Ultimate RAG Bootcamp Using Langchain,LangGraph & Langsmith
Build powerful RAG pipelines: Traditional, Advanced, Multimodal & Agentic AI with LangChain,LangGraph and Langsmith
Lo que serás capaz de hacer
Unlock the Power of Retrieval-Augmented Generation (RAG) – From Traditional to Advanced Agentic AI Systems In today’s AI-driven world, Retrieval-Augmented Generation (RAG) is one of the most impactful and in-demand techniques, powering everything from intelligent chatbots and personal assistants to automated research agents and enterprise AI systems. The Ultimate RAG Bootcamp is your complete, step-by-step guide to mastering RAG using the latest and most powerful tools — LangChain , LangGraph , and LangSmith . Whether you’re an AI beginner or an experienced developer, this course takes you from the fundamentals of RAG pipelines all the way to advanced Agentic RAG architectures used in production by leading companies. Why This Course? Unlike other courses that only touch on basic RAG concepts, this bootcamp goes deeper. You will: Learn traditional RAG step-by-step. Master advanced retrieval strategies like hybrid search, vector optimization, and multimodal RAG. Implement multi-agent, autonomous AI pipelines that can think, plan, and act collaboratively. Use LangSmith for experiment tracking, debugging, and performance optimization. Build real-world, deployable AI applications from start to finish. By the end, you won’t just understand RAG — you’ll be able to design, optimize, and deploy advanced AI systems for real-world scenarios. What You’ll Learn 1. RAG Foundations What RAG is and why it matters. Traditional RAG architecture: data ingestion, parsing, embeddings, and retrieval. Choosing and using vector databases effectively. Building retrieval + generation workflows with LangChain. 2. Advanced RAG Techniques Advanced chunking strategies for precision retrieval. Hybrid search: combining vector and keyword search. Multimodal RAG for text, images, and more. Persistent memory for context retention. Self-RAG for improving retrieval quality. Adaptive & Corrective RAG for dynamic and error-resistant pipelines. 3. Agentic RAG Pipelines Multi-agent architectures with LangGraph. Designing agents for research, summarization, and decision-making. Autonomous RAG with minimal human intervention. Collaborative AI reasoning with specialized agents. 4. LangSmith for RAG Evaluation & Optimization Tracking and managing RAG experiments. Debugging retrieval pipelines and fixing bottlenecks. Running evaluation metrics to boost accuracy. 5. Real-World RAG Projects Chatbot with domain-specific knowledge. Multi-agent research assistant for automated reports. Multimodal AI assistant with text and image retrieval. Deploying RAG applications to the cloud. Who This Course Is For AI developers & machine learning engineers. Data scientists integrating retrieval systems. Software developers building intelligent assistants. Researchers exploring advanced RAG workflows. Anyone aiming to master RAG from scratch to production-ready deployment. Tools & Frameworks You’ll Master LangChain – Build modular RAG pipelines. LangGraph – Create advanced agent-based workflows with memory. LangSmith – Track, debug, and evaluate RAG systems. Vector Databases – FAISS, Pinecone, Weaviate, and more. Cloud Deployment – Take AI apps from development to production. Your Learning Journey Understand RAG fundamentals. Build real-world retrieval pipelines. Advance to agentic and autonomous AI systems. Deploy and monitor in production. Optimize for continuous improvement. RAG is more than just an AI trend — it’s the foundation of intelligent, context-aware applications. By the end of this bootcamp, you’ll have hands-on, production-ready skills to build and deploy cutting-edge RAG pipelines with LangChain, LangGraph, and LangSmith. Join the Ultimate RAG Bootcamp today — and start building AI systems that truly understand, reason, and deliver results.
- Build traditional RAG pipelines for accurate and efficient information retrieval.
- Implement advanced retrieval methods like hybrid search, multimodal RAG, and persistent memory.
- Design multi-agent and autonomous RAG systems using LangGraph for collaborative AI reasoning.
- Use LangSmith for tracking, debugging, and optimizing RAG workflows in real-world projects.
- Integrate LangSmith for tracking, debugging, and optimizing RAG performance.
