Master Vector Database with Python for AI LLM Use Cases

Learn Vector Database using Python, Pinecone, LangChain, Open AI, Hugging Face and build out AI, ML , Chat applications
Master Vector Database with Python for AI LLM Use Cases
File Size :
4.34 GB
Total length :
7h 1m

Category

Instructor

Dr. KM Mohsin

Language

Last update

10/2023

Ratings

4.2/5

Master Vector Database with Python for AI LLM Use Cases

What you’ll learn

Pinecone Vector Database, LangChain, Transformer Models for vector embedding, Generative AI, Open AI API Usage, Hugging Face Models
Master the essential techniques for vector data embedding, indexing, and retrieval.
A Practical Code Along with Semantic Search Use Case in Detail with Named Entity Recognition
Developing an AI Chat Bot for Cognitive Search on Private Data Using LangChain
Understand the fundamentals of vector databases and their role in AI, generative AI, and LLM (Language Model Models).
Explore various vector database technologies, including Pinecone, and learn how to set up and configure a vector database environment.
Learn how vector databases enhance AI workflows by enabling efficient similarity search and nearest neighbor retrieval.
Gain practical knowledge on integrating vector databases with Python, utilizing popular libraries like NumPy, Pandas, and scikit-learn.
Implement code along exercises to build and optimize vector indexing systems for real-world applications.
Explore practical use cases of vector databases in AI, generative AI, and LLM, such as recommendation systems, content generation, and language translation.
Understand how vector databases can handle large-scale datasets and support real-time inference.
Gain insights into performance optimization techniques, scalability considerations, and best practices for vector database implementation.

Master Vector Database with Python for AI LLM Use Cases

Requirements

Basic understanding of programming concepts and experience with at least one programming language (such as Python, Java).
Good to have familiarity with basic data analysis, machine learning
Familiarity with databases and their basic principles, including tables, queries, and data manipulation.
Good to have familiarity with NumPy, Pandas for data manipulations
A working environment for running code and executing machine learning algorithms, such as Jupyter Notebook, Google Colab, or a local development setup.

Description

In this comprehensive course on Vector Databases, you will delve into the exciting world of cutting-edge technologies that are transforming the field of artificial intelligence (AI), particularly in generative AI. With a focus on Future-Proofing Generative AI, this course will equip you with the knowledge and skills to harness the power of Vector Databases for advanced applications, including Language Model Models (LLM), Generative Pretrained Transformers (GPT) like ChatGPT, and Artificial General Intelligence (AGI) development.Starting from the foundations, you will learn the fundamentals of Vector Databases and their role in revolutionizing AI workflows. Through practical examples and hands-on coding exercises, you will explore techniques such as vector data indexing, storage, retrieval, and conditionality reduction. You will also gain proficiency in integrating Pinecone Vector Data Base with other tools like LangChain, OpenAI API using Python to implement real-world use cases and unleash the full potential of Vector Databases.Throughout the course, we will uncover the limitless possibilities of Vector Databases in generative AI. You will discover how these databases enable content generation, recommendation systems, language translation, and more. Additionally, we will discuss performance optimization, scalability considerations, and best practices for efficient implementation.Led by an expert instructor with a PhD in computational nano science and extensive experience as a data scientist at leading companies, you will benefit from their deep knowledge, practical insights, and passion for teaching AI and Machine Learning (ML). Join us now to embark on this transformative learning journey and position yourself at the forefront of Future-Proofing Generative AI with Vector Databases. Enroll today and unlock a world of AI innovation!

Overview

Section 1: Introduction to Vector Database

Lecture 1 Course Overview

Lecture 2 Introduction to Vector Database

Lecture 3 Why Vector Database

Lecture 4 Vector Database Use Cases

Section 2: Vector Database Foundations

Lecture 5 Section Overview

Lecture 6 SQLite Database

Lecture 7 Storing and Retrieving Vector Data in SQLite

Lecture 8 Vector Similarity Search

Lecture 9 Chroma DB-Local Vector Data Base – Part 1: Setup & Data Insertion

Lecture 10 Chroma DB-Local Vector Data Base – Part 2: Query

Section 3: Pinecone Vector Database Environment Setup

Lecture 11 Pinecone Account Setup

Lecture 12 Pinecone DB Console Overview

Lecture 13 Setting Up Development Environment in Windows

Lecture 14 Setting Up Development Environment in Ubuntu

Lecture 15 “Hello World” Script for Vector DB

Section 4: Database Operations

Lecture 16 Database Operations: Create, Retrieve, Update and Deletion (CRUD)

Lecture 17 Insert Data

Lecture 18 Upsert: Insert and Update

Lecture 19 Query Vector Data

Lecture 20 Fetch Vectors by ID

Lecture 21 Delete Vector

Section 5: Data Base Management

Lecture 22 Concepts of Index and Collection

Lecture 23 Index Management

Lecture 24 What is collection

Lecture 25 Index Backup Part 1: Creating Collection

Lecture 26 Index Backup Part 2: Creating Index from Collection

Lecture 27 Partitioning Vectors

Lecture 28 Upsert using Namespace

Lecture 29 Vector Partitioning Using Metadata

Lecture 30 Distance Metrics

Section 6: Project 1: Application in Semantic Search

Lecture 31 Introduction to Semantic Search

Lecture 32 Medium Posts Data Obtaining

Lecture 33 Data Preprocessing

Lecture 34 Preparing for Upsert

Lecture 35 Vector Query: “Semantic Search”

Section 7: Project 2: Semantic Search Powered by Named Entity

Lecture 36 Concept of Named Entity Recognition (NER)

Lecture 37 NER Implementation Examples

Lecture 38 Setting up Environment for NER based Semantic Search

Lecture 39 Vector Embedding Models and Load Data

Lecture 40 Data Preparation

Lecture 41 Developing NER Helper Function

Lecture 42 Vector Embedding in Batches

Lecture 43 NER Extraction in Batches

Lecture 44 Metadata Processing

Lecture 45 Vector Upsert

Lecture 46 Vector Query: Semantic Search with NER

Section 8: Project 3: Building AI Chat Agent with LangChain and OpenAI

Lecture 47 Building an Retrieval AI Agent with LangChain and OpenAI

Lecture 48 Obtaining OpenAI API

Lecture 49 Data Load

Lecture 50 Vector Embedding Function

Lecture 51 Setup Vector DB

Lecture 52 Processing for Meta Data

Lecture 53 Embedding and OpenAI Rate Limit Workaround

Lecture 54 Indexing

Lecture 55 Semantic Search with OpenAI

Lecture 56 Embedding with OpenAI and LangChain

Lecture 57 Retrieval QA Agent- an example of retrieval augmented generation (RAG)

Lecture 58 Chat Agent

Section 9: Capstone Project

Data engineers, database administrators and data professionals curious about the emerging field of vector databases.,Data scientists and analysts interested in exploring advanced AI techniques.,Machine learning engineers seeking to enhance their knowledge of vector databases and their applications.,AI researchers and practitioners looking to leverage vector databases for generative AI models.,Software developers interested in integrating vector databases into their applications.,Students and academics studying AI, machine learning, or data science who want to expand their knowledge in this specialized area.,Individuals with a technical background or a strong interest in AI and databases, eager to explore cutting-edge technologies shaping the future of AI and ML.

Course Information:

Udemy | English | 7h 1m | 4.34 GB
Created by: Dr. KM Mohsin

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