Master Vector Database with Python for AI & LLM Use Cases from Udemy

What's inside

Learning objectives

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.
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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.
Show more
Show less

Syllabus

Introduction to Vector Database

Course Overview

Why Vector Database

Vector Database Use Cases

This quiz tests your knowledge on introduction to Vector Database.

Vector Database Foundations

Section Overview

SQLite Database

Storing and Retrieving Vector Data in SQLite

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 Insertion

Chroma DB-Local Vector Data Base - Part 2: Query

Pinecone Vector Database Environment Setup

Pinecone Account Setup

Pinecone DB Console Overview

Setting Up Development Environment in Windows

Setting Up Development Environment in Ubuntu

"Hello World" Script for Vector DB

This quiz is a knowledge test for environment setup.

Database Operations

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

Insert Data

Upsert: Insert and Update

Query Vector Data

Fetch Vectors by ID

Delete Vector

Data Base Management

Concepts of Index and Collection

Index Management

What is collection

Index Backup Part 1: Creating Collection

Index Backup Part 2: Creating Index from Collection

Partitioning Vectors

Upsert using Namespace

Vector Partitioning Using Metadata

Distance Metrics

Quiz on concepts of Pinecone database management

Project 1: Application in Semantic Search

Introduction to Semantic Search

Medium Posts Data Obtaining

Data Preprocessing

Preparing for Upsert

Vector Query: "Semantic Search"

This quiz tests your basic knowledge on "Semantic Search". Please review the relevant lecture if you feels like missed the basic.

Reading Assignment: Read Hugging Face Documents on a sentence transformer

Project 2: Semantic Search Powered by Named Entity

Concept of Named Entity Recognition (NER)

NER Implementation Examples

Setting up Environment for NER based Semantic Search

Vector Embedding Models and Load Data

Data Preparation

Developing NER Helper Function

Vector Embedding in Batches

NER Extraction in Batches

Metadata Processing

Vector Upsert

Vector Query: Semantic Search with NER

Retrieval Augmented Generation (RAG) - a technique to make LLM grounding technique to remove hallucination.

Building an Retrieval AI Agent with LangChain and OpenAI

Obtaining OpenAI API

Data Load

Vector Embedding Function

Setup Vector DB

Processing for Meta Data

Embedding and OpenAI Rate Limit Workaround

Indexing

Semantic Search with OpenAI

Embedding with OpenAI and LangChain

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

Chat Agent

Students will learn how to think about an AI project involving vector database and work towards their portfolio.

Build a video search engine based on audio content where "text" is search input.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches essential techniques for vector data embedding, indexing, and retrieval, which are highly relevant in data science and AI

Develops proficiency in integrating Vector Database with Python, utilizing popular libraries like NumPy, Pandas, and scikit-learn, which are industry standards for data manipulation and machine learning

Provides practical knowledge on how to build and optimize vector indexing systems for real-world AI applications, developing essential skills for AI practitioners

Covers performance optimization techniques, scalability considerations, and best practices for vector database implementation, which are crucial for building efficient and scalable AI systems

Instructed by Dr. KM Mohsin, who has a PhD in computational nanoscience and extensive experience as a data scientist at leading companies, ensuring students learn from an expert in the field

Requires extensive background knowledge in AI and machine learning, which may be a barrier for beginners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Master Vector Database with Python for AI & LLM Use Cases with these activities:

Read article on Vector Embeddings

Show steps

Refresh your knowledge on vector embeddings to enhance your understanding of the concepts discussed in the course.

Show steps

Find an article or blog post on vector embeddings.
Read the article thoroughly, taking notes on key concepts.
Summarize the main ideas of the article in your own words.

Solve coding exercises on Vector Distance Metrics

Show steps

Strengthen your understanding of vector distance metrics by solving coding exercises that involve calculating and comparing distances.

Browse courses on Cosine Similarity

Show steps

Find coding exercises on vector distance metrics.
Solve the exercises, implementing different distance metrics.
Compare the results of using different distance metrics.

Follow tutorial on Building a basic Vector Search Engine

Show steps

Gain hands-on experience by building a basic vector search engine to apply the concepts learned in the course.

Browse courses on Vector Database

Show steps

Find a tutorial on building a vector search engine.
Follow the tutorial step-by-step.
Test the vector search engine with your own data.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Discuss Vector Database Applications with peers

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Expand your knowledge by discussing various applications of vector databases with fellow students in a peer session.

Browse courses on Generative AI

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Find a study group or online forum where you can connect with peers.
Initiate a discussion on vector database applications.
Share your own ideas and learn from others' experiences.

Design a Vector Database Schema for a specific use case

Show steps

Apply your understanding of vector databases by designing a schema for a specific use case, such as image search or text classification.

