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