Master Vector Databases

Name: Master Vector Databases
Rating: 4.4 (243 reviews)

Master Vector Database using Python, Embeddings, Pinecone, ChromaDB, Facebook FAISS, Qdrant, LangChain, Open AI

Created byAdnan Waheed

Last updated 4/2024

English

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.

Course content

8 sections • 43 lectures • 7h 16m total length

Introduction to Vector Database15:19
Vectors and Embeddings9:58
Explain vector database like I'm 54:56
explain vector databases by grouping fruits by taste to find items similar to apples. show how a vector database stores data for fast similarity search using features.
How vector database store data6:36
How do vector database works?5:14
Vectors in 2D7:44

Setup and basic operations9:23
Creating, storing and retrieving vector data19:35
Finding nearest vector6:21
Explore how to store and deserialize vectors in binary format, then find the nearest vector to a numpy array using SQL calculation; learn about vector search extensions for semantic search.
Vector search using sqlite-vss extension15:11
Enable vector semantic search in SQLite with the sqlite-vss extension. Use LangChain and syntax transformer embeddings to split text into chunks and perform similarity queries.

Introduction to ChromaDB15:07
Explore chroma, an open source AI native vector database with built-in embedding capabilities, showing how to create collections, add documents with metadata and IDs, and perform semantic search with filtering.
Revolutionizing the Data access with Vector Database2:42
Methods on collections8:08
Storing "The Matrix" collections12:59
Adding document associated embeddings10:59
Query data with 'where' filter9:44
ChromaDB + Langchain - QA on multiple documents - Part 116:27
ChromaDB + Langchain - QA on multiple documents - Part 210:06
Learn how to set up ChromaDB, create a retriever, and perform multi-document QA with LangChain and GPT-3.5 turbo, controlling outputs and source documents.

Introduction to Pinecone19:24
Setup account, create an index, dashboard review14:45
Understanding index creation configuration9:00
Index management15:44
Insert vector data to an index15:26
Insert vector data into the index with upsert, formatting as a list of id and vector tuples, optionally adding metadata and using pandas for zip-based upserts.
Query vector data12:06
Upsert vector data in batches13:41
Learn how to upsert vector data in batches to a pinecone index, generating IDs and 64-d vectors, and chunking data for efficient batch uploads, with parallel upserts previewed.
Upsert batches in parallel1:52
Upsert with metadata0:11
Vector IDs must be string6:51
Sentence transformer embeddings9:45
Semantic search with metadata filtering - news articles32:38

Introduction to Qdrant vector database10:46
Connect with APIs8:38
Create a qdrant python client5:24
Connect to a fresh qdrant cluster via Python, install and import the qdrant client and LangChain components, configure host and API key, and prep for creating a collection.
Create a collection7:25
Create a vector store5:10
Create a vector store with LangChain by configuring a collection and cosine distance, then wire OpenAI embeddings and API keys to store and index vectors.
Add document to vector store on the cloud14:43
Query the document9:38
Query the document by embedding it into a vector store with OpenAI, using LangChain retrievers and a retrieval QA chain to extract answers and key points.
Create a streamlit QA app0:24

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!

Who this course is for:

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.

Master Vector Databases

What you'll learn

Explore related topics

Course content

Introduction6 lectures • 50min

The power of embeddings2 lectures • 13min

Using SQLite as vector storage4 lectures • 51min

ChromaDB8 lectures • 1hr 26min

Facebook AI Similarity Search (FAISS)2 lectures • 24min

Pinecone12 lectures • 2hr 31min

Qdrant8 lectures • 1hr 2min

Congratulations and Thank You!1 lecture • 1min

Requirements

Description

Who this course is for: