Generative AI & LLM Specialist | AI Automation Engineer | Transforming Businesses with LLMs, RAG, and Intelligent Automation |
load vs lazy_load in LangChain: load() - Eager Loading The load() method is the straightforward approach. When you call it, the document loader reads the entire source (e.g., a file, a directory of files, a website) and parses everything into a list of Document objects immediately. When to use it: You are working with a small number of files or a small amount of text. Your entire dataset can easily fit into your application's memory (RAM). You want simplicity and plan to use all documents immediately (e.g., for splitting and embedding). lazy_load() - Lazy Loading The lazy_load() method is designed for memory efficiency. Instead of returning a list, it returns a generator. A generator yields one Document at a time. This means you can process each document (e.g., split, embed, store in a database) without ever having the entire dataset in RAM. When to use it: You are working with a large number of files or a very large single file. The full dataset is too large to load into memory at once. You want to process documents in a streaming fashion.
