Published 3/2024
Created by Manas Dasgupta
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 18 Lectures ( 7h 42m ) | Size: 3.81 GB
Develop powerful RAG Applications using Open AI GPT APIs, LangChain LLM Framework and Vector Databases
What you’ll learn:
Fundamental of LLM Application Development
LLM Frameworks with LangChain
Using Open AI GPT API to develop RAG Applications
Engineering Optimized Prompts for your RAG Application
LangChain Loaders and Splitters
Using Chains and LCEL (LangChain Expression Language)
Using Retreivers, Agents and Tools
Conversational Memory
Multiple RAG Projects with various Source Types and Business Use
Requirements:
Basic Python Language
No Data Science experience needed
Description:
This course on developing RAG Applications using Open AI GPT APIs, LangChain LLM Framework and Vector Databases is intended to enable learners who want to build a solid conceptual and hand-on proficiency to be able to solve any RAG automation projects given to them. This course covers all the basics aspects of LLM and Frameworks like Agents, Tools, Chains, Retrievers, Output Parsers, Loaders and Splitters and so on in a very thorough manner with enough hands-on coding. It also takes a deep dive into concepts of Language Embeddings and Vector Databases to help you develop efficient semantic search and semantic similarity based RAG Applications.List of Projects Included:SQL RAG: Convert Natural Language to SQL Statements and apply on your MySQL Database to extract desired Results.CV Analysis: Load a CV document and extract JSON based key information from the document.Conversational HR Chatbot: Create a comprehensive HR Chatbot that is able to respond with answers from a HR Policy and Procedure database loaded into a Vector DB, and retain conversational memory like ChatGPT.Structured Data Analysis: Load structured data into a Pandas Dataframe and use a Few-Shot ReAct Agent to perform complex analytics.For each project, you will learn:- The Business Problem- What LLM and LangChain Components are used- Analyze outcomes- What are other similar use cases you can solve with a similar approach.
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