Topic
LangChain Document Loaders & Chunking
12 posts on langchain document loaders & chunking — part of framework tutorials on the n4n AI blog.
Web scraping documents into LangChain with WebBaseLoader
A practical guide to scraping web documents into LangChain using WebBaseLoader, covering authentication, JavaScript rendering, custom parsing, and pipeline integration.
Splitting code files in LangChain for RAG pipelines
Learn to split code files effectively in LangChain for RAG pipelines using language-aware splitters, with runnable examples and verification steps.
Semantic chunking in LangChain with embeddings
Learn to implement semantic chunking in LangChain using embeddings for better RAG retrieval — step-by-step with runnable code and evaluation techniques.
LangChain document loaders for Google Drive and S3
A step-by-step guide to loading documents from Google Drive and Amazon S3 using LangChain document loaders, with runnable code and verification steps.
LangChain CSV and JSON loaders for structured data
Practical guide to loading CSV and JSON files with LangChain document loaders, including nested structures, custom parsing, and chunking strategies for RAG pipelines.
Loading PDFs into LangChain with PyPDFLoader
Learn to load PDFs into LangChain using PyPDFLoader with step-by-step code examples, metadata extraction, text splitting, and error handling for production use.
LangChain document loaders for Notion and Confluence
A practical guide to loading Notion and Confluence content into LangChain with working code, chunking strategies, and verification steps.
LangChain RecursiveCharacterTextSplitter explained
A precise technical breakdown of LangChain's RecursiveCharacterTextSplitter: how its hierarchical separator recursion works, key params, and common pitfalls.
LangChain document loaders: PDF, HTML, and Markdown
Practical guide to langchain document loaders pdf html markdown: install, load each format with code, dodge pitfalls, and chunk for retrieval.
Chunking strategies in LangChain: fixed size vs semantic
A practitioner's head-to-head comparison of langchain chunking fixed size vs semantic across cost, latency, ergonomics, and limits, with code and a clear verdict.
Chunk overlap in LangChain: how much is too much
Practical guide to langchain chunk overlap tuning: how to set chunk_size and overlap to balance RAG retrieval quality, token cost, and latency.
Choosing the right chunk size for LangChain RAG apps
Practical guide to selecting chunk size for LangChain RAG apps: tradeoffs, code samples, and an ordered path to tune retrieval and context windows.
More topics in framework tutorials
- Building Chatbots with Vercel AI SDK & Next.js15
- LangChain RAG with Vector Databases15
- LangGraph Multi-Agent Workflows15
- LlamaIndex Query Engines for RAG15
- AutoGen Multi-Agent Conversations & Group Chat14
- Choosing an AI Framework by Use Case14
- CrewAI Agent Roles & Task Design14
- CrewAI Real-World Crew Examples14
- Haystack RAG Pipelines14
- LangChain Agents & Tool Calling14
- LangChain Multi-Model Fallback & Routing14
- LangChain.js for Node & TypeScript14