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A high-level, interpreted programming language and it is used for general purpose. Python is one of the most popular languages for data-intensive tasks and data science because of its rich library support for statistics, machine learning, and AI-related tasks.- Latest
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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
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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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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
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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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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
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
