RAG for Professionals with LangGraph, Python and OpenAI

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: 1. RAG inside a larger, agentic AI Framework You’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. 2. Fully explained, fully customizable Every step is explained in detail: - Multiple ways to load and split Documents - Different Summarization Strategies (Stuff, Map-Reduce, Refine) - Several Retrieval Strategies and their trade-offs - Alternatives and Options at each step - You’ll always see why something is done, what could go wrong, and how to adjust it to your own use case. 3. Dynamic, automated updates – production, not prototypes Real 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 Folders - Automatically Update Embeddings and Vectors in ChromaDB - Keep your RAG System in sync with your real document repositories This is the kind of workflow you need for Enterprise Scenarios. 4. 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 provider - Change Embedding Models for better data privacy - Replace Chroma with a more powerful Vector DB if your user base grows - Adjust prompts, retrievers and memory without rewriting everything - You’re not locked into a single vendor or toy stack. 5. Real-world Enterprise document scenario - You’ll work with a complex folder structure and multiple file types: PDFs, Word, PowerPoint, Text, CSV, Mixed directories - Exactly the kind of messy, heterogeneous data you’ll see in real organizations. What you’ll build Over the course you will: - Create a Basic Chatbot with LangChain & OpenAI - Implement Document Summarization Pipelines for small and very large files - Build your first RAG Chain with FAISS and LangChain - Add Retrieval Strategies like similarity search, thresholds and MMR - Use LangGraph to create a graph-based Chatbot with Memory - Extend it into an Agentic Workflow, where RAG could be one tool among others - Load and process multiple documents and formats from directories - Create and operate a dynamic Chroma Vector Database - Implement Metadata-based search & filtering (by document, page, date, etc.) - Detect file changes and automatically re-embed updated Documents - Bring it all together into a customizable, scalable, self-updating, Enterprise-ready RAG system

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