Videos
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
- Highest Rated
Video
Evaluating your RAG Applications
✅ Sign-up for a free cluster at → https://mdb.link/AH10-Ec0qG8-register ✅ Get help on our Community Forums → https://mdb.link/AH10-Ec0qG8-forums - In this livestream, we will deep-dive into evaluating LLM applications. We will talk about the evaluation process and metrics, along with a code walkthrough of how to evaluate a RAG application using the RAGAS framework. - Link to this article: https://mdb.link/AH10-Ec0qG8-article
Jun 04, 2024
Video
Fundamentals of Building a FARM Stack App in 5 min
❗ Here's the correct link - https://mongodb-celeb-search.com/ ✅ Sign-up for a free cluster at → https://mdb.link/LldmlWM1amg-register ✅ Get help on our Community Forums → https://mdb.link/LldmlWM1amg-forums - Join Associate Developer Advocate Anaiya Raisinghani as she talks about the fundamentals of building a FARM stack application in this quick 5 minute lightning talk. ✅ For more information on MongoDB’s FARM stack check out this article on our Developer Center → https://mdb.link/LldmlWM1amg-article - Subscribe to MongoDB YouTube→ https://mdb.link/subscribe #farm #react #fastapi #python #mongodb #developers
May 23, 2024
Video
Boosting AI: Build A Chatbot With Your Data | LangChain Templates Using the RAG Pattern
✅ Find the written article here → https://mdb.link/article-9zyL1_8X8rQ ✅ Sign-up for a free cluster → https://mdb.link/free-9zyL1_8X8rQ ✅ Get help on our Community Forums → https://mdb.link/community-9zyL1_8X8rQ We’ve partnered with Andrew Ng and DeepLearning AI to help you unlock the power of building efficient RAG applications through their new course, for FREE! Start learning now https://trymongodb.com/3LM0hny Discover how to enhance your AI chatbot using MongoDB Atlas Vector Search combined with LangChain Templates in our comprehensive tutorial. This video will guide you through the innovative process of integrating retrieval-augmented generation (RAG) pattern to improve your chatbot’s response accuracy and relevance. We'll walk you through the key technologies, including the use of MongoDB's Vector Search and the deployment of LangChain templates to seamlessly fuse external data with LLMs for more dynamic interactions. What You Will Learn: - How to use MongoDB Atlas Vector Search to pinpoint relevant data. - Setting up LangChain Templates for efficient AI integrations. - Step-by-step guide on deploying a chatbot using the RAG pattern. --- Subscribe to MongoDB YouTube→ https://mdb.link/subscribe
Apr 26, 2024
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 📚 Best Practices for FastAPI & MongoDB → https://mdb.link/best-practices-fastapi-65jdbqw8XHE 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/subscribe
Mar 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/subscribe
Mar 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 Rebecca Qian, 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. Rebecca 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-llamaindex
Mar 21, 2024
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-AWS
Mar 15, 2024
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/subscribe
Mar 08, 2024
Video
Semantic Search Made Easy With LangChain and MongoDB
"✅ Sign-up for a free cluster at → https://www.mongodb.com/cloud/atlas/register ✅ Get help on our Community Forums → https://www.mongodb.com/community/forums/ ✅ https://mdb.link/ZvwUzcMvKiI" target="_blank" rel="noreferrer">https://mdb.link/ZvwUzcMvKiI-tutorial" target="_blank" rel="noreferrer">https://mdb.link/ZvwUzcMvKiI" target="_blank" rel="noreferrer">https://mdb.link/ZvwUzcMvKiI-tutorial ✅ https://trymongodb.com/3H7kO3L" target="_blank" rel="noreferrer">https://trymongodb.com/3H7kO3L ✅ https://mdb.link/ZvwUzcMvKiI" target="_blank" rel="noreferrer">https://mdb.link/ZvwUzcMvKiI ✅ https://mdb.link/ZvwUzcMvKiI" target="_blank" rel="noreferrer">https://mdb.link/ZvwUzcMvKiI-community ✅ https://twitter.com/codestackr" target="_blank" rel="noreferrer">https://twitter.com/codestackr ✅ https://mdb.link/subscribe" target="_blank" rel="noreferrer">https://mdb.link/subscribe" target="_blank" rel="noreferrer">https://mdb.link/subscribe" target="_blank" rel="noreferrer">https://mdb.link/subscribe The video is a tutorial on how to enable semantic search on user-specific data using Lang chain and MongoDB Atlas. The process involves loading, transforming, embedding, and storing data before it can be queried. The tutorial uses the AT&T Wikipedia page as a data source and demonstrates how to use libraries from Lang chain to load, transform, embed, and store vectors. The video also explains how to retrieve and query relevant data using Vector search. 