Here are the repositories you can dive into:

🔹 MEAN - Sample CRUD Application with MEAN Stack
Build robust applications with MongoDB, Express.js, Angular, and Node.js. This template provides a sample CRUD application to help you get started.

🔹 MERN - MERN Stack Code for the MERN Tutorial
Get up and running with MongoDB, Express.js, React, and Node.js. Our template is designed to complement the MERN tutorial and get you coding in no time.

🔹 Java - Java Quick Start Code Samples
Perfect for those using Java, this repository contains all the code samples from our Java Quick Start blog post series.

🔹 Java Spring Boot - REST APIs with Java, Spring Boot, and MongoDB
Learn how to build powerful REST APIs using Java, Spring Boot, and MongoDB with this comprehensive template from our blog post.

Extra Templates for Vector Search and Vertex AI:

🔹Agentic HR Chatbot

🔹Build a JavaScript AI Agent With LangGraph.js and MongoDB

🔹Build Smart Applications With Atlas Vector Search and Google Vertex AI

🔹Add Memory to Your JavaScript RAG Application Using MongoDB and LangChain

🔹How to integrate MongoDB Atlas Search with the Kotlin Sync driver using a Spring Boot REST application

🔹Leveraging MongoDB Atlas Vector Search With LangChain

🔹Retrieval-Augmented Generation With MongoDB and Spring AI: Bringing AI to Your Java Applications

🔹Building a Kotlin App with Spring Boot and Atlas Search: A Complete Guide

🔹Vertex AI Agent SDK for building RAG with MongoDB Atlas

🔹 Unlocking Semantic Search: Building a Java-Powered Movie Search Engine with Atlas Vector Search and Spring Boot
Dive into semantic search with this project. Showcases how to build a Java-powered movie search engine using MongoDB's Vector Search and Spring Boot.

🔹 HR Chatbot with LangChain, MongoDB, OpenAI, and Google APIs
This project implements an HR Chatbot leveraging LangChain, MongoDB, OpenAI's language models, and Google APIs. It includes synthetic data generation, embedding creation, and a chatbot interface for querying HR-related information and interacting with Google services.

🔹 Internal Enterprise Search Chatbots and Customer Service Chatbots
Easily build internal enterprise search chatbots and customer service chatbots. This template provides the necessary tools and code to get started.

🔹 Public Repo for Reasoning Engine on Google Cloud Vertex AI
Need a public-facing repository with the code and steps for setting up a reasoning engine on Google Cloud Vertex AI? This template has you covered, including source code for creating Vertex AI extensions.

🔹 Vertex AI Extensions
MongoDB seamlessly integrates with Google Vertex AI Extensions

🔹 Google-Cloud-Semantic-Search

This is a demo of vector search using MongoDB Atlas and Google Cloud. The dataset is a catalogue of books. The project uses Node.js and express for the server and Angular for the client.

🔹 Google-Cloud-Generative-AI-Chatbot

This is a demo of a customer service chatbot using Generative AI through Google Cloud's Vertex AI PaLM APIs. The app also leverages MongoDB Atlas Search to look for relevant answers in a MongoDB Atlas database. Finally, the app can detect dissatisfied customers with sentiment analysis.

🔹 Google-Cloud-RAG-Langchain

This is a demo of a chatbot assistant using retrieval augmented generation (RAG) using LangChain.

🔹 GCP_RAG_Chatbot

This project consists of a frontend chat application built using React and Material UI, and a backend server built with Express.js, MongoDB, and Google Cloud AI Platform.The backend handles HTTP requests, interacts with a MongoDB database for persisting data, and uses Google Cloud's AI Platform for generating chatbot responses and text embeddings.

🔹  Google-Cloud-Sentiment-Chef

This is a demo of a sentiment analysis, tagging, and summarization Gemini — Google's next-generation AI model.