In this tutorial you will see how to build a RAG application utilizing the LangChain framework, OpenAI models, and Gradio for interface creation, we'll guide you through building a...

Jul 25, 2024 · A step-by-step approach to implement RAG ChatBot using Python Langchain Framework from scratch. What is RAG? All AI enthusiasts know what RAG [Retrieval Augmented Generation] is. I don't...

Feb 22, 2024 · This article presents how to leverage Gemma as the foundation model in a retrieval-augmented generation (RAG) pipeline or system, with supporting models provided by Hugging Face, a repository for open-source models, datasets, and compute resources.

These libraries simplify the development of a RAG system, reducing the complexity to a small amount of code: PyMongo: A Python library for interacting with MongoDB that enables functionalities to connect to a cluster and query data stored in collections and documents.

Sep 12, 2024 · In this tutorial, we will look into some scenarios where vector search alone is inadequate and see how to improve them using a technique called self-querying retrieval. Specifically, in this tutorial, we will cover the following: What is metadata? Why is it important for RAG? Metadata is information that describes your data.

RAG combines AI language generation with knowledge retrieval for more informative responses. LangChain simplifies building the chatbot logic, while MongoDB Atlas' Vector database capability provides a powerful platform for storing and searching the knowledge base that fuels the …

Retrieval-augmented generation (RAG) is an architecture used to augment large language models (LLMs) with additional data so that they can generate more accurate responses. You can implement RAG in your generative AI applications by combining an LLM with a retrieval system powered by Atlas Vector Search. Why use RAG?

In this post, we are going to show you how you can build your own RAG LLM system using Nomic and MongoDB. This tutorial will require you to have accounts on both Nomic Atlas and MongoDB. You can sign up for a Nomic Atlas account here. You also need to setup a MongoDB Atlas account. You can do this by visiting the MongoDB Atlas website.

Jan 11, 2024 · As part of a series of blog posts about the Together Embeddings endpoint release, we are excited to announce that you can build your own powerful RAG-based application right from the Together platform with MongoDB’s Atlas Vector Search. What is Retrieval Augmented Generation (RAG)?

A Python sample for implementing retrieval augmented generation using Azure Open AI to generate embeddings, Azure Cosmos DB for MongoDB vCore to perform vector search and semantic kernel. Deployed to Azure App service using Azure Developer CLI (azd).