• views: 168
• date: 19 November 2025
• posted by: voska89

Build production-ready AI Systems for internal Business Documents using LangChain, LangGraph, OpenAI, Chroma & Python
What you'll learn
Explain what RAG is, why it's needed, and when it outperforms plain LLMs
Design your own Enterprise RAG Solutions for internal Documents & Knowledge bases
Use LangChain to build Chatbots, Summarization Pipelines and RAG chains
Use LangGraph to design graph-based, agentic AI Workflows
Load, split and chunk Documents of different Types and sizes effectively
Apply different Summarization Strategies (Stuff, Map-Reduce, Refine)
Create Embeddings and use Vector Stores (FAISS, Chroma) for Retrieval
Evaluate and tune Retrieval Strategies (similarity, thresholds, MMR, multi-query)
Manage Vector Stores with Metadata for powerful filtering and search
Build a dynamic, persistent Chroma vector DB from scratch
Implement automated Vector DB updates based on File and Metadata Changes
Swap out LLMs, Embeddings and Vector DBs to meet Privacy & Scalability needs
Requirements
Comfortable with basic Python Programming
Ability to install software (Anaconda, Python packages) on your machine
Willingness to spend a few Dollars on API calls (less than 5 USD)
Stable Internet Connection and ability to Stream HD Videos
Optional but helpful: prior exposure to ChatGPT / LLMs conceptually
Description
Build Real-World, Enterprise-grade RAG systems – not just toy demos.Large Language Models (LLMs) like ChatGPT are powerful – but on their own they don't know your company's documents, policies or reports. That's where Retrieval Augmented Generation (RAG) comes in.In this course you'll learn, step by step, how to build professional, fully customizable RAG Applications in Python using LangChain, LangGraph, OpenAI and Chroma – tailored to internal Business Data, Knowledge and Documents.You won't just copy a toy example and get "some" result - you'll understand every Building Block: Loading and Chunking Documents, Embeddings, Vector Databases, Retrieval Strategies, Summarization methods, Conversational Memory, and automated Updates for your Vector Store.By the end, you'll be able to design, adapt and extend your own Enterprise RAG Pipelines with Confidence.What makes this course different?Most RAG tutorials stop after a simple "ask questions about this PDF" demo. This course goes several levels deeper:RAG inside a larger, agentic AI FrameworkYou'll integrate RAG into LangChain and LangGraph, so it can become one tool in a larger AI Agent that can decide when to use RAG – and when to follow other tools or workflows. This is how modern, Agentic AI systems are built in practice.Fully explained, fully customizableEvery step is explained in detail:Multiple ways to load and split DocumentsDifferent Summarization Strategies (Stuff, Map-Reduce, Refine)Several Retrieval Strategies and their trade-offsAlternatives and Options at each stepYou'll always see why something is done, what could go wrong, and how to adjust it to your own use case.Dynamic, automated updates – production, not prototypesReal companies don't have static PDFs. Files change all the time. You will build a system that can:Detect Content and Metadata Changes in Documents and FoldersAutomatically Update Embeddings and Vectors in ChromaDBKeep your RAG System in sync with your real document repositoriesThis is the kind of workflow you need for Enterprise Scenarios.Easily swappable Components (LLM, Embeddings, Vector DB, hosting)Because everything is built on LangChain and LangGraph, your system is modular:Swap OpenAI for Azure OpenAI or another providerChange Embedding Models for better data privacyReplace Chroma with a more powerful Vector DB if your user base growsAdjust prompts, retrievers and memory without rewriting everythingYou're not locked into a single vendor or toy stack.Real-world Enterprise document scenarioYou'll work with a complex folder structure and multiple file types: PDFs, Word, PowerPoint, Text, CSV, Mixed directoriesExactly the kind of messy, heterogeneous data you'll see in real organizations.What you'll buildOver the course you will:Create a Basic Chatbot with LangChain & OpenAIImplement Document Summarization Pipelines for small and very large filesBuild your first RAG Chain with FAISS and LangChainAdd Retrieval Strategies like similarity search, thresholds and MMRUse LangGraph to create a graph-based Chatbot with MemoryExtend it into an Agentic Workflow, where RAG could be one tool among othersLoad and process multiple documents and formats from directoriesCreate and operate a dynamic Chroma Vector DatabaseImplement Metadata-based search & filtering (by document, page, date, etc.)Detect file changes and automatically re-embed updated DocumentsBring it all together into a customizable, scalable, self-updating, Enterprise-ready RAG system
Who this course is for
Data Scientists, ML Engineers, and Developers who want to build real RAG Systems
AI/Analytics Professionals in Enterprises who work with internal knowledge bases, reports, manuals or document repositories
Technical Product Managers and Architects planning LLM-powered tools for document Q&A and Summarization
Advanced Python users who want to understand LangChain, LangGraph and Vector Databases in a structured, hands-on way
Homepage
https://www.udemy.com/course/rag-for-professionals-with-langgraph-python-and-openai/




   
   
   

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