How to Build an App with Semantic Search: Django, MongoDB Atlas & Voyage AI Tutorial
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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
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Access the Kaggle dataset here: https://www.kaggle.com/datasets/anaiya/guinnesswinebarsdublin
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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
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