- Use vector databases like FAISS, Pinecone, and Weaviate efficiently.
Temario
UNIDAD 1 - Introduction · 1 leccionesUNIDAD 1 - Introduction
UNIDAD 1 - Introduction
- Introduction
UNIDAD 2 - Introduction To RAG · 4 leccionesUNIDAD 2 - Introduction To RAG
UNIDAD 2 - Introduction To RAG
- Introduction To RAG
- Some Examples And Advantages Of Using RAG
- Business Uecases Impact With RAG
- Prompt Engineering Vs FineTuning Vs RAG
UNIDAD 3 - Core Components In RAG · 2 leccionesUNIDAD 3 - Core Components In RAG
UNIDAD 3 - Core Components In RAG
- Data Ingestion And Preprocessing
- Query Processing And Output Generation Phase
UNIDAD 4 - VS Code And Anaconda Installation · 1 leccionesUNIDAD 4 - VS Code And Anaconda Installation
UNIDAD 4 - VS Code And Anaconda Installation
- Anaconda Installation And VS Code Installation
UNIDAD 5 - Data Ingestion And Data Parsing Techniques · 10 leccionesUNIDAD 5 - Data Ingestion And Data Parsing Techniques
UNIDAD 5 - Data Ingestion And Data Parsing Techniques
- Project Structure And Environment Set Up With UV Package
- Document Structure In LangChain
- Ingesting And Parsing Text Data Using Document Loaders
- Text Splitting Techniques
- Ingestion And Parsing PDF Documents
- Handling Common PDF Issues
- Ingestion And Data Parsing Word Documents
- Parsing CSV And Excel Files
- Json files Parsing And Processing
- SQL Databases Parsing And Processing
UNIDAD 6 - Vector Embedding And Vector Databases · 5 leccionesUNIDAD 6 - Vector Embedding And Vector Databases
UNIDAD 6 - Vector Embedding And Vector Databases
- Introduction To Embeddings And Vector Databases
- Visualization Of Embedding And Consine Similarity
- Creating Your First Embeddings With HuggingFace Embedding Models
- Getting Started With OPENAI Embeddings
- Sematic And Similarity Search Uing Open AI Embedding Models
UNIDAD 7 - Vector Stores And Vector Databases · 13 leccionesUNIDAD 7 - Vector Stores And Vector Databases
UNIDAD 7 - Vector Stores And Vector Databases
- Vector Stores Vs Vector Databases
- Building Tradition RAG With ChromaDb Vector Store-Part 1
- Building Traditional RAG With ChromaDB Vector Store-Part 2
- Building Traditional RAG With ChromaDB Vector Store- Part 3
- Building RAG Pipeline Using LCEL(Langchain Expression Language)
- Adding New Documents To Existing Vector Store
- Advanced RAG Techniques-Conversational Memory
- How To Use GROQ LLM
- Building a RAG System with LangChain and FAISS- Part 1
- Building a RAG System with LangChain and FAISS- Part 2
- InMemory Vector Store
- Working With Full Fledged DataStax Astra VectorDB
- Working With PineCone VectorStore DB
UNIDAD 8 - Advanced Chunking And Preprocessing Techniques · 4 leccionesUNIDAD 8 - Advanced Chunking And Preprocessing Techniques
UNIDAD 8 - Advanced Chunking And Preprocessing Techniques
- Semantic Chunking With RAG
- Semantic Chunking With Python
- Building RAG Pipeline With Semantic Chunker
- Semantic Chunking With Langchain
UNIDAD 9 - Hybrid Search Strategies · 8 leccionesUNIDAD 9 - Hybrid Search Strategies
UNIDAD 9 - Hybrid Search Strategies
- Combining Dense And Sparse Matrix
- Combining Dense And Sparse Retriever With Langchain
- Benefits Of Combining Dense And Sparse Search
- Reranking Hybrid Search Statergy
- Reranking Hybrid Statergy Implementation
- Maximal Marginal Relevance Theoretical Explanation
- MMR Retriever Implementation
- When To And When Not To Use MMR
UNIDAD 10 - Query Enhancement · 4 leccionesUNIDAD 10 - Query Enhancement
