Python
Sign in to follow topics
Featured
.jpg&w=3840&q=75)
.png&w=3840&q=75)
All Python Content
- Latest
- Highest Rated
Video
How to Build an App with Semantic Search: Django, MongoDB Atlas & Voyage AI Tutorial
Read the written tutorial: https://dev.to/mongodb/grab-a-pint-with-django-mongodb-backend-voyage-ai-and-langchain-170n Watch the Django MongoDB Backend Quickstart tutorial: https://youtu.be/laXann1O0cg Sign-up for a free cluster ā https://mdb.link/5s5ngllTB8E-register Access the Kaggle dataset here: https://www.kaggle.com/datasets/anaiya/guinnesswinebarsdublin Subscribe to MongoDB YouTubeā https://mdb.link/subscribe Looking for the best places to grab a drink in Dublin? Stop searching and start building! With the power of the Django-MongoDB-Backend Python package and AI, you can find the exact drink you're looking for. Build your own intelligent Dublin pub finder! This tutorial shows you how to combine Django, MongoDB, Voyage AI, and LangChain to create an AI-powered app with semantic search. Learn to set up your backend, embed data, and implement smart search functionality to help anyone discover the perfect pub in Dublin. Dive into the exciting world where Python, databases, and AI converge! Chapters: 0:00 Introduction to the Dublin Pub Finder 0:28 What is Django MongoDB Backend? 1:07 Understanding LangChain & MongoDB Integration 1:58 Why Voyage AI for Embeddings? 2:30 Project Prerequisites 3:25 Demo: Our Intelligent Pub Finder in Action 4:18 Data Collection & Preparation (Google Places API) 5:39 Setting up Django MongoDB Backend 7:24 Defining Django Models (models.py) 8:55 Generating Embeddings with Voyage AI 10:19 Importing Data to MongoDB Atlas 11:46 Creating Your Atlas Vector Search Index 13:10 Integrating LangChain for Semantic Search 14:48 Building the Django Application (views.py & URLs) 16:00 Crafting the User Interface (HTML/CSS) 17:15 Running the Application 17:35 Conclusion & Key Takeaways This video is not affiliated with, endorsed by, or sponsored by Python. The use of any trademark is solely for informational and identification purposes, so that we may provide clear and accurate descriptions. All opinions and critiques provided in this video are those of the creator and do not reflect the views of Python or its affiliates. Visit Mongodb.com ā https://mdb.link/MongoDB Read the MongoDB Blog ā https://mdb.link/Blog Read the Developer Blog ā https://mdb.link/developerblogJun 23, 2025
Tutorial
How to Improve LLM Applications With Parent Document Retrieval Using MongoDB and LangChain
In this tutorial, you will learn about a technique called parent document retrieval and implement it in RAG and Agentic workflows using MongoDBās LangChain integration.May 29, 2025
Video
Experience the Beanie ODM: Build Robust Data Models in Python
ā Ā Try MongoDB 8.0 āĀ https://mdb.link/5XhDQQcWQm4 ā Ā Sign-up for a free cluster āĀ https://mdb.link/5XhDQQcWQm4-try - Episode description: Join oman Right, the creator of the Beanie ODM, and Shubham Ranjan, Product Manager at MongoDB, as they discuss all things Beanie and Python! In this episode, we will explore how Beanie uses Pythons async capabilities to simplify MongoDB data modeling and querying. Roman and Shubham will walk through defining schemas, executing advanced queries and aggregations, and integrating Beanie into various Python frameworks. We'll wrap up with a LIVE Q&A, so come get your questions answered from the experts. - ā Ā Beanie GitHub ā https://github.com/BeanieODM/beanie ā Ā Beanie Docs ā https://beanie-odm.dev/ ā Ā Build a Cocktail API with Beanie and MongoDB ā https://mdb.link/5XhDQQcWQm4-read - oman Right LinkedIn: https://www.linkedin.com/in/roman-right/ Shubham Ranjan LinkedIn: https://www.linkedin.com/in/shran/May 12, 2025
Tutorial
How to Use Cohere Embeddings and Rerank Modules With MongoDB Atlas
To avoid issues in leveraging AI search functionality or machine learning in your application, Cohere and MongoDB offer fully managed solutions.Mar 13, 2025
Quickstart
Building AI and RAG Apps With MongoDB, Anyscale and PyMongo
This tutorial guides you through integrating MongoDB Atlas with Anyscale and PyMongo to build scalable AI-powered retrieval-augmented generation (RAG) services. Learn how to deploy self-hosted models, set up a vector store, and connect your services to efficiently process and analyze large datasets using Ray's distributed computing capabilities.Mar 13, 2025
Tutorial
Building a Real-Time, Dynamic Seller Dashboard on MongoDB
In this article, we look at how a single query on MongoDB can power a real-time view of top-selling products, and deep-dive into the top-selling regions.
Mar 13, 2025
(+1)
Tutorial
Enhancing LLM Accuracy Using MongoDB Vector Search and Unstructured.io Metadata
This article provides a comprehensive guide on improving the precision of large language models using MongoDB's Vector Search and Unstructured.io's metadata extraction techniques, aiming to equip readers with the tools to produce well-sourced and contextually accurate AI outputs.
Mar 12, 2025
Tutorial
How to Seamlessly Use MongoDB Atlas and IBM watsonx.ai LLMs in Your GenAI Applications
Learn how to build a RAG framework using MongoDB Atlas Vector Search and IBM watsonx LLMs.Mar 12, 2025