Description
RAG for Professionals with LangGraph, Python and OpenAI is a course on how to build high-performance Retrieval Augmentative Generation (RAG) systems published by Udemy Online Academy. This is a hands-on, engineering-oriented course designed to teach developers how to build high-performance Retrieval Augmentative Generation (RAG) systems. The course covers the entire lifecycle of modern RAG pipelines – from data ingestion and embedding to indexing, retrieval optimization, and multi-stage reasoning with LangGraph. Using Python and OpenAI models, the course demonstrates how to design modular, production-ready architectures that handle long-running queries, tooling, workflows, and evaluation. Learners gain hands-on experience in improving response accuracy, reducing artifacts, and increasing reliability by integrating vector storage, in-memory systems, routing logic, and custom agents.
Key points include understanding RAG concepts and architecture, building retrieval pipelines with Python, using LangGraph to design workflow-based reasoning, integrating OpenAI models for production, optimizing embeddings and vector search, improving retrieval accuracy and context relevance, reducing artifacts through evaluation techniques, scaling RAG systems for production, adding tools and memory to agent workflows, and implementing robust debugging and monitoring practices. In the end, developers will learn not only how to build RAG, but also how to scale, debug, and adapt it to real-world organizational needs.
What you will learn in RAG for Professionals with LangGraph, Python and OpenAI:
- Explain what RAG is, why it is needed, and when it outperforms simple LLMs
- Design your own enterprise RAG solutions for internal document and knowledge bases
- Use LangChain to build chatbots, summarization pipelines, and RAG chains
- Use LangGraph to design graph and agent-based AI workflows
- Efficiently load, split, and slice documents of different types and sizes
- 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 searching
- Build a dynamic and persistent Chroma vector database from scratch
- Implement automatic Vector DB updates based on file and metadata changes
- And…
Course specifications
Publisher: Udemy
Instructors: Alexander Hagmann
Language: English
Level: Introductory to Advanced
Number of Lessons: 136
Duration: 10 hours and 20 minutes
Course topics
RAG for Professionals with LangGraph, Python and OpenAI Prerequisites
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
Pictures
RAG for Professionals with LangGraph, Python and OpenAI introduction video
Installation guide
After Extract, watch with your favorite Player.
English subtitle
Quality: 720p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
5.5 GB
