat-model.provider.ts
import { ChatGroq } from '@langchain/groq';import { Inject, Provider } from '@nestjs/common';import { ConfigService } from '@nestjs/config';import { GroqConfig } from '~configs/types/groq-config.type';import { GROQ_CHAT_MODEL } from '../constants/groq-chat-model.constant';
export function InjectChatModel() { return Inject(GROQ_CHAT_MODEL);}
export const GroqChatModelProvider: Provider<ChatGroq> = { provide: GROQ_CHAT_MODEL, useFactory: (configService: ConfigService) => { const { apiKey, model } = configService.get<GroqConfig>('groq'); return new ChatGroq({ apiKey, model, temperature: 0.3, maxTokens: 2048, streaming: false, }); }, inject: [ConfigService],};

// tool.provider.ts
import { DuckDuckGoSearch } from '@langchain/community/tools/duckduckgo_search';import { Tool } from '@langchain/core/tools';import { Provider } from '@nestjs/common';import { ConfigService } from '@nestjs/config';import { AngularDocsService } from '~angular-docs/application/angular-docs.service';import { DuckDuckGoConfig } from '~configs/types/duck-config.type';import { TOOLS } from '../constants/tools.constant';import { DragonBallService } from '../dragon-ball.service';
export const ToolsProvider: Provider<Tool[]> = { provide: TOOLS, useFactory: async (service: ConfigService, dragonBallService: DragonBallService, docsService: AngularDocsService) => { const { maxResults } = service.get<DuckDuckGoConfig>('duckDuckGo'); const duckTool = new DuckDuckGoSearch({ maxResults }); const characterFiltertool = dragonBallService.createCharactersFilterTool(); const retrieverTools = await docsService.createRetrieverTools(); return [duckTool, characterFiltertool, ...retrieverTools]; }, inject: [ConfigService, DragonBallService, AngularDocsService],};

import { ChatPromptTemplate } from '@langchain/core/prompts';import { Tool } from '@langchain/core/tools';import { ChatGroq } from '@langchain/groq';import { Inject, Provider } from '@nestjs/common';import { AgentExecutor, createToolCallingAgent } from 'langchain/agents';import { AGENT_EXECUTOR } from '../constants/agent.constant';import { GROQ_CHAT_MODEL } from '../constants/groq-chat-model.constant';import { TOOLS } from '../constants/tools.constant';
const prompt = ChatPromptTemplate.fromMessages([ ['system', 'You are a helpful assistant.'], ['placeholder', '{chat_history}'], ['human', '{input}'], ['placeholder', '{agent_scratchpad}'],]);
export function InjectAgent() { return Inject(AGENT_EXECUTOR);}
export const AgentExecutorProvider: Provider<AgentExecutor> = { provide: AGENT_EXECUTOR, useFactory: async (llm: ChatGroq, tools: Tool[]) => { const agent = await createToolCallingAgent({ llm, tools, prompt, streamRunnable: false }); console.log('tools', tools);
 return AgentExecutor.fromAgentAndTools({ agent, tools, verbose: true, }); }, inject: [GROQ_CHAT_MODEL, TOOLS],};

import { DynamicStructuredTool, tool } from '@langchain/core/tools';import { HttpService } from '@nestjs/axios';import { Injectable } from '@nestjs/common';import { z } from 'zod';import { CharacterFilter } from './types/character-filter.type';import { Character } from './types/character.type';
export const characterFilterSchema = z.object({ name: z.string().optional().describe('Name of a Dragon Ball Z character.'), gender: z.enum(['Male', 'Female', 'Unknown']).optional().describe('Gender of a Dragon Ball Z caracter.'), race: z.enum(['Human', 'Saiyan']) .optional() .describe('Race of a Dragon Ball Z character'), affiliation: z.enum(['Z Fighter', 'Red Ribbon Army', 'Namekian Warrior']) .optional() .describe('Affiliation of a Dragon Ball Z character.'),});
@Injectable()export class DragonBallService { constructor(private readonly httpService: HttpService) {}
 async getCharacters(characterFilter: CharacterFilter): Promise<string> { const filter = this.buildFilter(characterFilter);
 if (!filter) { return this.generateMarkdownList([]); }
 const characters = await this.httpService.axiosRef .get<Character[]>(`https://dragonball-api.com/api/characters?${filter}`) .then(({ data }) => data);
 return this.generateMarkdownList(characters); }
 createCharactersFilterTool(): DynamicStructuredTool<any> { return tool(async (input: CharacterFilter): Promise<string> => this.getCharacters(input), { name: 'dragonBallCharacters', description: `Call Dragon Ball filter characters API to retrieve characters by name, race, affiliation, or gender.`, schema: characterFilterSchema, }); }

