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Take your LangChain RAG app development skills to the next level with this intermediate playlist, designed for those who already know the basics. Learn how to build a production-ready Retrieval-Augmented Generation app with LangChain, covering advanced topics such as vector stores, document chunking, and embedding pipelines. This playlist covers the specific workflow of deploying a Q&A chatbot on real data, helping you to create more sophisticated and efficient applications.
Last updated: Jun 5, 2026
Build a Retrieval-Augmented Generation pipeline with LangChain and Pinecone
Deploy a Q&A chatbot with LangChain and real-world data using Supabase
Configure advanced vector store techniques to optimize LangChain RAG app performance
Integrate LangChain RAG app with other AI tools for enhanced functionality
Master document chunking and embedding pipelines with LangChain and Supabase
Watch the videos in order for the best learning experience.
Build Your Own POWERFUL RAG Chatbot | Python, LangChain, Streamlit
Thomas Janssen
Build an Agentic RAG Chatbot with LangChain and Supabase
Thomas Janssen
How to Set the Chunk Size in Document Splitter | RAG | LangChain
Prompt Engineering
RAG + Langchain Python Project: Easy AI/Chat For Your Docs
pixegami
Take your LangChain RAG app development skills to the next level with this intermediate playlist, designed for those who already know the basics. Learn how to build a production-ready Retrieval-Augmented Generation app with LangChain, covering advanced topics such as vector stores, document chunking, and embedding pipelines. This playlist covers the specific workflow of deploying a Q&A chatbot on real data, helping you to create more sophisticated and efficient applications.
Begin your journey with “Build Your Own POWERFUL RAG Chatbot | Python, LangChain, Streamlit”
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