This course uses Open AI GPT and Google Gemini APIs, LlamaIndex LLM Framework and Vector Databases like ChromaDB and Pinecone, and is intended to help you learn how to build LLM RAG applications through solid conceptual and hands-on sessions. This course covers all the basic aspects to learn LLM RAG apps and Frameworks like Agents, Tools, QueryPipelines, Retrievers, Query Engines in a crisp and clear manner. It also takes a 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 help make your RAG Applications more efficient.

List of Projects/Hands-on included:

Basic RAG: Chat with multiple PDF documents using VectorStore, Retriever, Nodepostprocessor, ResponseSynthesizer and Query Engine.

ReAct Agent: Create a Calculator using a ReAct Agent and Tools.

Document Agent with Dynamic Tools : Create multiple QueryEngineTools dynamically and Orchestrate queries through Agent.

Semantic Similarity: Try Semantic Similarity operations and get Similarity Score.

Sequential Query Pipeline: Create Simple Sequential Query Pipeline.

DAG Pipeline: Develop complex DAG Pipelines.

Dataframe Pipeline: Develop complex Dataframe Analysis Pipelines with Pandas Output Parser and Response Synthesizer.

Working with SQL Databases: Develop SQL Database ingestion bots using multiple approaches.

For each project, you will learn:

– The Business Problem

– What LLM and LlamaIndex Components are used

– Analyze outcomes

– What are other similar use cases you can solve with a similar approach.