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Gen AI - RAG Application Development using LlamaIndex - AD-TEAM - 12-14-2024
![[Image: 3a71d9dd1d690a994a88fdd5bc8b2df5.jpg]](https://i124.fastpic.org/big/2024/1214/f5/3a71d9dd1d690a994a88fdd5bc8b2df5.jpg)
Gen AI - RAG Application Development using LlamaIndex
Published 5/2024
Duration: 7h23m | .MP4 1280x720, 30 fps® | AAC, 44100 Hz, 2ch | 3.43 GB
Genre: eLearning | Language: English
Learn LlamaIndex to Develop RAG Applications using Open AI GPT, Gemini LLM and Vector Databases
What you'll learn
Fundamentals of LLM RAG Application Development
Using Open AI GPT API to develop RAG Applications
Prompt Engineering - Write Optimized Prompts for your RAG Application
Using LlamaIndex Query Engines, Retrievers and Query Pipelines
Building Conversational Memory
Using Data Connectors
Building Smart Agents and Tools
Language Embeddings and Vector Databases
Working with SQL Databases
Working with Structured Data and Dataframes in RAGs
Convert your LlamaIndex RAG as a FAST API
Requirements
Some Python background
Description
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.
Who this course is for:
Software Developers aspiring to use the power of LLMs to build Gan AI RAG Applications as part of their Project and Products
Software Developers looking to automate their Software Engineering processes using Gen AI
[To see links please register or login]
![[Image: q45e5KOw_o.jpg]](https://images2.imgbox.com/4f/dc/q45e5KOw_o.jpg)