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    Gen Ai - Llm Rag Two In One - Langchain + Llamaindex

    Posted By: ELK1nG
    Gen Ai - Llm Rag Two In One - Langchain + Llamaindex

    Gen Ai - Llm Rag Two In One - Langchain + Llamaindex
    Published 10/2024
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 4.88 GB | Duration: 9h 13m

    Gen AI - Learn to develop RAG Applications using LangChain an LlamaIndex Frameworks using LLMs and Vector Databases

    What you'll learn

    Be able to develop your own RAG Applications using either LangChain or LlamaIndex

    Be able to use Vector Databases effectively within your RAG Applications

    Craft Effective Prompts for your RAG Application

    Create Agents and Tools as parts of your RAG Applications

    Create RAG Conversational Bots

    Perform Tracing for your RAG Applications using LangGraph

    Requirements

    Python Programming Knowledge

    Description

    This course leverages the power of both LangChain and LlamaIndex frameworks, along with OpenAI GPT and Google Gemini APIs, and Vector Databases like ChromaDB and Pinecone. It is designed to provide you with a comprehensive understanding of building advanced LLM RAG applications through in-depth conceptual learning and hands-on sessions. The course covers essential aspects of LLM RAG apps, exploring components from both frameworks such as Agents, Tools, Chains, Memory, QueryPipelines, Retrievers, and Query Engines in a clear and concise manner. You'll also delve into Language Embeddings and Vector Databases, enabling you to develop efficient semantic search and similarity-based RAG applications. Additionally, the course covers various Prompt Engineering techniques to enhance the efficiency of your RAG applications.List of Projects/Hands-on included: Develop a Conversational Memory Chatbot using downloaded web data and Vector DBCreate a CV Upload and Semantic CV Search App Invoice Extraction RAG AppCreate a Structured Data Analytics App that uses Natural Language Queries ReAct Agent: Create a Calculator App using a ReAct Agent and ToolsDocument Agent with Dynamic Tools: Create multiple QueryEngineTools dynamically and orchestrate queries through AgentsSequential Query Pipeline: Create Simple Sequential Query PipelinesDAG Pipeline: Develop complex DAG PipelinesDataframe Pipeline: Develop complex Dataframe Analysis Pipelines with Pandas Output Parser and Response SynthesizerWorking with SQL Databases: Develop SQL Database ingestion BotThis twin-framework approach will provide you with a broader perspective on RAG development, allowing you to leverage the strengths of both LangChain and LlamaIndex in your projects.

    Overview

    Section 1: Introduction

    Lecture 1 Introduction to the Course

    Lecture 2 Introduction to Large Language Models (LLMs)

    Lecture 3 Introduction to Prompt Engineering

    Lecture 4 Prompts Advanced

    Section 2: Starting with LangChain

    Lecture 5 Introduction to LangChain

    Lecture 6 LangChain Environment Setup

    Lecture 7 Installing Dependencies

    Lecture 8 Using Google Gemini LLM

    Lecture 9 Our First LangChain Program

    Section 3: Learn LangChain through Projects

    Lecture 10 Working with SQL Data - RAG Application

    Lecture 11 Create a CV Upload and Search Application

    Lecture 12 Create an Invoice Extract RAG Application

    Lecture 13 Create a Conversational Chatbot for HR Policy Queries

    Lecture 14 Analysis of Structured Data using Natural Language

    Section 4: Getting Started with LlamaIndex

    Lecture 15 Introduction to LlamaIndex

    Lecture 16 LlamaIndex setup

    Lecture 17 Our First LlamaIndex Program

    Section 5: Learn LlamaIndex through Projects

    Lecture 18 RAG App using Chroma DB Vector Database

    Lecture 19 LlamaIndex RAG with SQL Database

    Lecture 20 LlamaIndex Query Pipelines

    Lecture 21 LlamaIndex Sequential Query Pipeline

    Lecture 22 LlamaIndex Complex DAG Pipeline

    Lecture 23 Setting up a DataFrame Pipeline

    Lecture 24 Working with Agents and Tools

    Lecture 25 Create a Calculator RAG App using ReAct Agents

    Lecture 26 Create a Document Agent with Dynamically built Tools

    Lecture 27 Create a Code Checker RAG App

    Software Developers, Data Scientists, ML Engineers, DevOps Engineers, Support Engineers, Test / QA Engineers