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Video
Going FARMing with Python & FastAPI!
✅ Sign-up for a free cluster at → https://mdb.link/65jdbqw8XHE-register ✅ Get help on our Community Forums → https://mdb.link/65jdbqw8XHE-forums - Every week you can watch Mark as he builds out a Data Access Layer in Python. The experimental docbridge library is an attempt to build a Pythonic framework that takes into account the various patterns that are used when designing document models with MongoDB. There are quite a few features in the docbridge library now! Let's build something with it, demonstrating some of the data modeling patterns it helps to abstract, and we can talk about how well it works with FastAPI and maybe some changes to make it even better. 📚 RESOURCES 📚 Six Principles of Resilient Evolvability → https://mdb.link/65jdbqw8XHE-blog Building with Patterns → https://mdb.link/65jdbqw8XHE-patterns Docbridge GitHub Repo → https://github.com/mongodb-developer/docbridge 💬 Connect with Mark: LinkedIn: https://www.linkedin.com/in/judy2k/ ------ ✅ Subscribe to our channel → https://mdb.link/subscribeMar 27, 2024
Video
Going Async with Python!
✅ Sign-up for a free cluster at - https://mdb.link/0_mMQrP9RCM-register ✅ Get help on our Community Forums - https://mdb.link/0_mMQrP9RCM-community - Every week you can watch Mark as he builds out a Data Access Layer in Python. The experimental docbridge library is an attempt to build a Pythonic framework that takes into account the various patterns that are used when designing document models with MongoDB. Docbridge was built using PyMongo, but now I want to use it with FastAPI, so it needs to be ported or adapted to work as an async library. We'll go deep into the complexities of async libraries and generators, and talk about good practices for updating the data in your MongoDB database. - 📚 RESOURCES 📚 ✅ Six Principles of Resilient Evolvability - https://mdb.link/0_mMQrP9RCM-principles ✅ Building with Patterns - https://mdb.link/0_mMQrP9RCM-summary ✅ Docbridge GitHub Repo - https://github.com/mongodb-developer/docbridge ------ Connect with Mark: LinkedIn: https://www.linkedin.com/in/judy2k/ ✅ Subscribe to our channel - https://mdb.link/subscribeMar 27, 2024
Video
Exploring the Edge of AI: MongoDB's New Frontier with Patronus.ai
✅ MongoDB and Patronus Blog Article - https://mdb.link/F6PeGjFztSQ-article ✅ Get help on our Community Forums - https://mdb.link/F6PeGjFztSQ-forums ✅ Register to MongoDB Atlas - https://mdb.link/F6PeGjFztSQ-register - Join us on this exciting episode of the MongoDB Podcast Live as we delve into the groundbreaking partnership between MongoDB and Patronus AI. We're honored to have Anand Kannappan, the innovative mind behind Patronus AI, to discuss how this collaboration is pushing the boundaries of AI technology. Discover how Patronus AI's unique features, like Evaluation Runs, Adversarial Testing Sets, and Benchmarking, are setting new standards in AI model performance. Anand will also shed light on the cutting-edge Retrieval-augmented Generation Analysis and how it ensures reliability in AI-driven solutions. Whether you're an AI enthusiast, a developer, or just curious about the future of technology, this episode offers a deep dive into the intersection of AI and database technology, highlighting how MongoDB's versatile platform is playing a crucial role in shaping the AI landscape. - ✅ Patronus AI website: https://www.patronus.ai/ ✅ Patronus AI/MongoDB Atlas Integration: https://www.patronus.ai/blog/the-10-minute-guide-to-reliable-rag-systems-using-patronus-ai-mongodb-atlas-and-llamaindexMar 21, 2024
Tutorial
Building a RAG System With Google's Gemma, Hugging Face and MongoDB
This article presents how to leverage Gemma as the foundation model in a Retrieval-Augmented Generation (RAG) pipeline or system, with supporting models provided by Hugging Face, a repository for open-source models, datasets and compute resources.Mar 21, 2024
Richmond Alake
Tutorial
Adding Semantic Caching and Memory to Your RAG Application Using MongoDB and LangChain
This guide outlines how to enhance Retrieval-Augmented Generation (RAG) applications with semantic caching and memory using MongoDB and LangChain. It explains integrating semantic caching to improve response efficiency and relevance by storing query results based on semantics. Additionally, it describes adding memory for maintaining conversation history, enabling context-aware interactions. The tutorial includes steps for setting up MongoDB, implementing semantic caching, and incorporating these features into RAG applications with LangChain, leading to improved response times and enriched user interactions through efficient data retrieval and personalized experiences.Mar 20, 2024
Richmond Alake (+1)
Tutorial
RAG Series Part 1: How to Choose the Right Embedding Model for Your Application
In this tutorial, we will see why embeddings are important for RAG, and how to choose the right embedding model for your RAG application.Mar 19, 2024
Apoorva Joshi
Tutorial
Coding With Mark: Abstracting Joins & Subsets in Python
Learn how to use advanced Python to abstract subsets and joins in MongoDB data models.Mar 19, 2024
Mark Smith
Video
Python Chat with Anaiya Raisinghani
Anaiya Raisinghani is a Developer Advocate at MongoDB, and she's been building some awesome things with Python and a cool serverless platform called Neurelo. We're going to have a wide-ranging chat about what she knows and loves about MongoDB and the kinds of things she likes to build. There's going to be some demos and Python code walkthroughs. It's going to be a whole load of fun. - Check out Anaiya's articles on Neurelo: Neurelo and MongoDB: Getting Started and Fun Extras - https://mdb.link/vtEtu9-v5ZM-getting-started Building a Restaurant Locator Using Atlas, Neurelo, and AWS Lambda - https://mdb.link/vtEtu9-v5ZM-neurelo-AWSMar 15, 2024
Quickstart
Getting Started with MongoDB and FastAPI
Getting started with MongoDB and FastAPIMar 11, 2024
Aaron Bassett (+1)
Video
Code With Mark: Let's go FARMing!
✅ Sign-up for a free cluster at → https://mdb.link/_hmOnVurboQ-register ✅ Get help on our Community Forums → https://mdb.link/_hmOnVurboQ-forums - Every week you can watch Mark as he builds out a Data Access Layer in Python. This library, called docbridge, is an attempt to build a Pythonic framework that takes into account the various patterns that are used when designing document models with MongoDB. FastAPI is becoming a go-to framework for Python developers building APIs and applications with MongoDB. I've built a blocking data access library, but FastAPI is built on asyncio! That means that the code needs to be rewritten to work as both blocking and async. Let's go deep under the covers of coroutines and asynchronous iterators, and find out what's going on! - RESOURCES ✅ Full Stack FastAPI App Generator → https://mdb.link/_hmOnVurboQ-generator ✅ The Six Principles for Building Flexible Data Applications → https://mdb.link/_hmOnVurboQ-principles ✅ Building with Patterns → https://mdb.link/_hmOnVurboQ-patterns ✅ Docbridge GitHub Repo → https://mdb.link/_hmOnVurboQ-docbridge Connect with Mark: LinkedIn: https://www.linkedin.com/in/judy2k/ ------ ✅ Subscribe to our channel → https://mdb.link/subscribeMar 08, 2024