Master Vector Databases

Master Vector Database using Python, Embeddings, Pinecone, ChromaDB, Facebook FAISS, Qdrant, LangChain, Open AI

What you'll learn

Master Vector Database, Embeddings, ChromaDB, FAISS, Qdrant and much more

Learn integration Vector databases with LangChain, Open AI

Master Embeddings

Transformer Models for vector embedding, Generative AI, Open AI API Usage

Understand the fundamentals of vector databases and their role in AI, generative AI, and LLM (Language Model Models).

Implement code along exercises to build and optimize vector indexing systems for real-world applications.

Requirements

Basic python knowledge

Description

Are you ready to ride the next wave in the realm of data management? Introducing our groundbreaking course: Vector Database Mastery. In this comprehensive program, we delve deep into the fascinating world of Vector Databases, equipping you with the skills and knowledge needed to navigate the data landscape of the future.Why Vector Databases? Traditional databases are evolving, and the next generation is here – Vector Databases. They are not just databases; they are engines of understanding. Harness the power of vectors to represent and comprehend complex data structures, bringing unprecedented efficiency and flexibility to your data management endeavors.Course Highlights:Foundations of Vectors: Dive into the basics of vectors, understanding their role as powerful mathematical entities in representing and manipulating data. Uncover the fundamental concepts that form the backbone of Vector Databases.Embeddings Techniques: Master the art of embeddings – the key to transforming data into a high-dimensional vector space. Explore techniques like Word Embeddings, Doc2Vec, and more, unleashing the potential to encode complex information into compact, meaningful vectors.SQLite as a Vector Database: Witness the fusion of traditional SQL databases with the dynamic capabilities of vectors. Learn how to leverage SQLite as a Vector Database, enabling you to handle intricate relationships and queries with ease.ChromaDB: Explore the cutting-edge ChromaDB, a revolutionary Vector Database that takes data representation to a whole new level. Delve into its architecture, functionalities, and real-world applications, paving the way for a new era of data management.Pinecone DB: Step-by-step walkthrough about creating an index, prepare data, creating embeddings, adding data to index, making queries, queries with metadata filters and much more.Qdrant Vector Database: Uncover the capabilities of Qdrant, a high-performance, open-source Vector Database designed for scalability and speed. Learn to implement and optimize Qdrant for various use cases, propelling your projects to new heights.Langchain for QA Applications: Revolutionize question-answering applications using Langchain. Integrate vector-based search techniques into your projects, enhancing the precision and relevance of your results.OpenAI Embeddings: Harness the power of OpenAI embeddings to elevate your natural language processing projects. Learn to integrate state-of-the-art language models into your applications, pushing the boundaries of what's possible in text-based data analysis.Join the Vector Revolution!Enroll now to future-proof your data management skills. The Vector Database Mastery course is not just a learning experience; it's your ticket to staying ahead in the rapidly evolving world of data. Don't miss out on the next wave – secure your spot today and become a master of Vector Databases!

Overview

Section 1: Introduction

Lecture 1 Introduction to Vector Database

Lecture 2 Vectors and Embeddings

Lecture 3 Explain vector database like I'm 5

Lecture 4 How vector database store data

Lecture 5 How do vector database works?

Lecture 6 Vectors in 2D

Section 2: The power of embeddings

Lecture 7 Create embeddings using OpenAI

Lecture 8 Sentence Embedding Models

Section 3: Using SQLite as vector storage

Lecture 9 Setup and basic operations

Lecture 10 Creating, storing and retrieving vector data

Lecture 11 Finding nearest vector

Lecture 12 Vector search using sqlite-vss extension

Section 4: ChromaDB

Lecture 13 Introduction to ChromaDB

Lecture 14 Revolutionizing the Data access with Vector Database

Lecture 15 Methods on collections

Lecture 16 Storing "The Matrix" collections

Lecture 17 Adding document associated embeddings

Lecture 18 Query data with 'where' filter

Lecture 19 ChromaDB + Langchain - QA on multiple documents - Part 1

Lecture 20 ChromaDB + Langchain - QA on multiple documents - Part 2

Section 5: Facebook AI Similarity Search (FAISS)

Lecture 21 Introduction to FAISS

Lecture 22 Using similarity search for nearest neighbours

Section 6: Pinecone

Lecture 23 Introduction to Pinecone

Lecture 24 Setup account, create an index, dashboard review

Lecture 25 Understanding index creation configuration

Lecture 26 Index management

Lecture 27 Insert vector data to an index

Lecture 28 Query vector data

Lecture 29 Upsert vector data in batches

Lecture 30 Upsert batches in parallel

Lecture 31 Upsert with metadata

Lecture 32 Vector IDs must be string

Lecture 33 Sentence transformer embeddings

Lecture 34 Semantic search with metadata filtering - news articles

Section 7: Qdrant

Lecture 35 Introduction to Qdrant vector database

Lecture 36 Connect with APIs

Lecture 37 Create a qdrant python client

Lecture 38 Create a collection

Lecture 39 Create a vector store

Lecture 40 Add document to vector store on the cloud

Lecture 41 Query the document

Lecture 42 Create a streamlit QA app

Section 8: Congratulations and Thank You!

Lecture 43 Your feedback is very valuable!

Anyone who want to explore the world of AI,Anyone who want to step into AI world with practical learning,Data engineers, database administrators and data professionals curious about the emerging field of vector databases.,Software developers interested in integrating vector databases into their applications.