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Udemy - Ultimate RAG Bootcamp Using Langchain,LangGraph & Langsmith 2025-8

0 students Last updated Aug 2025

About the course

Udemy - Ultimate RAG Bootcamp Using Langchain,LangGraph & Langsmith 2025-8

Course content

Sections: 24

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Activities: 1

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Resources: 118

Announcements
1 Introduction

2 Introduction To RAG
3 Some Examples And Advantages Of Using RAG
4 Business Uecases Impact With RAG
5 Prompt Engineering Vs FineTuning Vs RAG

6 Data Ingestion And Preprocessing
7 Query Processing And Output Generation Phase

8 Anaconda Installation And VS Code Installation

9 Project Structure And Environment Set Up With UV Package
10 Document Structure In LangChain
11 Ingesting And Parsing Text Data Using Document Loaders
12 Text Splitting Techniques
13 Ingestion And Parsing PDF Documents
14 Handling Common PDF Issues
15 Ingestion And Data Parsing Word Documents
16 Parsing CSV And Excel Files
17 Json files Parsing And Processing
18 SQL Databases Parsing And Processing

19 Introduction To Embeddings And Vector Databases
20 Visualization Of Embedding And Consine Similarity
21 Creating Your First Embeddings With HuggingFace Embedding Models
22 Getting Started With OPENAI Embeddings
23 Sematic And Similarity Search Uing Open AI Embedding Models

24 Vector Stores Vs Vector Databases
25 Building Tradition RAG With ChromaDb Vector StorePart 1
26 Building Traditional RAG With ChromaDB Vector StorePart 2
27 Building Traditional RAG With ChromaDB Vector Store Part 3
28 Building RAG Pipeline Using LCELLangchain Expression Language
29 Adding New Documents To Existing Vector Store
30 Advanced RAG TechniquesConversational Memory
31 How To Use GROQ LLM
32 Building a RAG System with LangChain and FAISS Part 1
33 Building a RAG System with LangChain and FAISS Part 2
34 InMemory Vector Store
35 Working With Full Fledged DataStax Astra VectorDB
36 Working With PineCone VectorStore DB

37 Semantic Chunking With RAG
38 Semantic Chunking With Python
39 Building RAG Pipeline With Semantic Chunker
40 Semantic Chunking With Langchain

41 Combining Dense And Sparse Matrix
42 Combining Dense And Sparse Retriever With Langchain
43 Benefits Of Combining Dense And Sparse Search
44 Reranking Hybrid Search Statergy
45 Reranking Hybrid Statergy Implementation
46 Maximal Marginal Relevance Theoretical Explanation
47 MMR Retriever Implementation
48 When To And When Not To Use MMR

49 Query Expansion Technique
50 Query Expansion Technique Implementation
51 Query Decomposition Understanding And Implementation
52 HyDE Technique And Implementation

53 Introduction To MultiModal RAG
54 MultiModal RAG Implementation PDF With Text And Images

55 AI Agents Vs Agentic AI
56 Some More Examples For AI Agents And Agentic AI

57 Getting Started With LangGraph
58 Langgraph Environment Set Up
59 Setting Up OpenAI API Key
60 Setting Up Groq API Key
61 Setting Up LangSmith API Key
62 Build Simple Graph Uing LangGraph
63 Simple Graph Construction and Workflow
64 Create LLM Powered Chatbot Using Langgraph
65 State Schema With Data Classes
66 Pydantic Data Validation
67 Chains Using Langgraph
68 Introduction To Router In LangGraph
69 Tools And Tools Node IntegrationPart 1
70 Tools And Tools Node Integration Part 2
71 Building Chatbot With Multiple Tool IntegrationPart1
72 Building Chatbot With Multiple Tool IntegrationPart2

73 Agents And ReACT Agent Architecture
74 ReAct Agent Architecture Implementation
75 Agent With Memory Implementation
76 Different Streaming Techniques
77 Streaming Using Astream And Stream Events
78 Debugging Wiht LangGraph Studio And Langsmith

79 Traditional RAG Vs Agentic RAG
80 Build A Basic RAG With LangGraph
81 ReAct Agent Architecture Implementation With LangGraph
82 Tool Creation For RAG Agents With LangGraph
83 Detailed Agentic RAG Project Implementation Part 1
84 Detailed Agentic RAG ImplementationPart 2

85 Chain OF Thoughts Implementation With RAG
86 Self Reflection Understanding and Implementation
87 Query Planning And Decomposition With Implementation
88 Iterative Retrieval With Self Reflection
89 Answer synthesis From Multiple sources
90 Autonomous RAG Summary

91 Multi Agents Network With RAG
92 MultiAgent With RAG Implementation
93 Supervisor Multi Agent RAG Implementation
94 Hierarichal Agents With RAG Understanding And Implementation

95 Corrective RAG Detailed Explanation
96 Corrective RAG Detailed Implementation

97 Adaptive RAG Theoretical Understanding
98 Adaptive RAG Implementation

99 RAG With Persistant Memory With LangGraph
100 Agent Rag With Persistant Memory With LangGraph

101 What Is CAG And Its Basic Implementation
102 Advance CAG Implementation With LangGraph

103 Evaluation Of Chatbot Data Input Creation With Schema
104 Evaluation Of Chatbots Defining Metrics LLM AS A JUDGE
105 Evaluation OF Chatbots Running Evaluation Metrics With Different LLMS
106 RAG Evaluation Build RAG And Create Test Dataset
107 RAG Evaluation Build Evaluators With Metrics

108 Introduction To Graph Databases And Cypher Query Language With Langchain
109 What is Knowledge Graph
110 Creating Your Neo4j AuraDB Database Instance
111 RDBMS VS Graph Database
112 Neo4j Property Graph Data Model
113 Getting Started With Cypher Query Language
114 Intermediate To Advance Cypher Query Language

115 Getting StartedCreating Environment
116 Inserting Data In Graph DB With Python And Langchain
117 Creating GraphQuery Chain With Langchain
118 Prompting Statergies GraphDB With LLM

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Udemy - Ultimate RAG Bootcamp Using Langchain,LangGraph & Langsmith 2025-8

Udemy - Ultimate RAG Bootcamp Using Langchain,LangGraph & Langsmith 2025-8

Course modified date: 19 Aug 2025

Udemy - Ultimate RAG Bootcamp Using Langchain,LangGraph & Langsmith 2025-8

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