Using LangChain to Get More from RAG (Chapter 11)

The Memriq AI Inference Brief – Engineering Edition

Unlock the full potential of Retrieval-Augmented Generation (RAG) with LangChain’s modular components in this episode of Memriq Inference Digest — Engineering Edition. We dive deep into Chapter 11 of Keith Bourne’s book, exploring how document loaders, semantic text splitters, and structured output parsers can transform your RAG pipelines for better data ingestion, retrieval relevance, and reliable downstream automation.

In this episode:

- Explore LangChain’s diverse document loaders for PDFs, HTML, Word docs, and JSON

- Understand semantic chunking with RecursiveCharacterTextSplitter versus naive splitting

- Learn about structured output parsing using JsonOutputParser and Pydantic models

- Compare tooling trade-offs for building scalable and maintainable RAG systems

- Hear real-world use cases across enterprise knowledge bases, customer support, and compliance

- Get practical engineering tips to optimize pipeline latency, metadata hygiene, and robustness

Key tools & technologies:

- LangChain document loaders (PyPDF2, BSHTMLLoader, Docx2txtLoader, JSONLoader)

- RecursiveCharacterTextSplitter

- Output parsers: StrOutputParser, JsonOutputParser with Pydantic

- OpenAI text-embedding-ada-002

Timestamps:

00:00 – Introduction and guest welcome

02:30 – The power of LangChain’s modular components

06:00 – Why LangChain’s approach matters now

08:30 – Core RAG pipeline architecture breakdown

11:30 – Tool comparisons: loaders, splitters, parsers

14:30 – Under the hood walkthrough

17:00 – Real-world applications and engineering trade-offs

19:30 – Closing thoughts and resources

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Visit Memriq.ai for more AI engineering deep dives and resources