UNIDAD 10 - Query Enhancement
- Query Expansion Technique
- Query Expansion Technique Implementation
- Query Decomposition Understanding And Implementation
- HyDE Technique And Implementation
UNIDAD 11 - MultiModal Multi-Modal RAG · 2 leccionesUNIDAD 11 - MultiModal Multi-Modal RAG
UNIDAD 11 - MultiModal Multi-Modal RAG
- Introduction To MultiModal RAG
- MultiModal RAG Implementation (PDF With Text And Images)
UNIDAD 12 - Getting Started With AI Agents And Agentic AI · 2 leccionesUNIDAD 12 - Getting Started With AI Agents And Agentic AI
UNIDAD 12 - Getting Started With AI Agents And Agentic AI
- AI Agents Vs Agentic AI
- Some More Examples For AI Agents And Agentic AI
UNIDAD 13 - Updated Langchain Hands On With Version V1 · 11 leccionesUNIDAD 13 - Updated Langchain Hands On With Version V1
UNIDAD 13 - Updated Langchain Hands On With Version V1
- Intro To Langchain Updates
- Creating Virtual Environment Using UV Package
- Create Agents Using Langchain
- Invoke And Batch Streaming
- Tools In Langchain
- Messages Types In Langchain
- LLM structured Output With Langchain Using Pydantic
- LLM structured Output With Langchain Using TypeDict
- LLM Structured Output With Langchain Using Data Classes
- MiddleWare Summarization With Langchain
- Human In the Loop Middleware With Langchain
UNIDAD 14 - Langgraph Basics · 16 leccionesUNIDAD 14 - Langgraph Basics
UNIDAD 14 - Langgraph Basics
- Getting Started With LangGraph
- Langgraph Environment Set Up
- Setting Up OpenAI API Key
- Setting Up Groq API Key
- Setting Up LangSmith API Key
- Build Simple Graph Uing LangGraph
- Simple Graph Construction and Workflow
- Create LLM Powered Chatbot Using Langgraph
- State Schema With Data Classes
- Pydantic Data Validation
- Chains Using Langgraph
- Introduction To Router In LangGraph
- Tools And Tools Node Integration-Part 1
- Tools And Tools Node Integration- Part 2
- Building Chatbot With Multiple Tool Integration-Part1
- Building Chatbot With Multiple Tool Integration-Part2
UNIDAD 15 - Agents Architecture · 6 leccionesUNIDAD 15 - Agents Architecture
UNIDAD 15 - Agents Architecture
- Agents And ReACT Agent Architecture
- ReAct Agent Architecture Implementation
- Agent With Memory Implementation
- Different Streaming Techniques
- Streaming Using Astream And Stream Events
- Debugging Wiht LangGraph Studio And Langsmith
UNIDAD 16 - Agentic RAG · 6 leccionesUNIDAD 16 - Agentic RAG
UNIDAD 16 - Agentic RAG
- Traditional RAG Vs Agentic RAG
- Build A Basic RAG With LangGraph
- ReAct Agent Architecture Implementation With LangGraph
- Tool Creation For RAG Agents With LangGraph
- Detailed Agentic RAG Project Implementation- Part 1
- Detailed Agentic RAG Implementation-Part 2
UNIDAD 17 - Autonomous RAG · 6 leccionesUNIDAD 17 - Autonomous RAG
UNIDAD 17 - Autonomous RAG
- Chain OF Thoughts Implementation With RAG
- Self Reflection Understanding and Implementation
- Query Planning And Decomposition With Implementation
- Iterative Retrieval With Self Reflection
- Answer synthesis From Multiple sources
- Autonomous RAG Summary
UNIDAD 18 - Multi Agents RAGS · 4 leccionesUNIDAD 18 - Multi Agents RAGS
UNIDAD 18 - Multi Agents RAGS
- Multi Agents Network With RAG
- MultiAgent With RAG Implementation
- Supervisor Multi Agent RAG Implementation
- Hierarichal Agents With RAG Understanding And Implementation
UNIDAD 19 - Corrective RAG · 2 leccionesUNIDAD 19 - Corrective RAG
UNIDAD 19 - Corrective RAG
