MongoDB Atlas Vector Database: Zero to Advanced with Python

A hands-on guide to mastering MongoDB Atlas and building Vector Databases with Python, Pymongo and Langchain

What you'll learn
- Retrieval-Augmented Generation in MongoDB Atlas
- Beginner Commands in MongoDB: Learn basic CRUD operations using MongoDB Shell and Compass.
- VectorSearch on the Embedding developed by OpenAI models
- Setting Up MongoDB Atlas: Configure and manage a cloud-hosted MongoDB database on MongoDB Atlas
- Full text Search, Regular Expression Search on text data
- Integration of LangChain with Pymongo
- Connecting Python with MongoDB Atlas: Use PyMongo to connect your Python applications to MongoDB Atlas
- Advanced CRUD Operations: Perform complex operations like updating multiple documents, using filters, and conditional queries.
- Indexing and Aggregation: Learn how to create indexes and build efficient aggregation pipelines to handle large datasets.
- Introduction to Vector Databases: Understand vector embeddings and their role in AI applications like similarity search.

Requirements
- Knowledge of Python (even beginner-level proficiency is sufficient).
- A laptop or desktop with at least 8GB RAM and a stable internet connection.
- Familiarity with basic computer operations and interest in databases.

Description
Mastering: MongoDB Atlas Vector Database: Zero to Advanced with Python

This comprehensive course takes you from MongoDB fundamentals to advanced AI-powered vector databases. Perfect for beginners and enthusiasts wanting to master modern database techniques and AI integration.

Course Sections

Section 1: MongoDB FundamentalsMaster MongoDB basics using Shell and Compass. Learn database setup, CRUD operations, and core concepts.

Section 2: PyMongo & Advanced QueriesDive into Python integration with PyMongo. Build complex queries performed in MongoDB Atlas

Section 3: Aggregate Pipeline in AtlasDeep dive into aggregate pipeline and stages like groupby, project, match, conditional statements, switch case and many more.

Section 4: Search TechniquesExplore text search, regex patterns, and full-text search capabilities within MongoDB.

Section 5: MongoDB Atlas & Vector SearchTransition to cloud with MongoDB Atlas. Implement vector embeddings for similarity search and semantic applications.

Section 6: Introduction to Langchain with OpenAI LLMsthere we give you introduction to LangChain OpenAI and how to generate the text and get embeddings using sophisticated OpenAI and API keys.

Section 5: RAG SystemsBuild intelligent Retrieval-Augmented Generation systems combining traditional databases with AI technologies in MongoDB Atlas.

Tools & Resources

Technologies:MongoDB Shell, Compass, PyMongo, MongoDB Atlas, Vector Search, LangChain, OpenAI Embeddings

Included Materials:

Complete Jupyter notebooks with step-by-step code

Sample datasets and real-world data

Configuration files and connection scripts

Project templates and starter code

Documentation and reference guides

Hands-on exercises and solutions

All code examples, datasets, and resources provided for immediate hands-on practice.

Who this course is for:
- Beginners who want to start their journey in database management and modern data technologies.
- Python developers looking to enhance their skills by integrating databases into applications.
- AI and Data Enthusiasts interested in vector search and building AI-driven solutions.
- Students and professionals aiming to work with scalable cloud databases like MongoDB Atlas.
- Small business owners and hobbyists seeking to manage their data efficiently.
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