MongoDB

Apoorva Joshi

Senior AI Developer Advocate

In this tutorial, we will see how to evaluate LLM applications using the RAGAS framework, taking a RAG system as an example.

RAG Series Part 2: How to Evaluate Your RAG Application

In this tutorial, we will see why embeddings are important for RAG, and how to choose the right embedding model for your RAG application.

RAG Series Part 1: How to Choose the Right Embedding Model for Your Application

Richmond Alake

This guide outlines how to enhance Retrieval-Augmented Generation (RAG) applications with semantic caching and memory using MongoDB and LangChain. It explains integrating semantic caching to improve response efficiency and relevance by storing query results based on semantics. Additionally, it describes adding memory for maintaining conversation history, enabling context-aware interactions. 

The tutorial includes steps for setting up MongoDB, implementing semantic caching, and incorporating these features into RAG applications with LangChain, leading to improved response times and enriched user interactions through efficient data retrieval and personalized experiences.

Apoorva Joshi

Content by Apoorva Joshi

RAG Series Part 2: How to Evaluate Your RAG Application

RAG Series Part 1: How to Choose the Right Embedding Model for Your Application

Adding Semantic Caching and Memory to Your RAG Application Using MongoDB and LangChain