- Corrective RAG Detailed Explanation
- Corrective RAG Detailed Implementation
UNIDAD 20 - Adaptive RAG · 2 leccionesUNIDAD 20 - Adaptive RAG
UNIDAD 20 - Adaptive RAG
- Adaptive RAG Theoretical Understanding
- Adaptive RAG Implementation
UNIDAD 21 - RAG With Persistant Memory · 2 leccionesUNIDAD 21 - RAG With Persistant Memory
UNIDAD 21 - RAG With Persistant Memory
- RAG With Persistant Memory With LangGraph
- Agent Rag With Persistant Memory With LangGraph
UNIDAD 22 - Cache RAG With LangGraph · 2 leccionesUNIDAD 22 - Cache RAG With LangGraph
UNIDAD 22 - Cache RAG With LangGraph
- What Is CAG And Its Basic Implementation
- Advance CAG Implementation With LangGraph
UNIDAD 23 - VectorLess RAG With PageIndex · 2 leccionesUNIDAD 23 - VectorLess RAG With PageIndex
UNIDAD 23 - VectorLess RAG With PageIndex
- Vectorless RAG With Page Index Tutorials
- Traditional Vs Vectorless RAG
UNIDAD 24 - Gaurdrails · 1 leccionesUNIDAD 24 - Gaurdrails
UNIDAD 24 - Gaurdrails
- Guardrails with LangChain
UNIDAD 25 - LLM Gateways · 1 leccionesUNIDAD 25 - LLM Gateways
UNIDAD 25 - LLM Gateways
- LLM Gateways And Implementation
UNIDAD 26 - Chatbot And RAG Evaluation · 5 leccionesUNIDAD 26 - Chatbot And RAG Evaluation
UNIDAD 26 - Chatbot And RAG Evaluation
- Evaluation Of Chatbot- Data Input Creation With Schema
- Evaluation Of Chatbots- Defining Metrics LLM AS A JUDGE
- Evaluation OF Chatbots - Running Evaluation Metrics With Different LLMS
- RAG Evaluation- Build RAG And Create Test Dataset
- RAG Evaluation- Build Evaluators With Metrics
UNIDAD 27 - Introduction To Graph Databases And Cypher Query Language With Langchain · 7 leccionesUNIDAD 27 - Introduction To Graph Databases And Cypher Query Language With Langchain
UNIDAD 27 - Introduction To Graph Databases And Cypher Query Language With Langchain
- Introduction To Graph Databases And Cypher Query Language With Langchain
- What is Knowledge Graph
- Creating Your Neo4j AuraDB Database Instance
- RDBMS VS Graph Database
- Neo4j Property Graph Data Model
- Getting Started With Cypher Query Language
- Intermediate To Advance Cypher Query Language
UNIDAD 28 - Practical Implementation With GraphDb With Langchain · 4 leccionesUNIDAD 28 - Practical Implementation With GraphDb With Langchain
UNIDAD 28 - Practical Implementation With GraphDb With Langchain
- Getting Started-Creating Environment
- Inserting Data In Graph DB With Python And Langchain
- Creating GraphQuery Chain With Langchain
- Prompting Statergies GraphDB With LLM
UNIDAD 29 - End To End RAG Document Search Project · 8 leccionesUNIDAD 29 - End To End RAG Document Search Project
UNIDAD 29 - End To End RAG Document Search Project
- Project Demo
- Creating Environment With UV Package Manager
- Defining Project Structure
- Document Ingestion And Document Processing Implementation
- VectorStore, Retriever And RAG State Implementation
- Graph Builder And Node Implementation Integration
- Build React Agent Architecture With Retriever And Wikipedia
- Building Streamlit App With React Agent RAG
UNIDAD 30 - RolePlay · 1 leccionesUNIDAD 30 - RolePlay
UNIDAD 30 - RolePlay
- Roleplay: Building an Agentic AI Application
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Muestra · verificable por folio y QR
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
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- 30 unidades · 142 lecciones · 60 h
- 12 meses de acceso
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