Vector Database Fundamentals

Vector Database Fundamentals, available at Free, has an average rating of 4.4, with 9 lectures, based on 5 reviews, and has 315 subscribers.

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You will learn about Implement Retrieval Augmented Generation (RAG) using KDB AI and OpenAI, including setting up a complete RAG pipeline. Gain hands-on experience in data preparation, embedding generation, vector database operations, and integration with language models. Master vector search techniques, advanced vector operations, and querying methods for efficient information retrieval. Explore practical applications of RAG in AI-powered systems and NLP projects. This course is ideal for individuals who are This course is ideal for software developers building AI applications, data scientists enhancing NLP projects, AI enthusiasts interested in vector databases and language models, and professionals implementing advanced search and question-answering systems. It is particularly useful for This course is ideal for software developers building AI applications, data scientists enhancing NLP projects, AI enthusiasts interested in vector databases and language models, and professionals implementing advanced search and question-answering systems.

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Summary

Title: Vector Database Fundamentals

Price: Free

Average Rating: 4.4

Number of Lectures: 9

Number of Published Lectures: 9

Number of Curriculum Items: 9

Number of Published Curriculum Objects: 9

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Implement Retrieval Augmented Generation (RAG) using KDB AI and OpenAI, including setting up a complete RAG pipeline.

Gain hands-on experience in data preparation, embedding generation, vector database operations, and integration with language models.

Master vector search techniques, advanced vector operations, and querying methods for efficient information retrieval.

Explore practical applications of RAG in AI-powered systems and NLP projects.

Who Should Attend

This course is ideal for software developers building AI applications, data scientists enhancing NLP projects, AI enthusiasts interested in vector databases and language models, and professionals implementing advanced search and question-answering systems.

Target Audiences

This course is ideal for software developers building AI applications, data scientists enhancing NLP projects, AI enthusiasts interested in vector databases and language models, and professionals implementing advanced search and question-answering systems.

Dive into the world of vector databases and Retrieval Augmented Generation (RAG) with our comprehensive KDB AI course. Learn how to efficiently store, search, and retrieve high-dimensional data using cutting-edge techniques.

Key topics include:

Vector search fundamentals and applications

Advanced metadata filtering

Implementing RAG pipelines to enhance AI applications

Choosing and optimizing embedding models

Mastering similarity metrics: Euclidean distance, cosine similarity, and dot product

Leveraging indexes like HNSW and IVF-PQ for improved performance

Building sophisticated query systems with metadata filtering

Practical demonstrations cover:

Creating and managing tables

Implementing a RAG pipeline from scratch

Using metadata filters to make complex queries with groupings and aggregations

Some questions you will be able to answer after this course:

How do I choose an index? What are the right algorithm parameters for my data?

How do I choose an embedding model?

How do I optimize RAG performance?

How do I use a vector database to gain insights from my unstructured data

Whether you’re a data scientist, ML engineer, or AI enthusiast, this course equips you with the skills to create powerful AI-driven applications. Learn to combine vector search with large language models, optimize query performance, and solve real-world problems across various industries.

Join us to unlock the full potential of semantic search and RAG with KDB AI Vector Database!

Gain hands-on experience with KDB AI Cloud instances. Master the intricacies of vector embeddings and learn to build scalable, efficient AI systems that push the boundaries of intelligent search and generation.

Course Curriculum

Chapter 1: Fundamentals of Vector Search and Databases

Lecture 1: Introduction to Vector Search

Lecture 2: Introduction to Vector Databases

Lecture 3: Similarity Metrics

Chapter 2: Advanced Vector Operations

Lecture 1: Vector Indexes

Lecture 2: Choosing an Embedding Model

Lecture 3: Managing Tables

Chapter 3: Querying and Search Techniques

Lecture 1: Querying Data

Chapter 4: Retrieval Augmented Generation (RAG)

Lecture 1: Introduction to RAG

Lecture 2: Building RAG from Scratch

Instructors

Michael Ryaboy
Developer Advocate at KDB.AI

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3 stars: 1 votes

4 stars: 2 votes

5 stars: 2 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!