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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
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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
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Article
Learn How to Leverage MongoDB Data Within Kafka with New Tutorials!
MongoDB Documentation has released a series of tutorials based on a self-hosted Docker compose environment that includes all components needed.
Sep 17, 2024
Article
Why Unstructured Data is a Good Fit for Java
This article focuses on how Java developers can effectively manage unstructured data using MongoDB. The schema-less architecture, paired with its natural integration with Java’s POJO model, streamlines the handling of unstructured data, offering agility and scalability for modern applications.
Sep 16, 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
Currency Analysis With Time Series Collections #3 — MACD and RSI Calculation
Previously, we calculated simple moving average/exponential moving average on currencies based on a time window. Now, we increase the complexity!
Sep 11, 2024
Article
3 Underused MongoDB Features
This article is about three features of MongoDB that deserve to be better known: TTL Indexes, Capped Collections, and Change Streams.
Sep 11, 2024