import { AIMessage, HumanMessage } from '@langchain/core/messages';import { Injectable } from '@nestjs/common';import { AgentExecutor } from 'langchain/agents';import { ToolExecutor } from './interfaces/tool.interface';import { InjectAgent } from './providers/agent-executor.provider';import { AgentContent } from './types/agent-content.type';
@Injectable()export class AgentExecutorService implements ToolExecutor { private chatHistory = [];
 constructor(@InjectAgent() private agentExecutor: AgentExecutor) {}
 async execute(input: string): Promise<AgentContent[]> { const { output } = await this.agentExecutor.invoke({ input, chat_history: this.chatHistory });
 this.chatHistory = this.chatHistory.concat([new HumanMessage(input), new AIMessage(output)]); if (this.chatHistory.length > 10) { // remove the oldest Human and AI Messages this.chatHistory.splice(0, 2); } return [ { role: 'Human', content: input, }, { role: 'Assistant', content: output, }, ]; }}

private chatHistory = [];if (this.chatHistory.length > 10) { // remove the oldest Human and AI Messages this.chatHistory.splice(0, 2);}

import { IsNotEmpty, IsString } from 'class-validator';
export class AskDto { @IsString() @IsNotEmpty() query: string;}

@Post() async ask(@Body() dto: AskDto): Promise<string> { const contents = await this.service.execute(dto.query); return toDivRows(contents); }

@Controller()export class AppController { @Render('index') @Get() async getHello(): Promise<Record<string, string>> { return { title: 'Langchain Search Agent', }; }}

default.hbs<!DOCTYPE html><html lang="en"> <head> <meta charset="utf-8" /> <meta name="description" content="Angular tech book RAG powed by gemma 2 LLM." /> <meta name="author" content="Connie Leung" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <title>{{{ title }}}</title> <style> *, *::before, *::after { padding: 0; margin: 0; box-sizing: border-box; }</style> <script src="https://cdn.tailwindcss.com?plugins=forms,typography"></script> </head> <body class="p-4 w-screen h-screen min-h-full"> <script src="https://unpkg.com/htmx.org@2.0.1" integrity="sha384-QWGpdj554B4ETpJJC9z+ZHJcA/i59TyjxEPXiiUgN2WmTyV5OEZWCD6gQhgkdpB/" crossorigin="anonymous"></script> <div class="h-full grid grid-rows-[70px_1fr_40px] grid-cols-[1fr]"> {{> header }} {{{ body }}} {{> footer }} </div> </body></html>

<div> <div class="mb-2 p-1 border border-solid border-[#464646] rounded-lg"> <p class="text-[1.25rem] mb-2 text-[#464646] underline">Architecture</p> <ul id="architecture" hx-trigger="load" hx-get="/agent/architecture" hx-target="#architecture" hx-swap="innerHTML"></ul> </div> <div id="results" class="mb-4 h-[300px] overflow-y-auto overflow-x-auto"></div> <form id="rag-form" hx-post="/agent" hx-target="#results" hx-swap="beforeend swap:1s"> <div> <label> <span class="text-[1rem] mr-1 w-1/5 mb-2 text-[#464646]">Question: </span> <input type="text" name="query" class="mb-4 w-4/5 rounded-md p-2" placeholder="Ask the agent" aria-placeholder="Placeholder to ask any question to the agent"></input> </label> </div> <button type="submit" class="bg-blue-500 hover:bg-blue-700 text-white p-2 text-[1rem] flex justify-center items-center rounded-lg"> <span class="mr-1">Send</span><img class="w-4 h-4 htmx-indicator" src="/images/spinner.gif"> </button> </form></div>