Browse courses on Data Modeling

Show steps

Identify a specific use case for a vector database.
Determine the data types and relationships involved in the use case.
Design a vector database schema that meets the requirements of the use case.
Document the schema design, including the rationale for the choices made.

Participate in a Hackathon that focuses on Vector Databases

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Challenge yourself and showcase your skills by participating in a hackathon that focuses on vector databases, enabling you to apply your knowledge in a competitive setting.

Show steps

Find a hackathon that has a focus on vector databases.
Form a team or work individually on a project idea.
Develop a solution that utilizes vector databases to solve a real-world problem.
Present your solution and compete against other teams.

Contribute to an Open-Source Vector Database Project

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Gain practical experience and contribute to the community by participating in an open-source vector database project.

Browse courses on Software Development

Show steps

Find an open-source vector database project that aligns with your interests.
Identify areas where you can contribute, such as bug fixes or feature enhancements.
Submit pull requests and collaborate with the project maintainers.

Career center

Learners who complete Master Vector Database with Python for AI & LLM Use Cases will develop knowledge and skills that may be useful to these careers:

Data Architect

Data Architects design and build data systems. They work with businesses to understand their data needs and develop data solutions that meet those needs. This course can help Data Architects develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Data Architects can design and build data systems that are efficient and scalable.

See salaries and explore the career path for Data Architect

Machine Learning Engineer

Machine Learning Engineers build and deploy machine learning models to solve real-world problems. They work closely with Data Scientists to develop and refine models, and they also work with software engineers to integrate models into production systems. This course can help Machine Learning Engineers develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Machine Learning Engineers can improve the efficiency and accuracy of their machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts help businesses understand their data and make informed decisions. They use a variety of statistical and data visualization techniques to identify trends and patterns in data. This course can help Data Analysts develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Data Analysts can improve the efficiency and accuracy of their data analysis workflows.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. They use a variety of statistical and machine learning techniques to extract meaningful insights from data. This course can help Data Scientists develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Data Scientists can improve the efficiency and accuracy of their data analysis workflows.

See salaries and explore the career path for Data Scientist

Academic

Academics teach and conduct research at colleges and universities. They work in a variety of disciplines, including computer science, biology, and physics. This course can help Academics develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Academics can improve the efficiency and accuracy of their research and teaching.

See salaries and explore the career path for Academic

Research Scientist

Research Scientists conduct research to advance scientific knowledge. They work in a variety of fields, including computer science, biology, and physics. This course can help Research Scientists develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Research Scientists can improve the efficiency and accuracy of their research.

See salaries and explore the career path for Research Scientist

Statistician

Statisticians use mathematical and statistical models to analyze data. They work in a variety of industries, including healthcare, finance, and education. This course can help Statisticians develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Statisticians can improve the efficiency and accuracy of their statistical models.

See salaries and explore the career path for Statistician

Business Analyst

Business Analysts help businesses understand their operations and make informed decisions. They use a variety of data analysis and modeling techniques to identify areas for improvement. This course can help Business Analysts develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Business Analysts can improve the efficiency and accuracy of their data analysis workflows.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They help investors make informed decisions about where to invest their money. This course can help Quantitative Analysts develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Quantitative Analysts can improve the efficiency and accuracy of their financial models.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This course can help Actuaries develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Actuaries can improve the efficiency and accuracy of their risk assessment models.

See salaries and explore the career path for Actuary

Fraud Analyst

Fraud Analysts help businesses detect and prevent fraud. They use a variety of data analysis and modeling techniques to identify suspicious activity. This course can help Fraud Analysts develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Fraud Analysts can improve the efficiency and accuracy of their fraud detection models.

See salaries and explore the career path for Fraud Analyst

Database Administrator

Database Administrators are responsible for managing and maintaining databases. They ensure that databases are running smoothly and efficiently, and they also help to protect data from unauthorized access. This course can help Database Administrators develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Database Administrators can improve the performance and reliability of their databases.

See salaries and explore the career path for Database Administrator

Risk Analyst

Risk Analysts help businesses identify and manage risks. They use a variety of data analysis and modeling techniques to assess the likelihood and impact of risks. This course can help Risk Analysts develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Risk Analysts can improve the efficiency and accuracy of their risk assessment models.

See salaries and explore the career path for Risk Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. They work on a variety of projects, from small personal projects to large enterprise systems. This course can help Software Engineers develop the skills they need to work with vector databases, which are becoming increasingly important for storing and processing high-dimensional data. By learning about vector indexing, storage, and retrieval, Software Engineers can improve the efficiency and performance of their software applications.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers are responsible for developing and launching new products. They work closely with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course can help Product Managers develop the skills they need to understand the potential of vector databases and how they can be used to improve the performance and features of their products.

See salaries and explore the career path for Product Manager

Master Vector Database with Python for AI & LLM Use Cases

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