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Article
Harnessing Natural Language for MongoDB Queries With Google Gemini
By integrating MongoDB Atlas with Vertex AI Extensions, we offer a solution that enhances the accessibility and usability of the database.
Dec 13, 2024
Article
Building a Foreign Correspondent With MongoDB, Anthropic's Claude, Python
Join guest author Marko Aleksendric to learn how to use MongoDB, Anthropic's Claude and Python to create a simple web application aimed to help a virtual friend in a foreign country translate the local news items.
Dec 09, 2024
Article
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
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
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
Article
Audio Find - Atlas Vector Search for Audio
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.
Sep 09, 2024
Article
Capturing and Storing Real-World Optics With MongoDB Atlas, OpenAI GPT-4o, and PyMongo
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.
Sep 04, 2024
