Advanced RAG Chunking: Contextual & Structural Chunking with LangChain & Ollama (100% Local)

Description

Complete tutorial and source code (requires MLExpert Pro): https://www.mlexpert.io/academy/v2/context-engineering/effective-chunking-strategies Simple chunking kills RAG performance. If you split a document every 500 characters, you sever tables, break sentences, and create "orphan chunks" that have no context. In this video, we'll build an advanced chunking pipeline that respects document structure and uses a local LLM to inject global context into every single chunk. We will move beyond RecursiveCharacterTextSplitter and implement a two-pass strategy: Markdown Header Splitting followed by LLM-based Contextual Enrichment. LangChain: https://python.langchain.com/ Ollama: https://ollama.com/ AI Academy: https://www.mlexpert.io/ LinkedIn: https://www.linkedin.com/in/venelin-valkov/ Follow me on X: https://twitter.com/venelin_valkov Discord: https://discord.gg/UaNPxVD6tv Subscribe: http://bit.ly/venelin-subscribe GitHub repository: https://github.com/curiousily/AI-Bootcamp 👍 Don't Forget to Like, Comment, and Subscribe for More Tutorials! 00:00 - The problem with naive chunking 00:38 - Chunking pipeline overview 03:08 - Chunk dataclass & metadata structure 05:09 - Chunking and enrichment with Ollama 08:51 - Looking at the resulting chunks 11:39 - Token inflation & trade-offs Join this channel to get access to the perks and support my work: https://www.youtube.com/channel/UCoW_WzQNJVAjxo4osNAxd_g/join