Name: Master Vector Database with Python for AI & LLM Use Cases
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Créé parDr. KM Mohsin

Dernière mise à jour : 04/2025

Anglais

Ce que vous apprendrez

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.

Contenu du cours

11 sections • 74 sessions • 8 h 50 min de durée totale

Course Overview3:34
Introduction to Vector Database7:12
Why Vector Database3:39
Vector Database Use Cases4:09
Quiz on Introduction to Vector Database

Section Overview2:17
Fundamentals of Vector Database10:44
SQLite Database7:06
Storing and Retrieving Vector Data in SQLite12:09
Vector Similarity Search8:03
We were using a SQL database to showcase how vectors can be stored and retrieved. SQL is no way the right tool to index vectors and map similar vectors together. At the end of the lesson you will see that I am using a SQL query like this to retrieve similar vector. I used this following command,
SELECT vector FROM vectors ORDER BY abs(vector - ?) ASC

But in order to find the actual distance between vectors we need to calculate the Euclidean distance. This can be achieved python/numpy easily but in SQL it is little bit complicated because you have to essentially do the math using SQL on binary data. That complex SQL is out of scope of this.
As expected because I didn't take the Euclidean distance between vectors to find which one is closer to our query_vector we ended up having a wrong vector.
Here is the way anyone would calculate in python:

import numpy as np
vect1 = np.array([1.2, 3.4, 2.1, 0.8])
vect2 = np.array([2.7, 1.5, 3.9, 2.3])
qry_vect = np.array([1.0, 3.2, 2.0, 0.5])

d1 = np.linalg.norm(vect1 - qry_vect)
d2 = np.linalg.norm(vect2 - qry_vect)
then sort d1 and d2 to find the closest vectors.
you will find [1.2, 3.4, 2.1, 0.8] is the closest to our query_vector instead of the other one showed in the video.
To do the all these in a vector we need a true vector database which is the topic of subsequent sections.
Chroma DB-Local Vector Data Base - Part 1: Setup & Data Insertion10:43
Chroma DB-Local Vector Data Base - Part 2: Query9:13
Vector database foundation knowledge check

Pinecone Account Setup3:27
Pinecone DB Console Overview5:10
Setting Up Development Environment in Windows2:57
Setting Up Development Environment in Ubuntu5:59
A note on Pinecone upgrade (v-2.2.1 to v-5.0.0)1:16
"Hello World" Script for Vector DB - [Pinecone v-5.0.0]3:59
[Older version] "Hello World" Script for Vector DB - [Pinecone v-2.2.1]5:25
If you are using older pinecone (v-2.2.1) this video tutorial is still relevant. If you upgraded to later version please skip this one and see the next lesson.
Environment Setup

Database Operations: Create, Retrieve, Update and Deletion (CRUD)4:05
You can skip this one if you have already completed it in previous section.
Insert Vectors5:27
Upsert: Insert and Update5:54
This lesson was prepared using Pinecone version 2.2.1. For newest version (v5.0.0) please refer to the attached notebook. Only initialization syntax got changed.
Query Vector Data4:16
Fetch Vectors by ID2:12
Delete Vector3:45

Prérequis

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!

À qui ce cours s'adresse-t-il ?

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.

Ce que vous apprendrez

Découvrir les sujets associés

Contenu du cours

Introduction to Vector Database4 sessions • 19 min

Vector Database Foundations7 sessions • 1 h

Pinecone Vector Database Environment Setup7 sessions • 28 min

Database Operations using Pinecone v5.0.0 [latest as of Oct 2024]6 sessions • 30 min

[Will be archived] Database Operations using Pinecone v2.2.1 (May 2023)6 sessions • 26 min

Data Base Management7 sessions • 59 min

Project 1: Application in Semantic Search5 sessions • 40 min

Project 2: Semantic Search Powered by Named Entity11 sessions • 1 h 38 min

Project 3: Building AI Chat Agent with LangChain and OpenAI12 sessions • 1 h 39 min

Project 4: Audio Similarity Search9 sessions • 1 h 11 min

Prérequis

Description

À qui ce cours s'adresse-t-il ?