Developing Gen AI - RAG Applications with LangChain

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.
• 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.