Scott Kurowski

Leverage the mathematical properties of a population of db AI-embedded vectors to extract potential novel business intelligence.

Discover Latent Semantic Structure With Vector Clustering

Adit Shrimal

Varun Hande

Neil Chitre

Manav Middha

In this article, we will compare four popular natural language processing (NLP) techniques to find the most optimal solution for retrieving the most relevant results for a search query from a large corpus of products. 

Comparing NLP Techniques for Scalable Product Search

Pavel Duchovny

Explore the synergy of MongoDB Atlas, LangChain, and OpenAI GPT-4 in our cutting-edge AI Shop application.

AI Shop: The Power of LangChain, OpenAI, and MongoDB Atlas Working Together

Richmond Alake

Han Heloir

Discover how to build powerful multi-agent AI systems using AutoGen and MongoDB. This guide explores the integration of Microsoft's AutoGen framework with MongoDB's Atlas Vector Search, enabling efficient retrieval-augmented generation (RAG) and collaborative AI agents. Learn step-by-step implementation, from environment setup to agent configuration, and unlock the potential of scalable, context-aware AI solutions for complex data-driven tasks.

Multi-agent Systems With AutoGen and MongoDB

This tutorial explores building a retrieval-augmented generation (RAG) pipeline by integrating Google’s Gemma 2 (2B) model, MongoDB, and LLM evaluation techniques. Gemma 2, a lightweight model with two billion parameters, is used for efficient response generation, while MongoDB acts as the vector database, enabling semantic search for relevant documents.

The tutorial demonstrates how to create an asset management assistant that analyzes market reports stored in MongoDB. It covers embedding generation, vector search, and the use of the DeepEval library to assess the relevance and faithfulness of LLM-generated responses. By combining these tools, the tutorial highlights an efficient approach to building AI-driven solutions with robust performance evaluation in a RAG pipeline.

Implementing Robust RAG Pipelines: Integrating Google's Gemma 2 (2B) Open Model, MongoDB, and LLM Evaluation Techniques

Ran Shir

Explore the creation of a music catalog system that leverages the power of MongoDB Atlas's vector search and a Python service for sound embedding.

Audio Find - Atlas Vector Search for Audio

Capture real-world data using MongoDB Atlas, PyMongo, and OpenAI’s GPT-4. Transform images into searchable JSON documents and interact with an AI agent.

Capturing and Storing Real-World Optics With MongoDB Atlas, OpenAI GPT-4o, and PyMongo

Robert Walters

MongoDB has released MongoDB Spark Connector V10. Learn how to read from and write to MongoDB through Spark Structured Streaming.

Streaming Data with Apache Spark and MongoDB

Streaming Data With Apache Spark and MongoDB

Ashiq Sultan

Create an e-commerce semantic search utilizing MongoDB Vector Search and OpenAI models

Build an E-commerce Search Using MongoDB Vector Search and OpenAI

Learn how new mongosh external modules can be used to generate MQL language via OpenAI engine. Transform simple text sentences into sophisticated queries. 

Generating MQL Shell Commands Using OpenAI and New mongosh Shell

Shiv Pullepu

Ashwin Gangadhar

Rajesh Vinayagam

In this article, we'll demonstrate how easy it is to build an ML-based fraud solution using MongoDB and Databricks.

AI

Articles

All AI Articles

Real-Time Card Fraud Solution Accelerator with MongoDB and Databricks