| 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 |
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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