📚 RESOURCES 📚 https://mdb.link/ZvwUzcMvKiI" target="_blank" rel="noreferrer">https://mdb.link/ZvwUzcMvKiI-tutorial" target="_blank" rel="noreferrer">https://mdb.link/ZvwUzcMvKiI" target="_blank" rel="noreferrer">https://mdb.link/ZvwUzcMvKiI-tutorial https://trymongodb.com/3H7kO3L" target="_blank" rel="noreferrer">https://trymongodb.com/3H7kO3L https://mdb.link/ZvwUzcMvKiI" target="_blank" rel="noreferrer">https://mdb.link/ZvwUzcMvKiI https://mdb.link/ZvwUzcMvKiI" target="_blank" rel="noreferrer">https://mdb.link/ZvwUzcMvKiI-community https://twitter.com/codestackr" target="_blank" rel="noreferrer">https://twitter.com/codestackr https://mdb.link/subscribe" target="_blank" rel="noreferrer">https://mdb.link/subscribe" target="_blank" rel="noreferrer">https://mdb.link/subscribe" target="_blank" rel="noreferrer">https://mdb.link/subscribe ⏱️ Timestamps ⏱️ Introduction to Semantic Search [00:00:00 - 00:02:35] The video begins with an explanation of the multi-step process of enabling semantic search on user-specific data. This process includes loading, transforming, embedding, and storing data before it can be queried. The speaker mentions the team at Lang chain, whose goal is to simplify this process. The tutorial will walk through each of these steps using MongoDB Atlas as the vector store and the AT&T Wikipedia page as the data source. Setting Up the Environment [00:02:36 - 00:05:11] In this chapter, the speaker guides the viewer through the process of setting up the environment. This includes creating a free MongoDB Atlas account, obtaining an OpenAI API key, and cloning a repository with all the necessary code. The speaker also explains how to add the OpenAI API key and MongoDB connection string to the project, create a new Python environment, and install all the required dependencies. Vectorizing and Querying Data [00:05:12 - 00:07:47] The final chapter focuses on vectorizing and querying data. The speaker demonstrates how to load, transform, embed, and store data using the AT&T Wikipedia page as the data source. The process of creating vector representations of data using an LLM is explained, as well as how to store these vectors in a MongoDB database. The speaker then shows how to query this data using vector search, and how to set up a vector search index on the vector embeddings field. The video concludes with a demonstration of a CLI-based query and the results of a semantic search. ------ ✅ Subscribe to our channel → https://mdb.link/subscribe" target="_blank" rel="noreferrer">https://mdb.link/subscribe" target="_blank" rel="noreferrer">https://mdb.link/subscribe" target="_blank" rel="noreferrer">https://mdb.link/subscribe"
Jan 09, 2024
Video
Build an AWS Lambda Serverless function with PyMongo & MongoDB
✅ Sign-up for a free cluster at → https://mdb.link/free--ZSKyLspT3Q ✅ Get help on our Community Forums → https://mdb.link/community--ZSKyLspT3Q Follow along as MongoDB Developer Advocate Anaiya Raisinghani explains how to use AWS Lambda with PyMongo, MongoDB's Python Driver! This tutorial will take you through setting up an Atlas Cluster, establishing an AWS Lambda function, connecting the cluster to Lambda, and loading in/reading back specific information. Watch through the end for information on utilizing MongoDB's Aggregation Pipeline structure. 🔗 Resources 🔗 ✅ For an article format of this video along with code samples, please view our Developer Center → https://mdb.link/article--ZSKyLspT3Q ✅ Connect with Anaiya → https://www.linkedin.com/in/anaiyaraisinghani/ ✅ View more of Anaiya's content → https://www.mongodb.com/developer/search/?s=anaiya+raisinghani&sortMode=0 ------ Don't forget to like, share, and subscribe for more awesome tutorials! 🙌 ✅ Subscribe to our channel → https://mdb.link/subscribe
Sep 25, 2023