Master Vector Database with Python for AI & LLM Use Cases
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!
Vector Database Foundations
Pinecone Vector Database Environment Setup
Database Operations
Data Base Management
Project 1: Application in Semantic Search
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24
Concepts of Index and Collection -
25
Index Management
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26
What is collection
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27
Index Backup Part 1: Creating Collection
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28
Index Backup Part 2: Creating Index from Collection
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29
Partitioning Vectors
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30
Upsert using Namespace
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31
Vector Partitioning Using Metadata
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32
Distance Metrics
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33
Quiz on concepts of Pinecone database management
Project 2: Semantic Search Powered by Named Entity
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34
Introduction to Semantic Search
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35
Medium Posts Data Obtaining
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36
Data Preprocessing
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37
Preparing for Upsert
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38
Vector Query: "Semantic Search"
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39
Quiz on Semantic Search
This quiz tests your basic knowledge on "Semantic Search". Please review the relevant lecture if you feels like missed the basic.
Project 3: Building AI Chat Agent with LangChain and OpenAI
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40
Concept of Named Entity Recognition (NER)
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41
NER Implementation Examples
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42
Setting up Environment for NER based Semantic Search
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43
Vector Embedding Models and Load Data
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44
Data Preparation
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45
Developing NER Helper Function
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46
Vector Embedding in Batches
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47
NER Extraction in Batches
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48
Metadata Processing
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49
Vector Upsert
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50
Vector Query: Semantic Search with NER
Capstone Project
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51
Building an Retrieval AI Agent with LangChain and OpenAI
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52
Obtaining OpenAI API
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53
Data Load
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54
Vector Embedding Function
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55
Setup Vector DB
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56
Processing for Meta Data
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57
Embedding and OpenAI Rate Limit Workaround
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58
Indexing
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59
Semantic Search with OpenAI
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60
Embedding with OpenAI and LangChain
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61
Retrieval QA Agent- an example of retrieval augmented generation (RAG)
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62
Chat Agent
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