Skip to main content

Quick Starts

These quickstart guides and our MAAP partners offer easy and fast deployment of AI-powered applications using MongoDB Atlas.

📄️ MongoDB - Cohere Quickstart

The MongoDB - Cohere Quickstart is a comprehensive, integrated end-to-end technology stack meticulously designed to facilitate the rapid development and seamless deployment of gen AI-powered applications. This innovative framework combines the robust capabilities of MongoDB Atlas for scalable data storage and advanced vector search functionalities with Cohere's state-of-the-art command-r-plus language model and Cohere's re-ranker for powerful natural language processing and retrieval.

📄️ MongoDB - IBM Quickstart

Welcome to the MongoDB - IBM Quickstart, we will be using a financial dataset containing customer details, transactions, spending insights, and metadata, which we are storing in MongoDB Atlas, a fully managed cloud database platform. These records represent real-world scenarios such as payments, savings, and expenses, making the dataset highly relevant for building an intelligent finance assistant. To generate the vector embeddings for storing and retrieving this data, we will use the Granite Embedding Models (ibm-granite/granite-embedding-125m-english) from IBM Watsonx.ai. These embeddings capture the semantic meaning of financial data, enabling efficient similarity searches and contextual data retrieval. In addition to embedding models, Watsonx.ai also provides large language models (LLMs) for conversational chat query and retrieval capabilities, in our use case we are using Granite 3.0 (ibm/granite-3-8b-instruct) model, which are integral to this tutorial. By the end of this tutorial, you’ll have a functional system ready to support real-time financial assistance and personalized recommendations.