Gen AI - RAG Application Development using LangChain

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. We will also cover multiple Prompt Engineering techniques that will make your RAG Applications more efficient.

List of Projects Included:

SQL RAG: Convert Natural Language to SQL Statements and apply on your MySQL Database to extract desired Results.

RAG with Conversational Memory: Create a simple RAG Application with Conversational Memory.

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. Build UI using Streamlit.

Structured Data Analysis: Load structured data into a Pandas Dataframe and use a Few-Shot ReAct Agent to perform complex analytics.

Invoice Data Extractor: Upload multiple Invoices and extract key information into a CSV format. Build UI using Streamlit.

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.

What You Will Learn!

• Fundamental of LLM RAG Application Development
• LLM Frameworks with LangChain
• Using Open AI GPT API to develop RAG Applications
• Prompt Engineering Techniques
• 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

Who Should Attend!

• Any Software Developer aspiring to use the power of LLMs to infuse Gen AI features in their Project and Products
• Software Developers looking to automate their Software Engineering processes

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