Udemy - The Complete Langchain and Rag Developer Course 2026

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Udemy - The Complete Langchain and Rag Developer Course 2026 (Size: 2.3 GB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~Get Your Files Here !
  1 - Introduction
  1 - Introduction.mp4 145.6 MB
  2 - Module 1 Rag Foundations Langchain Kickstart
  2 - Why Rag Changes Everything Beyond Traditional Llms.mp4 39.5 MB
  3 - Module 2 Document Loading Multiformat Data Ingestion
  10 - Reading Processing Docx Files.mp4 18.2 MB
  11 - Realworld Docx Integration Project.mp4 104.1 MB
  12 - Working With Structured Csv Data.mp4 11.3 MB
  13 - Build A Csv Data Loader Stepbystep.mp4 54.1 MB
  4 - Module 3 Smart Text Chunking Strategies Optimization
  14 - Why Chunking Is Critical For Rag Systems.mp4 13.9 MB
  15 - Mastering Langchain Text Splitters.mp4 62 MB
  16 - Popular Chunking Strategies Compared.mp4 29.9 MB
  17 - Recursive Chunking Deep Dive Industry Standard.mp4 58.7 MB
  5 - Module 4 Embeddings Semantic Search Vector Databases
  18 - Embeddings Explained Visually Intuitively.mp4 18.7 MB
  19 - How Semantic Similarity Search Actually Works.mp4 13.6 MB
  20 - Using Embedding Models In Langchain.mp4 12.6 MB
  21 - Vector Databases Explained For Beginners.mp4 13.1 MB
  22 - Handson With Faiss Vector Store.mp4 249.2 MB
  23 - Building Ai Search With Chromadb.mp4 115.4 MB
  6 - Module 5 Langchain Runnables Ai Pipeline Composition
  24 - Understanding Langchain Runnables.mp4 19.3 MB
  25 - Master Runnable Passthrough.mp4 24.9 MB
  26 - Creating Custom Logic With Runnable Lambda.mp4 39.4 MB
  27 - Parallel Execution With Runnable Parallel.mp4 49.2 MB
  28 - Build Powerful Chains Using Pipe Operators.mp4 41.4 MB
  7 - Module 6 Build A Complete Endtoend Rag Application
  29 - Complete Rag Workflow Recap.mp4 21.4 MB
  30 - Load Real Documents Into Your Pipeline.mp4 144.6 MB
  31 - Chunk Documents For Maximum Retrieval Accuracy.mp4 77.4 MB
  32 - Generate Highquality Embeddings.mp4 44.1 MB
  33 - Build A Semantic Retriever.mp4 128.1 MB
  34 - Create Your Runnablebased Ai Chain.mp4 54.7 MB
  35 - Combine Lambda Functions Advanced Runnables.mp4 70.9 MB
  36 - Prompt Engineering For Better Ai Responses.mp4 71.7 MB
  37 - Structured Output Parsing With Pydantic.mp4 103.2 MB
  38 - Assemble The Full Production Rag Pipeline.mp4 39.4 MB
  39 - Run Test Validate Your Ai Application.mp4 100.4 MB
  8 - Pdf Processing Fundamentals For Rag.mp4 24.5 MB
  9 - Handson Pdf Extraction With Langchain.mp4 107.4 MB
  3 - Understanding The Complete Rag Architecture.mp4 49.5 MB
  4 - Langchain Fundamentals Made Simple.mp4 43.2 MB
  5 - Generate Configure Your Openai Api Key.mp4 18.4 MB
  6 - Professional Project Setup With Virtual Environments.mp4 39.8 MB
  7 - Your First Working Llm App.mp4 96.8 MB

Description


The Complete Langchain & Rag Developer Course 2026
https://WebToolTip.com
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz

Language: English | Size: 2.31 GB | Duration: 3h 0m
Master LangChain, RAG, OpenAI, FAISS & ChromaDB to Build Production-Ready AI RAG Applications in Python
What you'll learn

Build complete Retrieval-Augmented Generation (RAG) applications from scratch using Python and LangChain.

Build AI applications using LangChain and OpenAI APIs.

Understand the fundamentals of RAG, embeddings, vector databases, and semantic search.

Process PDFs, CSVs, and DOCX files for Retrieval-Augmented Generation systems

Implement advanced chunking strategies for improved retrieval performance.

Create embeddings and perform similarity search using FAISS and ChromaDB.

Build scalable AI workflows using LangChain Runnables.

Engineer prompts for more accurate and reliable LLM responses.

Parse structured outputs using Pydantic models.

Assemble a complete production-ready RAG pipeline from scratch.

Gain hands-on experience through a real-world capstone project.
Requirements

Basic Python programming knowledge is recommended.

A computer with internet access (Windows, macOS, or Linux).

No prior experience with LangChain is required.

No prior knowledge of Retrieval-Augmented Generation (RAG) is required.

No Machine Learning or Deep Learning background is necessary.

Enthusiasm to build real-world AI applications using modern Generative AI tools.

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