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    Master Vector Databases

    Posted By: ELK1nG
    Master Vector Databases

    Master Vector Databases
    Published 11/2023
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 4.55 GB | Duration: 7h 16m

    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.