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MongoDB Atlas, the multi-cloud developer data platform.
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Discover Latent Semantic Structure With Vector Clustering
Leverage the mathematical properties of a population of db AI-embedded vectors to extract potential novel business intelligence.
Oct 11, 2024
Article
Exact Matches in Atlas Search: Beginners Guide
This tutorial will focus on the different ways users can achieve exact matches as well as the pros and cons of each.
Oct 09, 2024
Article
Keeping Your Costs Down With MongoDB Atlas Serverless Instances
Learn how to use the new MongoDB Atlas serverless instances to keep your usage costs down.
Oct 01, 2024
Article
The MongoDB Atlas Sample Datasets
Explaining the MongoDB Atlas Sample Data and diving into its various datasets
Oct 01, 2024
Article
Comparing NLP Techniques for Scalable Product Search
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.
Sep 23, 2024
Article
Using SuperDuperDB to Accelerate AI Development on MongoDB Atlas Vector Search
Discover how you can use SuperDuperDB to describe complex AI pipelines built on MongoDB Atlas Vector Search and state of the art LLMs.
Sep 18, 2024
Article
AI Shop: The Power of LangChain, OpenAI, and MongoDB Atlas Working Together
Explore the synergy of MongoDB Atlas, LangChain, and OpenAI GPT-4 in our cutting-edge AI Shop application._Spot_BS_ForestGreen.png&w=3840&q=75)
Sep 18, 2024
Article
Multi-agent Systems With AutoGen and MongoDB
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.
Sep 18, 2024
Article
Implementing Robust RAG Pipelines: Integrating Google's Gemma 2 (2B) Open Model, MongoDB, and LLM Evaluation Techniques
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.
Sep 12, 2024
